In 2015, the U.S. Geological Survey completed a geology-based assessment to estimate the volumes of undiscovered, technically recoverable petroleum resources in the Cherokee Platform Province area of southeastern Kansas, northeastern Oklahoma, and southwestern Missouri. The U.S. Geological Survey identified four stratigraphic intervals that contain petroleum source rocks: (1) thin shales in the Middle to Upper Ordovician Simpson Group, (2) shales within the Upper Devonian to Lower Mississippian Woodford Shale and stratigraphically equivalent Chattanooga Shale, (3) coals and coal-associated shales and mudstones in the Middle Pennsylvanian (Desmoinesian) Cherokee and Marmaton Groups, and (4) thin marine shales within the Marmaton Group and the Upper Pennsylvanian (Missourian) Kansas City and Lansing Groups. Based on the nature of the petroleum accumulations, the characterization of the compositions and thermal maturity of the organic matter in the rocks, and the compositions of the produced petroleum, the U.S. Geological Survey identified three total petroleum systems (TPS) containing four assessment units (AU): the Paleozoic Composite TPS with the Paleozoic Conventional Assessment Unit (AU), the Woodford/Chattanooga TPS with the Woodford Shale Oil AU and the Woodford Biogenic Gas AU, and the Desmoinesian Coal TPS with the Desmoinesian Coalbed Gas AU. Assessment unit summaries follow
1. Three source rock intervals have contributed geochemically distinct oils to reservoirs within the Paleozoic Conventional AU. These intervals are the Simpson Group; the Woodford and Chattanooga Shales; and the Marmaton, Kansas City, and Lansing Groups. The major petroleum source rocks are the Woodford and Chattanooga Shales. The Paleozoic Conventional AU includes reservoirs that range in age from the Upper Cambrian Arbuckle Group to the lower Permian Chase Group. Most oil production in the province has been from Pennsylvanian sandstone reservoirs. Estimated undiscovered petroleum resources for this AU are a mean of 3 million barrels of oil (MMBO), 140 billion cubic feet of gas (BCFG), and 4 million barrels of natural gas liquids (MMBNGL).
2. The Woodford Shale Oil AU contains undiscovered continuous petroleum resources within the Woodford Shale and Chattanooga Shale. The geologic model for the AU assumes that petroleum resources remain trapped within the shale following petroleum migration. For most of the AU, organic matter within the Woodford Shale and Chattanooga Shale is thermally mature with respect to petroleum generation as shown by vitrinite reflectance values between 0.6 and 1 percent. Petroleum has been produced from the Woodford Shale and Chattanooga Shale. Estimated undiscovered petroleum resources for this AU are means of 460 MMBO, 640 BCFG, and 7 MMBNGL.
3. The Woodford Shale Biogenic Gas AU contains undiscovered continuous petroleum resources in the east-central portion of the Cherokee Platform Province near the Ozark uplift where the Woodford Shale and Chattanooga Shale are at depths of 1,250 ft or shallower. At those depths, methanogenesis and(or) biodegradation of thermogenic natural gases can be found where the shale may be more fractured and more susceptible to groundwater penetrations. The mean assessed volume of undiscovered gas for this assessment unit is 416 BCFG and 1 MMBNGL.
4. The Desmoinesian Coalbed Gas AU contains undiscovered continuous petroleum resources within the Middle Pennsylvanian coals and coal-associated shales and mudstones. The boundaries for the Desmoinesian Coalbed Gas AU are, in part, defined by the extent, depth, and thickness of the coals. Within the Desmoinesian Coalbed Gas AU, a sweet spot area was delineated based on a 10 foot or greater net coal thickness. Gas analytical data show that natural gas produced from the coals has a mixed biogenic and thermogenic origin and that there is significant migration of natural gas into the coals from adjacent conventional sandstone reservoirs. The estimated mean volume of undiscovered gas is 10.0 trillion cubic ft of gas (TCFG), and 23 MMBNGL.
For the three continuous (unconventional) assessment units and one conventional assessment unit in the Cherokee Platform Province, total mean volumes of undiscovered petroleum resources are estimated to be 463 MMBO, 11.2 TCFG and 35 MMBNGL.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205110","issn":"978-1-4113-4399-3","usgsCitation":"Drake, R.M., II, and Hatch, J.R., 2021, Geologic assessment of undiscovered oil and gas resources in the Cherokee Platform area of Kansas, Oklahoma, and Missouri: U.S. Geological Survey Scientific Investigations Report 2020–5110, 39 p., https://doi.org/10.3133/sir20205110.","productDescription":"viii, 39 p.","onlineOnly":"N","ipdsId":"IP-069652","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":383204,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5110/sir20205110.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5110"},{"id":383203,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5110/coverthb2.jpg"}],"country":"United States","state":"Kansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.2392578125,\n 33.43144133557529\n ],\n [\n -93.251953125,\n 33.578014746143985\n ],\n [\n -93.2958984375,\n 40.04443758460856\n ],\n [\n -100.0634765625,\n 40.04443758460856\n ],\n [\n -100.2392578125,\n 33.43144133557529\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The efficiency of biodiversity assessments and biomonitoring studies is commonly challenged by limitations in taxonomic identification and quantification approaches. In this study, we assessed the effects of different taxonomic and numerical resolutions on a range of community structure metrics in invertebrate compositional data sets from six regions distributed across North and South America. We specifically assessed the degree of similarity in the metrics (richness, equitability, beta diversity, heterogeneity in community composition and congruence) for data sets identified to a coarse resolution (usually family level) and the finest taxonomic resolution practical (usually genus level, sometimes species or morphospecies) and by presence-absence and relative abundance numerical resolutions. Spearman correlations showed highly significant and positive associations between univariate metrics (richness and equitability) calculated for coarse- and finest-resolution datasets. Procrustes analysis detected significant congruence between composition datasets. Higher correlation coefficients were found for datasets with the same numerical resolutions regardless of the taxonomic level (about 90%), while the correlations for comparisons across numerical resolutions were consistently lower. Our findings indicate that family-level resolution can be used as a surrogate of finer taxonomic resolutions to calculate a range of biodiversity metrics commonly used to describe invertebrate community structure patterns in New World freshwater wetlands without significant loss of information. However, conclusions on biodiversity patterns derived from datasets with different numerical resolutions should be critically considered in studies on wetland invertebrates.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107437","usgsCitation":"Pires, M.M., Grech, M.G., Stenert, C., Maltchik, L., Epele, L.B., McLean, K., Kneitel, J., Bell, D., Greig, H.S., Gagne, C.R., and Batzer, D., 2021, Does taxonomic and numerical resolution affect the assessment of invertebrate community structure in New World freshwater wetlands?: Ecological Indicators, v. 125, 107437, 7 p., https://doi.org/10.1016/j.ecolind.2021.107437.","productDescription":"107437, 7 p.","ipdsId":"IP-122245","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":453432,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107437","text":"Publisher Index Page"},{"id":396106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina, Brazil, United States","state":"California, Chubut, 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B.","contributorId":279586,"corporation":false,"usgs":false,"family":"Epele","given":"Luis","email":"","middleInitial":"B.