Lake Sinai Viruses (Sinaivirus) are commonly detected in honey bees (Apis mellifera) but no disease phenotypes or fitness consequences have yet been demonstrated. This viral group is genetically diverse, lacks obvious geographic structure, and multiple lineages can co-infect individual bees. While phylogenetic analyses have been performed, the molecular evolution of LSV has not been studied extensively. Here, I use LSV isolates from GenBank as well as contigs assembled from honey bee Sequence Read Archive (SRA) accessions to better understand the evolutionary history of these viruses. For each ORF, substitution rate variation, codon usage, and tests of positive selection were evaluated. Outlier regions of high or low diversity were sought with sliding window analysis and the role of recombination in creating LSV diversity was explored. Phylogenetic analysis consistently identified two large clusters of sequences that correspond to the current LSV1 and LSV2 nomenclature, however lineages sister to LSV1 were the most frequently detected in honey bee SRA accessions. Different expression levels among ORFs suggested the occurrence of subgenomic transcripts. ORF1 and RNA-dependent RNA polymerase had higher evolutionary rates than the capsid and ORF4. A hypervariable region of the ORF1 protein-coding sequence was identified that had reduced selective constraint, but a site-based model of positive selection was not significantly more likely than a neutral model for any ORF. The only significant recombination signals detected between LSV1 and LSV2 initiated within this hypervariable region, but assumptions of the test (single-frame coding and independence of substitution rate by site) were violated. LSV codon usage differed strikingly from that of honey bees and other common honey-bee viruses, suggesting LSV is not strongly co-evolved with that host. LSV codon usage was significantly correlated with that of Varroa destructor, however, despite the relatively weak codon bias exhibited by the latter. While codon usage between the LSV1 and LSV2 clusters was similar for three ORFs, ORF4 codon usage was uncorrelated between these clades, implying rapid divergence of codon use for this ORF only. Phylogenetic placement and relative abundance of LSV isolates reconstructed from SRA accessions suggest that detection biases may be over-representing LSV1 and LSV2 in public databases relative to their sister lineages.
Occupancy models have become a valuable tool for estimating wildlife-habitat relationships and for predicting species distributions. Highly-mobile species often violate the assumption that sampling units are geographically closed shifting the probability of occupancy to be interpreted as the probability of use. We used occupancy models, in conjunction with noninvasive sampling, to estimate habitat use and predict the distribution of a highly-mobile carnivore, the American black bear (Ursus americanus) in New Mexico, USA. The top model indicated that black bears use areas with higher primary productivity and fewer roads. The predictive performance of such models is rarely validated with independent data, so we validated our model predictions with 2-independent datasets. We first assessed the correlation between predicted and observed habitat use for 28 telemetry-collared bears in the Jemez Mountains. Predicted habitat use was positively correlated with observed use for all 3 years (2012: ρ = 0.81; 2013: ρ = 0.87; 2014: ρ = 0.90). We then predicted the probability of use within a cell where a bear mortality was documented using 2043 mortality locations from sport harvest, depredation, and vehicle collisions. The probability of habitat use at a mortality location was also positively correlated with observed use by the species (2012: ρ = 0.74; 2013: ρ = 0.89; 2014: ρ = 0.93). Our validation procedure supports the notion that occupancy models can be an effective tool for estimating habitat use and predicting the distribution of highly-mobile species when the assumption of geographic closure has been violated. Our findings may be of interest to studies that are estimating habitat use for highly-mobile species that are secretive or rare, difficult to capture, or expensive to monitor with other more intensive methods.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2019.03.010","usgsCitation":"Gould, M.J., Gould, W., Cain, J.W., and Roemer, G.W., 2019, Validating the performance of occupancy models for estimating habitat use and predicting the distribution of highly-mobile species: A case study using the American black bear: Biological Conservation, v. 234, p. 28-36, https://doi.org/10.1016/j.biocon.2019.03.010.","productDescription":"9 p.","startPage":"28","endPage":"36","ipdsId":"IP-099292","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Sangre de Cristo, Sacramento, and Jemez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.479248046875,\n 32.37996146435729\n ],\n [\n -104.83154296875,\n 32.37996146435729\n ],\n [\n -104.83154296875,\n 36.712467243386264\n ],\n [\n -107.479248046875,\n 36.712467243386264\n ],\n [\n -107.479248046875,\n 32.37996146435729\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"234","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gould, Matthew J.","contributorId":201504,"corporation":false,"usgs":false,"family":"Gould","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gould, William R.","contributorId":244516,"corporation":false,"usgs":false,"family":"Gould","given":"William R.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":832574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roemer, Gary W.","contributorId":273109,"corporation":false,"usgs":false,"family":"Roemer","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":832576,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202482,"text":"sir20195013 - 2019 - Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota","interactions":[],"lastModifiedDate":"2019-03-26T08:18:03","indexId":"sir20195013","displayToPublicDate":"2019-03-21T09:45:09","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5013","displayTitle":"Hydraulic Conductivity Estimates from Slug Tests in the Big Sioux Aquifer Near Sioux Falls, South Dakota","title":"Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota","docAbstract":"Hydraulic conductivity estimates were made for 15 observation wells using slug-out (rising-head) tests in the Big Sioux aquifer near Sioux Falls, South Dakota, as part of a cooperative study with the City of Sioux Falls to characterize the hydrogeology and the extent of the Big Sioux aquifer north of the city. Well and aquifer data were collected from field measurements and drillers’ logs. Multiple slug tests were completed at each observation well with a transducer to record the change in water level and a U.S. Geological Survey standard mechanical slug to displace the well’s water column. In total, 110 slug-out test trials were completed among the 15 observation wells. Hydraulic conductivity was estimated by curve fitting with AQTESOLV Pro version 4.50.002. Hydraulic conductivity estimates ranged from 64 to 379 feet per day (ft/d). The mean, standard deviation, and median hydraulic conductivity for the 110 slug-out test trials were 171 ft/d, 73 ft/d, and 157 ft/d, respectively. The mean hydraulic conductivity calculated for each well ranged from 88 to 270 ft/d, the standard deviation ranged from 7 to 66 ft/d, and the median hydraulic conductivity ranged from 86 to 256 ft/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195013","collaboration":"Prepared in cooperation with the City of Sioux Falls","usgsCitation":"Eldridge, W.G., and Medler, C.J., 2019, Hydraulic conductivity estimates from slug tests in the Big Sioux Aquifer near Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5013, 23 p., https://doi.org/10.3133/sir20195013.","productDescription":"Report: v, 24 p., Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-100666","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":362206,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5013/coverthb.jpg"},{"id":362207,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5013/sir20195013.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5013"},{"id":362208,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release","linkHelpText":"Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","otherGeospatial":"Big Sioux Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.846997999996,\n 43.462111\n ],\n [\n -96.846997999996,\n 43.836203\n ],\n [\n -96.636738000004,\n 43.836203\n ],\n [\n -96.636738000004,\n 43.462111\n ],\n [\n -96.846997999996,\n 43.462111\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Quantitative landslide risk assessment is a key step in creating appropriate land use policies. The forced migration of those displaced by recent events in Syria has highlighted the need for studies to guide humanitarian aid and resettlement policies. In 2011, armed conflict in the region precipitated the largest refugee crisis in a generation. Over 1.5 million displaced Syrians now reside in Lebanon, rapidly changing the population distribution in geomorphically-active areas of the country. We use a multi-step process to quantitatively assess the landslide risk profile of Lebanon throughout the ongoing Syrian conflict. First, mode-specific geotechnical models are utilized to assess the individual hazard contributions of a suite of triggering scenarios and types of landslides appropriate to the varied terrain of Lebanon. Second, vulnerability estimates and population data from the United Nations High Commissioner for Refugees (UNHCR) are combined to produce scenario-specific risk. Finally, risk data is aggregated to create a comprehensive landslide risk profile for Syrian refugees in Lebanon and compared to that of the pre-conflict Lebanese population.
