Concentrations of particulate organic carbon (POC) and dissolved organic carbon (DOC), which together comprise total organic carbon, were measured in this reconnaissance study at sampling sites in the Upper Klamath River, Lost River, and Klamath Straits Drain in 2013–16. Optical absorbance and fluorescence properties of dissolved organic matter (DOM), which contains DOC, also were analyzed. Parallel factor analysis was used to decompose the optical fluorescence data into five key components for all samples. Principal component analysis (PCA) was used to investigate differences in DOM source and processing among sites.
At all sites in this study, average DOC concentrations were higher than average POC concentrations. The highest DOC concentrations were at sites in the Klamath Straits Drain and at Pump Plant D. Evaluation of optical properties indicated that Klamath Straits Drain DOM had a refractory, terrestrial source, likely extracted from the interaction of this water with wetland peats and irrigated soils. Pump Plant D DOM exhibited more labile characteristics, which could, for instance, indicate contributions from algal or microbial exudates. The samples from Klamath River also had more microbial or algal derived material, as indicated by PCA analysis of the optical properties. Most sites, except Pump Plant D, showed a linear relation between fluorescent dissolved organic matter (fDOM) and DOC concentration, indicating these measurements are highly correlated (R2=0.84), and thus a continuous fDOM probe could be used to estimate DOC loads from these sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171160","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Goldman, J.H., and Sullivan, A.B., 2017, Characteristics of dissolved organic matter in the Upper Klamath River, Lost River, and Klamath Straits Drain, Oregon and California: U.S. Geological Survey Open File Report 2017-1160, 21 p., https://doi.org/10.3133/ofr20171160.","productDescription":"Report: iv, 21 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-088888","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":349912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1160/coverthb.jpg"},{"id":349913,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1160/ofr20171160.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1160"},{"id":349914,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71Z42V4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data from an analysis of dissolved organic matter in the Upper Klamath River, Lost River, and Klamath Straits Drain, Oregon and California, 2013–16"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Lost River, Klamath Straits Drain, Upper Klamath River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05261230468751,\n 41.77131167976407\n ],\n [\n -121.0308837890625,\n 41.77131167976407\n ],\n [\n -121.0308837890625,\n 42.44980808481614\n ],\n [\n -122.05261230468751,\n 42.44980808481614\n ],\n [\n -122.05261230468751,\n 41.77131167976407\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
In the Western United States, shrubland ecosystems provide vital ecological, hydrological, biological, agricultural, and recreational services. However, disturbances such as livestock grazing, exotic species invasion, conversion to agriculture, climate change, urban expansion, and energy development are altering these ecosystems.
Improving our understanding of how shrublands are distributed, where they are changing, the extent of the historical change, and likely future change directions is critical for successful management of these ecosystems. Remote-sensing technologies provide the most likely data source for large-area monitoring of ecosystem disturbance—both near-real time and historically. A monitoring framework supported by remote-sensing data can offer efficient and accurate analysis of change across a range of spatial and temporal scales.
The U.S. Geological Survey has been working to develop new remote-sensing data, tools, and products to characterize and monitor these changing shrubland landscapes. Nine individual map products (components) have been developed that quantify the percent of shrub, sagebrush, big sagebrush, herbaceous, annual herbaceous, litter, bare ground, shrub height, and sagebrush height at 1-percent intervals in each 30-meter grid cell. These component products are designed to be combined and customized to widely support different applications in rangeland monitoring, analysis of wildlife habitat, resource inventory, adaptive management, and environmental review.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173084","usgsCitation":"Young, S.M., 2017, U.S. Geological Survey shrub/grass products provide new approach to shrubland monitoring: U.S. Geological Fact Sheet 2017–3084, 4 p., https://doi.org/10.3133/fs20173084.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-091949","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":349910,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3084/coverthb.jpg"},{"id":349911,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3084/fs20173084.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3084"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Understanding the effects of management practices on soil organic carbon (SOC) is important for designing effective policies to mitigate greenhouse gas emissions in agriculture. In the Midwest United States, management practices in the croplands have been improved to increase crop production and reduce SOC loss since the 1980s. Many studies of SOC dynamics in croplands have been performed to understand the effects of management, but the results are still not conclusive. This study quantified SOC dynamics in the Midwest croplands from 1980 to 2012 with the General Ensemble Biogeochemical Modelling System (GEMS) and available management data. Our results showed that the total SOC in the croplands decreased from 1190 Tg C in 1980 to 1107 TgC in 1995, and then increased to 1176 TgC in 2012. Continuous cropping and intensive tillage may have driven SOC loss in the early period. The increase of crop production and adoption of conservation tillage increased the total SOC so that the decrease in the total SOC stock after 32 years was only 1%. The small change in average SOC did not reflect the large spatial variations of SOC change in the region. Major SOC losses occurred in the north and south of the region, where SOC baseline values were high and cropland production was low. The SOC gains took place in the central part of the region where SOC baseline values were moderate and cropland production was higher than the other areas. We simulated multiple land-use land-cover (LULC) change scenarios and analyzed the results. The analysis showed that among all the LULC changes, agricultural technology that increased cropland production had the greatest impact on SOC changes, followed by the tillage practices, changes in crop species, and the conversions of cropland to other land use. Information on management practice induced spatial variation in SOC can be useful for policy makers and farm managers to develop long-term management strategies for increasing SOC sequestration in different areas.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2017.09.017","usgsCitation":"Li, Z., Liu, S., Tan, Z., Sohl, T.L., and Wu, Y., 2017, Simulating the effects of management practices on cropland soil organic carbon changes in the Temperate Prairies Ecoregion of the United States from 1980 to 2012: Ecological Modelling, v. 365, p. 68-79, https://doi.org/10.1016/j.ecolmodel.2017.09.017.","productDescription":"12 p.","startPage":"68","endPage":"79","ipdsId":"IP-087774","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":360356,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"365","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c18c425e4b006c4f856ace0","contributors":{"authors":[{"text":"Li, Zhen","contributorId":200957,"corporation":false,"usgs":false,"family":"Li","given":"Zhen","affiliations":[],"preferred":false,"id":754393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Shuguang 0000-0002-6027-3479 sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":754394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tan, Zhengxi 0000-0002-4136-0921 ztan@usgs.gov","orcid":"https://orcid.org/0000-0002-4136-0921","contributorId":2945,"corporation":false,"usgs":true,"family":"Tan","given":"Zhengxi","email":"ztan@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":754395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":754396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wu, Yiping ywu@usgs.gov","contributorId":987,"corporation":false,"usgs":true,"family":"Wu","given":"Yiping","email":"ywu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":754397,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191218,"text":"sir20175115 - 2017 - Evaluation and use of U.S. Environmental Protection Agency Clean Watersheds Needs Survey data to quantify nutrient loads to surface water, 1978–2012","interactions":[],"lastModifiedDate":"2017-12-08T09:45:41","indexId":"sir20175115","displayToPublicDate":"2017-12-07T15:45:00","publicationYear":"2017","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":"2017-5115","title":"Evaluation and use of U.S. Environmental Protection Agency Clean Watersheds Needs Survey data to quantify nutrient loads to surface water, 1978–2012","docAbstract":"Changes in municipal and industrial point-source discharges over time have been an important factor affecting nutrient trends in many of the Nation’s streams and rivers. This report documents how three U.S. Environmental Protection Agency (EPA) national datasets—the Permit Compliance System, the Integrated Compliance Information System, and the Clean Watersheds Needs Survey—were evaluated for use in the U.S. Geological Survey National Water-Quality Assessment project to assess the causes of nutrient trends. This report also describes how a database of total nitrogen load and total phosphorous load was generated for select wastewater treatment facilities in the United States based on information reported in the EPA Clean Watersheds Needs Survey. Nutrient loads were calculated for the years 1978, 1980, 1982, 1984, 1986, 1988, 1990, 1992, 1996, 2000, 2004, 2008, and 2012 based on average nitrogen and phosphorous concentrations for reported treatment levels and on annual reported flow values.