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kneitel, Jamie M.","contributorId":279587,"corporation":false,"usgs":false,"family":"Kneitel","given":"Jamie M.","affiliations":[{"id":57301,"text":"California State University, 6000 J St, Sacramento, CA 95819, USA","active":true,"usgs":false}],"preferred":false,"id":835160,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bell, Douglas A.","contributorId":279590,"corporation":false,"usgs":false,"family":"Bell","given":"Douglas A.","affiliations":[{"id":57302,"text":"East Bay Regional Park District, 2950 Peralta Oaks Court, Oakland, CA","active":true,"usgs":false}],"preferred":false,"id":835161,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Greig, Hamish S.","contributorId":279591,"corporation":false,"usgs":false,"family":"Greig","given":"Hamish","email":"","middleInitial":"S.","affiliations":[{"id":57280,"text":"University of Maine, 212 Deering Hall, Orono, ME","active":true,"usgs":false}],"preferred":false,"id":835162,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gagne, Chase R.","contributorId":279592,"corporation":false,"usgs":false,"family":"Gagne","given":"Chase","email":"","middleInitial":"R.","affiliations":[{"id":57280,"text":"University of Maine, 212 Deering Hall, Orono, ME","active":true,"usgs":false}],"preferred":false,"id":835163,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Batzer, Darold P.","contributorId":279593,"corporation":false,"usgs":false,"family":"Batzer","given":"Darold P.","affiliations":[{"id":57305,"text":"University of Georgia, 120 Cedar St, Athens, GA 30602, USA","active":true,"usgs":false}],"preferred":false,"id":835164,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70219170,"text":"70219170 - 2021 - Improving the ability of a BACI design to detect impacts within a kelp‐forest community","interactions":[],"lastModifiedDate":"2021-06-01T17:27:50.058416","indexId":"70219170","displayToPublicDate":"2021-02-15T07:46:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Improving the ability of a BACI design to detect impacts within a kelp‐forest community","docAbstract":"Distinguishing between human impacts and natural variation in abundance remains difficult because most species exhibit complex patterns of variation in space and time. When ecological monitoring data are available, a before‐after‐control‐impact (BACI) analysis can control natural spatial and temporal variation to better identify an impact and estimate its magnitude. However, populations with limited distributions and confounding spatial‐temporal dynamics can violate core assumptions of BACI‐type designs. In this study, we assessed how such properties affect the potential to identify impacts. Specifically, we quantified the conditions under which BACI analyses correctly (or incorrectly) identified simulated anthropogenic impacts in a spatially and temporally replicated data set of fish, macroalgal, and invertebrate species found on nearshore subtidal reefs in southern California, USA. We found BACI failed to assess very localized impacts, and had low power but high precision when assessing region‐wide impacts. Power was highest for severe impacts of moderate spatial scale, and impacts were most easily detected in species with stable, widely distributed populations. Serial autocorrelation in the data greatly inflated false impact detection rates, and could be partly controlled for statistically, while spatial synchrony in dynamics had no consistent effect on power or false detection rates. Unfortunately, species that offer high power to detect real impacts were also more likely to detect impacts where none had occurred. However, considering power and false detection rates together can identify promising indicator species, and collectively analyzing data for similar species improved the net ability to assess impacts. These insights set expectations for the sizes and severities of impacts that BACI analyses can detect in real systems, point to the importance of serial autocorrelation (but not of spatial synchrony), and indicate how to choose the species, and groups of species, that can best identify impacts.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2304","usgsCitation":"Rassweiler, A., Okamoto, D.K., Reed, D.C., Kushner, D.J., Schroeder, D., and Lafferty, K.D., 2021, Improving the ability of a BACI design to detect impacts within a kelp‐forest community: Ecological Applications, v. 31, no. 4, e02304, 15 p., https://doi.org/10.1002/eap.2304.","productDescription":"e02304, 15 p.","ipdsId":"IP-118865","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":384711,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Rassweiler, Andrew 0000-0002-8760-3888","orcid":"https://orcid.org/0000-0002-8760-3888","contributorId":203606,"corporation":false,"usgs":false,"family":"Rassweiler","given":"Andrew","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":813105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Okamoto, Daniel K","contributorId":256705,"corporation":false,"usgs":false,"family":"Okamoto","given":"Daniel","email":"","middleInitial":"K","affiliations":[{"id":51835,"text":"Department of Biological Science, Florida State University, Tallahassee, Florida, 32306 USA","active":true,"usgs":false}],"preferred":false,"id":813106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Daniel C.","contributorId":203607,"corporation":false,"usgs":false,"family":"Reed","given":"Daniel","email":"","middleInitial":"C.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":813107,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kushner, David J","contributorId":256706,"corporation":false,"usgs":false,"family":"Kushner","given":"David","email":"","middleInitial":"J","affiliations":[{"id":51836,"text":"Channel Islands National Park, Ventura, California, 93001 USA","active":true,"usgs":false}],"preferred":false,"id":813108,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schroeder, Donna M","contributorId":256707,"corporation":false,"usgs":false,"family":"Schroeder","given":"Donna M","affiliations":[{"id":51837,"text":"Bureau of Ocean Energy Management, Pacific OCS Region, 760 Paseo Camarillo, Camarillo, California, 93010 USA","active":true,"usgs":false}],"preferred":false,"id":813109,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813110,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220313,"text":"70220313 - 2021 - Cloud-native repositories for big scientific data","interactions":[],"lastModifiedDate":"2021-05-04T12:13:11.359426","indexId":"70220313","displayToPublicDate":"2021-02-15T07:08:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8579,"text":"Computing in Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Cloud-native repositories for big scientific data","docAbstract":"Demographic models enhance understanding of drivers of population growth and inform conservation efforts to prevent population declines and extinction. For species with complex life histories, however, parameterizing demographic models is challenging because some life stages can be difficult to study directly. Integrated population models (IPMs) empower researchers to estimate vital rates for organisms that have cryptic or widely dispersing early life stages by integrating multiple demographic data sources. For a stream‐inhabiting frog (Rana boylii) that is declining through much of its range in Oregon and California, USA, we collected egg‐mass counts and capture–mark–recapture data on adults from two populations in California to fit IPMs that estimate adult abundance and the survival rate of both marked and unobserved life stages. Estimates of adult abundance based on long‐term monitoring of egg‐mass counts showed that study populations fluctuated greatly inter‐annually but were stable at longer timescales (i.e., decades). Adult female survival during 5–6 yr of capture–mark–recapture study periods was nearly equal in each population. Survival rate of R. boylii eggs to the subadult stage is low on average (0.002) but highly variable among years depending on post‐oviposition stream flow. Population viability analysis showed that survival of adult and subadult life stages has the greatest proportional effect on population growth; the survival of egg and tadpole life stages, however, is more malleable by management interventions. For example, simulations showed head‐starting of tadpoles, salvaging stranded egg masses, and limiting aseasonal pulsed flows could dramatically reduce the threat of extirpation. This study demonstrates the value of integrating multiple demographic data sources to construct models of population dynamics in species with complex life histories.
