Groundwater makes up a primary portion of the water supply in many parts of Utah, with annual withdrawals estimated at more than 1,000,000 acre-feet per year. Increases to groundwater withdrawal and land use may negatively impact water availability. Ensuring availability of clean water requires understanding how water quality has changed over time and how natural and human activities and processes influence water quality. Changes in arsenic, nitrate, and dissolved-solids concentrations in the groundwater in basins with high groundwater withdrawals were evaluated between 1975 and 2015 as indicators of basinwide water quality and the suitability of water for drinking. Data were used from the U.S. Geological Survey’s National Water Information System (NWIS) database and the Safe Drinking Water Information System (SDWIS) maintained by the Utah Department of Environmental Quality, Division of Drinking Water. Mann-Kendall trend tests were used to assess temporal trends in decadal and 5-year (sub-decadal) median analyte concentrations in basins. Trends also were assessed in smaller parts of larger basins to focus on changes occurring at a smaller spatial scale. To evaluate the relationship between land-use change and water-quality changes, trends also were evaluated for wells where land use has changed. Trends in decadal and sub-decadal median arsenic, nitrate, and dissolved-solids concentrations over time were identified throughout the basins and sub-basins in this study. For combined NWIS and SDWIS data, rates of median arsenic concentration change in basins and sub-basins ranged between decreases of –0.24 microgram per liter (μg/L) per year and increases of 0.48 μg/L per year. Rates of median nitrate-concentration change ranged between decreases of –0.08 milligram per liter (mg/L) per year and increases of 0.02 mg/L per year. Rates of median dissolved solids concentration change ranged between decreases of –5 mg/L per year and increases of 7 mg/L per year. The rates of change for nitrate and dissolved solids were similar to or less than rates of change observed in other parts of the country. Trends were not directly related to land-use change approximal to a well, although more data from wells where land use has changed would improve this evaluation. These findings highlight that water quality at a well is related to a range of factors including land, demographics, and water use over a larger area surrounding and up-gradient from the well; rates and direction of groundwater movement; and geologic and hydrologic conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205047","collaboration":"Prepared in Cooperation with the Utah Department of Environmental Quality","usgsCitation":"Miller, O.L., 2020, Quantifying trends in arsenic, nitrate, and dissolved solids from selected wells in Utah: U.S. Geological Survey Scientific Investigations Report 2020–5047, 80 p., https://doi.org/10.3133/sir20205047.","productDescription":"viii, 80 p.","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-108685","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":374936,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5047/sir20205047.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374937,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5047/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
801-908-5000
Many coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large (>30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7–13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2020.00361","usgsCitation":"Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reiners, A., and Pearson, S., 2020, Hydro-morphological characterization of coral reefs for wave runup prediction: Frontiers in Marine Science, v. 7, 361, 20 p., https://doi.org/10.3389/fmars.2020.00361.","productDescription":"361, 20 p.","ipdsId":"IP-116940","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456686,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.00361","text":"Publisher Index Page"},{"id":436960,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C39WNE","text":"USGS data release","linkHelpText":"Coral reef profiles for wave-runup prediction"},{"id":376661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2020-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Fred","contributorId":229615,"corporation":false,"usgs":false,"family":"Scott","given":"Fred","email":"","affiliations":[{"id":27619,"text":"TU Delft","active":true,"usgs":false}],"preferred":false,"id":793676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antolinez, Jose A.A.","contributorId":177510,"corporation":false,"usgs":false,"family":"Antolinez","given":"Jose","email":"","middleInitial":"A.A.","affiliations":[],"preferred":false,"id":793677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCall, Robert T.","contributorId":148986,"corporation":false,"usgs":false,"family":"McCall","given":"Robert","email":"","middleInitial":"T.","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":793678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":793679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reiners, Ad","contributorId":229616,"corporation":false,"usgs":false,"family":"Reiners","given":"Ad","email":"","affiliations":[{"id":27619,"text":"TU Delft","active":true,"usgs":false}],"preferred":false,"id":793680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearson, Stuart","contributorId":193835,"corporation":false,"usgs":false,"family":"Pearson","given":"Stuart","affiliations":[],"preferred":false,"id":793681,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208929,"text":"sir20205053 - 2020 - Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018","interactions":[],"lastModifiedDate":"2020-05-21T11:43:03.892535","indexId":"sir20205053","displayToPublicDate":"2020-05-20T08:23:12","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":"2020-5053","displayTitle":"Using Remotely Sensed Data to Map Joshua Tree Distributions at Naval Air Weapons Station China Lake, California, 2018","title":"Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018","docAbstract":"Species distribution models (SDMs) that are derived through inference have been used to provide important insights toward species distributions. Their inferences can be robust in relation to known presences, but SDMs have error rates that cannot be quantified with certainty. For large plant species with unique signatures and in sparsely vegetated habitats, object-oriented satellite image interpretation provides a useful alternative to the more commonly used SDM approach. We tested visual image interpretation techniques in a pilot project to map the distribution of the Joshua tree (Yucca brevifolia), an arborescent succulent plant endemic to the Mojave Desert of North America. Naval Air Weapons Station China Lake (NAWS–CL) required assistance in mapping the distribution of Joshua trees across the 4,715 square kilometer (km2) military installation in support of their national security mission. Joshua trees were present on 1,307 1-km2 cells in the species distribution model, or 27.7 percent of the military installation. This increases the published range of Joshua trees at NAWS–CL by 90 percent and corrects for two stands of Joshua trees that were previously identified but do not exist. Remotely sensed satellite data in combination with ground surveys of Joshua trees produced a more accurate distribution map at a 1-kilometer resolution than did previous SDMs based on correlative modeling (area under the curve [AUC] 0.9064 versus 0.5848, respectively). Ancillary comparison with light detection and ranging (lidar) data indicated that satellite and lidar data were equally successful with slightly different sources of error, but that using them in combination produced the best results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205053","usgsCitation":"Esque, T.C., Baird, P.E., Chen, F.C., Housman, D., and Holton, J.T., 2020, Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5053, 13 p., https://doi.org/10.3133/sir20205053.","productDescription":"vi, 13 p.","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-106778","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":374807,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5053/sir20205053.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374806,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5053/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Naval Air Weapons Station China Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0316162109375,\n 35.04573815523954\n ],\n [\n -116.66931152343749,\n 34.99625375979014\n ],\n [\n -116.79565429687499,\n 36.461054075054314\n ],\n [\n -117.99316406249999,\n 36.474306755095235\n ],\n [\n -118.0316162109375,\n 35.04573815523954\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Model‐estimated monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff (R)) for 146 United States (U.S.) Geological Survey 8‐digit hydrologic units located in the Colorado River Basin (CRB) are used to examine the temporal and spatial variability of the CRB water balance for water years 1901 through 2014 (a water year is the period from October 1 of one year through September 30 of the following year). Results indicate that the CRB can be divided into six subregions with similar temporal variability in monthly R. The water balance analyses indicated that approximately 75% of total water‐year R is generated by just one CRB subregion and that most of the R in the basin is derived from surplus (S) water generated during the months of October through April. Furthermore, the analyses show that temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the northwestern conterminous U.S. (CONUS) and positive atmospheric pressure anomalies over the southeastern CONUS. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the North Pacific Ocean into the CRB, particularly the Upper CRB. Additionally, the occurrence of extreme dry and wet periods in the CRB appears to be related to variability of the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation.