The EPA Permit Compliance System (PCS) and Integrated Compliance Information System (ICIS), which monitor point-source facility discharges, together are the Nation’s most spatially comprehensive dataset for nutrients released to surface waters. However, datasets for many individual facilities are incomplete, the PCS/ICIS historical data date back only to 1989, and historical data are available for only a limited number of facilities. Additionally, inconsistencies in facility reporting make it difficult to track or identify changes in nutrient discharges over time. Previous efforts made by the U.S. Geological Survey to “fill in” gaps in the PCS/ICIS data were based on statistical methods—missing data were filled in through the use of a statistical model based on the Standard Industrial Classification code, size, and flow class of the facility and on seasonal nutrient discharges of similar facilities. This approach was used to estimate point-source loads for a single point in time; it was not evaluated for use in generating a consistent data series over time.
Another national EPA dataset that is available is the Clean Watersheds Needs Survey (CWNS), conducted every 4 years beginning 1973. The CWNS is an assessment of the capital needs of wastewater facilities to meet the water-quality goals set in the Clean Water Act. Data collected about these facilities include location and contact information for the facilities; population served; flow and treatment level of the facility; estimated capital needs to upgrade, repair, or improve facilities for water quality; and nonpoint-source best management practices.
Total nitrogen and total phosphorous load calculations for each of the CWNS years were based on treatment level information and average annual outflow (in million gallons per day) from each of the facilities that had reported it. Treatment levels categories (such as Primary, Secondary, or Advanced) were substituted with average total nitrogen and total phosphorous concentrations for each treatment level based on those reported in literature. The CWNS dataset, like the PCS/ICIS dataset, has years where facilities did not report either a treatment level or an annual average outflow, or both. To fill in the data gaps, simple linear assumptions were made based on each facility’s responses to the survey in years bracketing the data gap or immediately before or after the data gap if open ended. Treatment level and flow data unique to each facility were used to complete the CWNS dataset for that facility.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175115","usgsCitation":"Ivahnenko, Tamara, 2017, Evaluation and use of U.S. Environmental Protection Agency Clean Watersheds Needs Survey data to quantify nutrient loads to surface water, 1978–2012: U.S. Geological Survey Scientific Investigations Report 2017–5115, 11 p., https://doi.org/10.3133/sir20175115.","productDescription":"Report: iv, 11 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-082278","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":349388,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5115/coverthb.jpg"},{"id":349584,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MG7MNN","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"National USEPA Clean Watershed Needs Survey WWTP nutrient load data 1978 to 2012"},{"id":349389,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5115/sir20175115.pdf","text":"Report","size":"864 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5115"}],"contact":"Program Coordinator, National Water Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
In almost all past inversions of large-earthquake ground motions for rupture behavior, the goal of the inversion is to find the “best fitting” rupture model that predicts ground motions which optimize some function of the difference between predicted and observed ground motions. This type of inversion was pioneered in the linear-inverse sense by Olson and Apsel (1982), who minimized the square of the difference between observed and simulated motions (“least squares”) while simultaneously minimizing the rupture-model norm (by setting the null-space component of the rupture model to zero), and has been extended in many ways, one of which is the use of nonlinear inversion schemes such as simulated annealing algorithms that optimize some other misfit function. For example, the simulated annealing algorithm of Piatanesi and others (2007) finds the rupture model that minimizes a “cost” function which combines a least-squares and a waveform-correlation measure of misfit.
All such inversions that look for a unique “best” model have at least three problems. (1) They have removed the null-space component of the rupture model—that is, an infinite family of rupture models that all fit the data equally well have been narrowed down to a single model. Some property of interest in the rupture model might have been discarded in this winnowing process. (2) Smoothing constraints are commonly used to yield a unique “best” model, in which case spatially rough rupture models will have been discarded, even if they provide a good fit to the data. (3) No estimate of confidence in the resulting rupture models can be given because the effects of unknown errors in the Green’s functions (“theory errors”) have not been assessed. In inversion for rupture behavior, these theory errors are generally larger than the data errors caused by ground noise and instrumental limitations, and so overfitting of the data is probably ubiquitous for such inversions.
Recently, attention has turned to the inclusion of theory errors in the inversion process. Yagi and Fukahata (2011) made an important contribution by presenting a method to estimate the uncertainties in predicted large-earthquake ground motions due to uncertainties in the Green’s functions. Here we derive their result and compare it with the results of other recent studies that look at theory errors in a Bayesian inversion context particularly those by Bodin and others (2012), Duputel and others (2012), Dettmer and others (2014), and Minson and others (2014).
Notably, in all these studies, the estimates of theory error were obtained from theoretical considerations alone; none of the investigators actually measured Green’s function errors. Large earthquakes typically have aftershocks, which, if their rupture surfaces are physically small enough, can be considered point evaluations of the real Green’s functions of the Earth. Here we simulate smallaftershock ground motions with (erroneous) theoretical Green’s functions. Taking differences between aftershock ground motions and simulated motions to be the “theory error,” we derive a statistical model of the sources of discrepancies between the theoretical and real Green’s functions. We use this model with an extended frequency-domain version of the time-domain theory of Yagi and Fukahata (2011) to determine the expected variance 2 τ caused by Green’s function error in ground motions from a larger (nonpoint) earthquake that we seek to model.
We also differ from the above-mentioned Bayesian inversions in our handling of the nonuniqueness problem of seismic inversion. We follow the philosophy of Segall and Du (1993), who, instead of looking for a best-fitting model, looked for slip models that answered specific questions about the earthquakes they studied. In their Bayesian inversions, they inductively derived a posterior probability-density function (PDF) for every model parameter. We instead seek to find two extremal rupture models whose ground motions fit the data within the error bounds given by 2 τ , as quantified by using a chi-squared test described below. So, we can ask questions such as, “What are the rupture models with the highest and lowest average rupture speed consistent with the theory errors?” Having found those models, we can then say with confidence that the true rupture speed is somewhere between those values. Although the Bayesian approach gives a complete solution to the inverse problem, it is computationally demanding: Minson and others (2014) needed 1010 forward kinematic simulations to derive their posterior probability distribution. In our approach, only about107 simulations are needed. Moreover, in practical application, only a small set of rupture models may be needed to answer the relevant questions—for example, determining the maximum likelihood solution (achievable through standard inversion techniques) and the two rupture models bounding some property of interest.
The specific property that we wish to investigate is the correlation between various rupturemodel parameters, such as peak slip velocity and rupture velocity, in models of real earthquakes. In some simulations of ground motions for hypothetical large earthquakes, such as those by Aagaard and others (2010) and the Southern California Earthquake Center Broadband Simulation Platform (Graves and Pitarka, 2015), rupture speed is assumed to correlate locally with peak slip, although there is evidence that rupture speed should correlate better with peak slip speed, owing to its dependence on local stress drop. We may be able to determine ways to modify Piatanesi and others’s (2007) inversion’s “cost” function to find rupture models with either high or low degrees of correlation between pairs of rupture parameters. We propose a cost function designed to find these two extremal models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171151","usgsCitation":"Spudich, P., Cirella, A., Scognamiglio, L., and Tinti, E., 2017, Analysis of the variability in ground-motion synthesis and inversion: U.S. Geological Survey Open-File Report 2017–1151, 39 p., https://doi.org/10.3133/ofr20171151.","productDescription":"iv, 39 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-087954","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":349837,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1151/ofr20171151_.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1151"},{"id":349836,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1151/coverthb.jpg"}],"contact":"Director,
Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road
Mail Stop 977
Menlo Park, CA 94025
From January to April 2016, the U.S. Geological Survey (USGS), the Mojave Water Agency, and other local water districts made approximately 1,200 water-level measurements in about 645 wells located within 15 separate groundwater basins, collectively referred to as the Mojave River and Morongo groundwater basins. These data document recent conditions and, when compared with older data, changes in groundwater levels. A water-level contour map was drawn using data measured in 2016 that shows the elevation of the water table and general direction of groundwater movement for most of the groundwater basins. Historical water-level data stored in the USGS National Water Information System (https://waterdata.usgs.gov/nwis/) database were used in conjunction with data collected for this study to construct 37 hydrographs to show long-term (1930–2016) and short-term (1990–2016) water-level changes in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3391","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Dick, M.C., and Kjos, A.R., 2017, Regional water table (2016) in the Mojave River and Morongo groundwater basins, southwestern Mojave Desert, California: U.S. Geological Survey Scientific Investigations Map 3391, scale 1:170,000, https://doi.org/10.3133/sim3391.","productDescription":"Map: 42.62 x 37.53 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-083520","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":349478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3391/coverthb_.jpg"},{"id":349479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3391/sim3391.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3391"},{"id":349633,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GB2291","text":"Data Release","description":"SIM 3391","linkHelpText":"Regional Water Table (2016) in the Mojave River and Morongo Groundwater Basins, Southwestern Mojave Desert, California Data Release"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert, Mojave River and Morongo groundwater basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116,\n 34.0833\n ],\n [\n -117.8333,\n 34.0833\n ],\n [\n -117.8333,\n 35.25\n ],\n [\n -116,\n 35.25\n ],\n [\n -116,\n 34.0833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Availability of water is an important issue in northeastern Arizona because of continued water requirements for industrial and municipal use by a growing population and because of low precipitation in the arid climate of the Black Mesa area. Precipitation in the area typically is between 6 and 16 inches per year.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from January 2013 to December 2015. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In 2013, total groundwater withdrawals were 3,980 acre-feet (ft), in 2014 total withdrawals were 4,170 acre-ft, and in 2015 total withdrawals were 3,970 acre-ft. From 2013 to 2015 total withdrawals varied by less than 5 percent.