We used historical stocking and population survey records of Yellowstone Cutthroat Trout Oncorhynchus clarkii bouvieri and other salmonids in the North Fork Shoshone River drainage, Wyoming to summarize fish stocking history and population trends. Based on 98 years of historical records, we found that despite extensive stocking of Yellowstone Cutthroat Trout and minimal stocking of nonnative salmonids after about 1950, populations of wild Yellowstone Cutthroat Trout declined relative to those of nonnative salmonid species. The timing of increases in nonnative salmonids (1970s) did not coincide with their period of most intensive stocking (1935–1950). It is plausible that Yellowstone Cutthroat Trout populations persisted because of high levels of supplemental stocking from 1935 to 1965 and declined with reduced stocking efforts in the 1970s, thereby allowing the increase of introduced nonnative salmonids. The establishment of nonnative salmonids likely further reduced stocking success of Yellowstone Cutthroat Trout due to competition and hybridization. This study demonstrates that an understanding of long-term stocking records and population survey data can be useful for developing and implementing successful management frameworks for the conservation of imperiled fish populations across the United States.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10593","usgsCitation":"Nordberg, B.J., Mandeville, E., Walters, A.W., Burckhardt, J.C., and Wagner, C.E., 2021, Historical data provide important context for understanding declines in Cutthroat Trout: North American Journal of Fisheries Management, v. 41, no. 3, p. 809-819, https://doi.org/10.1002/nafm.10593.","productDescription":"11 p.","startPage":"809","endPage":"819","ipdsId":"IP-107228","costCenters":[{"id":683,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":393734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"North Fork Shoshone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110,\n 44.40\n ],\n [\n -109,\n 44.40\n ],\n [\n -109,\n 44.55\n ],\n [\n -110,\n 44.55\n ],\n [\n -110,\n 44.40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Nordberg, Brittany J.","contributorId":270690,"corporation":false,"usgs":false,"family":"Nordberg","given":"Brittany","email":"","middleInitial":"J.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":829772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":829773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":829771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burckhardt, Jason C.","contributorId":270692,"corporation":false,"usgs":false,"family":"Burckhardt","given":"Jason","email":"","middleInitial":"C.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":829774,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Catherine E.","contributorId":270693,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":829775,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219530,"text":"70219530 - 2021 - Would you like to know more? The effect of personalized wildfire risk information and social comparisons on information-seeking behavior in the wildland–urban interface","interactions":[],"lastModifiedDate":"2021-04-13T13:24:44.123688","indexId":"70219530","displayToPublicDate":"2021-02-13T08:23:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Would you like to know more? The effect of personalized wildfire risk information and social comparisons on information-seeking behavior in the wildland–urban interface","docAbstract":"Private landowners are important actors in landscape-level wildfire risk management. Accordingly, wildfire programs and policy encourage wildland–urban interface homeowners to engage with local organizations to properly mitigate wildfire risk on their parcels. We investigate whether parcel-level wildfire risk assessment data, commonly used to inform community-level planning and resource allocation, can be used to “nudge” homeowners to engage further with a regional wildfire organization. We sent 4564 households in western Colorado a letter that included varying combinations of risk information about their community, their parcels, and their neighbors’ parcels, and we measured follow-up visits to a personalized “Web site”. We find that the effect of providing parcel-specific information depends on baseline conditions: Informing homeowners about their property’s wildfire risk increases information-seeking among homeowners of the highest-risk parcels by about 5 percentage points and reduces information-seeking among homeowners of lower-risk parcels by about 6 percentage points. Parcel-specific information also increases the overall response in the lowest risk communities by more than 10 percentage points. Further, we find evidence of a 6-percentage point increase in response rate associated with receiving a social comparison treatment that signals neighboring properties as being either low or moderate risk on average. These results, especially considered against the 13 percent overall average response rate, offer causal evidence that providing parcel-specific wildfire risk information can influence behavior. As such, we demonstrate the effectiveness of simple outreach in engaging wildland–urban interface homeowners with wildfire risk professionals in ways that leverage existing data.
Determining whether seismicity near volcanoes is due primarily to tectonic or magmatic processes is a challenging but critical endeavor for volcanic eruption forecasting and detection, especially at poorly monitored volcanoes. Global statistics on the occurrence and timing of earthquakes near volcanoes both within and outside of eruptive periods reveal patterns in eruptive seismicity that may improve our ability to discern magmatically driven seismicity from purely tectonic seismicity. In this paper, we catalog magnitude four and greater (M4+) earthquakes near volcanoes globally and compute statistics on their occurrence with respect to various eruptive and volcanic attributes, evaluating their utility as diagnostic indicators of eruptions. Using a 2‐week time window and a 30 km radius around the volcanoes, we find that 11% of eruptions are preceded by at least one M4+ earthquake, but only 1% of such earthquakes is followed by eruption. However, earthquakes located 5–15 km from the volcano, those with normal faulting mechanisms and/or large nondouble‐couple components, and those occurring as groups are more commonly associated with eruptions, providing significant forecasting utility in some cases. Similarly, certain volcanoes are more likely to exhibit such precursors, such as those with long repose periods. We illustrate the use of these data in eruption forecasting scenarios, including rapid identification of analogous earthquake sequences at other volcanoes. When integrated within the context of multiparametric, multidisciplinary probabilistic assessments of volcanic activity, global earthquake statistics can improve eruption forecasts, and our work provides a model for use on other rapidly expanding global volcanological databases.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB021294","usgsCitation":"Pesicek, J.D., Ogburn, S.E., and Prejean, S., 2021, Indicators of volcanic eruptions revealed by global M4+ earthquakes: Journal of Geophysical Research, v. 126, no. 3, e2020JB021294, 28 p., https://doi.org/10.1029/2020JB021294.","productDescription":"e2020JB021294, 28 p.","ipdsId":"IP-124151","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":453474,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jb021294","text":"Publisher Index Page"},{"id":384229,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":811480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ogburn, Sarah E. 0000-0002-4734-2118","orcid":"https://orcid.org/0000-0002-4734-2118","contributorId":204751,"corporation":false,"usgs":true,"family":"Ogburn","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":811481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prejean, Stephanie G. 0000-0003-0510-1989 sprejean@usgs.gov","orcid":"https://orcid.org/0000-0003-0510-1989","contributorId":172404,"corporation":false,"usgs":true,"family":"Prejean","given":"Stephanie","email":"sprejean@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":811482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218675,"text":"70218675 - 2021 - Airborne geophysical imaging of weak zones on Iliamna Volcano, Alaska: Implications for slope stability","interactions":[],"lastModifiedDate":"2021-03-05T13:52:47.045359","indexId":"70218675","displayToPublicDate":"2021-02-12T07:44:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Airborne geophysical imaging of weak zones on Iliamna Volcano, Alaska: Implications for slope stability","docAbstract":"Water‐saturated, hydrothermally altered rocks reduce the strength of volcanic edifices and increase the potential for sector collapses and far‐traveled mass flows of unconsolidated debris. Iliamna Volcano is an andesitic stratovolcano located on the western side of the Cook Inlet, ∼225 km southwest of Anchorage and is a source of repeated avalanches. The widespread snow and ice cover on Iliamna Volcano make surface alteration difficult to identify. However, intense hydrothermal alteration significantly reduces both the electrical resistivity and magnetization of volcanic rock and can therefore be identified with airborne geophysical measurements. We use airborne electromagnetic and magnetic data to map snow and ice thickness and identify underlying alteration zones at Iliamna Volcano, Alaska. Resistivities were calculated to an average depth of >300 m, and a 3‐D susceptibility model extends from the surface to the base of the volcano, about 3,000 m below the summit. Geophysical models image low resistivity (<30 ohm‐m) and low susceptibilities near the summit of Iliamna and below its older vent complex, with the low susceptibilities indicating alteration up to ∼800 m in thickness. Thin conductors (∼50–100 m thick) on the edifice slopes coincide with recorded locations of repeated debris avalanches over the past ∼60 years and are attributed to saturated zones at high elevation. Three‐dimensional slope stability models based upon the geophysically constrained alteration distribution suggest the edifice of Iliamna is unstable and could lead to collapse scars ∼400 m deep near the current and former vent complexes.