Remote sensing has emerged as a powerful method of characterizing river systems but is subject to several important limitations. This study focused on defining the limits of spectrally based mapping in a large river. We used multibeam echosounder (MBES) surveys and hyperspectral images from a deep, clear-flowing channel to develop techniques for inferring the maximum detectable depth,
The Kankakee River in northern Indiana flows through the area once known as the Grand Marsh. Beginning in the 1860s, anthropogenic changes to the river within Indiana resulted in downstream flooding and additional transport of sediment and nutrients. In 2015, the U.S. Geological Survey, in cooperation with the Indiana Department of Environmental Management, upgraded the gaging station Kankakee River at Shelby, Indiana, to include the collection of water-quality data. By relating continuously monitored water-quality data to discrete data collected from December 2015 through May 2018, linear regression was used to develop models for estimating concentrations of suspended sediment, total nitrogen, and total phosphorus. Developed regression models indicated a strong correlation between turbidity and specific conductance with suspended-sediment concentration (adjusted coefficient of determination equals 0.92, predicted residual error sum of squares equals 0.151), nitrate plus nitrite and specific conductance with total nitrogen (adjusted coefficient of determination equals 0.95, predicted residual error sum of squares equals 0.0248), and turbidity with total phosphorus (adjusted coefficient of determination equals 0.89, predicted residual error sum of squares equals 0.0103).
Daily loads of suspended sediment, total nitrogen, and total phosphorus were computed as the product of daily mean regression model concentrations and daily mean streamflow. Rloadest models were used to compute daily loads of each constituent during gaps in regression model loads. For 2016 and 2017, the estimated annual suspended-sediment loads were 105,000 and 91,000 tons; estimated total nitrogen loads were 8,690 and 8,890 tons; and estimated total phosphorus loads were 265 and 236 tons, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195005","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Lathrop, T.R., Bunch, A.R., and Downhour, M.S., 2019, Regression models for estimating sediment and nutrient concentrations and loads at the Kankakee River, Shelby, Indiana, December 2015 through May 2018: U.S. Geological Survey Scientific Investigation Report 2019–5005, 13 p., https://doi.org/10.3133/sir20195005.","productDescription":"Report: v, 13 p.; 2 Data Releases","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101520","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":362192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5005/coverthb.jpg"},{"id":362193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5005/sir20195005.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5005"},{"id":362194,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PE9PTD","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and rloadest models for suspended sediment, total nitrogen, and total phosphorus for Kankakee River at Shelby, Indiana, January 5, 2016 to May 31, 2018"},{"id":362195,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90EKU6X","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and Surrogate Models for Suspended Sediment, Total Nitrogen, and Total Phosphorus for the Kankakee River at Shelby, Indiana, January 5, 2016 to May 31, 2018"}],"country":"United States","state":"Indiana","city":"Shelby","otherGeospatial":"Kankakee River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.81396484375,\n 41.34897943069752\n ],\n [\n -86.08337402343749,\n 41.34588656996287\n ],\n [\n -86.08337402343749,\n 41.76721469421018\n ],\n [\n -86.81259155273438,\n 41.76823896512856\n ],\n [\n -86.81396484375,\n 41.34897943069752\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Threats to aquatic biodiversity are expressed at broad spatial scales, but identifying regional trends in abundance is challenging owing to variable sampling designs, and temporal and spatial variation in abundance. We compiled a regional dataset of brook trout Salvelinus fontinalis counts across their southern range representing 326 sites from eight states between 1982-2014, and conducted a statistical power analysis using Bayesian state-space models to evaluate the ability to detect temporal trends by characterizing posterior distributions with three approaches. A combination of monitoring periods, number of sites and electrofishing passes, decline magnitude and different revisit patterns were tested. Power increased with monitoring periods and decline magnitude. Trends in adults were better detected than young-of-the-year fish, which showed greater inter-annual variation in abundance. The addition of weather covariates to account for the temporal variation increased power only slightly. Single- and three-pass electrofishing methods were similar in power. Finally, power was higher for sampling designs with more frequent revisits over the duration of the monitoring program. Our results provide guidance for broad-scale monitoring designs for temporal trend detection.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0241","usgsCitation":"Pregler, K.C., Hanks, R.D., Childress, E., Hitt, N.P., Hocking, D.J., Letcher, B., and Kanno, Y., 2019, State-space analysis of power to detect regional brook trout population trends over time: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 11, p. 2145-2155, https://doi.org/10.1139/cjfas-2018-0241.","productDescription":"11 p.","startPage":"2145","endPage":"2155","ipdsId":"IP-098679","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":362209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"11","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pregler, Kasey C.","contributorId":149616,"corporation":false,"usgs":false,"family":"Pregler","given":"Kasey","email":"","middleInitial":"C.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":759565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanks, R. Daniel","contributorId":214286,"corporation":false,"usgs":false,"family":"Hanks","given":"R.","email":"","middleInitial":"Daniel","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":759566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Childress, Evan S.","contributorId":214287,"corporation":false,"usgs":false,"family":"Childress","given":"Evan S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":759567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568 nhitt@usgs.gov","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":4435,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"nhitt@usgs.gov","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":759564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hocking, Daniel J.","contributorId":214288,"corporation":false,"usgs":false,"family":"Hocking","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":39006,"text":"Frostburg State University","active":true,"usgs":false}],"preferred":false,"id":759568,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":167313,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":759569,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kanno, Yoichiro","contributorId":210653,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":759570,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202439,"text":"tm7C22 - 2019 - User’s manual for the Draper climate-distribution software suite with data‑evaluation tools","interactions":[],"lastModifiedDate":"2019-07-26T12:05:14","indexId":"tm7C22","displayToPublicDate":"2019-03-20T11:25:22","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C22","displayTitle":"User’s Manual for the Draper Climate-Distribution Software Suite with Data-Evaluation Tools","title":"User’s manual for the Draper climate-distribution software suite with data‑evaluation tools","docAbstract":"Development of a time series of spatially distributed climate data is an important step in the process of developing physically based environmental models requiring distributed inputs of climate data beyond what is available from observations collected at climate stations. To prepare inputs required for model-mapping units across the study area, climate data (temperature and precipitation) are distributed by combining data from gridded surfaces of mean-monthly climate-data values with (often) widely spaced daily point observations. Examples of climate-data files used to develop PRMS-formatted input files for the Merced River Basin Precipitation-Runoff Modeling System (PRMS) are included in this manual.