This study created digital terrain models (DTMs) from historical aerial images using Structure from Motion (SfM) for a variety of image dates, resolutions, and photo scales. Accuracy assessments were performed on the SfM DTMs, and they were compared to the United States Geological Survey’s three-dimensional digital elevation program (3DEP) light detection and ranging (LiDAR) DTMs to evaluate geomorphic change thresholds based on vertical accuracy assessments and elevation change methodologies. The results of this study document a relationship between historical aerial photo scales and predicted vertical accuracy of the resultant DTMs. The results may be used to assess geomorphic change thresholds over multi-decadal timescales depending on spatial scale, resolution, and accuracy requirements. This study shows that if elevation changes of approximately ±1 m are to be mapped, historical aerial photography collected at 1:20,000 scale or larger would be required for comparison to contemporary LiDAR derived DTMs.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12101625","usgsCitation":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2020, Evaluating elevation change thresholds between structure-from-motion DEMs derived from historical aerial photos and 3DEP LiDAR data: Remote Sensing, v. 10, no. 12, 1625, 16 p., https://doi.org/10.3390/rs12101625.","productDescription":"1625, 16 p.","ipdsId":"IP-118392","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":456696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12101625","text":"Publisher Index Page"},{"id":376482,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","county":"Fairfax County","otherGeospatial":"Piney Branch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.30409622192383,\n 38.8538792131213\n ],\n [\n -77.23749160766602,\n 38.8538792131213\n ],\n [\n -77.23749160766602,\n 38.93230667504973\n ],\n [\n -77.30409622192383,\n 38.93230667504973\n ],\n [\n -77.30409622192383,\n 38.8538792131213\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Chirico, Peter G. 0000-0001-8375-5342","orcid":"https://orcid.org/0000-0001-8375-5342","contributorId":63838,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter","email":"","middleInitial":"G.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":793216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeWitt, Jessica D. 0000-0002-8281-8134 jdewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-8134","contributorId":5804,"corporation":false,"usgs":true,"family":"DeWitt","given":"Jessica","email":"jdewitt@usgs.gov","middleInitial":"D.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":793217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergstresser, Sarah E. 0000-0003-0182-5779 sbergstresser@usgs.gov","orcid":"https://orcid.org/0000-0003-0182-5779","contributorId":195556,"corporation":false,"usgs":true,"family":"Bergstresser","given":"Sarah","email":"sbergstresser@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":793218,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210676,"text":"70210676 - 2020 - Morphological, elemental, and boron isotopic insights into pathophysiology of diseased coral growth anomalies","interactions":[],"lastModifiedDate":"2020-06-16T14:55:19.53722","indexId":"70210676","displayToPublicDate":"2020-05-19T09:52:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Morphological, elemental, and boron isotopic insights into pathophysiology of diseased coral growth anomalies","docAbstract":"Growth anomalies (GAs) impact both coral skeleton and soft tissues and are detrimental to reef health. This tumor-like disease is increasingly found throughout the tropics and is commonly associated with high human population density, yet little is known about the etiology, pathology, or calcification behavior of the disease. Here, we investigate potential mechanisms involved in the development of GAs through chemical and morphological characterization of GA skeletons in Porites compressa from a site of high disease prevalence (Coconut Island, Hawaii). A comprehensive suite of trace elements and boron isotopes (δ11B) were measured in skeletal GAs to assess calcification behavior and uptake of essential and toxic metals. Scanning electron microscopy of GA skeleton revealed it to be highly porous consisting of a matrix with a disorganized crystal structure, in contrast to the dense well-organized normal skeleton of P. compressa. Elemental analyses revealed decreased Mg/Ca and increased U/Ca in GA skeletons relative to paired unaffected samples, suggesting a decreased abundance of rapidly accreting microstructures “centers of calcification” in the GAs. Estimates of carbonate system parameters based on δ11B and B/Ca measurements indicate reduced pH (–0.05 units) and [CO32–] within the calcifying fluid of GAs, which may have implications for GA calcification. Higher levels of essential (V/Ca and Mo/Ca) elements in GAs potentially indicate increased abundance of holobiont-associated, nitrogen-fixing bacteria and higher Sb/Ca and Nd/Ca indicate alteration in the accumulation/depuration of these toxic metals. In aggregate, our findings show that dystrophic calcification processes could explain structural differences seen in GA vs unaffected skeletons and highlight the use of approaches herein to shed light on disease pathophysiology in corals.","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-65118-6","usgsCitation":"Andersson, E., Stewart, J.A., Work, T.M., Woodley, C., Schock, T., and Day, R.D., 2020, Morphological, elemental, and boron isotopic insights into pathophysiology of diseased coral growth anomalies: Scientific Reports, v. 10, 8252, 13 p., https://doi.org/10.1038/s41598-020-65118-6.","productDescription":"8252, 13 p.","ipdsId":"IP-117757","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":456699,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-65118-6","text":"Publisher Index Page"},{"id":375618,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Andersson, Erik","contributorId":225365,"corporation":false,"usgs":false,"family":"Andersson","given":"Erik","affiliations":[{"id":25356,"text":"National Institute of Standards and Technology","active":true,"usgs":false}],"preferred":false,"id":790908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, Joseph A. E.","contributorId":211748,"corporation":false,"usgs":false,"family":"Stewart","given":"Joseph","email":"","middleInitial":"A. E.","affiliations":[],"preferred":false,"id":790909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":790910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodley, Cheryl M.","contributorId":225366,"corporation":false,"usgs":false,"family":"Woodley","given":"Cheryl M.","affiliations":[{"id":41087,"text":"Hollings Marine Laboratory, National Ocean Service, National Oceanic and Atmospheric Administration, Charleston, SC 29412, USA","active":true,"usgs":false}],"preferred":false,"id":790911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schock, Tracey B.","contributorId":225367,"corporation":false,"usgs":false,"family":"Schock","given":"Tracey B.","affiliations":[{"id":41088,"text":"Marine Biochemical Sciences, Chemical Sciences Division, National Institute of Standards and Technology, Hollings Marine Laboratory, Charleston, SC 29412, USA","active":true,"usgs":false}],"preferred":false,"id":790912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Day, Russell D.","contributorId":225368,"corporation":false,"usgs":false,"family":"Day","given":"Russell","email":"","middleInitial":"D.","affiliations":[{"id":41088,"text":"Marine Biochemical Sciences, Chemical Sciences Division, National Institute of Standards and Technology, Hollings Marine Laboratory, Charleston, SC 29412, USA","active":true,"usgs":false}],"preferred":false,"id":790913,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210166,"text":"70210166 - 2020 - Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling","interactions":[],"lastModifiedDate":"2020-05-19T14:46:15.127607","indexId":"70210166","displayToPublicDate":"2020-05-19T09:41:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling","docAbstract":"This study employed a novel combination of data (winter cover crop cost-share enrollment records, satellite remote sensing of wintertime vegetation, and results of Soil and Water Assessment Tool (SWAT) water quality simulations) to estimate the environmental performance of winter cover crops (WCC) at the watershed scale, from 2008 through 2017, within the Tuckahoe sub-watershed of the Choptank River. The Choptank is a river basin within the Chesapeake Bay watershed and, as a focus watershed for the U.S. Department of Agriculture’s Conservation Effects Assessment Project (CEAP), has been the subject of considerable study assessing