We utilize active‐source seismic interferometry with dense seismic arrays both offshore and onland to explore the utility of this method to create virtual sources and reveal body‐wave reflections in these two different environments. We first utilize data from an ocean‐bottom cable (OBC) array in the Gulf of Mexico with equal numbers of sources (160 airgun shots) and receivers (160 ocean‐bottom four‐component sensors). We next use data from a geophone array across the Bighorn Mountains of Wyoming with many receivers (1300 vertical‐component geophones) but a small number of sources (14 borehole active‐source shots). We find that the OBC virtual source results, which produce strong reflections from sub‐seafloor structures, are far superior to the onland results which lack usable reflections, and we explore reasons for these differences through a set of selective stacking approaches. We present techniques to account for the direction the seismic waves travel (directionality) and stationary phase and show that improvements can be made when incorporating these corrections. Although interferometric methods are based on assumptions of large numbers of widely distributed actual sources, we find that selective exclusion of potentially problematic source–receiver pairs can yield improved results. These geometric adjustments to active‐source interferometry methods have utility for dense‐nodal‐array surveys that are now common in academic studies, but that often suffer from sparse source geometry.
Harpacticoid copepods can be a substantial component of the meiobenthic community in lakes and serve an ecological role as detritivores. Here we present the first species-level lake-wide quantitative assessment of the harpacticoid assemblage of Lake Ontario with emphasis on the status of nonindigenous species. Additionally, we provide COI-5P sequences of harpacticoid taxa through Barcode of Life Data System (BOLD). Harpacticoids were collected at depths from 0.1 to 184 m and from a range of substrates from August to September 2018 as part of the Cooperative Science and Monitoring Initiative (CSMI) offshore benthic survey. Twenty-six meiobenthic samples were analyzed using microscopy for community composition analysis of harpacticoids. We found thirteen indigenous and three nonindigenous species of harpacticoid, with the introduced species dominating at shallow depths. The community transitioned from nonindigenous to indigenous species dominance as depth increased. Nonindigenous species accounted for 79% of the community (by abundance) at depths <20 m, 55% from 20 to 40 m, and only 24% at depths >40 m. The nonindigenous species encountered included the first detections of Schizopera borutzkyi (Monchenko, 1967) and Heteropsyllus nunni (Coull, 1975) from Lake Ontario. S. borutzkyi was the most abundant harpacticoid species in the lake, approaching a maximum density of 50,000/m2 and a lake-wide average density of 7,900/m2. Numerically important indigenous species included Bryocamptus nivalis (Willey, 1925), Canthocamptus robertcokeri (Wilson, 1958), Canthocamptus staphylinoides (Pearse, 1905), and Moraria cristata (Chappuis, 1929). The prevalence of nonindigenous harpacticoids in the meiobenthos of Lake Ontario suggests further investigations of Great Lakes meiofauna communities are warranted.
The Jerusalem cricket subfamily Stenopelmatinae is distributed from south-western Canada through the western half of the United States to as far south as Ecuador. Recently, the generic classification of this subfamily was updated to contain two genera, the western North American Ammopelmatus, and the Mexican, and central and northern South American Stenopelmatus. The taxonomy of the latter genus was also revised, with 5, 13 and 14 species being respectively validated, declared as nomen dubium and described as new. Despite this effort, the systematics of Stenopelmatus is still far from complete. Here, we generated sequences of the mitochondrial DNA barcoding locus and performed two distinct DNA sequence-based approaches to assess the species’ limits among several populations of Stenopelmatus, with emphasis on populations from central and south-east Mexico. We reconstructed the phylogenetic relationships among representative species of the main clades within the genus using nuclear 3RAD data and carried out a molecular clock analysis to investigate its biogeographic history. The two DNA sequence-based approaches consistently recovered 34 putative species, several of which are apparently undescribed. Our estimates of phylogeny confirmed the recent generic update of Stenopelmatinae and revealed a marked phylogeographic structure within Stenopelmatus. Based on our results, we propose the existence of four species-groups within the genus (the faulkneri, talpa, Central America and piceiventris species-groups). The geographic distribution of these species-groups and our molecular clock estimates are congruent with the geological processes that took place in mountain ranges along central and southern Mexico, particularly since the Neogene. Our study emphasises the necessity to continue performing more taxonomic and phylogenetic studies on Stenopelmatus to clarify its actual species richness and evolutionary history in Mesoamerica.
This report addresses system characterization of Satellogic’s NewSat satellite (also known as ÑuSat) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2016, Satellogic has launched 17 NewSat satellites. All NewSat satellites have four-band imagers with a 1-meter (m) ground sample distance, and values in pixels are identical to values in meters. All NewSats have been launched into Sun-synchronous orbits of about 475 kilometers, with inclinations of about 97.5 degrees. The satellites have expected lifetimes of about 3 years. More information on the Satellogic satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://satellogic.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the NewSat satellites have an interior geometric performance in the range of −0.119 (−0.119 pixel) to 0.020 m (0.020 pixel) in easting and −0.148 (−0.148 pixel) to 0.014 m (0.014 pixel) in northing in band-to-band registration, an exterior geometric performance of −9.04 (−9.04 pixels) to −5.84 m (−5.84 pixels) in easting and 1.25 (1.25 pixels) to 3.11 m (3.11 pixels) in northing offset in comparison to Sentinel-2, an exterior geometric performance using ground control points of a 6.5-m circular error (95 percent), a radiometric performance in the range of 0.034 to 0.081 in offset and 0.652 to 0.808 in slope, and a spatial performance in the range of 1.61 to 1.76 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.081 to 0.138.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030L","usgsCitation":"Vrabel, J.C., Bresnahan, P., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., and Park, S., 2022, System characterization report on the Satellogic NewSat multispectral sensor (ver. 1.1, April 2022), chap. L of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030L.","productDescription":"v, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-135435","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":393738,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/l/ofr20211030l.pdf","text":"Report","size":"7.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-L"},{"id":393737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/l/coverthb2.jpg"},{"id":399739,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/l/versionHist.txt","size":"1 kB"}],"edition":"Version 1.0: January 3, 2022; Version 1.1: April 28, 2022","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The revision of federal safety regulations for integrity management of gas transmission pipelines to require explicit consideration of seismicity increases the importance for operators to be actively identifying high-consequence areas (HCAs), evaluating seismic-related threats, and choosing a risk model to support risk management decisions. To ensure equal access to information by both operators and inspectors, the authors have compiled publicly available data and tools for practical seismic risk assessments, such as Microsoft building footprints, the USGS National Seismic Hazard Models, and the USGS Ground Failure product.