The Draper Climate-Distribution Software Suite (Draper Suite) consists of the Draper climate-distribution program (Draper) and several supporting pre- and post-processing applications. Draper combines spatially distributed input in the form of monthly averaged values for precipitation, maximum temperature, and minimum temperature with daily observed data from climate stations to estimate distributed climate-data values at predefined locations across a study area (typically a drainage basin) on a daily time step. Alternative methods are used when station data are limited or missing for a particular day. Draper uses a set of required and optional input and output files with defined formats and naming conventions. A shell application also is available to manage multiple runs of the Draper application.
Other applications in the Draper Suite include (1) a tool to find and interactively remove outliers in the input data, (2) a tool to check and enforce a minimum daily temperature range, and (3) a tool to view output diagnostic information as time-series graphs. These tools can be used iteratively to evaluate and improve the results from Draper as part of a workflow involving physically based environmental models, such as the Precipitation-Runoff Modeling System (PRMS).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C22","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Donovan, J.M., and Koczot, K.M., 2019, User’s manual for the Draper climate-distribution software suite with data‑evaluation tools: U.S. Geological Survey Techniques and Methods 7-C22, 55 p., https://doi.org/10.3133/tm7C22. ","productDescription":"viii, 55 p","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-086388","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":362190,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c22/coverthb.jpg"},{"id":362191,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c22/tm7c22.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C22"},{"id":365983,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://code.usgs.gov/cawsc/draper","text":"Source code and executables","linkHelpText":"- Users are required to create an account to access the distribution"}],"contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
By coupling with the ground, wind causes ground motion that appears on seismic records as noise across a wide bandwidth. This wind-generated noise can drown out important features such as small earthquakes and prevent observation of normal modes from large earthquakes. Because the wind field is heterogeneous at local scales due to structures, diurnal heating, and topography, wind-induced seismic noise may be different on seismometers installed just meters apart. We have investigated the spatial variability of wind-induced noise using two weather sensors separated by approximately ~100 m and co-located with one deep borehole and four near-surface broadband seismometers. We found that at longer periods (>5 s), increasing wind speed causes increases in noise on the horizontal components of seismometers. While this has been previously observed, we also measured a γ2-coherence of less than 0.2 between the wind speed, wind direction, and the pressure recorded by our weather stations. We further observed a loss of coherence between the vertical components of our seismometers from 8 s to 20 s period. The amplitude of the drop-in coherence appears to depend on the substrate surrounding the seismometer. Based on two previously-developed theoretical models, we found that the local material surrounding the sensor could be amplifying the wind-generated noise. We also investigated the frequency dependence of wind-induced noise and found that the dominant source of high-frequency seismic noise at some sites could be anthropogenic rather than induced by wind. Additionally, we estimated the linear relationship between the root mean squares (RMS) of wind speed and RMS seismic velocity for all sensors, finding substantial variability between different installments. A more detailed understanding of the complex processes by which wind-induced noise is generated can inform the installation of sensors and the development of methods for mitigation of these effects, thus improving the overall quality of seismic data.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120180227","usgsCitation":"Dybing, S., Ringler, A.T., Wilson, D.C., and Anthony, R.E., 2019, Characteristics and spatial variability of wind noise on near-surface broadband seismometers: Bulletin of the Seismological Society of America, v. 109, no. 3, p. 1082-1098, https://doi.org/10.1785/0120180227.","productDescription":"17 p.","startPage":"1082","endPage":"1098","ipdsId":"IP-103523","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":381855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Alburquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.84478759765624,\n 34.95799531086792\n ],\n [\n -106.46575927734375,\n 34.95799531086792\n ],\n [\n -106.46575927734375,\n 35.240011164750456\n ],\n [\n -106.84478759765624,\n 35.240011164750456\n ],\n [\n -106.84478759765624,\n 34.95799531086792\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"109","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Dybing, S. N.","contributorId":246021,"corporation":false,"usgs":false,"family":"Dybing","given":"S. 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During water year 2017 (October 1, 2016, through September 30, 2017), data presented in this report were collected at 72 stations: 70 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Among the 72 stations in this report, 4 stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticide compounds are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed represented by the station. Some stations have been classified based on the unique hydrology of the waterbodies they monitor. A summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented.
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\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Recent increases in earthquake occurrence rates in Oklahoma have been linked to the injection of large volumes of saltwater, a byproduct of oil and gas extraction. Here we present a detailed study of remote earthquake triggering in an area of active injection‐induced seismicity in northern Oklahoma using data from the LArge‐n Seismic Survey in Oklahoma (LASSO) temporary array and nearby permanent broadband seismic stations. We estimate changes in earthquake rates and calculate the Coulomb failure stress changes on potential receiver faults due to passing teleseismic surface waves. A statistically significant increase in seismicity is observed ∼8 hr after the 16 April 2016 Mw 7.8 Ecuador earthquake. The Coulomb stress changes associated with the Ecuador earthquake are on the order of ∼1 kPa. Physical mechanisms consistent with the observed dynamic stress threshold include failure driven by activation of aseismic slip or hydrological response of the fault system.