Choosing whether or not to migrate is an important life history decision for many fishes. Here we combine data from physical captures and detections on autonomous passive integrated transponder (PIT) tag antennas to study migration in an endangered fish, the humpback chub (Gila cypha). We develop hidden Markov mark-recapture models with and without antenna detections and find that the model fit without antenna detections misses a large proportion of fish and underestimates migration and survival probabilities. We then assess survival and growth differences associated with life history strategy and migration for different demographic groups (small male, small female, large male, large female). We find large differences in survival according to life history strategy, where residents had much lower over-winter survival than migrants. However, within the migratory life history strategy, survival and growth were similar for active migrants and skipped migrants for all demographic groups. We discuss some common challenges to incorporating detections from autonomous antennas into population models and demonstrate how these data can provide insight about fish movement and life history strategies.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2020-0291","usgsCitation":"Dzul, M.C., Kendall, W.L., Yackulic, C., Winkelman, D.L., Van Haverbeke, D.R., and Yard, M.D., 2021, Partial migration and spawning movements of humpback chub in the Little Colorado River are better understood using data from autonomous PIT tag antennas: Canadian Journal of Fisheries and Aquatic Sciences, v. 78, no. 8, p. 1057-1072, https://doi.org/10.1139/cjfas-2020-0291.","productDescription":"16 p.","startPage":"1057","endPage":"1072","onlineOnly":"N","ipdsId":"IP-121398","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436513,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95KA0XI","text":"USGS data release","linkHelpText":"Humpback chub spring and fall capture histories in the Little Colorado River, 2009-2019"},{"id":384979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Little Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.28851318359375,\n 35.67737855391475\n ],\n [\n -111.29425048828125,\n 35.67737855391475\n ],\n [\n -111.29425048828125,\n 36.43896124085945\n ],\n [\n -112.28851318359375,\n 36.43896124085945\n ],\n [\n -112.28851318359375,\n 35.67737855391475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William Louis 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":257230,"corporation":false,"usgs":false,"family":"Kendall","given":"William","email":"","middleInitial":"Louis","affiliations":[{"id":51981,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, 201 J.V.K. Wagar Building 1484 Campus Delivery, Fort Collins, CO 80523, USA","active":true,"usgs":false}],"preferred":false,"id":813825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":813827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Haverbeke, David Randall","contributorId":257231,"corporation":false,"usgs":false,"family":"Van Haverbeke","given":"David","email":"","middleInitial":"Randall","affiliations":[{"id":51983,"text":"Arizona Fish and Wildlife Conservation Office, U.S. Fish and Wildlife Service, 2500 S Pine Knoll Dr., Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":813828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813829,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218013,"text":"sir20205148 - 2021 - Nutrient concentrations, loads, and yields in the Middle Iowa River Basin, Iowa","interactions":[],"lastModifiedDate":"2021-02-12T12:56:57.224275","indexId":"sir20205148","displayToPublicDate":"2021-02-11T17:37:55","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5148","displayTitle":"Nutrient Concentrations, Loads, and Yields in the Middle Iowa River Basin, Iowa","title":"Nutrient concentrations, loads, and yields in the Middle Iowa River Basin, Iowa","docAbstract":"Concentrations, loads, and yields of nitrate plus nitrite, total nitrogen, and total phosphorus were assessed in the Iowa River upstream from the Coralville Reservoir in east-central Iowa. The results of this study describe baseline nutrient transport during two historical reference periods, 1980–96 and 2006–10, that can be used to evaluate the progress of the implementation of reduction strategies in the Middle Iowa River Basin. Where available, nutrient data during the more recent period 2011–18 are also described. Data included nutrient concentrations and streamflow from multiple Federal, State, and Tribal agencies, and loads were computed using multiple techniques to provide valuable insights, which would otherwise not be possible.
Despite an upward trend for mean annual and base streamflow (the trend in high streamflow was not significant), average nutrient loads and yields in the Iowa River were smaller in the recent period (2011–18) than in either historical reference period. Notably smaller loads during the 2012 drought, however, caused pronounced skewed average loads for 2011–18. Comparisons among periods were difficult to make because of a short period of data upstream from Marshalltown, Iowa, at the upstream boundary of the study area and a lack of recent data near Marengo, Iowa, at the downstream boundary of the study area. Though spring and summer loads were a disproportionate part of annual loads, up to 90 percent, seasonal load comparisons to determine load reduction were more sensitive to one or the other historical period than was assessment of annual loads. Runoff-transport relations may provide an additional tool to assess load reduction.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205148","collaboration":"Prepared in cooperation with the Sac and Fox Tribe of the Mississippi in Iowa","usgsCitation":"Garrett, J.D., and Kalkhoff, S.J., 2021, Nutrient concentrations, loads, and yields in the Middle Iowa River Basin, Iowa: U.S. Geological Survey Scientific Investigations Report 2020–5148, 22 p., https://doi.org/10.3133/sir20205148.","productDescription":"Report: vii, 22 p.; 1 Table; Dataset","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-116761","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":383240,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5148/sir20205148_table1.1.csv","text":"Table 1.1","size":"23.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5148 Table 1.1","linkHelpText":"— Tributary sites in the Middle Iowa River Basin, upstream of Coralville Reservoir"},{"id":383239,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5148/sir20205148_table1.1.xlsx","text":"Table 1.1","size":"28.1 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5148 Table 1.1","linkHelpText":"— Tributary sites in the Middle Iowa River Basin, upstream of Coralville Reservoir"},{"id":383241,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":383238,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5148/sir20205148.pdf","text":"Report","description":"SIR 2020–5148"},{"id":383237,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5148/coverthb.jpg"}],"country":"United States","state":"Iowa","otherGeospatial":"Middle Iowa River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.065673828125,\n 41.380930388318\n ],\n [\n -90.911865234375,\n 41.529141988723104\n ],\n [\n -90.9613037109375,\n 41.79179268262892\n ],\n [\n -91.2249755859375,\n 42.020732852644294\n ],\n [\n -91.5655517578125,\n 42.17968819665961\n ],\n [\n -91.900634765625,\n 42.34636533160187\n ],\n [\n -92.142333984375,\n 42.66224137632748\n ],\n [\n -92.3291015625,\n 42.984558134256076\n ],\n [\n -92.5323486328125,\n 43.18114705939968\n ],\n [\n -92.7685546875,\n 43.20917969039356\n ],\n [\n -92.9058837890625,\n 43.08894918346591\n ],\n [\n -92.8564453125,\n 42.807491865911544\n ],\n [\n -92.6641845703125,\n 42.55712670332118\n ],\n [\n -92.46093749999999,\n 42.28137302193453\n ],\n [\n -92.1148681640625,\n 42.00848901572399\n ],\n [\n -91.8731689453125,\n 41.70982942509964\n ],\n [\n -91.6644287109375,\n 41.40565583808169\n ],\n [\n -91.4117431640625,\n 41.1455697310095\n ],\n [\n -91.175537109375,\n 41.000629848685385\n ],\n [\n -91.0052490234375,\n 40.9964840143779\n ],\n [\n -90.911865234375,\n 41.017210578228436\n ],\n [\n -90.94482421875,\n 41.104190944576466\n ],\n [\n -90.99426269531249,\n 41.21585377825921\n ],\n [\n -91.04919433593749,\n 41.25716209782705\n ],\n [\n -91.065673828125,\n 41.380930388318\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
An aeromagnetic survey was conducted to improve understanding of the geology and structure in the area around Burney, northeastern California. The new data are a substantial improvement over existing data and reveal a prominent north northwest-trending magnetic grain that allows extension of mapped faults, delineation of plutons within the Mesozoic basement in the northern Sierra Nevada, and linear anomalies that limit the amount of strike-slip offset along various faults in the area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211006","usgsCitation":"Langenheim, V.E., 2021, Aeromagnetic map of Burney and the surrounding area, northeastern California: U.S. Geological Survey Open-File Report 2021–1006, 8 p., 1 sheet, scale 1:250:000, https://doi.org/10.3133/ofr20211006.","productDescription":"Report: iv, 8 p.; 1 Sheet: 30.37 x 34.42 inches; Data Release","numberOfPages":"8","ipdsId":"IP-111186","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":383220,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PUFYDD","linkHelpText":"Aeromagnetic data, grid data, and magnetization boundaries of a survey flown in the Burney region, northeastern California"},{"id":383219,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2021/1006/ofr20211006_sheet.pdf","text":"Sheet","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383217,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1006/covrthb.jpg"},{"id":383218,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1006/ofr20211006_pamphlet.pdf","text":"Pamphlet","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.607421875,\n 39.57182223734374\n ],\n [\n -120.0146484375,\n 39.57182223734374\n ],\n [\n -120.0146484375,\n 41.541477666790286\n ],\n [\n -122.607421875,\n 41.541477666790286\n ],\n [\n -122.607421875,\n 39.57182223734374\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.