","language":"English","publisher":"Endeavor Business Media","usgsCitation":"Kwong, N.S., Jaiswal, K.S., Baker, J.W., Luco, N., Ludwig, K.A., and Stephens, V.J., 2022, Open-source resources help navigate new IM regulations: Oil & Gas Journal, v. 120, no. 1, p. 46-53.","productDescription":"8 p.","startPage":"46","endPage":"53","ipdsId":"IP-133094","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":394019,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394000,"type":{"id":15,"text":"Index Page"},"url":"https://www.ogj.com/pipelines-transportation/article/14223160/opensource-resources-help-navigate-new-im-regulations"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 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Simon 0000-0003-3017-9585","orcid":"https://orcid.org/0000-0003-3017-9585","contributorId":241863,"corporation":false,"usgs":true,"family":"Kwong","given":"N.","email":"","middleInitial":"Simon","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":830280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":830281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, J. W. 0000-0003-2744-9599","orcid":"https://orcid.org/0000-0003-2744-9599","contributorId":198187,"corporation":false,"usgs":false,"family":"Baker","given":"J.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":830282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":830283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ludwig, K. A. 0000-0002-0935-9410 kaludwig@usgs.gov","orcid":"https://orcid.org/0000-0002-0935-9410","contributorId":596,"corporation":false,"usgs":true,"family":"Ludwig","given":"K.","email":"kaludwig@usgs.gov","middleInitial":"A.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":5059,"text":"Office of the Chief Scientist for National Hazards","active":true,"usgs":true}],"preferred":true,"id":830284,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephens, Vasey J. 0000-0003-2661-7861","orcid":"https://orcid.org/0000-0003-2661-7861","contributorId":269838,"corporation":false,"usgs":false,"family":"Stephens","given":"Vasey","email":"","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":false,"id":830285,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227263,"text":"70227263 - 2022 - Knowledge sharing for shared success in the decade on ecosystem restoration","interactions":[],"lastModifiedDate":"2022-01-05T12:48:00.184381","indexId":"70227263","displayToPublicDate":"2022-01-03T06:43:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9977,"text":"Ecological Solutions and Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Knowledge sharing for shared success in the decade on ecosystem restoration","docAbstract":"The size of an ecosystem affects ecological interactions, but less is known about how ecosystem size may affect social interactions. We posit that ecosystem size could serve as a basis for understanding and contextualizing social interactions, connecting how ecosystem size influences natural resource investment decisions and the use of ecosystem services. We leverage international (Canada, Czech Republic, Germany, United States of America) inland recreational fishery data to explore whether certain ecosystem sizes receive a disproportionate amount of fish stocked (a measure of resource investment) and attract more angler effort – our measure of an ecosystem service. We find that smaller lentic waterbodies receive a disproportionate amount of fish stocked per area and also attract more angler effort per area consistently in all four countries. Therefore, we find that resource use and resource investment is matched by ecosystem size. We conclude that small waterbodies are prioritized by both managers and users and contribute more (per area) to recreational fisheries compared to large and more visible waterbodies on the landscape. An increasing focus on smaller-sized lakes and rivers, also those anthropogenically created, in science, assessment, and management is warranted.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2022.106388","usgsCitation":"Kaemingk, M., Arlinghaus, R., Birdsong, M., Chizinski, C., Lyach, R., Wilson, K., and Pope, K.L., 2022, Matching of resource use and investment according to waterbody size in recreational fisheries: Fisheries Research, v. 254, 106388, 6 p., https://doi.org/10.1016/j.fishres.2022.106388.","productDescription":"106388, 6 p.","ipdsId":"IP-124536","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433561,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"254","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kaemingk, M.A.","contributorId":340850,"corporation":false,"usgs":false,"family":"Kaemingk","given":"M.A.","email":"","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":908908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arlinghaus, R.","contributorId":268274,"corporation":false,"usgs":false,"family":"Arlinghaus","given":"R.","affiliations":[{"id":55610,"text":"IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":908909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdsong, M.H.","contributorId":341801,"corporation":false,"usgs":false,"family":"Birdsong","given":"M.H.","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chizinski, C.J.","contributorId":340849,"corporation":false,"usgs":false,"family":"Chizinski","given":"C.J.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lyach, R.","contributorId":341802,"corporation":false,"usgs":false,"family":"Lyach","given":"R.","affiliations":[{"id":81789,"text":"Institute for Evaluations and Social Analyses,","active":true,"usgs":false}],"preferred":false,"id":908912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, K.L.","contributorId":341803,"corporation":false,"usgs":false,"family":"Wilson","given":"K.L.","email":"","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":908913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":908914,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228318,"text":"70228318 - 2022 - Pathways for avian influenza virus spread: GPS reveals wild waterfowl in commercial livestock facilities and connectivity with the natural wetland landscape","interactions":[],"lastModifiedDate":"2022-09-27T16:42:47.008718","indexId":"70228318","displayToPublicDate":"2022-01-02T06:47:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Pathways for avian influenza virus spread: GPS reveals wild waterfowl in commercial livestock facilities and connectivity with the natural wetland landscape","docAbstract":"Zoonotic diseases are of considerable concern to the human population and viruses such as avian influenza (AIV) threaten food security, wildlife conservation and human health. Wild waterfowl and the natural wetlands they use are known AIV reservoirs, with birds capable of virus transmission to domestic poultry populations. While infection risk models have linked migration routes and AIV outbreaks, there is a limited understanding of wild waterfowl presence on commercial livestock facilities, and movement patterns linked to natural wetlands. We documented 11 wild waterfowl (three Anatidae species) in or near eight commercial livestock facilities in Washington and California with GPS telemetry data. Wild ducks used dairy and beef cattle feed lots and facility retention ponds during both day and night suggesting use for roosting and foraging. Two individuals (single locations) were observed inside poultry facility boundaries while using nearby wetlands. Ducks demonstrated high site fidelity, returning to the same areas of habitats (at livestock facilities and nearby wetlands), across months or years, showed strong connectivity with surrounding wetlands, and arrived from wetlands up to 1251 km away in the week prior. Telemetry data provides substantial advantages over observational data, allowing assessment of individual movement behaviour and wetland connectivity that has significant implications for outbreak management. Telemetry improves our understanding of risk factors for waterfowl–livestock virus transmission and helps identify factors associated with coincident space use at the wild waterfowl–domestic livestock interface. Our research suggests that even relatively small or isolated natural and artificial water or food sources in/near facilities increases the likelihood of attracting waterfowl, which has important consequences for managers attempting to minimize or prevent AIV outbreaks. Use and interpretation of telemetry data, especially in near-real-time, could provide key information for reducing virus transmission risk between waterfowl and livestock, improving protective barriers between wild and domestic species, and abating outbreaks.
Lake wide acoustic (AC) and bottom trawl (BT) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The BT survey has been conducted each fall since 1973 using 12-m trawls at depths ranging from 9 to 110 m and includes 70 fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species [i.e., alewife (Alosa pseudoharengus), bloater (Coregonus hoyi), rainbow smelt (Osmerus mordax), deepwater sculpin (Myoxocephalus thompsonii), slimy sculpin (Cottus cognatus), round goby (Neogobius melanostomus), ninespine stickleback (Pungitius pungitius)] as well as for age-0 yellow perch (Perca flavescens) and large (> 350 mm) burbot (Lota lota). The AC survey has been conducted each late summer/early fall since 2004, and the 2021 survey consisted of 25 transects [507 km total (315 miles)] covering bottom depths ranging from 15 to 235 m and 42 midwater trawl tows covering bottom depths ranging 13 to 215 m; this survey estimates densities of three prey fish species (i.e., alewife, bloater, and rainbow smelt). The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks.