From 2014 to 2015, annually measured water levels in the Black Mesa area declined in 9 of 15 wells that were available for comparison in the unconfined areas of the N aquifer, and the median change was -0.1 feet. Water levels declined in 3 of 16 wells measured in the confined area of the aquifer. The median change for the confined area of the aquifer was 0.6 feet. From the prestress period (prior to 1965) to 2015, the median water-level change for 34 wells in both the confined and unconfined areas was -13.2 feet; the median water-level changes were -1.7 feet for 16 wells measured in the unconfined areas and -42.3 feet for 18 wells measured in the confined area.
Spring flow was measured at four springs in 2014. Flow fluctuated during the period of record for Burro Spring and Unnamed Spring near Dennehotso, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Pasture Canyon Spring. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Unnamed Spring near Dennehotso has fluctuated for the period of record. Trend analysis for discharge at Moenkopi and Pasture Canyon Springs yielded a slope significantly different (p<0.05) from zero.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2015), Dinnebito Wash near Sand Springs 09401110 (1993 to 2015), Polacca Wash near Second Mesa 09400568 (1994 to 2015), and Pasture Canyon Springs 09401265 (2004 to 2015). Median winter flows (November through February) of each water year were used as an index of the amount of groundwater discharge at the above-named sites. For the period of record of each streamflow-gaging station, the median winter flows have generally remained constant, which suggests no change in groundwater discharge.
In 2014, water samples collected from four springs in the Black Mesa area were analyzed for selected chemical constituents, and the results were compared with previous analyses. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 25 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. Concentrations of dissolved solids, chloride, and sulfate at Burro Spring and Unnamed Spring near Dennehotso have varied for the period of record, but there is no increasing or decreasing statistical trend in the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171127","collaboration":"Prepared in cooperation with the Navajo Nation and the Arizona Department of Water Resources","usgsCitation":"Macy, J.P., and Mason, J.P., 2017, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2013–2015: U.S. Geological Survey Open-File Report 2017–1127, 49 p., https://doi.org/10.3133/ofr20171127.","productDescription":"v., 49 p.","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-083213","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":349866,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1127/coverthb.jpg"},{"id":349867,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1127/ofr20171127.pdf","text":"Report","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1127"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 35.5\n ],\n [\n -109.5,\n 35.5\n ],\n [\n -109.5,\n 37\n ],\n [\n -111.5,\n 37\n ],\n [\n -111.5,\n 35.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The recent steep increase in seismicity rates in Oklahoma, southern Kansas, and other parts of the central United States led the U.S. Geological Survey (USGS) to develop, for the first time, a probabilistic seismic hazard forecast for one year (2016) that incorporates induced seismicity. In this study, we explore a process to ground‐truth the hazard model by comparing it with two databases of observations: modified Mercalli intensity (MMI) data from the “Did You Feel It?” (DYFI) system and peak ground acceleration (PGA) values from instrumental data. Because the 2016 hazard model was heavily based on earthquake catalogs from 2014 to 2015, this initial comparison utilized observations from these years. Annualized exceedance rates were calculated with the DYFI and instrumental data for direct comparison with the model. These comparisons required assessment of the options for converting hazard model results and instrumental data from PGA to MMI for comparison with the DYFI data. In addition, to account for known differences that affect the comparisons, the instrumental PGA and DYFI data were declustered, and the hazard model was adjusted for local site conditions. With these adjustments, examples at sites with the most data show reasonable agreement in the exceedance rates. However, the comparisons were complicated by the spatial and temporal completeness of the instrumental and DYFI observations. Furthermore, most of the DYFI responses are in the MMI II–IV range, whereas the hazard model is oriented toward forecasts at higher ground‐motion intensities, usually above about MMI IV. Nevertheless, the study demonstrates some of the issues that arise in making these comparisons, thereby informing future efforts to ground‐truth and improve hazard modeling for induced‐seismicity applications.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220170033","usgsCitation":"White, I., Liu, T., Luco, N., and Liel, A., 2017, Considerations in comparing the U.S. Geological Survey one‐year induced‐seismicity hazard models with “Did You Feel It?” and instrumental data: Seismological Research Letters, v. 89, no. 1, p. 127-137, https://doi.org/10.1785/0220170033.","productDescription":"11 p.","startPage":"127","endPage":"137","ipdsId":"IP-091828","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":351347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, 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\"}}]}","volume":"89","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-06","publicationStatus":"PW","scienceBaseUri":"5a7d6ffee4b00f54eb2441ba","contributors":{"authors":[{"text":"White, Isabel 0000-0003-3572-8969","orcid":"https://orcid.org/0000-0003-3572-8969","contributorId":201797,"corporation":false,"usgs":true,"family":"White","given":"Isabel","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":726974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Taojun","contributorId":201798,"corporation":false,"usgs":false,"family":"Liu","given":"Taojun","email":"","affiliations":[{"id":6713,"text":"University of Colorado, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":726975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":726976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liel, Abbie","contributorId":145731,"corporation":false,"usgs":false,"family":"Liel","given":"Abbie","affiliations":[{"id":16213,"text":"Dept. of Civil, Environ. and Architectural Engineering, University of Colorado","active":true,"usgs":false}],"preferred":false,"id":726977,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191367,"text":"sir20175122 - 2017 - Detection of microcystin and other cyanotoxins in lakes at Isle Royale National Park, Pictured Rocks National Lakeshore, and Sleeping Bear Dunes National Lakeshore, northern Michigan, 2012–13","interactions":[],"lastModifiedDate":"2018-09-12T17:05:27","indexId":"sir20175122","displayToPublicDate":"2017-12-05T16:20:00","publicationYear":"2017","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":"2017-5122","title":"Detection of microcystin and other cyanotoxins in lakes at Isle Royale National Park, Pictured Rocks National Lakeshore, and Sleeping Bear Dunes National Lakeshore, northern Michigan, 2012–13","docAbstract":"Although cyanotoxins released during algal blooms have become an increasing concern in surface waters across the United States, the presence of cyanotoxins in northern Michigan lakes had not been evaluated in detail. The U.S. Geological Survey and National Park Service (NPS) led a 2-year study (2012 and 2013) to determine the presence of microcystin and other algal toxins in several inland lakes at Isle Royale National Park (hereafter referred to as ISRO, Pictured Rocks National Lakeshore (hereafter referred to as PIRO), and Sleeping Bear Dunes National Lakeshore (hereafter referred to as SLBE). Samples also were collected at four sites in Lake Michigan within the SLBE. The two analytical techniques used in the study were enzyme-linked immunosorbent assays (ELISA) for microcystin, cylindrospermopsin, and saxitoxin; and liquid chromatography/tandem mass spectrometry (LC/MS/MS) for a larger suite of algal toxins. Neither cylindrospermopsin nor saxitoxin were detected in the 211 samples. Microcystin was detected in 31 percent of samples (65 of 211 samples) analyzed by the ELISA method, but no sample results exceeded the World Health Organization recreational health advisory standard for microcystin (10 micrograms per liter [µg/L]). However, about 10 percent of the samples (21 of 211 samples) that were collected from PIRO and SLBE and were analyzed by ELISA for microcystin had concentrations greater than the U.S. Environmental Protection Agency (EPA) drinking water 10-day health advisory of 0.3 µg/L for children preschool age and younger (less than 6-years old). One sample collected in 2012 from SLBE exceeded the EPA drinking water 10-day health advisory of 1.6 µg/L for school-age children through adults (6-years old and older). In 2012, the highest concentration of 2.7 µg/L was detected in Florence Lake within SLBE. Many visitors enjoy recreation in or on the water and camp in the backcountry at these national parks where the most common source of drinking water is filtered surface water.