","language":"English","publisher":"Department of Energy","doi":"10.2172/1769795","usgsCitation":"Varadharajan, C., Kumar, V., Willard, J., Zwart, J.A., Sadler, J.M., Weierbach, H., Perciano, T., Mueller, J., Hendrix, V., and Christianson, D., 2021, Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality: Technical Report, 5 p., https://doi.org/10.2172/1769795.","productDescription":"5 p.","ipdsId":"IP-126904","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":453494,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1769795","text":"External Repository"},{"id":421340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Varadharajan, Charuleka","contributorId":242712,"corporation":false,"usgs":false,"family":"Varadharajan","given":"Charuleka","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weierbach, Helen","contributorId":290549,"corporation":false,"usgs":false,"family":"Weierbach","given":"Helen","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883293,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perciano, Talita 0000-0002-2388-1803","orcid":"https://orcid.org/0000-0002-2388-1803","contributorId":290546,"corporation":false,"usgs":false,"family":"Perciano","given":"Talita","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883294,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Juliane 0000-0001-8627-1992","orcid":"https://orcid.org/0000-0001-8627-1992","contributorId":290539,"corporation":false,"usgs":false,"family":"Mueller","given":"Juliane","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883295,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hendrix, Valerie 0000-0001-9061-8952","orcid":"https://orcid.org/0000-0001-9061-8952","contributorId":290533,"corporation":false,"usgs":false,"family":"Hendrix","given":"Valerie","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883296,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Christianson, Danielle","contributorId":265829,"corporation":false,"usgs":false,"family":"Christianson","given":"Danielle","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":883297,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70223670,"text":"70223670 - 2021 - Biological and anthropogenic influences on macrophage aggregates in white perch Morone americana from Chesapeake Bay, USA","interactions":[],"lastModifiedDate":"2021-09-01T13:34:03.647085","indexId":"70223670","displayToPublicDate":"2021-02-11T08:18:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Biological and anthropogenic influences on macrophage aggregates in white perch Morone americana from Chesapeake Bay, USA","title":"Biological and anthropogenic influences on macrophage aggregates in white perch Morone americana from Chesapeake Bay, USA","docAbstract":"The response of macrophage aggregates in fish to a variety of environmental stressors has been useful as a biomarker of exposure to habitat degradation. Total volume of macrophage aggregates (MAV) was estimated in the liver and spleen of white perch Morone americana from Chesapeake Bay using stereological approaches. Hepatic and splenic MAV were compared between fish populations from the rural Choptank River (n = 122) and the highly urbanized Severn River (n = 131). Hepatic and splenic MAV increased with fish age, were greater in females from the Severn River only, and were significantly greater in fish from the more polluted Severn River (higher concentrations of polycyclic aromatic hydrocarbons, organochlorine pesticides, and brominated diphenyl ethers). Water temperature and dissolved oxygen had a significant effect on organ volumes, but not on MAV. Age and river were most influential on hepatic and splenic MAV, suggesting that increased MAV in Severn River fish resulted from chronic exposures to higher concentrations of environmental contaminants and other stressors. Hemosiderin was abundant in 97% of spleens and was inversely related to fish condition and positively related to fish age and trematode infections. Minor amounts of hemosiderin were detected in 30% of livers and positively related to concentrations of benzo[a] pyrene metabolite equivalents in the bile. This study demonstrated that hepatic and splenic MAV were useful indicators in fish from the 2 tributaries with different land use characteristics and concentrations of environmental contaminants. More data are needed from additional tributaries with a wider gradient of environmental impacts to validate our results in this species.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03555","usgsCitation":"Matsche, M.A., Blazer, V., Pulster, E., and Mazik, P.M., 2021, Biological and anthropogenic influences on macrophage aggregates in white perch Morone americana from Chesapeake Bay, USA: Diseases of Aquatic Organisms, v. 143, p. 79-100, https://doi.org/10.3354/dao03555.","productDescription":"22 p.","startPage":"79","endPage":"100","ipdsId":"IP-122515","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":388726,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.83837890625,\n 36.78289206199065\n ],\n [\n -75.65185546874999,\n 36.78289206199065\n ],\n [\n -75.65185546874999,\n 39.67337039176558\n ],\n [\n -76.83837890625,\n 39.67337039176558\n ],\n [\n -76.83837890625,\n 36.78289206199065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Matsche, Mark A","contributorId":194275,"corporation":false,"usgs":false,"family":"Matsche","given":"Mark","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":822263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":822264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pulster, Erin","contributorId":236999,"corporation":false,"usgs":false,"family":"Pulster","given":"Erin","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":822265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822266,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225537,"text":"70225537 - 2021 - Linking decomposition rates of soil organic amendments to their chemical composition","interactions":[],"lastModifiedDate":"2021-10-21T12:03:44.991455","indexId":"70225537","displayToPublicDate":"2021-02-11T07:00:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9533,"text":"Soil Research","active":true,"publicationSubtype":{"id":10}},"title":"Linking decomposition rates of soil organic amendments to their chemical composition","docAbstract":"The stock of organic carbon contained within a soil represents the balance between inputs and losses. Inputs are defined by the ability of vegetation to capture and retain carbon dioxide, effects that management practices have on the proportion of captured carbon that is added to soil and the application organic amendments. The proportion of organic amendment carbon retained is defined by its rate of mineralisation. In this study, the rate of carbon mineralisation from 85 different potential soil organic amendments (composts, manures, plant residues and biosolids) was quantified under controlled environmental conditions over a 547 day incubation period. The composition of each organic amendment was quantified using nuclear magnetic resonance and mid- and near-infrared spectroscopies. Cumulative mineralisation of organic carbon from the amendments was fitted to a two-pool exponential model. Multivariate chemometric algorithms were derived to allow the size of the fast and slow cycling pools of carbon to be predicted from the acquired spectroscopic data. However, the fast and slow decomposition rate constants could not be predicted suggesting that prediction of the residence time of organic amendment carbon in soil would likely require additional information related to soil type, environmental conditions, and management practices in use at the site of application.
The Soldier Meadows spring complex provides habitat for the desert dace, an endemic and threatened fish. The spring complex has been altered with the construction of irrigation ditches that remove water from natural stream channels. Irrigation ditches generally provide lower quality habitat for the desert dace. Land and wildlife management agencies are interested in increasing habitat extent and quality by filling in irrigation ditches and restoring streamflow to natural channels. The U.S. Geological Survey measured streamflow, surveyed topography, and combined light detection and ranging data to create a two-dimensional hydraulic model of the study area to understand how restoration would change streamflow extents and hydraulic characteristics. Streamflow measurements indicate that, except for a section of one irrigation ditch at the upstream end of the study area, the total volume of streamflow diverted into the irrigation ditches in the study area was minimal. Hydraulic modeling indicates filling in the irrigation ditch at the upper end of the study area would return streamflow to the natural channel, resulting in an increase in natural channel surface water extent, and a reduction of irrigation ditch surface water flow. The result would be a more heterogenous natural stream channel, ranging from shallow and slow to narrow and fast.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205143","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Morris, C.M., 2021, Evaluation of streamflow extent and hydraulic characteristics of a restored channel at Soldier Meadows, Black Rock Desert–High Rock Canyon Emigrant Trails National Conservation Area, Nevada: U.S. Geological Survey Scientific Investigations Report 2020–5143, 22 p., https://doi.org/10.3133/sir20205143.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-110000","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":383124,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O0GII7","linkHelpText":"Geospatial data and surface-water model archive for evaluation of streamflow extent and hydraulic characteristics of a restored channel at Soldier Meadows, Black Rock Desert–High Rock Canyon Emigrant Trails National Conservation Area, Nevada"},{"id":383123,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5143/images"},{"id":383122,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5143/sir20205143.xml"},{"id":383121,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5143/sir20205143.