The Missouri groundwater-level observation well network is a series of wells across the State of Missouri in which groundwater levels are monitored in real time and periodically. The wells monitor the water levels in multiple key aquifers, such as the Ozark aquifer in the Salem and Springfield Plateaus and the Mississippi Alluvial Plain aquifer in the South-eastern Lowlands. As of 2018, 150 real-time sites are operated as a cooperative effort between the Missouri Department of Natural Resources (MoDNR) and the U.S. Geological Survey. This fact sheet describes the network and well data from the network.
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U.S. Geological Survey
1400 Independence Road, MS-100
Rolla, MO 65401
Mercury monitoring results from about 300 Morone saxatilis (striped bass) muscle tissue samples collected by the State of Utah from Lake Powell resulted in a Utah/Arizona fish consumption advisory issued in 2012 for approximately the lower 100 kilometers of the reservoir. Chemical, physical, and biological data were collected during two synoptic sampling cruises on Lake Powell during May/June 2014 and August 2015 to test three hypotheses associated with a conceptual model developed to explain the observed geographic concentration gradient of Hg in fish tissue samples. This model proposes that in the transition from a primarily riverine system to a reservoir, there is a change in the concentration and composition of water-column particulate material, increasing in the proportion of organic content moving downstream, as the larger size fractions of the inorganic particulate load are deposited in the upper reservoir. This change alleviates light limitation of phytoplankton production and leads to a higher proportion of autochthonous primary production in the downstream direction. This, in turn, drives increased microbial methylmercury (MeHg) production in the benthos and potentially the water column, in the downstream direction, and results in the observed elevated fish Hg levels in the lower part of the reservoir. The model also proposes that there are differences between the main stem of Lake Powell and side canyons, embayments, or secondary rivers entering the reservoir, in terms of Hg cycling dynamics and bioaccumulations, driven mainly by differences in hydrology. Finally, seasonal differences in Hg dynamics within the reservoir are proposed, based on seasonal dynamics associated with primary production and the physical process of seasonal stratification.
A total of three statistically testable hypotheses were proposed and postulated that measurable differences in key Hg and non-Hg metrics exist between: (1) the upper and lower reservoir; (2) main stem and river arm/side canyon/embayment sites; and (3) early-season (May/June 2014, less stratified) and late-season (August 2015, stratified) conditions. Statistically modeled least square means in combination with the graphical analysis of Hg and non-Hg parameters were used to examine the data collected during the study and test these hypotheses. Data collected during the study are included in a U.S. Geological Survey data release and are available online at https://doi.org/10.5066/F74X560J.
In general, water-column, plankton, and surface sediment samples collected during the synoptic sampling cruises are supportive of the three hypotheses associated with the conceptual model. In support of hypothesis 1 (comparing upper and lower reservoir sites), the least square mean for turbidity was higher in the upper reservoir. In contrast, surface water particulate organic carbon (as a percentage of total particulate mass), particulate MeHg (by mass [in nanograms per gram] and as a percentage of total mercury [THg]), and particulate-dissolved partitioning coefficients for THg and MeHg were higher in the lower reservoir. Plankton THg concentrations also were significantly (probability [p] less than (<) 0.05) higher in the lower reservoir. Surface sediment metrics in support of hypothesis 1 include higher MeHg production potential rates in the lower reservoir. In contrast, there were no statistically significant differences between the upper and lower reservoir for surface sediment percent of MeHg and MeHg concentration, percent MeHg, or methylation rate constants. These spatial trends associated with hypothesis 1 indicate a pathway for enhanced Hg bioavailability in the lower reservoir.
Hypothesis 2, which tested for differences between main stem and river arm/side canyon/embayment sites, was supported by a number of water-column parameters, including particulate THg and MeHg concentrations by mass (in nanograms per gram) and percent particulate MeHg being significantly (p<0.05) higher in the river arms, side canyons, and embayments relative to the main stem channel. Plankton MeHg concentrations (by mass [in nanograms per gram] and volume [in nanograms per liter] and as a percentage of THg) were elevated in river arm/side canyon/embayment sites compared to main stem sites, indicating an enhanced potential for MeHg bioaccumulation at the base of the pelagic food web in river arms, side canyons, and embayments. In contrast, few of the sediment metrics differed between main stem and river arm/side canyon/embayment sampling sites; however, the potential for MeHg degradation in surface sediment was significantly higher in the main stem. The data indicate that river arm/side canyon/embayment sites may experience enhanced Hg bioaccumulation, compared to the main stem, because of higher MeHg levels at the base of the pelagic food web. This conclusion is supported by the elevated Hg detected in striped bass muscle tissue samples collected in the San Juan Arm during this study (2014). Fish collected from the lower reservoir exhibited a distinct Hg isotopic signature that was enriched in delta (δ)202Hg and capital delta (Δ)199Hg relative to fish samples collected from either Good Hope Bay or the San Juan Arm.
Hypothesis 3 tested for differences between early (May/June) high-flow and late (August) low-flow seasons. This test was supported by a range of non-Hg metrics (nitrate, phosphate, chlorophyll a, dissolved oxygen, fluorescent dissolved organic matter, temperature, and pH) that reflect the increase in chlorophyll a, decrease in nutrients, and buildup of stratified conditions in the transition from early- to late-season sampling periods. Significant seasonal differences also were noted for multiple Hg metrics, including (a) water-column filtered and particulate (by mass) MeHg and THg concentrations; (b) plankton MeHg and THg concentration (by mass); and (c) sediment percent MeHg, Hg(II)-methylation rate constant, and microbial ribosomal ribonucleic acid, small subunit 16 (16S rRNA) abundance, all of which were higher during the late-season synoptic sampling. Overall, the surface sediment metrics are consistent with a seasonal shift from the early-season synoptic results, when the availability of Hg(II) exerts a primary control on MeHg production, to the late-season synoptic sampling, when microbial activity is a dominant driver of MeHg production.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181159","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Naftz, D.L., Marvin-DiPasquale, M., Krabbenhoft, D.P., Aiken, G., Boyd, E.S., Conaway, C.H., Ogorek, J., and Anderson, G.M., 2019, Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015: U.S. Geological Survey Open-File Report 2018–1159, 81 p., https://doi.org/10.3133/ofr20181159.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-095917","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1159/coverthb.jpg"},{"id":359577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1159/ofr20181159.pdf","text":"Report","size":"9.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1159"},{"id":359578,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74X560J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for Biogeochemical and Physical Processes Controlling Mercury Methylation and Bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014–2015"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63551330566406,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 36.75594019674357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Utah Water Science Center
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2329 West Orton Circle West
Valley City, UT 84119
Context. Visual encounter surveying is a standard animal inventory method, modifications of which (e.g. distance sampling and repeated count surveys) are used for modelling population density. However, a variety of factors may bias visual survey counts.