linkages between land use and water quality. Farm enrollment data from the Maryland Agricultural Cost Share (MACS) program documented a strong increase in the use of WCC within the Tuckahoe watershed during the study period, from 27% of corn fields and 9% of soybean fields in 2008 to 89% of corn fields and 46% of soybean fields in 2016. Satellite remote sensing of wintertime ground cover detected increased wintertime vegetation following corn crops, in comparison to full season and double cropped soybean, consistent with patterns of cover crop implementation. Although inter-annual variation in climate strongly affected observed levels of vegetation, with warm winters resulting in increased vegetative cover, a 30-year analysis of wintertime greenness revealed significant increases in wintertime vegetation associated increased adoption of WCC. The predominant WCC species recorded by the MACS program as planted in the Tuckahoe watershed were wheat (68.1%), barley (16.1%), and rye (7.2%). The MACS WCC enrollment data were combined with output from the SWAT model, calibrated to streamflow and nutrient loading from the Tuckahoe watershed, to estimate water quality impacts based on known distribution of cover crop species and planting dates (2008 to 2017). Results indicated a 25% overall 10-year reduction in nitrate leaching from cropland resulting from cover crop adoption, rising to an estimated 38% load reduction in 2016 when 64% of fields were planted to cover crops. A large portion of WCC (39.3%) were planted late (after October 15) and planted to wheat (68.1%). Increased environmental benefits would be achieved by shifting agronomic methods away from late-planted wheat.","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.75.3.362","usgsCitation":"Hively, W.D., Lee, S., Sadeghi, A.M., McCarty, G.W., Lamb, B.T., Soroka, A.M., Keppler, J., Yeo, I., and Moglen, G.E., 2020, Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling: Journal of Soil and Water Conservation, v. 75, no. 3, p. 362-375, https://doi.org/10.2489/jswc.75.3.362.","productDescription":"14 p.","startPage":"362","endPage":"375","ipdsId":"IP-106326","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":456701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.75.3.362","text":"Publisher Index Page"},{"id":374921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.783203125,\n 36.98500309285596\n ],\n [\n -75.0146484375,\n 36.98500309285596\n ],\n [\n -75.0146484375,\n 39.57182223734374\n ],\n [\n -77.783203125,\n 39.57182223734374\n ],\n [\n -77.783203125,\n 36.98500309285596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Sangchul","contributorId":201237,"corporation":false,"usgs":false,"family":"Lee","given":"Sangchul","email":"","affiliations":[],"preferred":false,"id":789372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadeghi, Ali M.","contributorId":131147,"corporation":false,"usgs":false,"family":"Sadeghi","given":"Ali","email":"","middleInitial":"M.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":789373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":789374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lamb, Brian T.","contributorId":211092,"corporation":false,"usgs":false,"family":"Lamb","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":789375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Soroka, Alexander M. 0000-0002-8002-5229","orcid":"https://orcid.org/0000-0002-8002-5229","contributorId":201664,"corporation":false,"usgs":true,"family":"Soroka","given":"Alexander","email":"","middleInitial":"M.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keppler, Jason","contributorId":218039,"corporation":false,"usgs":false,"family":"Keppler","given":"Jason","email":"","affiliations":[{"id":39731,"text":"Maryland Department of Agriculture, Office of Resource Conservation","active":true,"usgs":false}],"preferred":false,"id":789377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yeo, In-Young","contributorId":131145,"corporation":false,"usgs":false,"family":"Yeo","given":"In-Young","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":789378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moglen, Glenn E.","contributorId":106585,"corporation":false,"usgs":false,"family":"Moglen","given":"Glenn","email":"","middleInitial":"E.","affiliations":[{"id":13220,"text":"The Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":789379,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70210258,"text":"70210258 - 2020 - Local to landscape-level controls of water fluxes through Hawaiian forests: Effects of invasive animals and plants on soil infiltration capacity across substrate and moisture gradients","interactions":[],"lastModifiedDate":"2020-05-27T14:25:26.537122","indexId":"70210258","displayToPublicDate":"2020-05-19T09:20:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Local to landscape-level controls of water fluxes through Hawaiian forests: Effects of invasive animals and plants on soil infiltration capacity across substrate and moisture gradients","docAbstract":"Given the potential effect of invasive plants and animals to water fluxes through forests, the invasive-driven degradation of native ecosystems is a topic of great concern for many downstream land and water managers. The infiltration rate determines the partitioning between runoff and infiltration into soil in Hawaiian forests and beyond. Thus, to explore the ecohydrological effects of plant and animal invasion in mesic and wet forests in Hawaii, we measured soil infiltration capacity in multiple fenced (i.e., ungulate-free)/unfenced and native/invaded forest sites along moisture and substrate age gradients across the islands of Hawai‘i and Kaua‘i. We also characterized forest composition and structure and soil characteristics at these sites to assess the direct and vegetation-mediated impacts of invasive species on infiltration capacity.\nInfiltration capacity is highly variable across forested sites and the wider landscape. Much of this variability is determined by a complex set of soil, vegetation, and disturbance factors that affect infiltration capacity at the immediate surrounding of measurement plots. Consequently, the effect of any given factor can be masked by variability in other factors. However, by controlling for variability in soil and vegetation conditions at a local plot level, we found that the presence of invasive species in forests has complex and sometimes non-intuitive effects on infiltration.\nOur final models showed that invasive ungulates negatively affect soil infiltration capacity consistently across the wide moisture and substrate age gradients considered. Additionally, because several soil characteristics known to be affected by ungulates were associated with local infiltration rates (e.g., soil organic matter, bare soil cover, soil depth), the long-term secondary effects of high ungulate densities in Hawaiian forests may be higher than effects observed in this study. These results provide clear evidence for land managers that ungulate control efforts likely improve ecohydrologic function to mesic and wet forest systems critical to protecting downstream and nearshore resources and maintaining groundwater recharge.\nCompared to ungulate effects, the effect of invasive plants on water infiltration capacity in Hawaiian forests appeared much more complex. In general, elements of forest structure including increased canopy, understory and floor cover, greater presence of large roots, and lower grass and bare soil covers were positively associated with water infiltration. Whether native or not, a plant species’ potential to alter infiltration rates in Hawaiian forests was likely to depend on its physiognomy and how it affects forest community structure. For instance, while the cover of native dominant tree ‘ōhi‘a, Metrosideros polymorpha, was found to be positively associated with infiltration capacity (perhaps as an indicator of overall forest integrity), invasive Himalayan ginger, Hedychium gardnerianum, was also positively correlated with infiltration capacity, possibly due to preferential flow channels created by the presence of large root mats.