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383120,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5143/covrthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Black Rock Desert, High Rock Canyon Emigrant Trails National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.40765380859375,\n 40.734770989672406\n ],\n [\n -118.35845947265625,\n 40.734770989672406\n ],\n [\n -118.35845947265625,\n 41.45919537950706\n ],\n [\n -119.40765380859375,\n 41.45919537950706\n ],\n [\n -119.40765380859375,\n 40.734770989672406\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Seasonal migrations of fish populations can have large effects on lake nutrient budgets and food web dynamics, but the addition of a migrating non‐native species may alter these dynamics. The Round Goby (Neogobius melanostomus) arrived in Lake Ontario (USA/Canada) about 20 years ago with a documented history of annual offshore–inshore migrations in its native range. Here we combined nearshore, fixed‐plot video with offshore trawl data to document the annual migration of this population over multiple years. This behaviour was correlated with seasonal, nearshore temperature changes. The population size structure and mean fish length of returning fish were smaller than those of out‐migrating fish. The out‐migrating population contained an estimated 37.7 metric tonnes of phosphorous; and we estimated roughly 20 metric tonnes were translocated to and remained in offshore waters over the winter months, representing an important nutrient subsidy to a variety of offshore piscivorous fish. Lake Sturgeon (Acipenser fulvescens) have incorporated Round Goby extensively into their diet and consume a size range of fish matching the size range of missing Round Goby that fail to return to the nearshore. We conclude Round Goby are an important prey within the food web of Lake Ontario and translocate roughly 6.5% of the monthly total phosphorous load entering from surface waters. Further investigations of the nutrient content, population size structure and fate of migrating Round Goby in Lake Ontario are warranted to clarify the extent of this prey and nutrient subsidy in ongoing assessments of lake condition.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12568","usgsCitation":"Pennuto, C., Mehler, K., Weidel, B., Lantry, B.F., and Bruestle, E., 2021, Dynamics of the seasonal migration of Round Goby (Neogobius melanostomus, Pallas 1814) and implications for the Lake Ontario food web: Ecology of Freshwater Fish, v. 30, no. 2, p. 151-161, https://doi.org/10.1111/eff.12568.","productDescription":"11 p.","startPage":"151","endPage":"161","ipdsId":"IP-118165","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.925537109375,\n 43.265206318396025\n ],\n [\n 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bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lantry, Brian F. 0000-0001-8797-3910 bflantry@usgs.gov","orcid":"https://orcid.org/0000-0001-8797-3910","contributorId":3435,"corporation":false,"usgs":true,"family":"Lantry","given":"Brian","email":"bflantry@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bruestle, Eric","contributorId":251746,"corporation":false,"usgs":false,"family":"Bruestle","given":"Eric","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":810467,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218701,"text":"70218701 - 2021 - Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers","interactions":[],"lastModifiedDate":"2021-04-16T13:59:46.362577","indexId":"70218701","displayToPublicDate":"2021-02-10T07:11:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7747,"text":"Acta Polytechnica","active":true,"publicationSubtype":{"id":10}},"title":"Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers","docAbstract":"Exsolution and re-dissolution of CO2 gas within heterogeneous porous media are investigated using experimental data and mathematical modeling. In a set of bench-scale experiments, water saturated with CO2 under a given pressure is injected into a 2-D water-saturated porous media system, causing CO2 gas to exsolve and migrate upwards. A layer of fine sand mimicking a heterogeneity within a shallow aquifer is present in the tank to study accumulation and trapping of exsolved CO2. Then, clean water is injected into the system and the accumulated CO2 dissolves back into the flowing water. Simulated exsolution and dissolution mass transfer processes are studied using both nearequilibrium and kinetic approaches and compared to experimental data under conditions that do and do not include lateral background water flow. The mathematical model is based on the mixed hybrid finite element method that allows for accurate simulation of both advection- and diffusion- dominated processes.
","language":"English","publisher":"Czech Technical University","doi":"10.14311/AP.2021.61.0077","usgsCitation":"Fucik, R., Solovsky, J., Plampin, M.R., Wu, H., Mikyska, J., and Illangasekare, T.H., 2021, Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers: Acta Polytechnica, v. 61, no. SI, 12 p., https://doi.org/10.14311/AP.2021.61.0077.","productDescription":"12 p.","ipdsId":"IP-114423","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":453509,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14311/ap.2021.61.0077","text":"Publisher Index Page"},{"id":384057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"SI","noUsgsAuthors":false,"publicationDate":"2021-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Fucik, Radek 0000-0001-7040-9184","orcid":"https://orcid.org/0000-0001-7040-9184","contributorId":254378,"corporation":false,"usgs":false,"family":"Fucik","given":"Radek","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Solovsky, Jakub","contributorId":254380,"corporation":false,"usgs":false,"family":"Solovsky","given":"Jakub","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plampin, Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":811429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Hao","contributorId":254382,"corporation":false,"usgs":false,"family":"Wu","given":"Hao","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":811430,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mikyska, Jiri","contributorId":254383,"corporation":false,"usgs":false,"family":"Mikyska","given":"Jiri","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811431,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Illangasekare, Tissa H.","contributorId":194933,"corporation":false,"usgs":false,"family":"Illangasekare","given":"Tissa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":811432,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217868,"text":"tm16B1 - 2021 - Multi-taxa database data dictionary","interactions":[],"lastModifiedDate":"2021-02-10T12:59:29.690403","indexId":"tm16B1","displayToPublicDate":"2021-02-09T15:16:40","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"16-B1","displayTitle":"Multi-Taxa Database Data Dictionary","title":"Multi-taxa database data dictionary","docAbstract":"The conservation of biological resources relies on the successful management of ecological and physiological research data. The Western Ecological Research Center of the U.S. Geological Survey is working with researchers, land managers, and decision makers from non-government organizations and city, county, state, and federal resource agencies to develop data management methods. Access to the most current and applicable research data available in making sound decisions to conserve species diversity is foundational. We sought to accomplish several goals in developing the data management strategy used in the Multi-Taxa database. Data persistence and availability are primary goals of well-developed databases. By documenting and sharing the structure and definitions of Multi-Taxa database, we hope to further the successful management of these crucial data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm16B1","collaboration":"Prepared in cooperation with San Diego Association of Governments (SanDAG)","usgsCitation":"Watson, E., Rochester, C.J., Brown, C.W., Holmes, D.A., Hathaway, S.A., and Fisher, R.N., 2021, Multi-taxa database data dictionary: U.S. Geological Survey Techniques and Methods 16–B1, 149 p., https://doi.org/10.3133/tm16B1.","productDescription":"Report: xvi, 149 p., 5 Appendixes; 3 Datasets","numberOfPages":"149","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119276","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":383110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/16/b1/covrthb.jpg"},{"id":383111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383112,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx1.pdf","text":"Appendix 1","size":"700 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of All Database Tables"},{"id":383113,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx2.pdf","text":"Appendix 2","size":"200 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of Tables Associated with Survey Events"},{"id":383114,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx3.pdf","text":"Appendix 3","size":"240 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of Tables Associated with Sites"},{"id":383115,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx4.pdf","text":"Appendix 4","size":"250 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of Tables Associated with Taxa Observations"},{"id":383116,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx5.pdf","text":"Appendix 5","size":"240 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of Tables Associated with Habitat Observations"},{"id":383117,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_field_def.csv","text":"Field Definitions","size":"175 KB","linkFileType":{"id":7,"text":"csv"}},{"id":383118,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_lookup_table_def.csv","text":"Lookup Table Definitions","size":"25 KB","linkFileType":{"id":7,"text":"csv"}},{"id":383119,"rank":10,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_table_def.csv","text":"Table Definitions","size":"10 KB","linkFileType":{"id":7,"text":"csv"}}],"contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Wetland managers in the Central Valley of California, a dynamic hydrological landscape, require information regarding the amount and location of existing wetland habitat to make decisions on how to best use water resources to support multiple wildlife objectives, particularly during