Approximately 18 percent of the samples (39 of 211 samples) were analyzed by LC/MS/MS to confirm the ELISA results and to evaluate the samples for a larger suite of algal toxins. In general, the microcystin results between the ELISA and LC/MS/MS methods were similar; although, the ELISA results tended to be slightly higher than the summation of LC/MS/MS microcystin congeners. The slightly higher ELISA results might be because the ELISA microcystin method is reactive with the ADDA functional group common to all microcystins, and because not all microcystin congeners are included in the LC/MS/MS method. The LC/MS/MS method indicated that the congener microcystin-LR was the most frequently detected, followed by microcystin-WR and microcystin-YR.
Sixteen of the lakes included in this study also were monitored by the NPS for nutrients. Total phosphorus (TP) concentrations were, on average, highest at the ISRO lakes, whereas total nitrogen (TN) concentrations were highest at SLBE. The average annual TN:TP ratios for the 16 lakes within the national park and national lakeshores ranged from ratios of 20 to 89. Overall, results indicated a slight increase in percentage of microcystin detections with an increase in the TN:TP ratio (R-squared 0.269 and 0.340, respectively [2012 and 2013 combined dataset] derived from linear regression).
This study also indicated that even in the absence of visible algal blooms, microcystin may be present. Most microcystin concentrations did not exceed the EPA’s 10-day health advisory drinking-water benchmark. In general, these results provide a useful baseline with which to evaluate potential future changes in algal toxin concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175122","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fuller, L.M., Brennan, A.K., Fogarty, L.R., Loftin, K.A., Johnson, H.E., VanderMeulen, D.D., and Lafrancois, B.M., 2017, Detection of microcystin and other cyanotoxins in lakes at Isle Royale National Park, Pictured Rocks National Lakeshore, and Sleeping Bear Dunes National Lakeshore, northern Michigan, 2012–13: U.S. Geological Survey Scientific Investigations Report 2017–5122, 44 p., https://doi.org/10.3133/sir20175122.","productDescription":"vi, 44 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-071309","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":349614,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5122/coverthb.jpg"},{"id":349615,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5122/sir20175122.pdf","text":"Report","size":"9.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5122"}],"country":"United States","state":"Michigan","otherGeospatial":"Isle Royale National Park, Pictured Rocks National Lakeshore, Sleeping Bear Dunes National Shoreline","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.32571411132812,\n 47.8085431415187\n ],\n [\n -88.3795166015625,\n 47.8085431415187\n ],\n [\n -88.3795166015625,\n 48.20728655738642\n ],\n [\n -89.32571411132812,\n 48.20728655738642\n ],\n [\n -89.32571411132812,\n 47.8085431415187\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.63131713867188,\n 46.417979059090115\n ],\n [\n -86.00234985351562,\n 46.417979059090115\n ],\n [\n -86.00234985351562,\n 46.693725378358955\n ],\n [\n -86.63131713867188,\n 46.693725378358955\n ],\n [\n -86.63131713867188,\n 46.417979059090115\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.17469787597656,\n 44.8281172855491\n ],\n [\n -85.79704284667969,\n 44.8281172855491\n ],\n [\n -85.79704284667969,\n 45.16509478442965\n ],\n [\n -86.17469787597656,\n 45.16509478442965\n ],\n [\n -86.17469787597656,\n 44.8281172855491\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way
Suite 5
Lansing, MI 48911
Connectivity among individual marine protected areas (MPAs) is one of the most important considerations in the design of integrated MPA networks. To provide such information for managers in Hawaii, USA, a numerical circulation model was developed to determine the role of ocean currents in transporting coral larvae from natal reefs throughout the high volcanic islands of the Maui Nui island complex in the southeastern Hawaiian Archipelago. Spatially- and temporally-varying wind, wave, and circulation model outputs were used to drive a km-scale, 3-dimensional, physics-based circulation model for Maui Nui. The model was calibrated and validated using satellite-tracked ocean surface current drifters deployed during coral-spawning conditions, then used to simulate the movement of the larvae of the dominant reef-building coral, Porites compressa, from 17 reefs during eight spawning events in 2010–2013. These simulations make it possible to investigate not only the general dispersal patterns from individual coral reefs, but also how anomalous conditions during individual spawning events can result in large deviations from those general patterns. These data also help identify those reefs that are dominated by self-seeding and those where self-seeding is limited to determine their relative susceptibility to stressors and potential roadblocks to recovery. Overall, the numerical model results indicate that many of the coral reefs in Maui Nui seed reefs on adjacent islands, demonstrating the interconnected nature of the coral reefs in Maui Nui and providing a key component of the scientific underpinning essential for the design of a mutually supportive network of MPAs to enhance conservation of coral reefs.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2017.00381","usgsCitation":"Storlazzi, C.D., van Ormondt, M., Chen, Y., and Elias, E.P., 2017, Modeling fine-scale coral larval dispersal and interisland connectivity to help designate mutually-supporting coral reef marine protected areas: Insights from Maui Nui, Hawaii: Frontiers in Marine Science, v. 4, 381, 14 p., https://doi.org/10.3389/fmars.2017.00381.","productDescription":"381, 14 p.","ipdsId":"IP-074125","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469246,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2017.00381","text":"Publisher Index Page"},{"id":438128,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NK3C59","text":"USGS data release","linkHelpText":"Physics-based numerical circulation model outputs of ocean surface circulation during the 2010-2013 summer coral-spawning seasons in Maui Nui, Hawaii, USA"},{"id":349887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kahoolawe, Lanai, Maui, Molokai","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.40386962890625,\n 20.406420474920292\n ],\n [\n -155.85479736328125,\n 20.406420474920292\n ],\n [\n -155.85479736328125,\n 21.299610604945606\n ],\n [\n -157.40386962890625,\n 21.299610604945606\n ],\n [\n -157.40386962890625,\n 20.406420474920292\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-05","publicationStatus":"PW","scienceBaseUri":"5a60faeee4b06e28e9c229bc","contributors":{"authors":[{"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":724730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Ormondt, Maarten","contributorId":200365,"corporation":false,"usgs":false,"family":"van Ormondt","given":"Maarten","email":"","affiliations":[],"preferred":false,"id":724731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chen, Yi-Leng","contributorId":173747,"corporation":false,"usgs":false,"family":"Chen","given":"Yi-Leng","email":"","affiliations":[{"id":27289,"text":"Department of Meteorology, University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":724732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elias, Edwin P. L.","contributorId":194055,"corporation":false,"usgs":false,"family":"Elias","given":"Edwin","email":"","middleInitial":"P. L.","affiliations":[],"preferred":false,"id":724733,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207060,"text":"70207060 - 2017 - Assessing the global distribution of river fisheries harvest: A systematic map protocol","interactions":[],"lastModifiedDate":"2020-12-08T17:49:51.927554","indexId":"70207060","displayToPublicDate":"2017-12-04T15:55:06","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5897,"text":"Environmental Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the global distribution of river fisheries harvest: A systematic map protocol","docAbstract":"Although surface freshwater comprises < 0.01% of the total water volume of earth, freshwater inland capture fisheries and aquaculture represent 40% of the global reported finfish harvest. While the social, economic, and ecological importance of inland fish and fisheries is difficult to overstate, they are often undervalued and underappreciated. Accurate information about these highly dispersed fisheries is inherently difficult to acquire, often unreported, and not collected in a standardized format globally. A standardized river fishery database is needed for managing aquatic systems as well as for defining relevant development policies. Here, we describe our methodology to search, identify, and describe available river fisheries information to create a harmonized global database of river fisheries harvest. This database will provide the first global database of spatially and temporally explicit river fisheries data. The database can be used to identify locations, hotspots of data collection, and gaps in existing knowledge and will be especially important to inform studies and management at larger spatial scales (i.e., watershed, regional, or global scales). This database will also be critical for developing fish biomass models for rivers, which can provide managers with information critical for decision-making, such as improved valuation methods for river fish and fisheries.