\nFew studies have conducted comprehensive integrated ecological and hydrological sampling in forests of high conservation value. While we show there are large benefits to understanding how conservation efforts may help shape water fluxes, we also found that the commonly used study design for infiltration studies used here and elsewhere (i.e., adjacent paired sites) could be modified to provide more accurate effects of invasion in future studies for ecosystems in Hawaii and beyond.","language":"English","publisher":"Hawai‘i Cooperative Studies Unit","collaboration":"Kohala Watershed Partnership; Three Mountain Alliance; Hawaii Water Resource Commissioner; The Nature Conservancy, Hawaii Department of Land and Natural Resources; USGS PIWSC; University of California – Santa Barbara; University of Hawai‘i at Hilo – Hawai‘i Cooperative Studies Unit","usgsCitation":"Fortini, L., Leopold, C., Perkins, K., Chadwick, O.A., Yelenik, S.G., Jacobi, J.D., Bishaw, K., Gregg, M., and Rosa, S.N., 2020, Local to landscape-level controls of water fluxes through Hawaiian forests: Effects of invasive animals and plants on soil infiltration capacity across substrate and moisture gradients, vii, 86 p.","productDescription":"vii, 86 p.","ipdsId":"IP-116705","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":375076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375053,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/5282"}],"country":"United 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Scaphirhynchus sturgeons in the lower Missouri River","title":"Differences in macronutrient content of common aquatic macroinvertebrates available as prey for young-of-the-year Scaphirhynchus sturgeons in the lower Missouri River","docAbstract":"Nutrient availability in prey items can have important consequences for the growth, reproduction, survival, and recruitment into adulthood of juvenile fish. For young of the year sturgeon, which are highly dependent on macroinvertebrates as prey, knowing the nutritional content across various prey items within their habitats can help managers during habitat restoration. The objective of this study was to test for differences in the macronutrient composition of major invertebrate groups commonly consumed by young of the year sturgeon in the lower Missouri River in the summer, when sturgeon habitat assessments occur. Potential prey vary considerably in size. In addition, there were significant differences in the concentrations of nutrients. The lowest concentration of lipid was found in Odonata (2.36 ± 1.83 mg 100 mg−1; mean ± pooled variance standard error) and the highest was in Diptera (14.49 ± 3.30 mg 100 mg−1). The lowest concentration of protein was found in Ephemeroptera (58.98 ± 1.90 mg 100 mg−1) and the highest concentration was in Trichoptera (70.07 ± 3.26 mg 100 mg−1). Some spatial differences were found in energy derived from protein in Ephemeroptera in the lower Missouri River, but not in energy derived from lipid. Our findings provide useful information that can contribute to adaptive management efforts for sturgeons in the lower Missouri River.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2020.1767705","usgsCitation":"Gonzalez, A., Barnes, C.L., Wilder, S.M., and Long, J.M., 2020, Differences in macronutrient content of common aquatic macroinvertebrates available as prey for young-of-the-year Scaphirhynchus sturgeons in the lower Missouri River, v. 35, no. 1, p. 191-202, https://doi.org/10.1080/02705060.2020.1767705.","productDescription":"12 p.","startPage":"191","endPage":"202","ipdsId":"IP-106495","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456703,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2020.1767705","text":"Publisher Index Page"},{"id":393420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Kansas, Missouri, Nebraska, South Dakota","otherGeospatial":"lower Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.31640625,\n 39.16414104768742\n ],\n [\n -92.98828125,\n 39.16414104768742\n ],\n [\n -92.98828125,\n 43.46886761482925\n ],\n [\n -99.31640625,\n 43.46886761482925\n ],\n [\n -99.31640625,\n 39.16414104768742\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez, Alin","contributorId":270391,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Alin","email":"","affiliations":[],"preferred":false,"id":829134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, C. 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Both resident and migratory species need to contend with seasonality and balance settling in favorable areas with tracking favorable environmental conditions during the year. We present an exploratory framework to jointly investigate a species' niche in geographic and ecological spaces, applied to wood storks (Mycteria americana), which are partially migratory wading birds, in the southeastern United States. We concurrently described monthly geographic distributions and climatic niches based on temperature and precipitation. Geographic distributions of wood storks were more similar throughout the year than were climatic niches, suggesting that birds stay within specific areas seasonally, rather than tracking areas of similar climate. However, wood storks expressed consistent selection of warm areas during the winter, and wet areas during the summer, indicating that the selection of seasonal ranges may be directly related to environmental conditions across the entire range. Our flexible framework, which simultaneously considered geographic and ecological spaces, suggested that tracking climate alone did not explain seasonal distributions of wood storks in breeding and non‐breeding areas.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3110","usgsCitation":"Basille, M., Watling, J.I., Romanach, S., and Borkhataria, R.R., 2020, Joint seasonality in geographic and ecological spaces, illustrated with a partially migratory bird: Ecosphere, v. 11, no. 5, e03110, 12 p., https://doi.org/10.1002/ecs2.3110.","productDescription":"e03110, 12 p.","ipdsId":"IP-102424","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456706,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3110","text":"Publisher Index 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\"}}]}","volume":"11","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Basille, Mathieu","contributorId":175274,"corporation":false,"usgs":false,"family":"Basille","given":"Mathieu","email":"","affiliations":[],"preferred":false,"id":789860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watling, James I.","contributorId":175275,"corporation":false,"usgs":false,"family":"Watling","given":"James","email":"","middleInitial":"I.","affiliations":[{"id":27555,"text":"John Carroll University","active":true,"usgs":false}],"preferred":false,"id":789861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":220093,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":789862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Borkhataria, Rena R.","contributorId":197425,"corporation":false,"usgs":false,"family":"Borkhataria","given":"Rena","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":789863,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227318,"text":"70227318 - 2020 - Golden Eagle perch site use in the U.S. southern plains: Understanding electrocution risk","interactions":[],"lastModifiedDate":"2022-01-10T13:38:35.904154","indexId":"70227318","displayToPublicDate":"2020-05-19T07:35:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Golden Eagle perch site use in the U.S. southern plains: Understanding electrocution risk","docAbstract":"Electrocution on overhead electric systems is a primary cause of anthropogenic mortality for Golden Eagles (Aquila chrysaetos) in North America. Distribution poles supporting energized equipment are most often involved in electrocutions, but the frequency with which Golden Eagles perch on pole supporting equipment is unknown. To resolve questions of perch frequency, and by extension, electrocution risk and mitigation prioritization, we used Google Earth to identify perch locations of GPS-transmittered preadult Golden Eagles, and specifically to identify perching on poles supporting transformers. We used transformer poles as a proxy for electrocution risk because transformers are visible in Google Earth imagery. We examined 105 randomly selected “perch events” for each of 10 Golden Eagles (n = 1050 perch events total) tracked for a mean of 16 consecutive mo after fledging. The most frequently used perch sites were cliffs (24.6%), trees (21.2%), and hills (16.6%). Across individuals, 10.8% of perches were on overhead electric systems (individual ranges = 0.0–34.3%). Seven Golden Eagles perched on a distribution pole at least once. Of these, five perched on a transformer pole at least once. Perching on transformer poles occurred more frequently than expected given the proportion of transformer poles present (Yates’ χ2 = 26.5, P < 0.001). Given the frequency of perching on transformer poles revealed in this study and the frequency of electrocution on equipment poles revealed in previous studies, the data suggest that electrocution mitigation measures should be focused on equipment poles. Future research should quantify perching across a wider variety of habitats and Golden Eagle age and sex to identify whether the patterns reported here occur more broadly.