drought. Scientists from the U.S. Geological Survey Western Ecological Research Center (WERC), Point Blue Conservation Science (Point Blue), and the U.S. Fish and Wildlife Service (USFWS) partnered to learn how wetland and flooded agricultural habitats used by waterfowl and shorebirds change during the non-breeding season (July–April) particularly during drought. During extreme drought conditions, the ability to provide sufficient water for wildlife often depends on the timing of water deliveries to managed wetlands and winter-flooded crop fields and decisions on whether to fallow croplands. Waterfowl and shorebirds could be particularly affected by these decisions because they typically rest and feed in flooded habitats. Poor habitat conditions resulting from spatially or temporally suboptimal water deliveries (that is, mismatch between waterfowl habitat needs and timing and location of flooded habitats) could reduce waterfowl hunting opportunities and body condition. Point Blue scientists developed a system for near real-time tracking of habitats used by waterfowl, shorebirds, and some other wetland-dependent “waterbirds” (www.pointblue.org/watertracker) and to assess the impacts of drought on habitat availability and on waterfowl and shorebird bioenergetics. The WERC researchers linked these data with near real-time tracking (telemetry) data of duck locations throughout the Valley. The team used these two datasets to relate duck locations to open-water characteristics and to learn how waterfowl use habitats under spatially and temporally changing conditions during drought and non-drought periods. We found that recent extreme drought (2013–15) significantly changed the timing and magnitude of flooding and consequently reduced the availability of habitats used by waterfowl and shorebirds more than other recent historic droughts 2000–11. Drought reduced irrigations of moist soil seed plants and thus there was lower food energy available for waterfowl. Analyses using bioenergetics models indicated that, overall, extreme drought increased food energy deficits (total number of deficit days) for shorebirds and waterfowl. Our analysis indicated a strong direct relationship between duck locations and classified habitat derived from open-water data during the wintering period (October–March). This result helps confirm the application of dynamic water data to identify flooded areas that provide waterfowl habitat. Presence of open water at a 1-hectare resolution can be used effectively to identify flooded landscape areas available as habitat for ducks. Our discoveries from evaluating use of space by ducks also indicated that nighttime feeding locations of ducks were concentrated nearby primary roosts and that foraging distances could depend on hydrologic dynamics of location (Suisun Marsh versus California excluding Suisun Marsh) and time of season (early, middle, late). Other results indicated that some areas on the California landscape with extremely reliable water supplies could receive consistent use by ducks year after year (in essence, almost drought proof). The Water Tracker is set up to automatically track wetland habitat and food availability each year and is making these data available to water and wetland managers. Results from this research are a significant step toward understanding how waterfowl and shorebird habitats can be optimally managed on the landscape to support desired populations of these migratory birds during extreme drought.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201102","collaboration":"Prepared in cooperation with the Southwest Climate Adaptation Science Center of the U.S. Geological Survey and the Regional Inventory and Monitoring Program of the U.S. Fish and Wildlife Service","usgsCitation":"Matchett, E.L., Reiter, M., Overton, C.T., Jongsomjit, D., and Casazza, M.L., 2021, Using high resolution satellite and telemetry data to track flooded habitats, their use by waterfowl, and evaluate effects of drought on waterfowl and shorebird bioenergetics in California: U.S. Geological Survey Open-File Report 2020–1102, 59 p., https://doi.org/10.3133/ofr20201102.","productDescription":"Report: xi, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","ipdsId":"IP-102884","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":383074,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2020/1102/images"},{"id":383073,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P922KDU6","linkHelpText":"Classification of waterfowl habitat and quantification of interannual space use and movement distance from primary roosts to night feeding locations by waterfowl in California for October–March of 2015 through 2018"},{"id":383071,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1102/ofr20201102.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1102/covrthb.jpg"},{"id":383072,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2020/1102/ofr20201102.xml"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 36.06686213257888\n ],\n [\n -119.44335937499999,\n 35.137879119634185\n ],\n [\n -118.828125,\n 34.813803317113155\n ],\n [\n -118.30078125,\n 35.137879119634185\n ],\n [\n -118.49853515625,\n 35.71083783530009\n ],\n [\n -119.39941406249999,\n 37.33522435930639\n ],\n [\n -120.47607421874999,\n 38.16911413556086\n ],\n [\n -120.89355468749999,\n 38.58252615935333\n ],\n [\n -121.22314453124999,\n 39.11301365149975\n ],\n [\n -121.640625,\n 39.977120098439634\n ],\n [\n -121.97021484374999,\n 40.74725696280421\n ],\n [\n -122.3876953125,\n 41.0130657870063\n ],\n [\n -122.84912109375,\n 40.613952441166596\n ],\n [\n -122.87109375,\n 40.07807142745009\n ],\n [\n -122.6953125,\n 38.993572058209466\n ],\n [\n -122.08007812499999,\n 37.68382032669382\n ],\n [\n -121.37695312499999,\n 36.96744946416934\n ],\n [\n -120.234375,\n 35.99578538642032\n ],\n [\n -120.234375,\n 36.06686213257888\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The mitigation of losses due to extreme climate events and long-term climate adaptation requires climate informed decision-making. In the past few decades, several remote sensing and modeled-based Earth observations (EOs) have been developed to provide an unprecedented global overview and routine monitoring of climate and its impacts on vegetation and hydrologic conditions, with the goal of supporting informed decision-making. However, their usage in decision-making is particularly limited in climate-risk vulnerable and in situ data-scarce regions such as sub-Saharan Africa, due to lack of access to EOs. Here, we describe the Early Warning eXplorer (EWX), which was developed to address this crucial limitation and facilitate the application of EOs in decision-making, particularly in the food and water-insecure regions of the world. First, the EWX's core framework, which includes (i) the Viewer, (ii) GeoEngine, and (iii) Support Applications, is described. Then, a comprehensive overview of the Viewer, which is a web-based interface used to access EOs, is provided. This includes a description of (i) the maps and associated features to access gridded EO data and anomalies for different temporal averaging periods, (ii) time series graphs and associated features to access EOs aggregated over polygons such as administrative boundaries, and (iii) commonly used EOs served by the EWX that provide assessments of climate and vegetation conditions. Next, examples are provided to demonstrate how EWX can be used to monitor development, progression, spatial extent, and severity of climate-driven extreme events to support timely decisions related to mitigation of food insecurity and flooding impacts. Finally, the value of a regional implementation of EWX at the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya, is highlighted. Regional implementation of the EWX facilitates access to regionally focused EOs and their availability at polygon boundaries most relevant to the local decision-makers. Similar instances of EWX implemented in other regions, especially those susceptible to food and water security, will likely further enhance the application of EOs for informed decision-making.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2020.583509","usgsCitation":"Shukla, S., Landsfeld, M., Anthony, M., Budde, M., Husak, G., Rowland, J., and Funk, C., 2021, Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX): Frontiers in Climate, v. 2, 583509, 16 p., https://doi.org/10.3389/fclim.2020.583509.","productDescription":"583509, 16 p.","ipdsId":"IP-120483","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453527,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2020.583509","text":"Publisher Index Page"},{"id":429328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":901558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":901559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anthony, Michelle 0000-0001-6646-2134","orcid":"https://orcid.org/0000-0001-6646-2134","contributorId":336955,"corporation":false,"usgs":false,"family":"Anthony","given":"Michelle","affiliations":[{"id":80923,"text":"KBR Technical Support Services Contract (TSSC)","active":true,"usgs":false}],"preferred":false,"id":901560,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Husak, Greg 0000-0003-2647-7870","orcid":"https://orcid.org/0000-0003-2647-7870","contributorId":331302,"corporation":false,"usgs":false,"family":"Husak","given":"Greg","email":"","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":901562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901563,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901564,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220098,"text":"70220098 - 2021 - Landsat 8 thermal infrared sensor scene select mechanism open loop operations","interactions":[],"lastModifiedDate":"2021-04-19T12:55:23.904501","indexId":"70220098","displayToPublicDate":"2021-02-09T07:52:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7131,"text":"MDPI Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat 8 thermal infrared sensor scene select mechanism open loop operations","docAbstract":"The Landsat 8 (L8) spacecraft and its two instruments, the operational land imager (OLI) and thermal infrared sensor (TIRS), have been consistently characterized and calibrated since its launch in February 2013. These performance metrics and