Aims. The aim of the present study was to evaluate three observer-related biases: (1) whether fatigue compromises detection rate as a survey occasion progresses; (2) whether long-term fatigue or boredom compromise detection rates over the course of a survey period; and (3) whether observers exhibit biases in detection rates of different animal taxa.
Methods. We analysed >2.3 × 104 observations of lizards and small mammals from nocturnal pedestrian visual encounter surveys, each 4 h in duration, conducted by a pool of 29 observers, each of whom surveyed for up to 31 nights.
Key results. Detections of sleeping (diurnal) emerald tree skinks (Lamprolepis smaragdina) exhibited a small but statistically verified decline as the evening progressed, whereas detections of sleeping (diurnal) green anoles (Anolis carolinensis) increased as the evening progressed. Detections of nocturnal geckos (several species pooled) showed a weak and non-significant declining trend. Small mammal sightings (rats, shrews and mice pooled) declined strongly over the course of an evening. The participants saw greater or equal numbers of animals the more nights they surveyed. Most participants exhibited statistically significant, and often strong, taxonomic detection bias compared with the pool of peer observers. The skills of some observers appeared to be consistently above average; others consistently below average.
Conclusions. Data on sleeping lizards suggest that neither short-term nor long-term observer fatigue is of much concern for 4-h visual searches. On the contrary, differences among observers in taxonomic bias and overall detection skills pose a problem for data interpretation.
Implications. By comparing temporal detection patterns of immobile (e.g. sleeping) with actively moving animal taxa, sampling biases attributable to searcher fatigue versus the animals’ circadian rhythm can be disentangled and, if need be, statistically corrected for. Observer skill differences and observer-specific taxonomic biases may hamper efforts to statistically evaluate survey results, unless explicitly included as covariates in population models.
","language":"English","publisher":"BioOne","doi":"10.1071/WR18016","usgsCitation":"Lardner, B., Yackel Adams, A.A., Knox, A.J., Savidge, J.A., and Reed, R., 2019, Do observer fatigue and taxon-bias compromise visual encounter surveys for small vertebrates?: Wildlife Research, v. 46, no. 2, p. 127-135, https://doi.org/10.1071/WR18016.","productDescription":"9 p.","startPage":"127","endPage":"135","ipdsId":"IP-102306","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467807,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr18016","text":"Publisher Index Page"},{"id":437539,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QTSAHY","text":"USGS data release","linkHelpText":"Visual Surveys Rapid Response Saipan 2016"},{"id":382552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lardner, Bjorn","contributorId":225066,"corporation":false,"usgs":false,"family":"Lardner","given":"Bjorn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":803721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knox, Adam J","contributorId":244306,"corporation":false,"usgs":false,"family":"Knox","given":"Adam","email":"","middleInitial":"J","affiliations":[{"id":40374,"text":"Maui Invasive Species Committee","active":true,"usgs":false}],"preferred":false,"id":803722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":803724,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215505,"text":"70215505 - 2019 - Imaging spectroscopy for the detection, assessment and monitoring of natural and anthropogenic hazards","interactions":[],"lastModifiedDate":"2020-10-21T14:38:05.881233","indexId":"70215505","displayToPublicDate":"2019-03-18T09:28:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3503,"text":"Surveys in Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Imaging spectroscopy for the detection, assessment and monitoring of natural and anthropogenic hazards","docAbstract":"Natural and anthropogenic hazards have the potential to impact all aspects of society including its economy and the environment. Diagnostic data to inform decision-making are critical for hazard management whether for emergency response, routine monitoring or assessments of potential risks. Imaging spectroscopy (IS) has unique contributions to make via the ability to provide some key quantitative diagnostic information. In this paper, we examine a selection of key case histories representing the state of the art to gain an insight into the achievements and perspectives in the use of visible to shortwave infrared IS for the detection, assessment and monitoring of a selection of significant natural and anthropogenic hazards. The selected key case studies examined provide compelling evidence for the use of the IS technology and its ability to contribute diagnostic information currently unattainable from operational spaceborne Earth observation systems. User requirements for the applications were also evaluated. The evaluation showed that the projected launch of spaceborne IS sensors in the near-, mid and long term future, together with the increasing availability, quality and moderate cost of off the shelf sensors, the possibilities to couple unmanned autonomous systems with miniaturized sensors, should be able to meet these requirements. The challenges and opportunities for the scientific community in the future when such data become available will then be ensuring consistency between data from different sensors, developing techniques to efficiently handle, process, integrate and deliver the large volumes of data, and most importantly translating the data to information that meets specific needs of the user community in a form that can be digested/understood by them. The latter is especially important to transforming the technology from a scientific to an operational tool. Additionally, the information must be independently validated using current trusted practices and uncertainties quantified before IS derived measurement can be integrated into operational monitoring services.
Solid organic matter (OM) in sedimentary rocks produces petroleum and solid bitumen when it undergoes thermal maturation. The solid OM is a ‘geomacromolecule’, usually representing a mixture of various organisms with distinct biogenic origins, and can have high heterogeneity in composition. Programmed pyrolysis is a common method to reveal bulk geochemical characteristics of the dominant OM, while detailed organic petrography is required to reveal information about the biogenic origin of contributing macerals. Despite the advantages of programmed pyrolysis, it cannot provide information about the heterogeneity of chemical compositions present in the individual OM types. Therefore, other analytical techniques such as Raman spectroscopy are necessary.
In this study, we compared geochemical characteristics and Raman spectra of two sets of naturally and artificially matured Bakken source rock samples. A continuous Raman spectral map on solid bitumen particles was created from the artificially matured hydrous pyrolysis residues, in particular, to show the systematic chemical modifications in microscale. Spectroscopic data was plotted for both sets against thermal maturity to compare maturation rate/path for these two separate groups. The outcome showed that artificial maturation through hydrous pyrolysis does not follow the same trend as naturally-matured samples although having similar solid bitumen reflectance values (%SBRo).