Strong seismic waves from the July 2019 Ridgecrest, California, earthquakes displaced rocks in proximity to the M 7.1 mainshock fault trace at several locations. In this report, we document large boulders that were displaced at the Wagon Wheel Staging Area (WWSA), approximately 4.5 km southeast of the southern terminus of the large M 6.4 foreshock rupture (hereafter “the large foreshock”) and 9 km southwest of the nearest approach of the M 7.1 mainshock surface rupture. Some boulders appear to have slid along essentially flat surfaces, which suggest that dynamic stresses overcame the coefficient of friction. Other boulders appear to have rocked within their sockets. In both cases, we use simple mechanical models to estimate total peak dynamic accelerations between 0.5 and 1g, commensurate with modified Mercalli intensity 9. It is unclear if the strongest shaking at this location occurred during the large foreshock or the M 7.1 mainshock. The inferred accelerations are higher than predicted mainshock ground motions at WWSA, although local high accelerations could have been generated by path, site, or source effects. Gaps between boulders and their sockets are easily visible in the immediate aftermath of earthquakes and provide a quick indication of strong shaking. More importantly, the gaps quickly fill with surficial organic debris, including seeds and leaves of the year, that quickly become entombed. Boulders may thus potentially be extracted to examine gap fillings associated with past earthquakes, providing a new datable paleoseismic method.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200029","usgsCitation":"Sleep, N., and Hough, S.E., 2020, Mild displacements of boulders during the 2019 Ridgecrest Earthquakes: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1579-1588, https://doi.org/10.1785/0120200029.","productDescription":"10 p.","startPage":"1579","endPage":"1588","ipdsId":"IP-119088","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.71929444649237,\n 35.663769671293025\n ],\n [\n -117.71929444649237,\n 35.5783607422547\n ],\n [\n -117.59492272074345,\n 35.5783607422547\n ],\n [\n -117.59492272074345,\n 35.663769671293025\n ],\n [\n -117.71929444649237,\n 35.663769671293025\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Sleep, Norman","contributorId":245424,"corporation":false,"usgs":false,"family":"Sleep","given":"Norman","affiliations":[{"id":49192,"text":"Stanford","active":true,"usgs":false}],"preferred":false,"id":927666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927667,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210123,"text":"fs20203031 - 2020 - Ten ways Mount St. Helens changed our world—The enduring legacy of the 1980 eruption","interactions":[],"lastModifiedDate":"2020-05-19T11:43:08.986286","indexId":"fs20203031","displayToPublicDate":"2020-05-18T13:10:40","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3031","displayTitle":"Ten Ways Mount St. Helens Changed Our World—The Enduring Legacy of the 1980 Eruption","title":"Ten ways Mount St. Helens changed our world—The enduring legacy of the 1980 eruption","docAbstract":"Mount St. Helens was once enjoyed for its serene beauty and was considered one of America’s most majestic volcanoes because of its perfect cone shape, similar to Japan’s beloved Mount Fuji. Nearby residents assumed that the mountain was solid and enduring. That perception changed during the early spring of 1980. Then, on May 18, 1980, following 2 months of earthquakes and small explosions, the volcano’s over-steepened north flank collapsed in a colossal landslide and triggered a near-horizontal blast, followed by a powerful vertical eruption. The high-speed, rock-filled, and gas-charged blast quickly evolved into a gravitationally driven pyroclastic flow, which leveled millions of trees, stripped them of their branches and bark, and scoured soil from bedrock. The vertical eruption that followed fed a towering plume of ash for more than 9 hours. Winds carried the ash from the volcano and deposited it hundreds of miles away. Lahars (volcanic mudflows) buried river valleys. These catastrophic events caused the worst volcanic disaster in the recorded history of the conterminous United States. The events violently transformed Mount St. Helens and left a lasting impression on the hearts and minds of people living in the Pacific Northwest and beyond.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203031","issn":"2327-6916","collaboration":"Prepared in cooperation with the U.S. Forest Service and Washington State Parks and Recreation Commission","usgsCitation":"Driedger, C.L., Major, J.J., Pallister, J.S., Clynne, M.A., Moran, S.C., Westby, E.G., and Ewert, J.W., 2020, Ten ways Mount St. Helens changed our world—The enduring legacy of the 1980 eruption: U.S. Geological Survey Fact Sheet 2020-3031, 6 p., https://doi.org/10.3133/fs20203031.","productDescription":"6 p.","numberOfPages":"6","ipdsId":"IP-115978","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"links":[{"id":374839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3031/coverthb.jpg"},{"id":374840,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3031/fs20203031.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.3052978515625,\n 46.186486044787195\n ],\n [\n -122.06497192382811,\n 46.186486044787195\n ],\n [\n -122.06497192382811,\n 46.32559414426375\n ],\n [\n -122.3052978515625,\n 46.32559414426375\n ],\n [\n -122.3052978515625,\n 46.186486044787195\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Volcano Hazards Program
U.S. Geological Survey
12201 Sunrise Valley Dr., MS 905
Reston, VA 20192
The Sierra Madre fault zone is a south-vergent, active reverse fault that accommodates shortening between basins on the northern margin of the Los Angeles region and the San Gabriel Mountains. The preservation of late Quaternary alluvial fill and fan surfaces in the hanging wall of the fault provides evidence of long-term uplift. Surface rupture from the 1971 Mw 6.6 San Fernando earthquake and evidence of large prehistoric displacements from trenching investigations emphasize the ongoing hazard posed by the fault system to the region. This one-day field trip visits some of the key locations near Pasadena and San Fernando, California, where slip rates have been determined from cosmogenic and luminescence dating of abandoned surfaces dating to 50–70, ca. 30, and ca. 12 ka and surface offsets measured from lidar and pre-development topographic maps. Another stop is the site of a paleoseismic trench, which provided key evidence on the timing and displacement of past ruptures on the fault. In combination, results from these field investigations converge on a slip rate for the eastern ~100 km of the fault zone of 1–2 mm/yr, which matches or exceeds the rates for other reverse faults in southern California. This rate, in combination with trenching data that show no evidence of post–mid Holocene ruptures along the central and eastern portions of the fault, indicate the fault zone poses a significant seismic hazard to the region.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From the islands to the mountains: A 2020 view of geologic excursions in Southern California","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2020.0059(01)","usgsCitation":"Burgette, R., Scharer, K., and Lindvall, S., 2020, Late Quaternary slip rates on the Sierra Madre fault zone and paleoseismic evidence on the size and frequency of past ruptures, chap. 1 of From the islands to the mountains: A 2020 view of geologic excursions in Southern California, v. 59, p. 1-20, https://doi.org/10.1130/2020.0059(01).","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-116598","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":377832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Madre fault zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.21289062499999,\n 33.829356907739296\n ],\n [\n -115.62561035156249,\n 33.829356907739296\n ],\n [\n -115.62561035156249,\n 34.74161249883172\n ],\n [\n -118.21289062499999,\n 34.74161249883172\n ],\n [\n -118.21289062499999,\n 33.829356907739296\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"59","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burgette, Reed J.","contributorId":175465,"corporation":false,"usgs":false,"family":"Burgette","given":"Reed J.","affiliations":[{"id":49682,"text":"Dept of Geolgical Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":797257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindvall, Scott","contributorId":224667,"corporation":false,"usgs":false,"family":"Lindvall","given":"Scott","affiliations":[{"id":40908,"text":"Lettis Consultants International","active":true,"usgs":false}],"preferred":false,"id":797259,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213123,"text":"70213123 - 2020 - Abundant spontaneous and dynamically