For the BT survey, total biomass density of prey fish equaled only 2.4 kg/ha, the 5th lowest estimate of the time series and well below the long-term average total biomass of 34.28 kg/ha. For the AC survey, total biomass density of prey fish equaled 6.61 kg/ha, 50% higher than the longterm average total biomass of 4.28 kg/ha.
The AC survey reported bloater to be the dominant species (by biomass) among prey fishes, while the BT survey reported co-dominance of alewife, bloater, and round goby. Mean biomass of yearling and older (YAO) alewives in 2021 was 1.71 kg/ha in the AC survey and 0.504 kg/ha in the BT survey. Catchability of YAO alewives continues to be substantially lower for the BT survey since 2014.
Comparing the acoustic estimate to previous years, YAO alewife biomass was 10% higher than the 2019 estimate and less than the average from 2004-2019. Numeric density of age-0 alewife from the AC survey was 352 fish/ha in 2021, which is 71% of the long-term mean of 499 fish/ha. The alewife age distribution remained truncated, with age-0 fish and age-1 fish dominating the population. Biomass density of YAO bloater was 3.7 kg/ha in the AC survey and 0.43 kg/ha in the BT survey- each at least an order of magnitude lower than what was estimated by the BT survey between 1981 and 1998. Numeric density of age-0 bloater was the highest ever measured for the AC survey at 1,037 fish/ha while for the BT survey, it was 20 fish/ha. Biomass density of YAO rainbow smelt was 0.13 kg/ha in the AC survey and 0.005 kg/ha in the BT survey, continuing the trend of low rainbow smelt biomass that has been observed since 2001. Numeric density of age-0 rainbow smelt was 84 fish/ha in the AC survey and 1.9 fish/ha in the BT survey, indicating a weak year-class. All four prey fish species sampled only by the BT survey indicated below average biomass densities. Deepwater sculpin was estimated at 0.45 kg/ha, which makes 11 of the past 12 years when biomass was <1 kg/ha. Slimy sculpin was estimated at 0.05 kg/ha, the sixth lowest density ever measured. Round goby was estimated at 0.63 kg/ha, which was below the average biomass of 0.84 kg/ha since 2008. Ninespine stickleback density was < 1 fish/ha. Burbot biomass remained near record low levels, and only three age-0 yellow perch were caught in all trawls, indicating a weak yellow perch year-class in 2021.
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III 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":189812,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","suffix":"III","email":"","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":851149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":851150,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turschak, Benjamin A.","contributorId":150497,"corporation":false,"usgs":false,"family":"Turschak","given":"Benjamin","email":"","middleInitial":"A.","affiliations":[{"id":18038,"text":"University of Wisconsin, Milwaukee","active":true,"usgs":false}],"preferred":true,"id":851151,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Dale","contributorId":190498,"corporation":false,"usgs":false,"family":"Hanson","given":"Dale","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":851152,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228830,"text":"70228830 - 2022 - Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix A: Statistical hydrology","interactions":[],"lastModifiedDate":"2024-03-26T17:00:56.038518","indexId":"70228830","displayToPublicDate":"2022-01-01T11:53:47","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix A: Statistical hydrology","docAbstract":"Statistical analysis of the observational record from U.S. Geological Survey (USGS) streamgaging stations and other historical information provides an informative means of estimating flood flow frequency. Flood flow frequency is defined by values or quantiles of discharge for selected annual exceedance probabilities (AEPs) (England and others, 2018). The annual peak discharge data as part of systematic operation of a streamgaging station provides the foundation for a detailed analysis of peak discharge, but additional historical information pertaining to peak discharges also can be used. An annual peak discharge is defined as the maximum instantaneous discharge for a streamgaging station for a given water year, and annual peak discharge data for USGS streamgaging stations can be acquired through the USGS National Water Information System (NWIS) database (USGS, 2018). The statistical analyses are based on water-year increments. A water year is the 12-month period from October 1 of a given year through September 30 of the following year designated by the calendar year in which it ends.
For the statistical hydrology portion of the multi-layered analysis, InFRM team members from the USGS analyzed annual peak discharge records for the 15 USGS streamgaging stations (gages) shown on Figure A.1. Information on the period of record data for those USGS gages are listed in Table A.1.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency","usgsCitation":"Wallace, D., 2022, Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix A: Statistical hydrology: Interagency Flood Risk Management Report, 64 p.","productDescription":"64 p.","ipdsId":"IP-101867","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396304,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/#ha"}],"country":"United States","state":"Texas","otherGeospatial":"Neches River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96,\n 32\n ],\n [\n -96,\n 30\n ],\n [\n -94,\n 30\n ],\n [\n -94,\n 32\n ],\n [\n -96,\n 32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835668,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230312,"text":"70230312 - 2022 - Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin","interactions":[],"lastModifiedDate":"2022-09-13T16:42:30.131021","indexId":"70230312","displayToPublicDate":"2022-01-01T11:39:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin","docAbstract":"Anvil Lake, a relatively shallow seepage lake in northern Wisconsin, USA, has experienced dramatic changes in water level since elevation records began in 1938 in response to changes in meteorological and climatic conditions (Figure 1. Robertson et al., 2018). Anvil Lake’s water level record shows a pronounced 10–15-yr cycle, with recurring highs and lows with a typical swing of over 1 m. Although experiencing large cycles in water levels, the long-term average levels were relatively stable until about 1987, when water level dropped dramatically by an additional 1 m (in 2016). Water levels then rebounded dramatically, reaching near “normal” water levels in 2020. At its lowest level, the lake had a maximum depth of 8.2 m (mean depth of 4.7 m) and an area of 128 ha.\nLike most long-term records, Anvil Lake’s water level record has been measured by several observers using various techniques. To verify the consistency of the various datums used throughout this period, historical photographs with the water’s edge identified were obtained, tied to NAVD 1988 using a Real Time Kinematic satellite global positioning system, and compared with the measured water levels (See Figure 1).\nTo determine the causes of the changes in water level, a complete water budget was estimated for Anvil Lake from 1980 to 2014. Water levels in Anvil Lake were simulated (Figure 1) using a hydrodynamic model (General Lake Model, GLM), with daily lake evaporation estimated by\nGLM, monthly lake/groundwater exchange estimated with a groundwater model (MODFLOW), daily precipitation from the North American Land Data Assimilation System (NLDAS), and stream inflow and outflow were set as zero because the lake has no inlets or outlet. Atmospheric fluxes (precipitation minus evaporation) primarily drove the lake-level fluctuations and trends, but sub-decadal fluctuations in net groundwater exchange (groundwater inflow minus lake seepage) either enhanced or reduced the lake level response to the atmospheric drivers.\nThe changes in water levels were shown to affect the extent of stratification and water quality in the lake (Robertson et al., 2018). During periods of lower precipitation and lower water levels, Anvil Lake was a polymictic lake, whereas during periods of higher precipitation and higher water levels the lake was a dimictic lake with stratification lasting throughout summer. During periods with higher water levels, the water quality in the lake was shown to improve slightly as a result of the nutrients being diluted in a larger volume of water. If precipitation increases in the future, as results from many General Circulation Models (GCMs) suggest (Robertson et al., 2016), and if that outweighs the effects of increased evaporation caused by increased air temperatures, water levels in Anvil Lake may be expected to fluctuate at a higher level. Higher water levels in Anvil Lake are expected to result in the lake becoming more strongly stratified and have slightly improved water quality (lower nutrient and algal concentrations and increased water clarity) (Robertson et al., 2018).","language":"English","publisher":"Wisconsin Department of Natural Resources","usgsCitation":"Robertson, D., 2022, Response in the water level of Anvil Lake, Wisconsin, to changes in meteorological and climatic changes, Wisconsin, 2 p.","productDescription":"2 p.","ipdsId":"IP-130734","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":406607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398290,"type":{"id":15,"text":"Index Page"},"url":"https://wicci.wisc.edu/water-resources-working-group/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","otherGeospatial":"Anvil Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07800674438477,\n 45.93515431167519\n ],\n [\n -89.05139923095703,\n 45.93515431167519\n ],\n [\n -89.05139923095703,\n 45.9536560062781\n ],\n [\n -89.07800674438477,\n 45.9536560062781\n ],\n [\n -89.07800674438477,\n 45.93515431167519\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":839932,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70228833,"text":"70228833 - 2022 - Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses","interactions":[],"lastModifiedDate":"2024-03-27T15:12:27.207997","indexId":"70228833","displayToPublicDate":"2022-01-01T10:07:41","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses","docAbstract":"RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Neches River Basin.