This systematic map protocol describes the methodology to search, identify, and describe available information on river fish and fisheries across the globe. We define river fisheries as “both capture and aquaculture of river finfish species for food, income, or recreation”. River fish species are those finfish that live part, or all of their lives in rivers. The searches will be conducted for the period from 1950 to present using bibliographic databases and grey literature sources. To identify relevant evidence, pre-defined inclusion and exclusion criteria will be used to screen articles at title, abstract, and full text. A searchable database containing extracted meta-data from relevant included studies will be developed and presented as a geodatabase. The final systematic map will consist of a descriptive narrative report of the distribution and content of river fish literature including a geodatabase of available information.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-017-0107-x","usgsCitation":"Romulo, C., Basher, Z., Lynch, A., Kao, Y., and Taylor, W., 2017, Assessing the global distribution of river fisheries harvest: A systematic map protocol: Environmental Evidence, v. 6, 29, 10 p., https://doi.org/10.1186/s13750-017-0107-x.","productDescription":"29, 10 p.","ipdsId":"IP-085655","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":461325,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-017-0107-x","text":"Publisher Index Page"},{"id":369924,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2017-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Romulo, Chelsie 0000-0003-1612-1969","orcid":"https://orcid.org/0000-0003-1612-1969","contributorId":221032,"corporation":false,"usgs":false,"family":"Romulo","given":"Chelsie","email":"","affiliations":[],"preferred":false,"id":776684,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Basher, Zeenatul 0000-0002-6439-8324 zbasher@usgs.gov","orcid":"https://orcid.org/0000-0002-6439-8324","contributorId":48118,"corporation":false,"usgs":true,"family":"Basher","given":"Zeenatul","email":"zbasher@usgs.gov","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":false,"id":776685,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynch, Abigail 0000-0001-8449-8392 ajlynch@usgs.gov","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":169460,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","email":"ajlynch@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":776686,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kao, Yu-Chun 0000-0001-5552-909X ykao@usgs.gov","orcid":"https://orcid.org/0000-0001-5552-909X","contributorId":192240,"corporation":false,"usgs":true,"family":"Kao","given":"Yu-Chun","email":"ykao@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":776687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, William W.","contributorId":49735,"corporation":false,"usgs":false,"family":"Taylor","given":"William W.","affiliations":[],"preferred":false,"id":776688,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179694,"text":"70179694 - 2017 - New method to integrate remotely sensed hydrothermal alteration mapping into quantitative mineral resource assessments","interactions":[],"lastModifiedDate":"2019-03-27T09:58:49","indexId":"70179694","displayToPublicDate":"2017-12-04T09:57:04","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"New method to integrate remotely sensed hydrothermal alteration mapping into quantitative mineral resource assessments","docAbstract":"There are many examples where the use of chemicals have had profound unintended consequences, such as fertilizers reducing crop yields (paradox of enrichment) and insecticides increasing insect pests (by reducing natural biocontrol). Recently, the application of agrochemicals, such as agricultural disinfectants and fungicides, has been explored as an approach to curb the pathogenic fungus, Batrachochytrium dendrobatidis (Bd), which is associated with worldwide amphibian declines. However, the long-term, net effects of early-life exposure to these chemicals on amphibian disease risk have not been thoroughly investigated. Using a combination of laboratory experiments and analysis of data from the literature, we explored the effects of fungicide exposure on Bd infections in two frog species. Extremely low concentrations of the fungicides azoxystrobin, chlorothalonil, and mancozeb were directly toxic to Bd in culture. However, estimated environmental concentrations of the fungicides did not reduce Bd on Cuban tree frog (Osteopilus septentrionalis) tadpoles exposed simultaneously to any of these fungicides and Bd, and fungicide exposure actually increased Bd-induced mortality. Additionally, exposure to any of these fungicides as tadpoles resulted in higher Bd abundance and greater Bd-induced mortality when challenged with Bd post-metamorphosis, an average of 71 d after their last fungicide exposure. Analysis of data from the literature revealed that previous exposure to the fungicide itraconazole, which is commonly used to clear Bd infections, made the critically endangered booroolong frog (Litoria booroolongensis) more susceptible to Bd. Finally, a field survey revealed that Bd prevalence was positively associated with concentrations of fungicides in ponds. Although fungicides show promise for controlling Bd, these results suggest that, if fungicides do not completely eliminate Bd or if Bd recolonizes, exposure to fungicides has the potential to do more harm than good. To ensure that fungicide applications have the intended consequence of curbing amphibian declines, researchers must identify which fungicides do not compromise the pathogen resistance mechanisms of amphibians.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1607","usgsCitation":"Rohr, J.R., Brown, J., Battaglin, W.A., McMahon, T.A., and Reylea, R.A., 2017, A pesticide paradox: Fungicides indirectly increase fungal infections: Ecological Applications, v. 27, no. 8, p. 2290-2302, https://doi.org/10.1002/eap.1607.","productDescription":"13 p.","startPage":"2290","endPage":"2302","ipdsId":"IP-079804","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":469247,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5711531","text":"External Repository"},{"id":349651,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-29","publicationStatus":"PW","scienceBaseUri":"5a60faf5e4b06e28e9c22a0a","contributors":{"authors":[{"text":"Rohr, Jason R.","contributorId":18502,"corporation":false,"usgs":true,"family":"Rohr","given":"Jason","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":724345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jenise","contributorId":201101,"corporation":false,"usgs":false,"family":"Brown","given":"Jenise","email":"","affiliations":[],"preferred":false,"id":724346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Battaglin, William A. 0000-0001-7287-7096 wbattagl@usgs.gov","orcid":"https://orcid.org/0000-0001-7287-7096","contributorId":1527,"corporation":false,"usgs":true,"family":"Battaglin","given":"William","email":"wbattagl@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":724344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McMahon, Teagan A.","contributorId":201102,"corporation":false,"usgs":false,"family":"McMahon","given":"Teagan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reylea, Rick A.","contributorId":201103,"corporation":false,"usgs":false,"family":"Reylea","given":"Rick","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724348,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202035,"text":"70202035 - 2017 - 3D Pressure‐limited approach to model and estimate CO2 injection and storage capacity: saline Mount Simon Formation","interactions":[],"lastModifiedDate":"2019-02-07T13:49:56","indexId":"70202035","displayToPublicDate":"2017-12-01T13:47:19","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5800,"text":"Greenhouse Gases: Science and Technology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"3D Pressure‐limited approach to model and estimate CO2 injection and storage capacity: saline Mount Simon Formation","title":"3D Pressure‐limited approach to model and estimate CO2 injection and storage capacity: saline Mount Simon Formation","docAbstract":"To estimate the carbon dioxide (CO2) injection and storage capacity of saline formations, we used Tough2‐ECO2N simulation software to develop a pressure‐limited (dynamic) simulation approach based on applying three‐dimensional (3D) numerical simulation only on the effective injection area (Aeff) surrounding each injection well. A statistical analysis was performed to account for existing reservoir heterogeneity and property variations. The accuracy of the model simulation results (such as CO2 plume extension and induced injection well bottomhole pressure values) were tested and verified against the data obtained from the Decatur CO2 injection study of the Mount Simon Formation. Next, we designed a full‐field CO2 injection pattern by populating the core sections of this formation with a series of the simulated effective injection areas such that each simulated Aeff acts as a closed domain. The results of this analysis were used to estimate the optimum number and location of the required CO2 injection wells, along with the dynamic annual CO2 injection rate and overall pressure‐limited storage capacity of this formation. This approach enabled us to model separate CO2 injection activities independently at different sections of the same saline formation and to model and simulate faults and natural barriers by considering them as boundary conditions for each simulated Aeff without constructing full‐field models. Using this approach, a series of modeled Aeff with relevant properties may be redesigned to model any other saline formation with a similar structure.