Furthermore, Raman spectroscopy of solid bitumen from artificially matured samples indicated the heterogeneity of OM decreases as maturity increases. This may represent an alteration in chemical structure towards more uniform compounds at higher maturity. This study may emphasize the necessity of using analytical methods such as Raman spectroscopy along with conventional geochemical methods to better reveal the underlying chemical structure of OM. Finally, observation by Raman spectroscopy of chemical alteration of OM during artificial maturation may assist in the proposal of improved pyrolysis protocols to better resemble natural geologic processes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2019.03.009","usgsCitation":"Khatibi, S., Ostadhassan, M., Hackley, P.C., Tuschel, D., Abarghani, A., and Bubach, B., 2019, Understanding organic matter heterogeneity and maturation rate by Raman spectroscopy: International Journal of Coal Geology, v. 206, p. 46-64, https://doi.org/10.1016/j.coal.2019.03.009.","productDescription":"19 p.","startPage":"46","endPage":"64","ipdsId":"IP-101108","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":467811,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2019.03.009","text":"Publisher Index Page"},{"id":437540,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P975KILE","text":"USGS data release","linkHelpText":"Analyzing Heterogeneity in Artificially Matured Samples of Bakken Shales (2018)"},{"id":384583,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.029541015625,\n 46.28622391806706\n ],\n [\n -98.93188476562499,\n 46.28622391806706\n ],\n [\n -98.93188476562499,\n 49.001843917978526\n ],\n [\n -104.029541015625,\n 49.001843917978526\n ],\n [\n -104.029541015625,\n 46.28622391806706\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"206","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Khatibi, Seyedalireza","contributorId":255596,"corporation":false,"usgs":false,"family":"Khatibi","given":"Seyedalireza","email":"","affiliations":[{"id":51594,"text":"Univ. North Dakota","active":true,"usgs":false}],"preferred":false,"id":812636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ostadhassan, Mehdi","contributorId":255578,"corporation":false,"usgs":false,"family":"Ostadhassan","given":"Mehdi","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":812637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":812638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tuschel, David","contributorId":255597,"corporation":false,"usgs":false,"family":"Tuschel","given":"David","email":"","affiliations":[{"id":51595,"text":"HORIBA Scientific","active":true,"usgs":false}],"preferred":false,"id":812639,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Abarghani, Arash","contributorId":255576,"corporation":false,"usgs":false,"family":"Abarghani","given":"Arash","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":812640,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bubach, Bailey","contributorId":255598,"corporation":false,"usgs":false,"family":"Bubach","given":"Bailey","email":"","affiliations":[{"id":51594,"text":"Univ. North Dakota","active":true,"usgs":false}],"preferred":false,"id":812641,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202629,"text":"70202629 - 2019 - Detrital K-feldspar Pb isotopic evaluation of extraregional sediment transported through an Eocene tectonic breach of southern California's Cretaceous batholith","interactions":[],"lastModifiedDate":"2019-03-14T16:30:35","indexId":"70202629","displayToPublicDate":"2019-03-14T16:30:31","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Detrital K-feldspar Pb isotopic evaluation of extraregional sediment transported through an Eocene tectonic breach of southern California's Cretaceous batholith","docAbstract":"Sedimentary provenance studies have come to be overwhelmingly based upon U–Pb geochronologic measurements performed with detrital zircon while alternative and potentially complementary approaches such as conglomerate clast studies and heavy mineral analysis have faded in importance. Measurement of Pb isotopic compositions in detrital K-feldspar is among the under-utilized approaches available to ascertain sedimentary source regions. While it has been long recognized that common Pb isotope compositions recorded by K-feldspar vary widely and reflect the crustal provinces from which the host basement rocks crystallized, use of the approach has suffered due to a lack of appropriate statistical models and ground truth compositional data from source regions. In this paper, we: (1) present high-throughput LA-ICPMS analysis protocols needed to generate statistically meaningful detrital K-feldspar Pb isotope data sets; (2) develop an interpretative approach based upon 208Pb/206Pb vs. 207Pb/206Pb that incorporate information from the U- and Th-decay systems into one two-dimensional plot that is amenable to analysis using two-dimensional Kolmogorov–Smirnoff statistical tests; (3) generate new Pb isotopic data from basement rocks from southwestern North America to improve knowledge of the Pb isotopic properties of potential source regions; and (4) generate new Pb isotopic data from Lower Eocene to Lower Miocene sedimentary rocks to evaluate changes in drainage patterns that occurred in response to deformation that affected the southern California margin. Through this case study, we demonstrate how our new analytical and interpretative methods could be profitably applied to future geochemical and provenance studies and tectonically driven re-organization of drainage patterns.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2018.11.040","usgsCitation":"Shulaker, D.Z., Grove, M., Hourigan, J.K., Van Buer, N., Sharman, G.R., Howard, K.A., Miller, J., and Barth, A.P., 2019, Detrital K-feldspar Pb isotopic evaluation of extraregional sediment transported through an Eocene tectonic breach of southern California's Cretaceous batholith: Earth and Planetary Science Letters, v. 508, p. 4-17, https://doi.org/10.1016/j.epsl.2018.11.040.","productDescription":"14 p.","startPage":"4","endPage":"17","ipdsId":"IP-103612","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":362078,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"508","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shulaker, Danielle Ziva","contributorId":214181,"corporation":false,"usgs":false,"family":"Shulaker","given":"Danielle","email":"","middleInitial":"Ziva","affiliations":[{"id":38987,"text":"Stanford U.","active":true,"usgs":false}],"preferred":false,"id":759295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grove, Marty","contributorId":211570,"corporation":false,"usgs":false,"family":"Grove","given":"Marty","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":759296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hourigan, Jeremy K.","contributorId":99023,"corporation":false,"usgs":true,"family":"Hourigan","given":"Jeremy","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":759297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Buer, Nicholas","contributorId":214183,"corporation":false,"usgs":false,"family":"Van Buer","given":"Nicholas","email":"","affiliations":[{"id":38988,"text":"Cal State Poly Pomona","active":true,"usgs":false}],"preferred":false,"id":759298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":759299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Howard, Keith A. 0000-0002-6462-2947 khoward@usgs.gov","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":3439,"corporation":false,"usgs":true,"family":"Howard","given":"Keith","email":"khoward@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":759294,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Jonathan","contributorId":214184,"corporation":false,"usgs":false,"family":"Miller","given":"Jonathan","affiliations":[{"id":38989,"text":"San