The RiverWare model described in this chapter presents development of the Neches River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer streamgage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Neches River Basin POR hydrology will be in a later section.
Calibration results will also be shown that illustrate model performance since the Salt Water Barrier (SWB) construction was completed in 2005. The time window simulation run is for water year (WY) 2005 – WY 2018. This time window also captures the time when Hurricane Harvey occurred (late August of 2017). Each simulated water year was inspected individually to better validate the results.
After calibration, a general run for January 01, 1929 through WY 2018 was made. Historical pool elevations along with observed inflows and outflows were compared against the model simulated results. More emphasis was put on B.A. Steinhagen’s operations because the dam captures two major rivers (i.e. the Angelina and the Neches Rivers). Results were inspected closely for B.A. Steinhagen’s pool and releases, the simulated discharges at the Neches at Evadale gage, and the simulated discharges at the SWB at Beaumont, Texas.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"U.S. Army Corps of Engineers, Federal Emergency Management Agency","usgsCitation":"Wallace, D., 2022, Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses: Interagency Flood Risk Management Report, 66 p.","productDescription":"66 p.","ipdsId":"IP-113418","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396305,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/#ha"}],"country":"United States","state":"Texas","otherGeospatial":"Neches River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96,\n 32\n ],\n [\n -96,\n 30\n ],\n [\n -94,\n 30\n ],\n [\n -94,\n 32\n ],\n [\n -96,\n 32\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David S. 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":205198,"corporation":false,"usgs":true,"family":"Wallace","given":"David S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835669,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70229700,"text":"70229700 - 2022 - Estimates of metals contained in abyssal manganese nodules and ferromanganese crusts in the global ocean based on regional variations and genetic types of nodules","interactions":[],"lastModifiedDate":"2025-07-11T18:52:30.497681","indexId":"70229700","displayToPublicDate":"2022-01-01T09:44:27","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Estimates of metals contained in abyssal manganese nodules and ferromanganese crusts in the global ocean based on regional variations and genetic types of nodules","docAbstract":"Deep-ocean ferromanganese crusts and manganese nodules are important marine repositories for global metals. Interest in these minerals as potential resources has led to detailed sampling in many regions of the global ocean, allowing for updated estimates of their global extent. Here, we present global estimates of total tonnage as well as contained metal concentrations and tonnages for ferromanganese crusts and manganese nodules using the most extensive compilation of geochemical data collected to date, along with updated boundaries of regions of interest for these minerals. We present results from mean composition calculated in two ways: first, a global flat average of regional mean compositions, and second, a regionally weighted average that considers differences in chemistry among genetic types and/or oceanographic and geologic settings for these mineral occurrences. For nodules, we use the three genetic types: (1) hydrogenetic, typified by nodules from the West Pacific Nodule Field and Penrhyn Basin; (2) diagenetic, typified by nodules from the Peru Basin; (3) mixed hydrogenetic-diagenetic, typified by nodules from the Clarion–Clipperton Zone and the Central Indian Ocean Basin, and Atlantic Ocean regional type hydrogenetic nodules. All crusts considered here are of hydrogenetic origin, which we divide into seven regional types that reflect a combination of ocean basin and other source inputs. Crust types include Arctic Ocean, Atlantic Ocean, Indian Ocean, Continental Margin, Prime Crust Zone (PCZ), North Pacific (non PCZ), and South Pacific. Based on our areal estimates, we find that abyssal regions likely to contain hydrogenetic-type nodules are by far the most widespread in the global ocean (47% of total area), Atlantic Ocean (28%) are next, followed by mixed diagenetic-hydrogenetic (22%) and diagenetic (3%) types. For crusts, the Prime Crust Zone is the most extensive global region (27% of total area) followed by South Pacific (20%), Indian Ocean (18%), North Pacific (12%), Continental Margins (11%), Atlantic Ocean (10%), and Arctic Ocean (2%) types. The global total tonnage estimates that we calculated from this method are 21 × 1010 dry tons for manganese nodules, within the range of previous estimates, and 93 × 1010 dry tons for ferromanganese crusts, which is 4.5 times higher than the 20 × 1010dry tons reported by Hein et al. (2003). This geology and oceanography driven approach to marine mineral quantification contrasts with estimates typically carried out for terrestrial mineral resource deposits. Nevertheless, these estimates and the data that support them demonstrate that marine minerals are an impressive repository for global metals.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Perspectives on deep-sea mining","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-87982-2_3","usgsCitation":"Mizell, K., Hein, J.R., Au, M., and Gartman, A., 2022, Estimates of metals contained in abyssal manganese nodules and ferromanganese crusts in the global ocean based on regional variations and genetic types of nodules, chap. of Perspectives on deep-sea mining, p. 53-80, https://doi.org/10.1007/978-3-030-87982-2_3.","productDescription":"28 p.","startPage":"53","endPage":"80","ipdsId":"IP-131627","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":492156,"rank":2,"type":{"id":12,"text":"Errata"},"url":"https://doi.org/10.1007/978-3-030-87982-2_24","text":"Correction","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Correction to: Estimates of Metals Contained in Abyssal Manganese Nodules and Ferromanganese Crusts in the Global Ocean Based on Regional Variations and Genetic Types of Nodules"},{"id":397113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Mizell, Kira 0000-0002-5066-787X kmizell@usgs.gov","orcid":"https://orcid.org/0000-0002-5066-787X","contributorId":4914,"corporation":false,"usgs":true,"family":"Mizell","given":"Kira","email":"kmizell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":140835,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Au, Manda Viola","contributorId":288485,"corporation":false,"usgs":true,"family":"Au","given":"Manda Viola","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gartman, Amy 0000-0001-9307-3062 agartman@usgs.gov","orcid":"https://orcid.org/0000-0001-9307-3062","contributorId":177057,"corporation":false,"usgs":true,"family":"Gartman","given":"Amy","email":"agartman@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":838000,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229656,"text":"70229656 - 2022 - San Francisco Estuary chlorophyll sensor and sample analysis intercomparison","interactions":[],"lastModifiedDate":"2022-03-11T15:26:53.094975","indexId":"70229656","displayToPublicDate":"2022-01-01T09:19:02","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":10383,"text":"Intercomparison Report","active":true,"publicationSubtype":{"id":3}},"title":"San Francisco Estuary chlorophyll sensor and sample analysis intercomparison","docAbstract":"This report presents an assessment of chlorophyll collection methods and anonymous results of field and laboratory comparisons in 2018 - 2019 by agencies in the San Francisco Estuary (SFE). The methods assessment and comparison exercises, with funding provided by the Delta Regional Monitoring Program and Bay Nutrient Management Strategy and in-kind contributions from participating agencies, are a first step to facilitate future comparisons and syntheses of data and inform best science practices in the region. In situ sonde comparison exercises found general agreement between two models of Yellow Springs Instrument (YSI) sensors, but the newer sensor (EXO v2 - total algae) measured higher chlorophyll fluorescence (fCHL) relative to the older YSI sensor (6-series 6025). Results may be attributed to the use of a two-point calibration and the fluorescence response of algal cultures in sensor development by the manufacturer. The laboratory comparison included participation by 12 distinct field - laboratory pairs (or groups), with one group analyzing filters using two analytical methods. Filters were collected in triplicate across three sampling events in 2018, and all sample results were pooled together. Results of statistical analyses indicated that nominal filter pore size, the grinding method associated with pigment extraction, and analytical methods do not introduce variability to the chlorophyll-a measurement (Chl-a). When Chl-a results were assessed by sample event, however, significant differences between nominal pore size and analytical methods existed; these differences could be attributed to the small sample size per event. Consistent reporting units and high-concentration calibration standards for field sensors among data collection agencies would improve the consistency and comparability of data collected in the SFE. More routine split sampling events, longer term sensor comparison exercises, and further processing and analytical comparisons that control for individual filterers may also enhance comparability in the region.