","language":"English","publisher":"Wiley","doi":"10.1002/ghg.1701","usgsCitation":"Jahediesfanjani, H., Warwick, P., and Anderson, S.T., 2017, 3D Pressure‐limited approach to model and estimate CO2 injection and storage capacity: saline Mount Simon Formation: Greenhouse Gases: Science and Technology, v. 7, no. 6, p. 1080-1096, https://doi.org/10.1002/ghg.1701.","productDescription":"17 p.","startPage":"1080","endPage":"1096","ipdsId":"IP-084315","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":469251,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ghg.1701","text":"Publisher Index Page"},{"id":361078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mount Simon Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91,\n 37\n ],\n [\n -84,\n 37\n ],\n [\n -84,\n 41\n ],\n [\n -91,\n 41\n ],\n [\n -91,\n 37\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"6","noUsgsAuthors":false,"publicationDate":"2017-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Jahediesfanjani, Hossein 0000-0001-6281-5166 hjahediesfanjani@usgs.gov","orcid":"https://orcid.org/0000-0001-6281-5166","contributorId":193397,"corporation":false,"usgs":false,"family":"Jahediesfanjani","given":"Hossein","email":"hjahediesfanjani@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":756795,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":207248,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":756796,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":756797,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192271,"text":"ds1070 - 2017 - Single-beam bathymetry data collected in 2015 from Grand Bay, Alabama-Mississippi","interactions":[],"lastModifiedDate":"2025-05-13T16:26:15.809543","indexId":"ds1070","displayToPublicDate":"2017-12-01T12:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1070","title":"Single-beam bathymetry data collected in 2015 from Grand Bay, Alabama-Mississippi","docAbstract":"As part of the Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey within the estuarine, open-bay, and tidal creek environments of Grand Bay, Alabama-Mississippi, from May to June 2015. The goal of the SSIEES project is to assess the physical controls of sediment and material exchange between wetlands and estuarine environments along the northern Gulf of Mexico, specifically Grand Bay, Alabama-Mississippi; Vermilion Bay, Louisiana; and, along the east coast, within Chincoteague Bay, Virginia-Maryland. The data described in this report provide baseline bathymetric information for future research investigating wetland-marsh evolution, sediment transport, erosion, recent and long-term geomorphic change, and can also support the modeling of changes in response to restoration and storm impacts. The survey area encompasses more than 40 square kilometers of Grand Bay’s waters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1070","usgsCitation":"DeWitt, N.T., Stalk, C.A., Smith, C.G., Locker, S.D., Fredericks, J.J., McCloskey, T.A., and Wheaton, C.J., 2017, Single-beam bathymetry data collected in 2015 from Grand Bay, Alabama-Mississippi: U.S. Geological Survey Data Series 1070, https://doi.org/10.3133/ds1070.","productDescription":"HTML Document; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081056","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":349002,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1070","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1070"},{"id":349004,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7NP22M2","text":"USGS data release","description":"USGS data release","linkHelpText":"Single-Beam Bathymetry Data Collected in 2015 from Grand Bay, Mississippi/Alabama"},{"id":349001,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1070/coverthb.jpg"}],"country":"United States","state":"Alabama, Mississippi","otherGeospatial":"Grand Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.41487884521484,\n 30.328434677542585\n ],\n [\n -88.30467224121092,\n 30.328434677542585\n ],\n [\n -88.30467224121092,\n 30.419960083267238\n ],\n [\n -88.41487884521484,\n 30.419960083267238\n ],\n [\n -88.41487884521484,\n 30.328434677542585\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Climatic flux together with anthropogenic changes in land use and land cover pose major threats to wildlife, but our understanding of their combined impacts is limited. In arid southwestern North America, ferruginous pygmy-owls (Glaucidium brasilianum) are of major conservation concern due to marked declines in abundance linked to changes in land use and land cover during the past century. We reassessed abundance trends of pygmy-owls in northern Mexico across 17 years (2000-2016), which included data gathered over four additional years since inferences were last reported. We also assessed spatiotemporal trends in territory occupancy (n = 151 territories) across a much larger area that spanned 14 watershed regions in northern Mexico and adjacent Arizona over 16 years (2001-2016). Finally, we evaluated the influence of temperature, precipitation, land-use and land-cover change, spatial variation in local habitat quality, and interactions among these factors on occupancy dynamics. Large increases in abundance in 2015 and 2016 eliminated evidence of population declines that was described recently (e.g., Flesch 2014a) based on two modeling approaches. Moreover, there was little evidence of systematic temporal declines in territory occupancy across the broader bi-national study area, or for population units in Mexico and the adjacent U.S. Instead, occupancy dynamics varied at smaller spatial scales among watershed regions. We found that subpopulations in six regions declined or marginally declined across time, including two in the U.S. that declined to extinction; subpopulations in six other regions were stable; and those in two regions increased or marginally increased. Although variation in territory occupancy was associated with changes in temperature, precipitation, anthropogenic disturbance, and local differences in habitat quality, evidence for interactions among these factors was much greater than that for additive relationships. Territory occupancy declined with rising minimum air temperatures during winter at a much greater rate in disturbed landscapes compared to those with little to no anthropogenic disturbance. Moreover, occupancy increased with annual precipitation at increasingly positive rates as local territory quality increased. Such results suggest a complex set of processes simultaneously drove changes in territory occupancy, likely by influencing food abundance and the quantity, connectivity, and quality of habitat. Management focused on 1) protecting high-quality habitat, 2) enhancing and creating habitat (e.g., nest-cavity augmentation, riparian restoration), 3) reducing deleterious changes in land use and land cover, and 4) increasing landscape connectivity through both passive (e.g., landscape planning and restoration) and active (e.g., facilitated dispersal, translocations) techniques will enhance recovery prospects for pygmyowls.
","language":"English","publisher":"University of Arizona","collaboration":"U.S. Fish and Wildlife Service","usgsCitation":"Flesch, A., Nagler, P.L., Jarchow, C., and Alexander, R.B., 2017, Population trends, extinction risk, and conservation guidelines for ferruginous pygmy-owls in the Sonoran Desert, 38 p.","productDescription":"38 p.","ipdsId":"IP-088307","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Sonoran Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.92108833595975,\n 32.77933725310251\n ],\n [\n -112.92108833595975,\n 30.54552222291086\n ],\n [\n -109.9911552778319,\n 30.54552222291086\n ],\n [\n -109.9911552778319,\n 32.77933725310251\n ],\n [\n -112.92108833595975,\n 32.77933725310251\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Flesch, Aaron","contributorId":213954,"corporation":false,"usgs":false,"family":"Flesch","given":"Aaron","affiliations":[{"id":38937,"text":"School of Natural Resources and the Environment, University of Arizona, The Desert Laboratory - 1675 Anklam Rd., Tucson, AZ 85745 flesch@email.arizona.edu","active":true,"usgs":false}],"preferred":false,"id":758803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":758802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":758804,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Richard B. 0000-0001-9166-0626 ralex@usgs.gov","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":541,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","email":"ralex@usgs.gov","middleInitial":"B.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":953605,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199082,"text":"70199082 - 2017 - A diatom voucher flora from selected southeast rivers (USA)","interactions":[],"lastModifiedDate":"2018-09-04T10:23:31","indexId":"70199082","displayToPublicDate":"2017-12-01T10:14:12","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3081,"text":"Phytotaxa","active":true,"publicationSubtype":{"id":10}},"title":"A diatom voucher flora from selected southeast rivers (USA)","docAbstract":"This flora is intended to serve as an image voucher for samples analyzed for the U.S. Geological Survey Southeast Stream Quality Assessment (SESQA). The SESQA study included measurement of watershed and water quality parameters to determine the factors that have the greatest potential to alter biotic condition. Algal samples were collected at 108 sites in 2014, from streams representing gradients in chemical and physical alteration across the southeast region. More than 375 taxa were identified during analysis for species composition and abundance. This manuscript documents the flora with light micrographs of specimens representative of their morphologic range. We define “voucher flora” as images of specimens and the names applied to those specimens for a given project. Taxonomic vouchers from federal programs have generally not been made public, yet they are a salient element of a well-documented species dataset, particularly for long-term studies. This study is part of a broader effort to improve and encourage taxonomic consistency in federal, state and local programs by accessible identification resources and inter-lab comparisons.