Jose State U.","active":true,"usgs":false}],"preferred":false,"id":759300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barth, Andrew P.","contributorId":214136,"corporation":false,"usgs":false,"family":"Barth","given":"Andrew","email":"","middleInitial":"P.","affiliations":[{"id":38983,"text":"Indiana University - Purdue University","active":true,"usgs":false}],"preferred":false,"id":759301,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202630,"text":"70202630 - 2019 - Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover","interactions":[],"lastModifiedDate":"2019-06-18T10:54:15","indexId":"70202630","displayToPublicDate":"2019-03-14T16:27:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover","docAbstract":"Wildlife reservoirs of infectious disease are a major source of human‐wildlife conflict because of the risk of potential spillover associated with commingling of wildlife and livestock. In the Greater Yellowstone Ecosystem, the presence of brucellosis (Brucella abortus) in free‐ranging elk (Cervus canadensis) populations is of significant management concern because of the risk of disease transmission from elk to livestock. We identified how spillover risk changes through space and time by developing resource selection functions using telemetry data from 223 female elk to predict the relative probability of female elk occurrence daily during the transmission risk period. We combined these spatiotemporal predictions with elk seroprevalence, demography, and transmission timing data to identify when and where abortions (the primary transmission route of brucellosis) were most likely to occur. Additionally, we integrated our predictions of transmission risk with spatiotemporal data on areas of potential livestock use to estimate the daily risk to livestock. We predicted that approximately half of the transmission risk occurred on areas where livestock may be present (i.e., private property or grazing allotments). Of the transmission risk that occurred in livestock areas, 98% of it was on private ranchlands as opposed to state or federal grazing allotments. Disease prevalence, transmission timing, host abundance, and host distribution were all important factors in determining the potential for spillover risk. Our fine‐resolution (250‐m spatial, 1‐day temporal), large‐scale (17,732 km2) predictions of potential elk‐to‐livestock transmission risk provide wildlife and livestock managers with a useful tool to identify higher risk areas in space and time and proactively focus actions in these areas to separate elk and livestock to reduce spillover risk.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21645","usgsCitation":"Rayl, N., Proffitt, K., Almberg, E.S., Jones, J.D., Merkle, J., Gude, J., and Cross, P.C., 2019, Modeling elk‐to‐livestock transmission risk to predict hotspots of brucellosis spillover: Journal of Wildlife Management, v. 83, no. 4, p. 817-829, https://doi.org/10.1002/jwmg.21645.","productDescription":"13 p.","startPage":"817","endPage":"829","ipdsId":"IP-100336","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467814,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21645","text":"Publisher Index Page"},{"id":362077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","volume":"83","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rayl, Nathaniel D.","contributorId":199082,"corporation":false,"usgs":false,"family":"Rayl","given":"Nathaniel D.","affiliations":[],"preferred":false,"id":759303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Proffitt, Kelly 0000-0001-5528-3309","orcid":"https://orcid.org/0000-0001-5528-3309","contributorId":210093,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly","email":"","affiliations":[{"id":38065,"text":"Montana Fish, Wildlife and Parks, Bozeman, Montana","active":true,"usgs":false}],"preferred":false,"id":759305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Almberg, Emily S.","contributorId":207014,"corporation":false,"usgs":false,"family":"Almberg","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":759304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Jennifer D.","contributorId":145754,"corporation":false,"usgs":false,"family":"Jones","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[{"id":16227,"text":"Institute on Ecosystems,Montana State University MT, 59715 USA","active":true,"usgs":false}],"preferred":false,"id":759308,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merkle, Jerod","contributorId":172972,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","affiliations":[{"id":35288,"text":"Wyoming Cooperative Fish and Wildlife Research Unit, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":759306,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gude, Justin A.","contributorId":210094,"corporation":false,"usgs":false,"family":"Gude","given":"Justin A.","affiliations":[{"id":38066,"text":"Montana Fish, Wildlife and Parks,","active":true,"usgs":false}],"preferred":false,"id":759307,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":759302,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202298,"text":"sir20195003 - 2019 - Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","interactions":[],"lastModifiedDate":"2019-03-15T16:14:59","indexId":"sir20195003","displayToPublicDate":"2019-03-14T16:15:18","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5003","displayTitle":"Climate, Streamflow, and Lake-Level Trends in the Great Lakes Basin of the United States and Canada, Water Years 1960–2015","title":"Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","docAbstract":"Water levels in the Great Lakes fluctuate substantially because of complex interactions among inputs (precipitation and streamflow), outputs (evaporation and outflow), and other factors. This report by the U.S. Geological Survey in cooperation with the Great Lakes Restoration Initiative was completed to describe trends in climate, streamflow, lake levels, and major water-budget components within the Great Lakes Basin for water years (WYs) 1960–2015 (study period). Resulting trends are applicable only to the study period and should not be considered indicative of longer-term trends.
Analyses of climate trends used monthly data from the Parameter-elevation Regressions on Independent Slopes Model, which are available only for the United States. Trend tests were completed for annual and seasonal time series of monthly means for total precipitation, daily minimum air temperature (Tmin), and daily maximum air temperature (Tmax). Statistical significance for all time-trend tests (climate, streamflow, and lake levels) was determined using the Mann‑Kendall test for probability values less than or equal to 0.10. Trend analyses were completed without adjustments for serial correlation; however, a modified Mann-Kendall test was subsequently used to examine potential effects of short-term persistence in time-series data. Effects of short-term persistence were considered inconsequential for climate data and minor for streamflow data; however, the presence of short-term persistence in water-budget components had more substantial effects on trend analyses.
Spatial distributions of trends in climatic data for WYs 1960–2015 for the U.S. part of the Great Lakes Basin (land only) indicate (1) generally ubiquitous upward trends in Tmin and (2) a sharp transition from neutral or downward trends in precipitation northwest of Lake Michigan to generally upward trends east of Lake Michigan. Trends in Tmax were not statistically significant. Analyses of annual climatic data aggregated for the U.S. land part of the Great Lakes Basin indicated statistically significant upward trends for precipitation and Tmin, and similar statistically significant trends existed for all the individual lake subbasins except Lake Superior.