triggered submarine landslides in the Gulf of Mexico","interactions":[],"lastModifiedDate":"2020-09-10T14:44:11.00321","indexId":"70213123","displayToPublicDate":"2020-05-18T09:39:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Abundant spontaneous and dynamically triggered submarine landslides in the Gulf of Mexico","docAbstract":"Submarine landslides that occur offshore are common along the U.S. continental margins. These mass wasting events can trigger tsunamis and hence potentially devastate coastal communities and damage offshore infrastructure. However, the initiation and failure processes of submarine landslides are poorly understood. Here, we identify and locate 85 previously unknown submarine landslides in the Gulf of Mexico from 2008 to 2015. Ten of these landslides failed spontaneously while the remaining 75 were dynamically triggered by passing seismic surface waves from distant earthquakes with magnitudes as small as ∼5. Our observations demonstrate ongoing submarine landslide activity in the Gulf of Mexico where dense energy industry infrastructure is present and that the region is prone to secondary seismic hazard despite the low local seismicity rate. Our results should facilitate future investigations to identify unstable offshore slopes, to illuminate dynamic processes of landslides, and perhaps to apply remote detection technology in tsunami warning systems.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL087213","usgsCitation":"Fan, W., McGuire, J., and Shearer, P.M., 2020, Abundant spontaneous and dynamically triggered submarine landslides in the Gulf of Mexico: Geophysical Research Letters, v. 47, e2020GL087213, 10 p., https://doi.org/10.1029/2020GL087213.","productDescription":"e2020GL087213, 10 p.","ipdsId":"IP-114130","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456708,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl087213","text":"Publisher Index Page"},{"id":378311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.23291015625,\n 24.926294766395593\n ],\n [\n -82.44140625,\n 26.60817437403311\n ],\n [\n -83.0126953125,\n 27.858503954841247\n ],\n [\n -82.85888671875,\n 28.844673680771795\n ],\n [\n -84.13330078125,\n 29.916852233070173\n ],\n [\n -84.8583984375,\n 29.477861195816843\n ],\n [\n -86.41845703124999,\n 30.20211367909724\n ],\n [\n -89.12109375,\n 30.06909396443887\n ],\n [\n -89.2529296875,\n 29.630771207229\n ],\n [\n -88.79150390625,\n 28.844673680771795\n ],\n [\n -89.84619140625,\n 29.152161283318915\n ],\n [\n -90.19775390625,\n 28.844673680771795\n ],\n [\n -92.94433593749999,\n 29.439597566602902\n ],\n [\n -94.3505859375,\n 29.401319510041485\n ],\n [\n -95.91064453125,\n 28.401064827220896\n ],\n [\n -97.03125,\n 27.605670826465445\n ],\n [\n -97.2509765625,\n 26.667095801104814\n ],\n [\n -96.70166015624999,\n 25.740529092773226\n ],\n [\n -97.2509765625,\n 25.06569718553588\n ],\n [\n -97.53662109375,\n 23.845649887659352\n ],\n [\n -97.5146484375,\n 22.39071391683855\n ],\n [\n -96.9873046875,\n 20.879342971957897\n ],\n [\n -95.8447265625,\n 19.269665296502332\n ],\n [\n -94.76806640624999,\n 18.60460138845525\n ],\n [\n -94.21875,\n 18.437924653474408\n ],\n [\n -92.96630859375,\n 18.521283325496277\n ],\n [\n -92.13134765625,\n 18.8543103618898\n ],\n [\n -91.38427734374999,\n 18.999802829053262\n ],\n [\n -90.439453125,\n 21.105000275382064\n ],\n [\n -89.84619140625,\n 21.555284406923192\n ],\n [\n -88.505859375,\n 21.657428197370653\n ],\n [\n -85.18798828125,\n 22.715390019335942\n ],\n [\n -81.23291015625,\n 24.926294766395593\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2020-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Fan, Wenyuan","contributorId":174007,"corporation":false,"usgs":false,"family":"Fan","given":"Wenyuan","email":"","affiliations":[{"id":6728,"text":"Scripps Inst Oceanography","active":true,"usgs":false}],"preferred":false,"id":798294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":798295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shearer, Peter M.","contributorId":197012,"corporation":false,"usgs":false,"family":"Shearer","given":"Peter","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":798296,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211000,"text":"70211000 - 2020 - Specialized meltwater biodiversity persists despite widespread deglaciation","interactions":[],"lastModifiedDate":"2020-07-10T13:25:29.284896","indexId":"70211000","displayToPublicDate":"2020-05-18T08:23:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Specialized meltwater biodiversity persists despite widespread deglaciation","docAbstract":"Glaciers are important drivers of environmental heterogeneity and biological diversity across mountain landscapes. Worldwide, glaciers are receding rapidly due to climate change, with important consequences for biodiversity in mountain ecosystems. However, the effects of glacier loss on biodiversity have never been quantified across a mountainous region, primarily due to a lack of adequate data at large spatial and temporal scales. Here, we combine high-resolution biological and glacier change (ca. 1850–2015) datasets for Glacier National Park, USA, to test the prediction that glacier retreat reduces biodiversity in mountain ecosystems through the loss of uniquely adapted meltwater stream species. We identified a specialized cold-water invertebrate community restricted to the highest elevation streams primarily below glaciers, but also snowfields and groundwater springs. We show that this community and endemic species have unexpectedly persisted in cold, high-elevation sites, even in catchments that have not been glaciated in ∼170 y. Future projections suggest substantial declines in suitable habitat, but not necessarily loss of this community with the complete disappearance of glaciers. Our findings demonstrate that high-elevation streams fed by snow and other cold-water sources continue to serve as critical climate refugia for mountain biodiversity even after glaciers disappear.","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2001697117","usgsCitation":"Muhlfeld, C.C., Cline, T.J., Giersch, J.J., Peitzsch, E.H., Florentine, C., Jacobsen, D., and Hotaling, S., 2020, Specialized meltwater biodiversity persists despite widespread deglaciation: Proceedings of the National Academy of Sciences of the United States of America, v. 117, no. 22, p. 12208-12214, https://doi.org/10.1073/pnas.2001697117.","productDescription":"7 p.","startPage":"12208","endPage":"12214","ipdsId":"IP-114696","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":456727,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2001697117","text":"Publisher Index Page"},{"id":436963,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCMMKL","text":"USGS data release","linkHelpText":"Glacier National Park alpine aquatic invertebrates, 2011-2013"},{"id":376249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.17517089843749,\n 46.86770273172814\n ],\n [\n -112.1319580078125,\n 46.86770273172814\n ],\n [\n -112.1319580078125,\n 48.99463598353405\n ],\n [\n -115.17517089843749,\n 48.99463598353405\n ],\n [\n -115.17517089843749,\n 46.86770273172814\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Muhlfeld, Clint C. 0000-0002-4599-4059 cmuhlfeld@usgs.gov","orcid":"https://orcid.org/0000-0002-4599-4059","contributorId":924,"corporation":false,"usgs":true,"family":"Muhlfeld","given":"Clint","email":"cmuhlfeld@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":792386,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cline, Timothy Joseph 0000-0002-4955-654X","orcid":"https://orcid.org/0000-0002-4955-654X","contributorId":228871,"corporation":false,"usgs":true,"family":"Cline","given":"Timothy","email":"","middleInitial":"Joseph","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792387,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giersch, J. Joseph 0000-0001-7818-3941 jgiersch@usgs.gov","orcid":"https://orcid.org/0000-0001-7818-3941","contributorId":198074,"corporation":false,"usgs":true,"family":"Giersch","given":"J.","email":"jgiersch@usgs.gov","middleInitial":"Joseph","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792388,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792389,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792390,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jacobsen, Dean 0000-0001-5137-297X","orcid":"https://orcid.org/0000-0001-5137-297X","contributorId":198314,"corporation":false,"usgs":false,"family":"Jacobsen","given":"Dean","email":"","affiliations":[],"preferred":false,"id":792391,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hotaling, Scott 0000-0002-5965-0986","orcid":"https://orcid.org/0000-0002-5965-0986","contributorId":176860,"corporation":false,"usgs":false,"family":"Hotaling","given":"Scott","email":"","affiliations":[],"preferred":false,"id":792392,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215085,"text":"70215085 - 2020 - Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers","interactions":[],"lastModifiedDate":"2020-10-07T13:12:42.218246","indexId":"70215085","displayToPublicDate":"2020-05-18T08:08:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers","docAbstract":"Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate‐model simulations based on user‐defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high‐resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