","language":"English","publisher":"Delta Regional Monitoring Program","usgsCitation":"Stumpner, E.B., Yin, J.S., Heberger, M., Wu, J., Wong, A., and Saraceno, J., 2022, San Francisco Estuary chlorophyll sensor and sample analysis intercomparison: Intercomparison Report, 61 p.","productDescription":"61 p.","ipdsId":"IP-123558","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":397022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397021,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://deltarmp.org/documents/"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.67608642578126,\n 37.36579146999664\n ],\n [\n -121.4,\n 37.36579146999664\n ],\n [\n -121.4,\n 38.348118547988065\n ],\n [\n -122.67608642578126,\n 38.348118547988065\n ],\n [\n -122.67608642578126,\n 37.36579146999664\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stumpner, Elizabeth B. 0000-0003-2356-2244 estumpner@usgs.gov","orcid":"https://orcid.org/0000-0003-2356-2244","contributorId":181854,"corporation":false,"usgs":true,"family":"Stumpner","given":"Elizabeth","email":"estumpner@usgs.gov","middleInitial":"B.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yin, Jamie S.","contributorId":288390,"corporation":false,"usgs":false,"family":"Yin","given":"Jamie","email":"","middleInitial":"S.","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heberger, Matthew","contributorId":288391,"corporation":false,"usgs":false,"family":"Heberger","given":"Matthew","email":"","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Jing","contributorId":191126,"corporation":false,"usgs":false,"family":"Wu","given":"Jing","email":"","affiliations":[],"preferred":false,"id":837828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wong, Adam","contributorId":288392,"corporation":false,"usgs":false,"family":"Wong","given":"Adam","affiliations":[{"id":61747,"text":"San Francisco Estuary Institute - Aquatic Science Center","active":true,"usgs":false}],"preferred":false,"id":837829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Saraceno, John Franco 0000-0003-0064-1820","orcid":"https://orcid.org/0000-0003-0064-1820","contributorId":217534,"corporation":false,"usgs":false,"family":"Saraceno","given":"John Franco","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":837830,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237985,"text":"70237985 - 2022 - Whooping crane stay length in relation to stopover site characteristics","interactions":[],"lastModifiedDate":"2022-11-09T15:12:18.689229","indexId":"70237985","displayToPublicDate":"2022-01-01T08:59:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12807,"text":"Proceedings of the North American Crane Workshop","active":true,"publicationSubtype":{"id":10}},"title":"Whooping crane stay length in relation to stopover site characteristics","docAbstract":"Whooping crane (Grus americana) migratory stopovers can vary in length from hours to more than a month. Stopover sites provide food resources and safety essential for the completion of migration. Factors such as weather, climate, demographics of migrating groups, and physiological condition of migrants influence migratory movements of cranes (Gruidae) to varying degrees. However, little research has examined the relationship between habitat characteristics and stopover stay length in cranes. Site quality may relate to stay length with longer stays that allow individuals to improve body condition, or with shorter stays because of increased foraging efficiency. We examined this question using habitat data collected at 605 use locations from 449 stopover sites throughout the United States Great Plains visited by 58 whooping cranes from the Aransas–Wood Buffalo Population tracked with platform transmitting terminals. Research staff compiled land cover (e.g., hectares of corn; landscape level) and habitat metric (e.g., maximum water depth; site level) data for day use and evening roost locations via site visits and geospatial mapping. We used Random Forest regression analyses to estimate importance of covariates for predicting stopover stay length. Site-level variables explained 9% of variation in stay length, whereas landscape-level variables explained 43%. Stay length increased with latitude and the proportion of land cover as open-water slough with emergent vegetation as well as alfalfa, whereas stay length decreased as open-water lacustrine wetland land cover increased. At the site-level, stopover duration increased with wetted width at riverine sites but decreased with wetted width at palustrine and lacustrine wetland sites. Stopover duration increased with mean distance to visual obstruction as well as where management had reduced the height of vegetation through natural (e.g., grazing) or mechanical (e.g., harvesting) means and decreased with maximum water depth. Our results suggest that stopover length increases with the availability of preferred land cover types for foraging. High quality stopover sites with abundant forage resources may help whooping cranes maintain fat reserves important to their annual life cycle.