Noninvasive geophysical site characterization methods were used in two recent projects to obtain shear-wave velocity (VS) profiles to a minimum depth of 30 m and the time-averaged VS of the upper 30 meters (VS30) at seismic station sites. These projects include the 2009 American Recovery and Reinvestment Act (ARRA) funded U.S. Geological Survey site characterization project for 191 sites in California and the Central-eastern United States (CEUS), and the 2012 Electric Power Research Institute (EPRI) funded project for 33 additional CEUS sites. These sites are located in rural to urban settings with topographic conditions ranging from relatively flat sedimentary basins to mountaintop ridges. About 60 percent of the ARRA sites and 80 percent of the EPRI sites are located on rock or have thin sediment cover over rock, including Quaternary volcanic rock, Tertiary sediments and sedimentary rock, and Mesozoic (or older) crystalline or sedimentary rock. The remaining sites consist of thick sequences of Quaternary sediments overlying older sediments and rock.
ARRA sites were characterized using non-invasive active and passive surface-wave methods, including the horizontal-tovertical spectral ratio (HVSR) method and one or more of the following: spectral analysis of surface waves (SASW), multichannel analysis of surface waves (MASW; Rayleigh and Love waves) and, occasionally, array microtremor (linear and 2-D arrays) methods. P-wave seismic refraction data were also acquired at rock and shallow-rock sites. S-wave seismic refraction and/or Love-wave MASW methods were applied at sites where characterization proved difficult with Rayleighwave methods. Based on our experience from the ARRA project, we acquired Rayleigh- and Love-wave based MASW and P- and S-wave refraction data for the EPRI project at CEUS sites.
The HVSR method was found to be useful for identifying shallow-rock sites and for evaluating the relative variability of the depth-to-rock interface beneath the seismic station and the testing array(s). The fundamental mode modeling assumption was generally valid at most of these sites; nevertheless, multi-mode or effective-mode modeling routines were occasionally required, particularly in the case of shallow high-velocity layers. Deep sediment sites were characterized using active and, when appropriate, passive surface-wave based methods. Rock and shallow sediment sites were generally more challenging to characterize than deep sediment sites. About 10 percent of rock sites could not be characterized using surface wave methods, thus these sites were characterized using body-wave refraction methods. Love wave methods were found to be more effective than Rayleigh wave methods at some rock and shallow-rock sites (e.g., sites with shallow rock and sites with a thin low-velocity, highly attenuating surface layer). Lateral velocity variability was found to be very common at rock and shallow-rock sites, often causing significant scatter in the surface-wave dispersion data. Seismic refraction models have demonstrated that it may not be unusual for VS30 to vary by 20 percent, or more, over small distances (several tens of meters) at such sites. Based on these experiences, it is important to consider the application of combinations of methods when using noninvasive geophysical approaches to characterize seismic site conditions.
","conferenceTitle":"16th World Conference on Earthquake Engineering, 16WCEE 2017","conferenceDate":"January 9-13, 2017","conferenceLocation":"Santiago, Chile","language":"English","publisher":"International Association for Earthquake Engineering","usgsCitation":"Martin, A., Yong, A., Stephenson, W.J., Boatwright, J., and Diehl, J., 2017, Geophysical characterization of seismic station sites in the United States – The importance of a flexible, multi-method approach, 16th World Conference on Earthquake Engineering, 16WCEE 2017, Santiago, Chile, January 9-13, 2017, 19 p.","productDescription":"19 p.","ipdsId":"IP-080676","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":481977,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.wcee.nicee.org/wcee/sixteenth_conf_Santiago/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.07855248193185,\n 32.53666625001908\n ],\n [\n -114.30928863190336,\n 32.63838082249163\n ],\n [\n -114.18940679698169,\n 34.53764946137473\n ],\n [\n -119.39424118924363,\n 38.572056705043025\n ],\n [\n -123.29737686526406,\n 37.63629841187134\n ],\n [\n -120.31399583477251,\n 34.0619554841515\n ],\n [\n -117.07855248193185,\n 32.53666625001908\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.56029556803429,\n 36.997446902950074\n ],\n [\n -99.86195232972082,\n 34.14086982696833\n ],\n [\n -91.74686942614534,\n 33.02956856409422\n ],\n [\n -84.68084295679529,\n 31.754844006785248\n ],\n [\n -81.4025521969889,\n 32.03675981430574\n ],\n [\n -76.00210444134004,\n 35.756079891470606\n ],\n [\n -71.27311244517016,\n 44.469348399372535\n ],\n [\n -74.529776508883,\n 45.025831483992505\n ],\n [\n -77.26839093048407,\n 43.03532376235151\n ],\n [\n -81.20361675467349,\n 41.569243273876594\n ],\n [\n -86.76267624679905,\n 41.9776316577331\n ],\n [\n -91.49798799968512,\n 40.22868842092885\n ],\n [\n -98.56029556803429,\n 36.997446902950074\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, Antony","contributorId":243672,"corporation":false,"usgs":false,"family":"Martin","given":"Antony","affiliations":[],"preferred":false,"id":927099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stephenson, William J. 0000-0001-8699-0786 wstephens@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-0786","contributorId":695,"corporation":false,"usgs":true,"family":"Stephenson","given":"William","email":"wstephens@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":927101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boatwright, J.","contributorId":87297,"corporation":false,"usgs":true,"family":"Boatwright","given":"J.","email":"","affiliations":[],"preferred":false,"id":927172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diehl, John","contributorId":350842,"corporation":false,"usgs":false,"family":"Diehl","given":"John","affiliations":[{"id":83844,"text":"GEOVision, Incorporated","active":true,"usgs":false}],"preferred":false,"id":927102,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198869,"text":"70198869 - 2017 - Preface: The lunar reconnaissance orbiter","interactions":[],"lastModifiedDate":"2018-08-24T12:21:07","indexId":"70198869","displayToPublicDate":"2017-12-01T08:54:57","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Preface: The lunar reconnaissance orbiter","docAbstract":"When the call for papers for a special issue of Icarus devoted to analysis of data from the Lunar Reconnaissance Orbiter mission was announced in March 2015 we envisioned a single issue with only a possibility of a second. We certainly were gratified by the response from within and outside the LRO instrument teams such that we were compelled to publish this the third and final volume. It is a testament to the Moon as object that enhances our understanding of the history of the Earth-Moon system, the Solar System as a whole, and geologic processes that take place on the Moon and other atmosphere-less bodies. Many of the publications included lead authors outside the LRO team of co-investigators, using data from multiple instruments from LRO and other recent missions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2017.09.001","usgsCitation":"Keller, J., Gaddis, L.R., Petro, N.E., and Aharonson, O., 2017, Preface: The lunar reconnaissance orbiter: Icarus, v. 298, p. 1-1, https://doi.org/10.1016/j.icarus.2017.09.001.","productDescription":"1 p.","startPage":"1","endPage":"1","ipdsId":"IP-099396","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":356688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"298","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a365e4b0702d0e843048","contributors":{"authors":[{"text":"Keller, John W","contributorId":207212,"corporation":false,"usgs":false,"family":"Keller","given":"John W","affiliations":[{"id":37479,"text":"NASA Goddard Space Flight Center, Greenbelt, MD","active":true,"usgs":false}],"preferred":false,"id":743185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaddis, Lisa R. 0000-0001-9953-5483 lgaddis@usgs.gov","orcid":"https://orcid.org/0000-0001-9953-5483","contributorId":2817,"corporation":false,"usgs":true,"family":"Gaddis","given":"Lisa","email":"lgaddis@usgs.gov","middleInitial":"R.