No abstract available.
","language":"English","publisher":"Springer","doi":"10.1007/s00027-020-00722-2","usgsCitation":"Baron, J., Chandra, S., and Elser, J.J., 2020, Understanding mountain lakes in a changing world: Introduction to the special issue: Aquatic Sciences, v. 82, 57, 2 p., https://doi.org/10.1007/s00027-020-00722-2.","productDescription":"57, 2 p.","ipdsId":"IP-116697","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":374951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","noUsgsAuthors":false,"publicationDate":"2020-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Baron, Jill S. 0000-0002-5902-6251","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":215101,"corporation":false,"usgs":true,"family":"Baron","given":"Jill S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":789510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandra, Sudeep 0000-0002-9297-8211","orcid":"https://orcid.org/0000-0002-9297-8211","contributorId":224786,"corporation":false,"usgs":false,"family":"Chandra","given":"Sudeep","email":"","affiliations":[{"id":32871,"text":"University of Nevada at Reno","active":true,"usgs":false}],"preferred":false,"id":789511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elser, James J. 0000-0002-1460-2155","orcid":"https://orcid.org/0000-0002-1460-2155","contributorId":224787,"corporation":false,"usgs":false,"family":"Elser","given":"James","email":"","middleInitial":"J.","affiliations":[{"id":40941,"text":"University of Montana Flathead Lake Biological Station","active":true,"usgs":false}],"preferred":false,"id":789512,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70248918,"text":"70248918 - 2020 - Local magnitude, coda magnitude, and radiated energy of volcanic tectonic earthquakes from October 2010 to December 2011 at Sinabung volcano, Indonesia","interactions":[],"lastModifiedDate":"2023-09-26T12:17:04.967803","indexId":"70248918","displayToPublicDate":"2020-05-18T07:13:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Local magnitude, coda magnitude, and radiated energy of volcanic tectonic earthquakes from October 2010 to December 2011 at Sinabung volcano, Indonesia","docAbstract":"In August 2010, Sinabung volcano began erupting after more than a thousand years of dormancy. Following several weeks of phreatic eruptions, the eruptions ceased and Sinabung entered what became an inter-eruptive period of dominantly seismic unrest. While standard equations for understanding the size of an earthquake (local magnitude (ML), coda magnitude (MC), and seismic energy release (ER)) have long been developed, it is best practice to fine tune these relations for a given region and period of study to more accurately describe seismicity and to directly compare it with other volcanic systems. More accurate descriptions of magnitudes and energy release are vital to accurate volcanic eruption forecasting and evaluation of seismic and volcanic risk. In this study, we use high-frequency volcano-tectonic (VT) earthquakes recorded on a temporary three-component network installed between October 2010 and December 2011 in the region around Sinabung volcano to better constrain the seismic parameters of and better understand this previously unstudied volcano. We determine region-specific formulas for ML, MC, and ER as follows:
where A, r, and tcoda are maximum amplitude on a Wood-Anderson seismogram, hypocentral distance (km), and the coda duration (s), respectively. Constants in the ML equation have physically interpretable meanings. The constant for the geometrical spreading term (log10r term) equals one for perfect spherical spreading of the waveform. Our value is greater than one and thus suggests that wavefronts spread at a slightly different rate than for simple spherical spreading. The constant for the attenuation term (r term) is consistent with locally mapped attenuative deposits (limestones and tuffs) and previous 3D tomographic results. Our MC equation differs from a previous study, likely because different data in a different time period were used. Earthquake hypocenters are consistent with those located in previous tomographic studies, and we interpret the earthquakes in this study as distal VT earthquakes induced by continued magmatic intrusion at Sinabung over the period of October 2010–December 2011.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-01383-7","usgsCitation":"Pagacancang, A., McCausland, W.A., Hamidah, N.N., Kristianto, Basuki, A., and Indrastuti, N., 2020, Local magnitude, coda magnitude, and radiated energy of volcanic tectonic earthquakes from October 2010 to December 2011 at Sinabung volcano, Indonesia: Bulletin of Volcanology, v. 83, 45, 16 p., https://doi.org/10.1007/s00445-020-01383-7.","productDescription":"45, 16 p.","ipdsId":"IP-112724","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":421164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sinabung volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 94.80408011956274,\n 6.071558619518001\n ],\n [\n 94.80408011956274,\n 1.292805138355149\n ],\n [\n 101.37390433831303,\n 1.292805138355149\n ],\n [\n 101.37390433831303,\n 6.071558619518001\n ],\n [\n 94.80408011956274,\n 6.071558619518001\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationDate":"2020-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Pagacancang, Afnimar","contributorId":330169,"corporation":false,"usgs":false,"family":"Pagacancang","given":"Afnimar","email":"","affiliations":[{"id":78836,"text":"Bandung Institute of Technology (ITB)","active":true,"usgs":false}],"preferred":false,"id":884204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCausland, Wendy A. 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":204380,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":884205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamidah, Nimas Nurul","contributorId":330170,"corporation":false,"usgs":false,"family":"Hamidah","given":"Nimas","email":"","middleInitial":"Nurul","affiliations":[{"id":78836,"text":"Bandung Institute of Technology (ITB)","active":true,"usgs":false}],"preferred":false,"id":884206,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kristianto","contributorId":330171,"corporation":false,"usgs":false,"family":"Kristianto","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":884207,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Basuki, Ahmad","contributorId":330172,"corporation":false,"usgs":false,"family":"Basuki","given":"Ahmad","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":884208,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Indrastuti, Novianti","contributorId":204389,"corporation":false,"usgs":false,"family":"Indrastuti","given":"Novianti","email":"","affiliations":[{"id":36928,"text":"Center for Volcanology and Geological Hazard Mitigation, Bandung, Indonesia","active":true,"usgs":false}],"preferred":false,"id":884209,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209232,"text":"ofr20201030 - 2020 - Louisiana Barrier Island Comprehensive Monitoring Program: Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16","interactions":[],"lastModifiedDate":"2020-05-19T11:53:31.168523","indexId":"ofr20201030","displayToPublicDate":"2020-05-18T07:11:23","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":"2020-1030","displayTitle":"Louisiana Barrier Island Comprehensive Monitoring Program: Mapping Habitats in Beach, Dune, and Intertidal Environments Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16","title":"Louisiana Barrier Island Comprehensive Monitoring Program: Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16","docAbstract":"Barrier islands, headlands, and coastal shorelines provide numerous valuable ecosystem goods and services, including storm protection and erosion control for the mainland, habitat for fish and wildlife, salinity regulation in estuaries, carbon sequestration in marshes, and areas for recreation and tourism. These coastal features are dynamic environments because of their position at the land-sea interface. Storms, wave energy, tides, currents, and relative sea-level rise are powerful forces that shape local geomorphology and habitat distribution. In order to make more informed decisions, coastal resource managers require insights into how these dynamic systems are changing through time.