Of 103 U.S. Geological Survey streamgages analyzed for streamflow trends, 71 had significant annual trends (54 upward and 17 downward). Downward trends in annual streamflow are concentrated northwest of Lake Michigan (16 streamgages), and upward trends are concentrated east of Lake Michigan (53 streamgages). Of the 71 streamgages with significant annual trends, 70 had at least one season with a significant trend that matched the annual trend direction.
Of 35 Environment and Climate Change Canada streamgages analyzed, 22 had significant upward trends in annual streamflow, and all but 1 of these 22 had at least one season with a significant upward trend. None of the Environment and Climate Change Canada streamgages had significant downward annual trends, and only one had a significant downward seasonal trend.
Trends in lake levels and several major water-budget components affecting lake levels were analyzed for the study period. Significant downward trends in lake level and outflow for Lake Superior are driven primarily by low lake levels and outflows during WYs 1998–2014. A significant downward trend in runoff from the contributing drainage area also is indicated, which is consistent with numerous streamgages northwest of Lake Michigan with significant downward trends in annual streamflow. A significant upward trend in annual overlake evaporation also is indicated, which is consistent with the spatially distributed upward trends in annual Tmin.
The sum of overlake precipitation and runoff from the contributing drainage area for each of the Great Lakes, less overlake evaporation, composes a variable called net basin supply (NBS). A significant downward trend in NBS is indicated for Lake Superior, which is consistent with significant trends for individual components of runoff (downward) and evaporation (upward) that contributed to a significant downward trend for lake outflow. Statistically significant upward trends in NBS for Lake Saint Clair and Lake Ontario offset the downward trend for Lake Superior and combine with nonsignificant upward trends in NBS for Lakes Michigan and Huron and Lake Erie to produce a neutral trend in NBS for the basin.
A predictable pattern in monthly mean lake levels is noted for Lake Superior, with the minimum for each year usually during or near March and the maximum commonly during or near September or October. When an October lake level is in a period of substantial decline, potential for an ensuing short-term period of below-mean lake levels is enhanced. Downstream from Lake Superior, monthly lake levels have sawtooth patterns that somewhat resemble those for Lake Superior but with decreased predictability in timing.
Similar to Lake Superior, Lakes Michigan and Huron, Lake Saint Clair, and Lake Erie all have a prolonged period of low lake levels around WYs 1998–2014; however, a significant downward trend is indicated only for Lakes Michigan and Huron. All these lakes also have a period of low lake levels before about WY 1968, when minimum lake levels were lower than during WYs 1998–2014. The significant downward trend of outflow from Lake Superior is carried downstream into Lakes Michigan and Huron; however, trends in outflow from the next three lakes downstream (Lakes Saint Clair, Erie, and Ontario) are offset by increased precipitation and runoff and are not significant.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195003","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Norton, P.A., Driscoll, D.G., and Carter, J.M., 2019, Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015: Scientific Investigations Report 2019–5003, 47 p., https://doi.org/10.3133/sir20195003.","productDescription":"Report: vi, 47 p.; Appendix Figures; Appendix Tables: 5","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-089551","costCenters":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":362031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5003/coverthb.jpg"},{"id":362032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003.pdf","text":"Report","size":"22.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362033,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.1_to_1.103.pdf","text":"Appendix figures 1.1–1.103","size":"940 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362034,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.104_to_1.138.pdf","text":"Appendix figures 1.104–1.138","size":"333 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362035,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_tables_1.1_to_1.5.xlsx","text":"Appendix tables 1.1–1.5","size":"132 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5003"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.4716796875,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is derived from groundwater pumping along North Fork Eagle Creek in the Eagle Creek Basin near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial losses of vegetation in the basin, the monitoring of North Fork Eagle Creek for short-term geomorphic change has been required by the U.S. Department of Agriculture Forest Service, Lincoln National Forest, as part of the permitting decision that allows for the continued pumping of the production wells. The monitoring of short-term geomorphic change in North Fork Eagle Creek began in June 2017 with a geomorphic survey of the stream reach located between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (USGS site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The 2017 geomorphic survey was conducted by the U.S. Geological Survey (USGS), in cooperation with the Village of Ruidoso, and was the first in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey are summarized and interpreted in this report and are provided in their entirety in its companion data release.
The study reach is 1.86 miles long, and large sections of the reach are characterized by intermittent streamflow. Where water is normally present (including at the upper and lower portions of the reach near the streamflow-gaging stations), the discharge typically remains below 2 cubic feet per second throughout the year. Therefore, if geomorphic change is to occur, it will likely be driven by seasonal high-flow events. Discharge records from streamflow-gaging stations in the Eagle Creek Basin indicated that high-flow events in the basin (with peaks above 50 cubic feet per second) typically occurred during the North American monsoon months of July, August, and September. Additionally, the records appear to indicate that, as expected, overland runoff and “flashy” responses to rainfall have increased in the 5 years since the 2012 Little Bear Fire.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach. Cross-section survey results indicated that channel characteristics (including channel width and area) varied widely along the study reach. Also, as part of the survey, woody debris accumulations and pools in the channel of the study reach were identified, cataloged, photographed, and surveyed for location. There were 58 woody debris accumulations and 14 pools found in the study reach. On the basis that debris jams could be a driver of geomorphic change in North Fork Eagle Creek, woody debris accumulations were classified according to their debris jam potential. The burn marks found on some woody debris indicated that the 2012 Little Bear Fire may be a contributing factor to the volume of debris in North Fork Eagle Creek. However, the woody debris present at the time of the survey did not appear to have substantially affected the geomorphic state of the study reach. Further, the structure and composition of the woody debris accumulations indicated that, under high-flow conditions, most woody debris would likely be transported downstream and out of the study reach without causing substantial geomorphic change through further jamming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181187","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2019, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2017: U.S. Geological Survey Open-File Report 2018–1187, 28 p., https://doi.org/10.3133/ofr20181187.","productDescription":"Report: v., 28 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","ipdsId":"IP-093851","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":362041,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PR7TX3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2017 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":362039,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1187/coverthb.jpg"},{"id":362040,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1187/ofr20181187.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1187"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.98236083984375,\n 33.02939031998959\n ],\n [\n -104.98260498046875,\n 33.02939031998959\n ],\n [\n -104.98260498046875,\n 33.68549637289138\n ],\n [\n -105.98236083984375,\n 33.68549637289138\n ],\n [\n -105.98236083984375,\n 33.02939031998959\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, NM 87113