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":743184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petro, Noah E.","contributorId":193909,"corporation":false,"usgs":false,"family":"Petro","given":"Noah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":743186,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aharonson, Oded","contributorId":207213,"corporation":false,"usgs":false,"family":"Aharonson","given":"Oded","email":"","affiliations":[{"id":37480,"text":"Planetary Science Institute, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":743187,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195081,"text":"70195081 - 2017 - Dynamic rupture modeling of the M7.2 2010 El Mayor-Cucapah earthquake: Comparison with a geodetic model","interactions":[],"lastModifiedDate":"2018-02-08T12:43:29","indexId":"70195081","displayToPublicDate":"2017-12-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic rupture modeling of the M7.2 2010 El Mayor-Cucapah earthquake: Comparison with a geodetic model","docAbstract":"The 2010 Mw 7.2 El Mayor-Cucapah earthquake is the largest event recorded in the broader Southern California-Baja California region in the last 18 years. Here we try to analyze primary features of this type of event by using dynamic rupture simulations based on a multifault interface and later compare our results with space geodetic models. Our results show that starting from homogeneous prestress conditions, slip heterogeneity can be achieved as a result of variable dip angle along strike and the modulation imposed by step over segments. We also considered effects from a topographic free surface and find that although this does not produce significant first-order effects for this earthquake, even a low topographic dome such as the Cucapah range can affect the rupture front pattern and fault slip rate. Finally, we inverted available interferometric synthetic aperture radar data, using the same geometry as the dynamic rupture model, and retrieved the space geodetic slip distribution that serves to constrain the dynamic rupture models. The one to one comparison of the final fault slip pattern generated with dynamic rupture models and the space geodetic inversion show good agreement. Our results lead us to the following conclusion: in a possible multifault rupture scenario, and if we have first-order geometry constraints, dynamic rupture models can be very efficient in predicting large-scale slip heterogeneities that are important for the correct assessment of seismic hazard and the magnitude of future events. Our work contributes to understanding the complex nature of multifault systems.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014294","usgsCitation":"Kyriakopoulos, C., Oglesby, D.D., Funning, G.J., and Ryan, K., 2017, Dynamic rupture modeling of the M7.2 2010 El Mayor-Cucapah earthquake: Comparison with a geodetic model: Journal of Geophysical Research B: Solid Earth, v. 122, no. 12, p. 10263-10279, https://doi.org/10.1002/2017JB014294.","productDescription":"17 p.","startPage":"10263","endPage":"10279","ipdsId":"IP-085833","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469265,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jb014294","text":"Publisher Index Page"},{"id":351348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.5,\n 33\n ],\n [\n -114.5,\n 33\n ],\n [\n -114.5,\n 31.5\n ],\n [\n -116.5,\n 31.5\n ],\n [\n -116.5,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-19","publicationStatus":"PW","scienceBaseUri":"5a7d7000e4b00f54eb2441db","contributors":{"authors":[{"text":"Kyriakopoulos, Christos","contributorId":201722,"corporation":false,"usgs":false,"family":"Kyriakopoulos","given":"Christos","affiliations":[{"id":12655,"text":"University of California, Riverside","active":true,"usgs":false}],"preferred":false,"id":726852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oglesby, David D.","contributorId":201723,"corporation":false,"usgs":false,"family":"Oglesby","given":"David","email":"","middleInitial":"D.","affiliations":[{"id":12655,"text":"University of California, Riverside","active":true,"usgs":false}],"preferred":false,"id":726853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Funning, Gareth J. 0000-0002-8247-0545","orcid":"https://orcid.org/0000-0002-8247-0545","contributorId":172418,"corporation":false,"usgs":false,"family":"Funning","given":"Gareth","email":"","middleInitial":"J.","affiliations":[{"id":6984,"text":"UC Riverside","active":true,"usgs":false}],"preferred":false,"id":726854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryan, Kenneth 0000-0003-3933-3163 kryan@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-3163","contributorId":191921,"corporation":false,"usgs":true,"family":"Ryan","given":"Kenneth","email":"kryan@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":726851,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195170,"text":"70195170 - 2017 - Developing enterprise tools and capacities for large-scale natural resource monitoring: A visioning workshop","interactions":[],"lastModifiedDate":"2018-02-08T16:05:38","indexId":"70195170","displayToPublicDate":"2017-12-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Developing enterprise tools and capacities for large-scale natural resource monitoring: A visioning workshop","docAbstract":"In October 2016, the U.S. Geological Survey (USGS), in collaboration with the Pacific Northwest Aquatic Monitoring Partnership (PNAMP, www.pnamp.org), convened a 30-person workshop, https://www.pnamp.org/event/5509, to identify and prioritize development of enterprise systems for programs that monitor the status and trends of species populations and their terrestrial, aquatic, and marine habitats. Participants included representatives from federal natural resource research and land management organizations and nongovernmental organizations that manage natural resource monitoring programs.\nObjectives of the workshop were: 1) identify resources that support natural resource monitoring programs working across the data life cycle; 2) prioritize desired capacities and tools to facilitate monitoring design and implementation; 3) identify standards and best practices that improve discovery, accessibility, and interoperability of data across programs and jurisdictions; and 4) contribute to an emerging community of practice focused on natural resource monitoring.","language":"English","publisher":"Pacific Northwest Aquatic Monitoring Partnership","usgsCitation":"Bayer, J.M., Weltzin, J., and Scully, R.A., 2017, Developing enterprise tools and capacities for large-scale natural resource monitoring: A visioning workshop, v, 44 p.","productDescription":"v, 44 p.","numberOfPages":"49","ipdsId":"IP-092921","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":351384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":351133,"type":{"id":15,"text":"Index Page"},"url":"https://www.pnamp.org/document/5990"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7d6ffee4b00f54eb2441c6","contributors":{"authors":[{"text":"Bayer, Jennifer M. 0000-0001-9564-3110 jbayer@usgs.gov","orcid":"https://orcid.org/0000-0001-9564-3110","contributorId":3393,"corporation":false,"usgs":true,"family":"Bayer","given":"Jennifer","email":"jbayer@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":727287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weltzin, Jake 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":196323,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":727286,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scully, Rebecca A. 0000-0003-0704-8907 rscully@usgs.gov","orcid":"https://orcid.org/0000-0003-0704-8907","contributorId":191891,"corporation":false,"usgs":true,"family":"Scully","given":"Rebecca","email":"rscully@usgs.gov","middleInitial":"A.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":727288,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192105,"text":"70192105 - 2017 - Connecting the Soda–Avawatz and Bristol–Granite Mountains faults with gravity andaeromagnetic data, Mojave Desert, California","interactions":[],"lastModifiedDate":"2017-12-15T13:15:56","indexId":"70192105","displayToPublicDate":"2017-12-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Connecting the Soda–Avawatz and Bristol–Granite Mountains faults with gravity andaeromagnetic data, Mojave Desert, California","docAbstract":"The Soda–Avawatz and Bristol–Granite Mountains faults are considered by some to form the northeastern margin of the eastern California shear zone yet their connectivity and extents are obscured by surficial deposits and the estimates of total right-lateral offset from geologic data range from 0 to as much as 24 km. We use gravity and recently released detailed aeromagnetic data to map strands of these faults, examine structure within the fault zones and provide estimates of right-lateral offset. Gradients in gravity and aeromagnetic data define physical property contrasts that coincide with mapped strands of the faults and allow for extension of these faults, where concealed, to indicate continuity between the Soda–Avawatz and Bristol–Granite Mountains faults. Gravity data reveal local tectonic basins west of Silver Lake, beneath Soda Lake, and southwest of the Marble Mountains that are approximately 9–15 km long, 3–5 km wide, and 1–1.5 km deep. The basins are located where the local fault traces strike more northerly than the overall fault zone strike, suggesting that these basins are transtensional (pull-apart). If the lengths of these basins can be used as a proxy for rightlateral offset, the Soda–Avawatz and Bristol–Granite Mountains faults may have up to 9–15 km of post-early Miocene offset, consistent with our offset estimates from correlative magnetic anomalies across the fault zone.
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