","language":"English","publisher":"North American Crane Working Group","usgsCitation":"Caven, A.J., Pearse, A.T., Brandt, D.A., Harner, M.J., Wright, G.D., Baasch, D.M., Brinley Buckley, E.M., Metzger, K.L., Rabbe, M.R., and Lacy, A.E., 2022, Whooping crane stay length in relation to stopover site characteristics: Proceedings of the North American Crane Workshop, v. 15, p. 6-33.","productDescription":"28 p.","startPage":"6","endPage":"33","ipdsId":"IP-123212","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":409262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":409261,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.nacwg.org/proceedings15.html","linkFileType":{"id":5,"text":"html"}}],"volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Caven, Andrew J.","contributorId":177586,"corporation":false,"usgs":false,"family":"Caven","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":856431,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":856432,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":856433,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harner, Mary J.","contributorId":177584,"corporation":false,"usgs":false,"family":"Harner","given":"Mary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":856434,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Greg D.","contributorId":177585,"corporation":false,"usgs":false,"family":"Wright","given":"Greg","email":"","middleInitial":"D.","affiliations":[{"id":12957,"text":"Chippewa Ottawa Resource Authority","active":true,"usgs":false}],"preferred":false,"id":856435,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baasch, David M.","contributorId":147145,"corporation":false,"usgs":false,"family":"Baasch","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":16795,"text":"Headwaters Corp, Kearney, NE","active":true,"usgs":false}],"preferred":false,"id":856436,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brinley Buckley, Emma M.","contributorId":198370,"corporation":false,"usgs":false,"family":"Brinley Buckley","given":"Emma","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":856437,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Metzger, Kristine L.","contributorId":147144,"corporation":false,"usgs":false,"family":"Metzger","given":"Kristine","email":"","middleInitial":"L.","affiliations":[{"id":16794,"text":"USFWS, Div of Biol Serv, Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":856438,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rabbe, Matthew R","contributorId":298794,"corporation":false,"usgs":false,"family":"Rabbe","given":"Matthew","email":"","middleInitial":"R","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":856439,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lacy, Anne E","contributorId":174362,"corporation":false,"usgs":false,"family":"Lacy","given":"Anne","email":"","middleInitial":"E","affiliations":[{"id":16606,"text":"International Crane Foundation","active":true,"usgs":false}],"preferred":false,"id":856440,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70231346,"text":"70231346 - 2022 - Seismic site characterization with shear wave (SH) reflection and refraction methods","interactions":[],"lastModifiedDate":"2022-09-01T14:33:22.896544","indexId":"70231346","displayToPublicDate":"2022-01-01T08:44:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2453,"text":"Journal of Seismology","active":true,"publicationSubtype":{"id":10}},"title":"Seismic site characterization with shear wave (SH) reflection and refraction methods","docAbstract":"Reflection and critically refracted seismic methods use traveltime measurements of body waves propagating between a source and a series of receivers on the ground surface to calculate subsurface velocities. Body wave energy is refracted or reflected at boundaries where there is a change in seismic impedance, defined as the product of material density and seismic velocity. This article provides practical guidance on the use of horizontally propagating shear wave (SH-wave) refraction and reflection methods to determine shear wave velocity as a function of depth for near-surface seismic site characterizations. Method principles and the current state of engineering practice are reviewed, along with discussions of limitations and uncertainty assessments. Typical data collection procedures are described using basic survey equipment, along with information on more advanced applications and emerging technologies. Eight case studies provide examples of the techniques in real-world seismic site characterizations performed in a variety of geological settings.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10950-021-10042-z","usgsCitation":"Hunter, J.A., Crow, H.L., Stephenson, W.J., Pugin, A.J., Williams, R., Harris, J.B., Odum, J.K., and Woolery, E.W., 2022, Seismic site characterization with shear wave (SH) reflection and refraction methods: Journal of Seismology, v. 26, p. 631-652, https://doi.org/10.1007/s10950-021-10042-z.","productDescription":"22 p.","startPage":"631","endPage":"652","ipdsId":"IP-130350","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":449309,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10950-021-10042-z","text":"Publisher Index Page"},{"id":400276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2022-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hunter, James A.","contributorId":291430,"corporation":false,"usgs":false,"family":"Hunter","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":62701,"text":"Geological Survey of Canada, Ottawa, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":842341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crow, Heather L. 0000-0002-1575-0862","orcid":"https://orcid.org/0000-0002-1575-0862","contributorId":291431,"corporation":false,"usgs":false,"family":"Crow","given":"Heather","email":"","middleInitial":"L.","affiliations":[{"id":62701,"text":"Geological Survey of Canada, Ottawa, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":842342,"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":842343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pugin, Andre J.-M.","contributorId":291432,"corporation":false,"usgs":false,"family":"Pugin","given":"Andre","email":"","middleInitial":"J.-M.","affiliations":[{"id":62701,"text":"Geological Survey of Canada, Ottawa, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":842344,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Robert 0000-0002-2973-8493 rawilliams@usgs.gov","orcid":"https://orcid.org/0000-0002-2973-8493","contributorId":140741,"corporation":false,"usgs":true,"family":"Williams","given":"Robert","email":"rawilliams@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":842345,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harris, James B. 0000-0003-1515-9025","orcid":"https://orcid.org/0000-0003-1515-9025","contributorId":291433,"corporation":false,"usgs":false,"family":"Harris","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":62704,"text":"Millsaps College, Department of Geosciences, Jackson, Mississippi, USA","active":true,"usgs":false}],"preferred":false,"id":842346,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Odum, Jackson K. 0000-0002-3162-0355 odum@usgs.gov","orcid":"https://orcid.org/0000-0002-3162-0355","contributorId":291434,"corporation":false,"usgs":true,"family":"Odum","given":"Jackson","email":"odum@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":842347,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Woolery, Edward W 0000-0003-3398-5830","orcid":"https://orcid.org/0000-0003-3398-5830","contributorId":192994,"corporation":false,"usgs":false,"family":"Woolery","given":"Edward","email":"","middleInitial":"W","affiliations":[],"preferred":false,"id":842348,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70236142,"text":"70236142 - 2022 - Extensive droughts in the conterminous United States during multiple centuries","interactions":[],"lastModifiedDate":"2022-08-30T13:12:56.867999","indexId":"70236142","displayToPublicDate":"2022-01-01T08:09:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Extensive droughts in the conterminous United States during multiple centuries","docAbstract":"Extensive and severe droughts have substantial effects on water supplies, agriculture, and aquatic ecosystems. To better understand these droughts, we used tree-ring-based reconstructions of the Palmer drought severity index (PDSI) for the period 1475–2017 to examine droughts that covered at least 33% of the conterminous United States (CONUS). We identified 37 spatially extensive drought events for the CONUS and examined their spatial and temporal patterns. The duration of the extensive drought events ranged from 3 to 12 yr and on average affected 43% of the CONUS. The recent (2000–08) drought in the southwestern CONUS, often referred to as the turn-of-the-century drought, is likely one of the longest droughts in the CONUS during the past 500 years. A principal components analysis of the PDSI data from 1475 through 2017 resulted in three principal components (PCs) that explain about 48% of the variability of PDSI and are helpful to understand the temporal and spatial variability of the 37 extensive droughts in the CONUS. Analyses of the relations between the three PCs and well-known climate indices, such as indices of El Niño–Southern Oscillation, indicate statistically significant correlations; however, the correlations do not appear to be large enough (all with an absolute value less than 0.45) to be useful for the development of drought prediction models.
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Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70234293,"text":"70234293 - 2022 - An assessment of uncertainties in VS profiles obtained from microtremor observations in the phased 2018 COSMOS blind trials","interactions":[],"lastModifiedDate":"2022-09-01T14:54:40.235385","indexId":"70234293","displayToPublicDate":"2022-01-01T06:17:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"An assessment of uncertainties in VS profiles obtained from microtremor observations in the phased 2018 COSMOS blind trials","docAbstract":"Site response is a critical consideration when assessing earthquake hazards. Site characterization is key to understanding site effects as influenced by seismic site conditions of the local geology. Thus, a number of geophysical site characterization methods were developed to meet the demand for accurate and cost-effective results. As a consequence, a number of studies have been administered periodically as blind trials to evaluate the state-of-practice on-site characterization. We present results from the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) blind trials, which used data recorded from surface-based microtremor array methods (MAM) at four sites where geomorphic conditions vary from deep alluvial basins to an alpine valley. Thirty-four invited analysts participated. Data were incrementally released to 17 available analysts who participated in all four phases: (1) two-station arrays, (2) sparse triangular arrays, (3) complex nested triangular or circular arrays, and (4) all available geological control site information including drill hole data. Another set of 17 analysts provided results from two sites and two phases only. Although data from one site consisted of recordings from three-component sensors, the other three sites consisted of data recorded only by vertical-component sensors. The sites cover a range of noise source distributions, ranging from one site with a highly directional microtremor wave field to others with omni-directional (azimuthally distributed) wave fields. We review results from different processing techniques (e.g., beam-forming, spatial autocorrelation, cross-correlation, or seismic interferometry) applied by the analysts and compare the effectiveness between the differing wave field distributions. We define the M index as a quality index based on estimates of the time-averaged shear-wave velocity of the upper 10 (VS10), 30 (VS30), 100 (VS100), and 300 (VS300) meters and show its usefulness in quantitative comparisons of VS profiles from multiple analysts. Our findings are expected to aid in building an evidence-based consensus on preferred cost-effective arrays and processing methodology for future studies of seismic site effects.
Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( Rrs) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to Rrs spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ~73% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.
","language":"English","publisher":"IEEE","doi":"10.1109/TGRS.2021.3114635","usgsCitation":"Zolfaghari, K., Pahlevan, N., Binding, C., Gurlin, D., Simis, S.G., Verdu, A.R., Li, L., Crawford, C., VanderWoude, A., Errera, R., Zastepa, A., and Duguay, C.R., 2022, Impact of spectral resolution on quantifying cyanobacteria in lakes and reservoirs: A machine-learning assessment: IEEE Transactions in Geoscience and Remote Sensing, v. 60, 5515520, 20 p., https://doi.org/10.1109/TGRS.2021.3114635.","productDescription":"5515520, 20 p.","ipdsId":"IP-132686","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449319,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/tgrs.2021.3114635","text":"Publisher Index Page"},{"id":390590,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zolfaghari, Kiana","contributorId":267804,"corporation":false,"usgs":false,"family":"Zolfaghari","given":"Kiana","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":825333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pahlevan, Nima","contributorId":267805,"corporation":false,"usgs":false,"family":"Pahlevan","given":"Nima","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":825334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binding, Caren","contributorId":267806,"corporation":false,"usgs":false,"family":"Binding","given":"Caren","affiliations":[],"preferred":false,"id":825335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gurlin, Daniela","contributorId":267807,"corporation":false,"usgs":false,"family":"Gurlin","given":"Daniela","email":"","affiliations":[],"preferred":false,"id":825336,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simis, Stefan G.H.","contributorId":267808,"corporation":false,"usgs":false,"family":"Simis","given":"Stefan","email":"","middleInitial":"G.H.","affiliations":[],"preferred":false,"id":825337,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Verdu, Antonio Ruiz","contributorId":267809,"corporation":false,"usgs":false,"family":"Verdu","given":"Antonio","email":"","middleInitial":"Ruiz","affiliations":[],"preferred":false,"id":825338,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Lin","contributorId":267810,"corporation":false,"usgs":false,"family":"Li","given":"Lin","email":"","affiliations":[],"preferred":false,"id":825339,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":825340,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"VanderWoude, Andrea","contributorId":267811,"corporation":false,"usgs":false,"family":"VanderWoude","given":"Andrea","email":"","affiliations":[],"preferred":false,"id":825341,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Errera, Reagan","contributorId":267812,"corporation":false,"usgs":false,"family":"Errera","given":"Reagan","email":"","affiliations":[],"preferred":false,"id":825342,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zastepa, Arthur","contributorId":267813,"corporation":false,"usgs":false,"family":"Zastepa","given":"Arthur","email":"","affiliations":[],"preferred":false,"id":825343,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Duguay, Claude R.","contributorId":267814,"corporation":false,"usgs":false,"family":"Duguay","given":"Claude","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":825344,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70262190,"text":"70262190 - 2022 - Broad-scale geographic and temporal assessment of northern long-eared bat (Myotis septentrionalis) maternity colony-landscape association","interactions":[],"lastModifiedDate":"2025-01-15T17:13:38.964566","indexId":"70262190","displayToPublicDate":"2022-01-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Broad-scale geographic and temporal assessment of northern long-eared bat (Myotis septentrionalis) maternity colony-landscape association","docAbstract":"As the federally threatened northern long-eared bat Myotis septentrionalis continues to decline due to white-nose syndrome (WNS) impacts, the application of effective conservation measures is needed but often hindered by the lack of ecological data. To date, recommended management practices have been adopted in part from other federally listed sympatric species such as the endangered Indiana bat M. sodalis. During the maternity season, these measures have largely focused on conservation of known day-roost habitat, often with little consideration for foraging habitat, particularly riparian areas. We examined acoustic activity of northern long-eared bats relative to day-roost and capture data at coastal and interior sites in the District of Columbia, New York, Pennsylvania, Virginia, and West Virginia, USA, over the course of 6 summers (2015-2020), where maternity activity was still documented after the initial arrival and spread of WNS. Acoustic activity of northern long-eared bats relative to forest cover decreased at the acoustic site level (fine scale) but increased at the sampling region level (coarse scale). We observed a positive association of northern long-eared bat acoustic activity with riparian areas. Additionally, we observed higher levels of activity during pregnancy through early lactation period of the reproductive cycle prior to juvenile volancy. Our findings suggest the need for more explicit inclusion of forested riparian habitats in northern long-eared bat conservation planning. Acoustic sampling in spring and early summer rather than mid- to late summer and in forested riparian areas is the most effective strategy for identifying potential active northern long-eared bat maternity colonies on the local landscape.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01170","usgsCitation":"Gorman, K., Deeley, S., Barr, E., Freeze, S., Kalen, N., Muthersbaugh, M., and Ford, W., 2022, Broad-scale geographic and temporal assessment of northern long-eared bat (Myotis septentrionalis) maternity colony-landscape association: Endangered Species Research, v. 77, p. 119-130, https://doi.org/10.3354/esr01170.","productDescription":"12 p.","startPage":"119","endPage":"130","ipdsId":"IP-129463","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467209,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01170","text":"Publisher Index Page"},{"id":466435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New York, Pennsylvania, Virginia, and West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.63860968932777,\n 41.75751355656877\n ],\n [\n -79.25398225412287,\n 39.52289159017691\n ],\n [\n -77.93035543937282,\n 37.3515339549044\n ],\n [\n -75.77459039650464,\n 37.3515339549044\n ],\n [\n -75.87176521681832,\n 39.47700295265774\n ],\n [\n -74.87678100280708,\n 40.46398555372922\n ],\n [\n -73.88179678879584,\n 41.450968154800705\n ],\n [\n -76.63860968932777,\n 41.75751355656877\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"77","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gorman, Katherine M.","contributorId":348401,"corporation":false,"usgs":false,"family":"Gorman","given":"Katherine M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":923435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Deeley, Sabrina M.","contributorId":348402,"corporation":false,"usgs":false,"family":"Deeley","given":"Sabrina M.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":923436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barr, Elaine L.","contributorId":348403,"corporation":false,"usgs":false,"family":"Barr","given":"Elaine L.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":923437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeze, Samuel R.","contributorId":348404,"corporation":false,"usgs":false,"family":"Freeze","given":"Samuel R.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":923438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kalen, Nicholas","contributorId":348405,"corporation":false,"usgs":false,"family":"Kalen","given":"Nicholas","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":923439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muthersbaugh, Michael S.","contributorId":348406,"corporation":false,"usgs":false,"family":"Muthersbaugh","given":"Michael S.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":923440,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":923434,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227422,"text":"70227422 - 2022 - Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics","interactions":[],"lastModifiedDate":"2022-01-14T15:32:34.362133","indexId":"70227422","displayToPublicDate":"2021-12-31T09:21:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9944,"text":"Remote Sensing of the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics","docAbstract":"Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIRv), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIRv and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIRv offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIRv and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. Future efforts to fully integrate the complementary strengths of NIRv and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112858","usgsCitation":"Wang, X., Biederman, J.A., Knowles, J.F., Scott, R.L., Turner, A.J., Dannenberg, M.P., Kohler, P., Frankenberg, C., Litvak, M.E., Flerchinger, G.N., Law, B.E., Kwon, H., Reed, S., Parton, W.J., Barron-Gafford, G.A., and Smith, W.K., 2022, Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics: Remote Sensing of the Environment, v. 270, 112858, 11 p., https://doi.org/10.1016/j.rse.2021.112858.","productDescription":"112858, 11 p.","ipdsId":"IP-133234","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":449323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1981744","text":"Publisher Index 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Philipp","contributorId":271093,"corporation":false,"usgs":false,"family":"Kohler","given":"Philipp","email":"","affiliations":[{"id":33000,"text":"Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA","active":true,"usgs":false}],"preferred":false,"id":830802,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frankenberg, Christian","contributorId":271094,"corporation":false,"usgs":false,"family":"Frankenberg","given":"Christian","email":"","affiliations":[{"id":33000,"text":"Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA","active":true,"usgs":false}],"preferred":false,"id":830803,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Litvak, Marcy E","contributorId":271095,"corporation":false,"usgs":false,"family":"Litvak","given":"Marcy","email":"","middleInitial":"E","affiliations":[{"id":34162,"text":"Department of Biology, University of New Mexico, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":830804,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Flerchinger, Gerald N.","contributorId":257377,"corporation":false,"usgs":false,"family":"Flerchinger","given":"Gerald","email":"","middleInitial":"N.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":830805,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Law, Beverly E.","contributorId":222527,"corporation":false,"usgs":false,"family":"Law","given":"Beverly","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":830806,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kwon, Hyojung","contributorId":271096,"corporation":false,"usgs":false,"family":"Kwon","given":"Hyojung","email":"","affiliations":[{"id":56277,"text":"Department of Forest Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, OR, USA","active":true,"usgs":false}],"preferred":false,"id":830807,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":830808,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Parton, William J","contributorId":271097,"corporation":false,"usgs":false,"family":"Parton","given":"William","email":"","middleInitial":"J","affiliations":[{"id":16129,"text":"Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA","active":true,"usgs":false}],"preferred":false,"id":830809,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Barron-Gafford, Greg A.","contributorId":19058,"corporation":false,"usgs":false,"family":"Barron-Gafford","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":830810,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Smith, William K. 0000-0002-5785-6489","orcid":"https://orcid.org/0000-0002-5785-6489","contributorId":239667,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":830811,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70227324,"text":"70227324 - 2022 - Acoustic and genetic data can reduce uncertainty regarding populations of migratory tree-roosting bats impacted by wind energy","interactions":[],"lastModifiedDate":"2022-01-10T13:10:25.611928","indexId":"70227324","displayToPublicDate":"2021-12-30T07:06:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5762,"text":"Animals","active":true,"publicationSubtype":{"id":10}},"title":"Acoustic and genetic data can reduce uncertainty regarding populations of migratory tree-roosting bats impacted by wind energy","docAbstract":"Frequency‐dependent horizontal‐to‐vertical spectral ratios (HVSRs) of Fourier amplitudes from three‐component recordings can provide useful information for site response modeling. However, such information is not incorporated into most ground‐motion models, including those from Next‐Generation Attenuation projects, which instead use the time‐averaged shear‐wave velocity (
Considering reservoirs as linear fragments in a basin's river network could improve understanding, predictability, and management efficiency. We looked for general cascading spatial patterns across five categories of reservoir attributes: land cover, morphology and hydrology, fish habitat, fish assemblages, and fisheries. Attributes were pulled from various databases for large reservoirs (>100 ha) located in the United States. 16 widely distributed river basins, each including a minimum of 15 large reservoirs, were selected for analysis. Using analysis of covariance with basin as the class variable, we tested each attribute as a linear function of catchment area, which is an index of reservoir position in the basin. The majority of reservoir attributes displayed log-linear patterns as catchment area increased, indicating that reservoirs act as members of a larger network just as river reaches do. Several patterns were detected including attributes with no apparent lengthwise arrangement along the basin; cascading spatial patterns in which attributes increase or decrease from upstream to downstream within a basin; and attributes that increase with catchment area in some basins, decrease in others, or may simply remain constant throughout the basin. We conclude that each pattern may have different implications for management, and that the effectiveness with which most management activities influence reservoirs is likely to increase or decrease along river basins.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR029910","usgsCitation":"Faucheux, N., Sample, A., Aldridge, C., Norris, D., Owens, C., Starnes, V.R., VanderBloemen, S., and Miranda, L.E., 2022, Reservoir attributes display cascading spatial patterns along river basins: Water Resources Research, v. 58, no. 1, e2021WR029910, 14 p., https://doi.org/10.1029/2021WR029910.","productDescription":"e2021WR029910, 14 p.","ipdsId":"IP-119850","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Faucheux, N.M.","contributorId":341806,"corporation":false,"usgs":false,"family":"Faucheux","given":"N.M.","affiliations":[{"id":81792,"text":"Mississippi State Uni","active":true,"usgs":false}],"preferred":false,"id":908915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sample, A.R.","contributorId":341807,"corporation":false,"usgs":false,"family":"Sample","given":"A.R.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, C.A.","contributorId":275883,"corporation":false,"usgs":false,"family":"Aldridge","given":"C.A.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norris, D.M.","contributorId":341780,"corporation":false,"usgs":false,"family":"Norris","given":"D.M.","email":"","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":908918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Owens, C.","contributorId":341808,"corporation":false,"usgs":false,"family":"Owens","given":"C.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Starnes, Victoria R.","contributorId":343988,"corporation":false,"usgs":false,"family":"Starnes","given":"Victoria","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":908920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"VanderBloemen, S.","contributorId":341810,"corporation":false,"usgs":false,"family":"VanderBloemen","given":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908921,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908922,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}