calibration updates are determined through the United States Geological Survey (USGS) Landsat image assessment system (IAS), which has been performing this function since its launch. The TIRS on-orbit geometric calibration procedures in-clude TIRS-to-OLI alignment, TIRS sensor chip assembly (SCA) alignment, and TIRS band align-ment. In December 2014, the TIRS instrument experienced an anomalous condition related to the instrument’s ability to accurately measure the location of the scene select mechanism (SSM). The SSM is a rotating mirror that allows the instrument’s field of view to be pointed at the Earth, for normal imaging, or at either deep space or an onboard black body, for radiometric calibration purposes. This anomalous condition in the SSM’s position sensor made it necessary to implement a new mode of operation for this mirror, termed mode-0. Mode-0 involves operating the mirror in an open-loop control state during normal mission operations when acquiring Earth data. Closed-loop mode-4 is needed for directing the mirror towards the radiometric calibration targets and is used approximately once every two weeks to collect radiometric calibration data. Mode-0 is used for most operational imaging because it does not require SSM encoder data, thereby allowing the SSM en-coder electronics to remain unpowered most of the time, reducing its use throughout the lifetime of the TIRS instrument, thus helping to preserve its nominal behavior during it use. This paper dis-cusses the geometric calibration of the SSM mirror during its current normal mode-0 set of image operations, as its open-loop control allows the mirror to drift over time in its uncontrolled state and its impacts on products. The results shown in this paper demonstrate that the ability to have on-going updates to the modeling of the TIRS SSM mirror model, in both an automated fashion and with a set of more manual operations, allows accuracy that approaches mode-4 results within days from the start of a mode-0 event.","language":"English","publisher":"MDPI","doi":"10.3390/rs13040617","usgsCitation":"Choate, M.J., Rengarajan, R., Storey, J.C., and Beckmann, T., 2021, Landsat 8 thermal infrared sensor scene select mechanism open loop operations: MDPI Remote Sensing, v. 13, no. 4, 617, 15 p., https://doi.org/10.3390/rs13040617.","productDescription":"617, 15 p.","ipdsId":"IP-124617","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13040617","text":"Publisher Index Page"},{"id":385187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":216866,"corporation":false,"usgs":true,"family":"Choate","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, R. 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":56036,"corporation":false,"usgs":true,"family":"Rengarajan","given":"R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":814473,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beckmann, Tim 0000-0002-2557-0638","orcid":"https://orcid.org/0000-0002-2557-0638","contributorId":87995,"corporation":false,"usgs":true,"family":"Beckmann","given":"Tim","affiliations":[],"preferred":false,"id":814474,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218666,"text":"70218666 - 2021 - Improving remotely sensed river bathymetry by image-averaging","interactions":[],"lastModifiedDate":"2021-03-04T13:53:00.641289","indexId":"70218666","displayToPublicDate":"2021-02-09T07:50:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Improving remotely sensed river bathymetry by image-averaging","docAbstract":"Basic data on river bathymetry is critical for numerous applications in river research and management and is increasingly obtained via remote sensing, but the noisy, pixelated appearance of image‐derived depth maps can compromise subsequent analyses. We hypothesized that this noise originates from reflectance from an irregular water surface and introduced a framework for mitigating these effects by Inferring Bathymetry from Averaged River Images (IBARI). This workflow produces time‐averaged images from video frames stabilized to account for platform motion and/or computes a spatial average from an ensemble simulated by randomly shifting images relative to themselves. We used field observations of water depth and helicopter‐based videos from a clear‐flowing river to assess the potential of this approach to improve depth retrieval. Our results indicated that depths inferred from averaged images were more accurate and precise than those inferred from single frames; observed versus predicted regression R2 increased from 0.80 to 0.88. In addition, IBARI significantly enhanced the texture of image‐derived depth maps, leading to smoother, more coherent representations of channel morphology. Depth retrieval improved with image sequence duration, but the number of images was more important than the length of time encompassed; shorter acquisitions at higher frame rates would economize data collection. We also demonstrated the potential to scale up the IBARI workflow by producing a mosaic of bathymetric maps derived from averaged images acquired at several hovering waypoints distributed along a 2.36 km reach. This approach is well‐suited to data collected from helicopters and small unmanned aircraft systems.
In the spring of 2019, ice sheets transported down-stream during a large streamflow rise event in the lower Minnesota River destroyed an index-velocity streamgage at the Minnesota River at Fort Snelling State Park, Minnesota (U.S. Geological Survey station 05330920; hereafter referred to as “Ft. Snelling”). The streamgage previously used an acoustic Doppler velocity meter to provide instantaneous streamflow and suspended-sedimentation concentration (SSC) data in backwater conditions caused by the confluence with the Mississippi River. In response, the U.S. Geological Survey cooperated with the U.S. Army Corps of Engineers and Lower Minnesota River Watershed District to develop linear regression models that estimate SSCs and suspended-sand concentrations (sand) at the destroyed streamgage using streamflow data from an upstream site Minnesota River near Jordan, Minn. (U.S. Geological Survey station 05330000, hereafter referred to as “Jordan”).
Simple linear regression models were developed for selected positions on the streamflow hydrograph to estimate SSC and sand at Ft. Snelling from the streamflow at Jordan. Statistically significant models could not be developed for estimating SSC at low streamflows and sand at high streamflows. Models developed to estimate sand were more uncertain than models used to estimate SSC, and models using streamflow to predict SSC and sand were more uncertain than models using acoustic backscatter to predict SSC. Annual loads of SSC and sand estimated from these models show the dynamic nature of sediment transport and storage in this section of the lower Minnesota River. These models and the associated ancillary data can help with management decisions that are crucial in managing aquatic habitat, supporting power production, and commercial navigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211005","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers and Lower Minnesota River Watershed District","usgsCitation":"Groten, J.T., Hendrickson, J.S., and Loomis, L.R., 2021, Estimation of suspended sediment at a discontinued streamgage on the lower Minnesota River at Fort Snelling State Park, Minnesota: U.S. Geological Survey Open-File Report 2021–1005, 12 p., https://doi.org/10.3133/ofr20211005.","productDescription":"Report: vi, 12 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-121668","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":383100,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1005/ofr20211005.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1005"},{"id":383101,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AIULOQ","text":"USGS data release","linkHelpText":"Suspended-sediment and sand concentrations, streamflow, acoustic data, linear regression models, and loads for the Lower Minnesota River, 2012 -2019"},{"id":383108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1005/coverthb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Fort Snelling State Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.22311401367188,\n 44.819107339295684\n ],\n [\n -93.1801986694336,\n 44.85124448203336\n ],\n [\n -93.16577911376953,\n 44.879471418146686\n ],\n [\n -93.14414978027344,\n 44.89187715629887\n ],\n [\n -93.15067291259766,\n 44.89503897537852\n ],\n [\n -93.17436218261719,\n 44.89601180781499\n ],\n [\n -93.19427490234375,\n 44.89114748105545\n ],\n [\n -93.19599151611328,\n 44.87557887053108\n ],\n [\n -93.21247100830078,\n 44.859275967357476\n ],\n [\n -93.22208404541016,\n 44.85270483540896\n ],\n [\n -93.23856353759766,\n 44.84102097157541\n ],\n [\n -93.23856353759766,\n 44.82763029742812\n ],\n [\n -93.22895050048828,\n 44.81862027505869\n ],\n [\n -93.22311401367188,\n 44.819107339295684\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
The objective of the US Geological Survey’s mineral deposit database project (USMIN) is to develop a comprehensive twenty-first century geospatial database that is the authoritative source of the most important mines, mineral deposits, and mineral districts of the US. Since May 2017, the project has focused on critical minerals. Data for critical minerals that are produced as products are relatively robust, whereas data for critical minerals that may be recovered as byproducts are commonly of much poorer quality. Similarly, more is known about critical minerals that occur in conventional deposits than where those critical minerals occur in unconventional deposits. For example, rare earth elements occur principally in deposits hosted by alkaline igneous rocks, but there is potential for their production from phosphate rock mining, which is less documented. Lithium (Li) has been recovered from pegmatites and brines, but other Li-bearing deposit types have been delineated that may go into production. Cobalt may be produced as a byproduct or coproduct from a wide range of mineral deposit types, whereas rhenium is a byproduct of copper ore. Significant opportunities for research exist that could help identify new sources of critical minerals, and may also help increase production and recovery from existing sources.