In 2005, Louisiana’s Coastal Protection and Restoration Authority, in partnership with the University of New Orleans and the U.S. Geological Survey, developed the Barrier Island Comprehensive Monitoring (BICM) Program. The goal of the BICM Program is to develop long-term datasets for habitat coverage, shoreline assessments, shoreline position, topobathymetric changes, and sediment characterization to assist with planning, designing, evaluating, and maintaining current and future barrier shorelines. The overall objectives of the study described in this report were to (1) map habitats for 2008 and 2015–16 for BICM coastal reaches and (2) map habitat change between these two time periods.
This report highlights the second phase of habitat analyses for the BICM Program. This work builds on a previous habitat analysis conducted by the University of New Orleans, which included the development of habitat maps for 1996/1998, 2001, 2004, and 2005, along with habitat change maps. For this current effort, a new 15-class habitat scheme was developed from the original BICM scheme to further delineate various dune habitats, including meadow habitat found along the backslopes of dunes, to distinguish between marsh and mangrove, and to distinguish between beach and unvegetated barrier flat habitats. Additionally, a geographic object-based image analysis-based mapping framework was used to incorporate relative topography and address elevation uncertainty in light detection and ranging data to assist with mapping dune and intertidal habitats.
For the entire BICM region, the area experiencing a change in a land/water category (that is, land gain or land loss) was 3.4 percent, of which, 59.2 percent was land gain and 40.8 percent was land loss. Areal coverages of meadow, mangrove, scrub/shrub, and vegetated dune increased from 2008 to 2015–16, whereas areal coverages of beach, grassland, and intertidal decreased. The decrease in intertidal, however, was largely due to differing water levels in the orthophotography between the two time periods. Regional analyses of habitat coverage and habitat change captured the dynamic nature of these systems and the effects of restoration efforts, most notably in the Late Lafourche Delta, Modern Delta, and Chandeleur Islands regions. For instance, in the Modern Delta region there was a marked increase in unvegetated flat, meadow, mangrove, scrub/shrub, beach, unvegetated dune, and vegetated dune. As a result, this region experienced the highest percent change for land/water classes (6.6 percent) with land gain accounting for much of this change (70.8 percent). In contrast, the Acadiana Bays region had the highest relative percent loss of all regions. The region had a percent change for land/water classes of 2.8 percent, of which, 79.7 percent was land loss.
The results of this study provide information about the areal coverage and distribution of habitats for two recent time periods and change over about an 8-year period. These data can be used to evaluate changes along the Louisiana Gulf of Mexico shoreline, including gradual changes caused by coastal processes, restoration actions, and (or) episodic events, such as hurricanes and extreme storms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201030","collaboration":"Prepared in cooperation with the Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Enwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020–1030, 57 p., https://doi.org/10.3133/ofr20201030.","productDescription":"Report: ix, 57 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-114268","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":436983,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRT54Z","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, Chandeleur Islands Region"},{"id":436982,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E94E33","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 habitat map, Chandeleur Islands Region"},{"id":436981,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UBUO7C","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2016 habitat change, Chandeleur Islands Region"},{"id":436980,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LUPB9N","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2015 habitat change, East Chenier Region"},{"id":436979,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N0GKPB","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2016 habitat change, Acadiana Bays Region"},{"id":436978,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ABPHMC","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 Habitat Map, Acadiana Bays Region"},{"id":436977,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91F6GQY","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, East Chenier Region"},{"id":436976,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KSG6WX","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2015 Habitat Map, East Chenier Region"},{"id":436975,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SKS31W","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2015/16 Habitat Map, Acadiana Bays Region"},{"id":436974,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DW2Y25","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 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U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506–3152
Radon (Rnair) was monitored in open air in publicly accessible areas surrounding the Pinenut uranium (U) mine during mining and reclamation activities in 2015–16 to address concerns about mining related effects to areas surrounding Grand Canyon National Park (GCNP) in Arizona, USA. During July 2015, Rnair concentrations associated with the ore storage pile monitoring site were larger than those at the mine vent monitoring site and likely resulted from the relatively large amount of ore stored on site during this period. Higher wind velocities at the ore pile monitoring site generally resulted in lower Rnair concentrations; however, wind velocity did not appear to be an important factor in controlling Rnair concentrations at the mine vent monitoring site. Physical disturbances of the ore pile by heavy equipment did not coincide with elevated Rnair concentrations at the ore storage pile or mine vent monitoring sites. The relative size of the ore storage pile showed a positive trend with the daily mean Rnair concentration measured at the ore pile monitoring site. Principal component analysis (PCA) was applied to the ore pile and mine vent multivariate data sets for simultaneous comparison of all measured variables during 230 days of the study period. A significant positive coefficient for Rnair was associated with a significant negative coefficient for wind speed for principal component (PC) 2ore pile. Significant, positive PC2mine vent coefficients included Rnair, wind direction, and relative ore pile size indicating that Rnair variations at the mine vent monitoring site may be affected by Rn sourced from the ore pile. The ore pile is located about 200 m south of the mine vent Rn monitor with the prevalent wind direction coming from the south. All data generated during the field study and laboratory verification tests were published by Naftz et al. (2018) and are available online at: