Over the last decade, studies at the Shale Hills Critical Zone Observatory (Shale Hills) have greatly expanded knowledge of weathering in previously understudied, shale-mantled terrains, as well as Earth's Critical Zone as a whole. Among the many discoveries made was the importance of redistribution and losses of micron-sized particles during development of shale-derived soils. A geochemical fingerprint of this process for Al and Fe was illustrated quantitatively by Jin et al. (2010). Subsequent papers, too numerous to list in a Comment, built upon this new recognition by evaluating the spatial and temporal aspects element mobilization. Recently, Kim et al. (2018) examined the composition of suspended, generally micron-sized particles in the Shale Hills stream, along with the dissolved load, across seasons and ranges of discharge.
One prominent conclusion from Kim et al. (2018) is that Zr is essentially immobile at Shale Hills. Such a broad conclusion is in direct contradiction with one from Bern and Yesavage (2018) that Zr has been mobilized from soils at Shale Hills, and the losses relative to soil parent material are significant (median 41%). The point is important, because assuming Zr immobility is necessary to index gains and losses of other elements using the open-chemical-system transport function (τ). Both papers draw upon patterns and calculations using elemental concentration data from Shale Hills and attempt to construct conceptual frameworks to explain the results. Here, the argument is made that the understanding of substantial Zr mobility from soils at Shale Hills described by Bern and Yesavage (2018) is more accurate. Additionally, issues with adaptations of the standard τ equations used in Kim et al. (2018) and some previous papers are also addressed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.02.014","usgsCitation":"Bern, C.R., and Yesavage, T., 2019, Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”: Earth and Planetary Science Letters, v. 514, p. 166-168, https://doi.org/10.1016/j.epsl.2019.02.014.","productDescription":"3 p.","startPage":"166","endPage":"168","ipdsId":"IP-102182","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":363221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Shale Hills Critical Zone Observatory ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.17596435546875,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.4323142901375\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"514","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bern, Carleton R. 0000-0002-8980-1781 cbern@usgs.gov","orcid":"https://orcid.org/0000-0002-8980-1781","contributorId":201152,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton","email":"cbern@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yesavage, Tiffany 0000-0001-9433-763X","orcid":"https://orcid.org/0000-0001-9433-763X","contributorId":215057,"corporation":false,"usgs":false,"family":"Yesavage","given":"Tiffany","email":"","affiliations":[{"id":39167,"text":"USGS Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":false}],"preferred":false,"id":761538,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203164,"text":"70203164 - 2019 - Calcrete uranium deposits in the Southern High Plains, USA","interactions":[],"lastModifiedDate":"2019-04-25T05:57:51","indexId":"70203164","displayToPublicDate":"2019-04-25T05:53:27","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Calcrete uranium deposits in the Southern High Plains, USA","docAbstract":"The Southern High Plains (SHP) is a new and emerging U.S. uranium province. Here, uranyl vanadates form deposits in Pliocene to Pleistocene sandstone, dolomite, and limestone. Fifteen calcrete uranium occurrences are identified; two of these, the Buzzard Draw and Sulfur Springs Draw deposits, have combined in-place resources estimated at about 4 million pounds of U3O8. Ore minerals carnotite and finchite are hosted in dolomite at the Sulfur Springs Draw deposit, with accessory fluorite, celestine, smectite/illite, autunite, and strontium carbonate. Host carbonate at the Sulfur Springs Draw deposit is ∼190 ka and mineralization mobilized as recently as 3.8 ka. Ash collected near the deposit is 631 ka and erupted from the Yellowstone caldera complex. The Triassic Dockum Group that contains sandstone-hosted uranium deposits throughout the region and underlies the SHP is a potential source for uranium and vanadium. Regional uplift and dissection reintroduced oxygenated groundwater into the Dockum Group, mobilizing uranium. Additional uranium may have been contributed to groundwater by weathering of volcanic ash in Pliocene and Pleistocene host rocks. The locations of the uranium occurrences are mostly in modern drainage systems in the southeast portion of the SHP. Modelling of modern groundwater in the SHP carried out in a parallel study shows that a single fluid could form carnotite through evaporation, and that fluids of the requisite composition are more prevalent in the southern portion of the SHP. The southeastern portion of the SHP hosts more uranium occurrences due to a variety of factors including (1) upward transport of groundwater and connectivity between source and host rock, (2) higher uranium and vanadium content of groundwater, (3) higher rates of groundwater recharge in this region to drive the mineralizing system, and (4) shallower groundwater facilitating surface evaporation. Ongoing erosion of host rocks challenges preservation of deposits and may limit their size.
Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias. This work was aimed to (1) synthesize and compare WRTDS estimation bias for constituent concentrations and fluxes for rivers and streams in the Chesapeake Bay watershed (including headwater sites) and (2) identify controlling factors from five broad categories (watershed size, sampling practice, concentration and discharge conditions, land use, and geology). Five major constituents were considered, namely, suspended sediment (SS), total phosphorus (TP), total nitrogen (TN), orthophosphate (PO4), and nitrate-plus-nitrite (NOx). For both concentration and flux, estimation bias follows the general order of SS > TP > PO4 > TN ≈ NOx. Median TN and NOx bias statistics were near zero, with an equal distribution of small positive and negative bias. TP, PO4, and SS each showed a median positive bias across sites of <18% for flux and <7% for concentration. Particulate constituents, especially SS, tend to have larger bias at sites with smaller sampling frequencies, shorter sampling record lengths, and smaller watershed sizes. Results of multivariate models showed that both flux and concentration biases are most affected by concentration and discharge variabilities and the length of concentration record. In comparison, flux bias of particulate constituents is more affected by flow variability, whereas flux bias of dissolved constituents is more affected by concentration variability. Moreover, analysis using classification and regression trees provided additional information on how the factors affected flux bias: when all site-constituent combinations are considered, large flux biases are more likely associated with sites that have large concentration and discharge variabilities, small lengths of concentration record, and small sampling frequencies. These results may be useful for identifying sites with large biases, modifying monitoring practice at existing sites to reduce those biases, and choosing new monitoring locations in the Chesapeake watershed and beyond.
The Little Colorado River in Arizona, U.S.A. has undergone substantial geomorphic change since the early 1900s. We analyzed hydrologic and geomorphic data at different spatial and temporal scales to determine the type, magnitude, and rate of geomorphic change that has occurred since the early 20th century. Since the 1920s, there have been 4 alternating periods of high and low total-annual flow. Peak-flow magnitude, however, has progressively declined. In some reaches, the channel has narrowed between 72 and 88% since the 1930s. Increases in sinuosity in wide alluvial valleys have resulted in reductions in channel slope by ~21 to 32%; channel bed aggradation up to 1.4 m has also occurred in some reaches. Newly developed floodplains have been colonized by dense stands of vegetation that appear to have stabilized these surfaces. Large, long duration floods may cause some channel widening, and meander migration, however, these floods are infrequent, and narrowing resumes shortly thereafter. Channel narrowing, increases in sinuosity, decreases in slope, and increases in vegetative roughness appear to have caused biogeomorphic feedbacks, thereby exacerbating sediment deposition, and disrupting flood conveyance. In recent decades, there has been an increase in the travel time of floods up to ~100% compared to floods of the 1940s and 1950s, and this has likely led to increased flood attenuation, contributing to decreases in peak-flow magnitude. The progressive increase in water development in parts of the basin has also likely played some role in the progressive declines in peak flow over the duration of the study.
","language":"English","publisher":"The Geological Society of America","doi":"10.1130/B35047.1","usgsCitation":"Dean, D.J., and Topping, D.J., 2019, Geomorphic change and biogeomorphic feedbacks in a dryland river: The Little Colorado River, Arizona, USA: GSA Bulletin, Repository Item: 2019158; 23 p., https://doi.org/10.1130/B35047.1.","productDescription":"Repository Item: 2019158; 23 p.","ipdsId":"IP-099021","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437486,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XPWIBM","text":"USGS data release","linkHelpText":"Geomorphic Change Data for the Little Colorado River, Arizona, USA"},{"id":363278,"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 -111.412353515625,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 35.54116627999815\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":215067,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761570,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203195,"text":"70203195 - 2019 - Modeling barrier island habitats using landscape position information","interactions":[],"lastModifiedDate":"2019-08-19T16:53:07","indexId":"70203195","displayToPublicDate":"2019-04-24T16:23:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Modeling barrier island habitats using landscape position information","docAbstract":"Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.","language":"English","publisher":"MDPI","doi":"10.3390/rs11080976","usgsCitation":"Enwright, N., Lei Wang, Wang, H., Osland, M., Feher, L., Borchert, S., and Day, R., 2019, Modeling barrier island habitats using landscape position information: Remote Sensing, v. 11, no. 8, Article 976; 24 p., https://doi.org/10.3390/rs11080976.","productDescription":"Article 976; 24 p.","ipdsId":"IP-105601","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460395,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11080976","text":"Publisher Index Page"},{"id":437488,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90MACYS","text":"USGS data release","linkHelpText":"Modeling barrier island habitats using landscape position information for Dauphin Island, Alabama"},{"id":363276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":215077,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lei Wang","contributorId":215078,"corporation":false,"usgs":false,"family":"Lei Wang","affiliations":[{"id":39170,"text":"Department of Geography and Anthropology, Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":761586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hongqing 0000-0002-2977-7732 wangh@usgs.gov","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":215079,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","email":"wangh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":215080,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":215081,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Borchert, Sinéad M. 0000-0002-6665-7115","orcid":"https://orcid.org/0000-0002-6665-7115","contributorId":193278,"corporation":false,"usgs":false,"family":"Borchert","given":"Sinéad M.","affiliations":[],"preferred":false,"id":761590,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":215082,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761591,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202128,"text":"ofr20191010 - 2019 - Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas","interactions":[],"lastModifiedDate":"2019-04-26T15:38:27","indexId":"ofr20191010","displayToPublicDate":"2019-04-24T14:35:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1010","displayTitle":"Geochemistry and Mineralogy of Soils Collected in the Lower Rio Grande Valley, Texas","title":"Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas","docAbstract":"Presented in this report are the chemical and mineralogical results of a soil study conducted in the lower Rio Grande valley, Texas. Samples were collected from soils formed on Holocene alluvial flood-plain and distributary channel deposits of the Rio Grande, flood plain and meander-belt deposits of the Pliocene Goliad Formation, and the Pleistocene Lissie and Beaumont Formations. The lower Rio Grande valley is located on the old distributary delta of the Rio Grande. The watersheds on the U.S. side of the delta no longer drain into the Rio Grande but are part of a complex system of irrigation channels and wastewater drains that flow into the lower Laguna Madre. The results of the study have been used to map concealed geologic units and identify potential mosquito breeding habitat.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191010","collaboration":" ","usgsCitation":"Whitney, H.A., Solano, F., and Hubbard, B.E., 2019, Geochemistry and mineralogy of soils collected in the lower Rio Grande valley, Texas: U.S. Geological Survey Open-File Report 2019–1010, 92 p., https://doi.org/10.3133/ofr20191010.","productDescription":"Report: v, 92 p.; 6 Tables","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-062701","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":363123,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1010/coverthb.jpg"},{"id":363124,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1010"},{"id":363125,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table01.xlsx","text":"Table 1","size":"70.9 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Geochemical analyses of soil samples collected in 2003–04, by element and method of analysis, lower Rio Grande valley, Texas\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":363126,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table02.xlsx","text":"Table 2","size":"64.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Geochemical analyses of soil samples collected in 2007, by element and method of analysis, lower Rio Grande valley, Texas"},{"id":363127,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table03.xlsx","text":"Table 3","size":"18.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Univariate statistics and percentiles of analytical results for soil samples collected in 2003 and 2004, lower Rio Grande valley, Texas"},{"id":363128,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table04.xlsx","text":"Table 4","size":"19.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Univariate statistics and percentiles of analytical results for soil samples collected in 2007, lower Rio Grande valley, Texas"},{"id":363129,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table05.xlsx","text":"Table 5","size":"31.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Mineralogy of all soil samples collected in 2003, 2004, and 2007, lower Rio Grande valley, Texas"},{"id":363130,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1010/ofr20191010_table06.xlsx","text":"Table 6","size":"16.4 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary statistics of mineral content of soils by geologic formation (Page and others, 2005) as determined by x‐ray diffraction"}],"country":"United States","state":"Texas","otherGeospatial":"Rio Grande Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1845703125,\n 25.686087780724858\n ],\n [\n -97.1136474609375,\n 25.686087780724858\n ],\n [\n -97.1136474609375,\n 26.76277822801415\n ],\n [\n -99.1845703125,\n 26.76277822801415\n ],\n [\n -99.1845703125,\n 25.686087780724858\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Mineral and Energy Resources Center
U.S. Geological Survey
MS 954 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
The Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic responses and streamflow. The U.S. Geological Survey (USGS), in cooperation with the New Mexico Department of Homeland Security and Emergency Management, developed procedures to apply the PRMS model to simulate the effects of fire on hydrologic responses.
A PRMS model was built of the upper Rio Hondo Basin from the headwaters to approximately 19 miles downstream from the USGS streamgage Rio Hondo above Chavez Canyon near Hondo, New Mexico, by using 24 hydrologic response units (HRUs), or hydrologically similar subareas, from the National Hydrologic Model. A quasi-graphical user interface was created to easily query and analyze published PRMS sensitivity-analysis data. Simulation of mean daily streamflow was most sensitive to parameters related to snowmelt or infiltration throughout the upper Rio Hondo Basin. In the basin’s eastern and northern HRUs, flashiness and timing of streamflow were most sensitive to interflow; in many western-basin HRUs (higher elevations), flashiness of streamflow was most sensitive to soil moisture parameters, and timing of streamflow was most sensitive to infiltration and evapotranspiration parameters.
The PRMS model was calibrated for the fire-affected North Fork Eagle Creek subwatershed by comparing modeled to observed daily streamflow for the nonfrozen (May through October) period for a prefire and postfire time period. The prefire model was calibrated for the period 2007–12 before the 2012 fire, and the postfire model was calibrated for a 2-year (2014–15) period after the fire. Model parameterization combined manual adjustment of 8 parameters on the basis of prior knowledge and automated adjustment of the most sensitive parameters by using the Let Us Calibrate interface. A gridded, daily precipitation dataset that captured the spatial heterogeneity across the study watershed was used as the precipitation input for calibration. Model performance was assessed as satisfactory by using standard statistical measures for prefire and postfire periods.
The calibrated model was run by using data from a single precipitation gage to better represent the effect of localized, extreme storms on postfire hydrologic response. The calibrated models for prefire and postfire conditions simulated streamflows with greater consistency than the uncalibrated model for the corresponding (prefire or postfire) period of hydrographic record. The effect of fire on streamflow was found to be primarily a shift from streamflow dominated by base flow prior to fire to streamflow dominated by surface runoff after fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195022","collaboration":"Prepared in cooperation with the New Mexico Department of Homeland Security and Emergency Management","usgsCitation":"Douglas-Mankin, K.R., and Moeser, C.D., 2019, Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5022, 25 p., https://doi.org/10.3133/sir20195022.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"36","ipdsId":"IP-094970","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":363146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KD1X7Q","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model input and output for prefire and postfire hydrologic simulations in the Upper Rio Hondo Basin, New Mexico using the Precipitation-Runoff Modeling System (PRMS)"},{"id":363157,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5022/coverthb2.jpg"},{"id":363145,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5022/sir20195022.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5022"}],"country":"United States","state":"New Mexico","county":"Lincoln County, Otero County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.83610534667969,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.33741240611175\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113
A lagoon-wide, point-sampling survey of eelgrass (Zostera marina) abundance was conducted in Izembek Lagoon, Alaska, August 7–16, 2018, the ninth year of annual surveys (2007–11, 2015–18). Mean predicted aboveground biomass of eelgrass across 116 sampled points was 238 grams per square meter (g m-2) (95 percent confidence interval: 203–278 g m-2) in 2018, an increase of 240 percent from the previous year’s low estimate of 97 g m-2 (95 percent confidence interval: 78–120 g m-2). The increase marked the third year since 2015 where eelgrass biomass was above the long-term mean (158 g m-2). Eelgrass biomass was stable over the 9 years of this survey. A separate (transect) survey for eelgrass abundance at Grant Point-Old Boat Launch showed annual trends in eelgrass biomass similar to the lagoon-wide survey, but over a slightly longer time (2007–18). The estimates of above-average eelgrass biomass in Izembek Lagoon were likely influenced by relatively warm air temperatures and little or no ice in winter (air temperatures 2.7 degrees Celsius greater than the 12-year mean) and average (cool) air temperatures during the growing season (April–August) in 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191042","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., and Amundson, C.L., 2019, Monitoring annual trends in abundance of eelgrass (Zostera marina) at Izembek National Wildlife Refuge, Alaska, 2018: U.S. Geological Survey Open-File Report 2019-1042, 8 p., https://doi.org/10.3133/ofr20191042.","productDescription":"iv, 8 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-105984","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437489,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13EG9KS","text":"USGS data release","linkHelpText":"Eelgrass Biomass Model"},{"id":363193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1042/ofr20191042.pdf","text":"Report","size":"532 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1042"},{"id":363192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1042/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":" Izembek National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.42987060546875,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.02802211299252\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
This report provides a synthesis of geologic mapping and geochronologic research along the South Platte River between the town of Masters and the city of Fort Morgan, northeastern Colorado. This work was undertaken to better understand landscape development along this part of the river corridor. The focus is on times of rapid change within the fluvial system that had a marked effect on the landscape. The study area is susceptible to drought, which destabilizes vegetation and makes the landscape vulnerable to eolian activity. This is reflected in a landscape that is largely covered by eolian sand and lesser amounts of loess. Past glaciation of the river’s headwaters had a major influence on river discharge and sediment supply, as have major flood events particularly on unglaciated tributaries heading on the piedmont.
In the mapping area, fluvial deposits of the South Platte River system span the Pliocene and early Pleistocene(?) deposits of Nussbaum Alluvium to present-day deposits of the active channel and floodplain. Results of the study indicate that along this stretch of the South Platte River, the early Pleistocene and first half of the middle Pleistocene were times of net incision, periodically interrupted by episodes of aggradation that resulted in deposition of alluvium that has been correlated to Rocky Flats Alluvium, Verdos Alluvium, and Slocum Alluvium. Net incision between depositional events formed a series of poorly preserved terrace deposits along the valley sides that are now largely covered by eolian deposits. Sometime after about 380 thousand years, the river cut a deep paleovalley into Upper Cretaceous Pierre Shale that was then filled with a thick sequence of inferred Louviers Alluvium (coeval with Bull Lake glaciation). Net aggradation continued during the late Pleistocene, resulting in burial of the Louviers paleovalley with a thick sequence of mainstream and sidestream Broadway Alluvium (coeval with Pinedale glaciation). Subsequent incision during the late Pleistocene–Holocene transition formed the Kersey (Broadway) terrace, whose riser forms a prominent bluff on the south side of the river valley. This episode of incision spanned a very short period and was followed by renewed aggradation that deposited the next-lower terrace alluvium (Kuner terrace alluvium). The Kuner terrace level was probably abandoned sometime around the beginning of the middle Holocene. Low terraces on the valley floor indicate that the river has been primarily cutting and backfilling laterally rather than incising during the late Holocene.
Synthesis of geologic mapping and chronologic data generated in this study indicate that the South Platte River in northeastern Colorado likely was highly sensitive to rapidly changing environmental conditions or crossed threshold conditions that triggered rapid geomorphic response during major climate changes associated with the late Pleistocene–Holocene transition. Historical times have been another period marked by rapid incision, reflected by gully incision and headward erosion in tributary valleys draining the north side of the South Platte River. This historical erosion could be related at least in part to extensive construction of irrigation ditches and reservoirs in the late 1800s–early 1900s, which altered drainage paths and groundwater flow and could have amplified natural factors such as climate change or intrinsic geomorphic instabilities within the system.
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS 980
Denver, CO 80225
Mammoth Mountain, California, has exhibited unrest over the past ~30 years, characterized by seismicity over a broad range of depths, elevated 3He/4He ratios in fumarolic gas, and large-scale diffuse CO2 emissions. This activity has been attributed to magmatic intrusion, but minimal ground deformation and the presence of a shallow crustal gas reservoir beneath Mammoth Mountain pose a challenge for estimating magma supply rate. Here, we use the record of fumarolic 3He/4He ratios and CO2 emissions to estimate that of the ~5.2 Mt of CO2 released from Mammoth Mountain between 1989 and 2016, 1.6 Mt was associated with active intrusion and degassing of ~0.05–0.07 km3 of basaltic magma. Intrusion at an average rate of ~0.002–0.003 km3/year into a postulated zone of partial melt at ~15-km depth could occur without detection by local Global Navigation Satellite System stations.
","language":"English","publisher":"Wiley","doi":"10.1029/2019GL082487","usgsCitation":"Lewicki, J.L., Evans, W.C., Montgomery-Brown, E.K., Mangan, M.T., King, J., and Hunt, A., 2019, Rate of magma supply beneath Mammoth Mountain, California based on helium isotopes and CO2 emissions: Geophysical Research Letters, v. 46, no. 9, p. 4636-4644, https://doi.org/10.1029/2019GL082487.","productDescription":"9 p.","startPage":"4636","endPage":"4644","ipdsId":"IP-105520","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":437490,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FGZ0ED","text":"USGS data release","linkHelpText":"Fumarole gas geochemistry and tree-ring radiocarbon data at Mammoth Mountain, California (1989-2016)"},{"id":387401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mammoth Mountain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.07772064208983,\n 37.61232767789535\n ],\n [\n -118.97832870483398,\n 37.61232767789535\n ],\n [\n -118.97832870483398,\n 37.66289614387081\n ],\n [\n -119.07772064208983,\n 37.66289614387081\n ],\n [\n -119.07772064208983,\n 37.61232767789535\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"9","noUsgsAuthors":false,"publicationDate":"2019-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Lewicki, Jennifer L. 0000-0003-1994-9104 jlewicki@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-9104","contributorId":5071,"corporation":false,"usgs":true,"family":"Lewicki","given":"Jennifer","email":"jlewicki@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":819778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, William C. 0000-0001-5942-3102 wcevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5942-3102","contributorId":2353,"corporation":false,"usgs":true,"family":"Evans","given":"William","email":"wcevans@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":819779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montgomery-Brown, Emily K. 0000-0001-6787-2055","orcid":"https://orcid.org/0000-0001-6787-2055","contributorId":214074,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":819780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangan, Margaret T. 0000-0002-5273-8053 mmangan@usgs.gov","orcid":"https://orcid.org/0000-0002-5273-8053","contributorId":3343,"corporation":false,"usgs":true,"family":"Mangan","given":"Margaret","email":"mmangan@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":819781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, John","contributorId":243582,"corporation":false,"usgs":false,"family":"King","given":"John","affiliations":[{"id":48739,"text":"Lon Pine Research","active":true,"usgs":false}],"preferred":false,"id":819782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hunt, Andrew G. 0000-0002-3810-8610","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":206197,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":819783,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215491,"text":"70215491 - 2019 - Fault slip associated with the 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake and aftershock sequence","interactions":[],"lastModifiedDate":"2021-01-22T19:21:30.773896","indexId":"70215491","displayToPublicDate":"2019-04-23T13:21:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Fault slip associated with the 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake and aftershock sequence","docAbstract":"The 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake is one of the largest earthquakes in southern Idaho since the 1983 M 6.9 Borah Peak earthquake. It was followed by a vigorous aftershock sequence for nearly two weeks that included five events above M 4.5. The coseismic and early postseismic deformation was measured with both Interferometric Synthetic Aperture Radar and Global Positioning System (GPS), yielding up to 3 cm subsidence southwest of the mainshock epicenter and horizontal motions of
Hydrocarbon reserves and technically recoverable undiscovered resources in continuous accumulations are present in Upper Ordovician strata in the Appalachian Basin Province. The province includes parts of New York, Pennsylvania, Ohio, Maryland, West Virginia, Virginia, Kentucky, Tennessee, Georgia, and Alabama. The Upper Ordovician strata are part of the previously defined Utica-Lower Paleozoic Total Petroleum System (TPS) that extends from New York and southern Canada to Tennessee. This publication presents a revision to the hydrocarbon source rocks in the TPS, a change to the name of the TPS, and changes to the geographic extent of the Utica-Lower Paleozoic TPS. The revision to the TPS recognizes the Upper Ordovician Point Pleasant Formation as a major hydrocarbon source rock in this TPS. Consequently, the name of the TPS is changed to Ordovician Point Pleasant/Utica-Lower Paleozoic TPS. The most significant modification to the boundary of the newly defined Ordovician Point Pleasant/Utica-Lower Paleozoic TPS is a westward extension in the southwesterly portion of the TPS, adding areas in Ohio, Indiana, Kentucky, and Tennessee in order to include Ordovician strata, including potential petroleum source rocks, from the subsurface to their near-surface exposure. Also, portions of the former Utica-Lower Paleozoic TPS are now excluded from the newly defined TPS in a portion of northwestern Ohio and adjacent States to eliminate overlap with the Ordovician to Devonian Composite TPS in the Michigan basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195025","collaboration":" ","usgsCitation":"Enomoto, C.B., Trippi, M.H., and Higley, D.K., 2019, Ordovician Point Pleasant/Utica-Lower Paleozoic Total Petroleum System—Revisions to the Utica-Lower Paleozoic Total Petroleum System in the Appalachian Basin Province: U.S. Geological Survey Scientific Investigations Report 2019–5025, \n6 p., https://doi.org/10.3133/sir20195025. ","productDescription":"Report: iii, 14 p.; 1 Figure","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099699","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":363034,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5025/sir20195025.pdf","text":"Report","size":"3.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5025"},{"id":363033,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5025/coverthb.jpg"},{"id":363035,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5025/sir20195025_fig2.pdf","text":"Figure 2","size":"345 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Correlation chart of the stratigraphic units in the Ordovician Point Pleasant/Utica-Lower Paleozoic Total Petroleum System"}],"country":"United States","otherGeospatial":"Appalachian Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86,\n 36\n ],\n [\n -74,\n 36\n ],\n [\n -74,\n 43\n ],\n [\n -86,\n 43\n ],\n [\n -86,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
954 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The use of single nucleotide polymorphism (SNP) arrays to generate large SNP datasets for comparison purposes have recently become an attractive alternative to other genotyping methods. Although most SNP arrays were originally developed for domestic organisms, they can be effectively applied to wild relatives to obtain large panels of SNPs. In this study, we tested the cross-species application of the Affymetrix 600K Chicken SNP array in five species of North American prairie grouse (Centrocercus and Tympanuchus genera). Two individuals were genotyped per species for a total of ten samples. A high proportion (91%) of the total 580 961 SNPs were genotyped in at least one individual (73–76% SNPs genotyped per species). Principal component analysis with autosomal SNPs separated the two genera, but failed to clearly distinguish species within genera. Gene ontology analysis identified a set of genes related to morphogenesis and development (including genes involved in feather development), which may be primarily responsible for large phenotypic differences between Centrocercus and Tympanuchus grouse. Our study provided evidence for successful cross-species application of the chicken SNP array in grouse which diverged ca. 37 mya from the chicken lineage. As far as we are aware, this is the first reported application of a SNP array in non-passerine birds, and it demonstrates the feasibility of using commercial SNP arrays in research on non-model bird species.
Tidal wetland fluxes of particulate organic matter and carbon (POM, POC) are important terms in global budgets but remain poorly constrained. Given the link between sediment fluxes and wetland stability, POM and POC fluxes should also be related to stability. We measured POM and POC fluxes in eight microtidal salt marsh channels, with net POM fluxes ranging between −121 ± 33 (export) and 102 ± 28 (import) g OM·m−2·year−1 and net POC fluxes ranging between −52 ± 14 and 43 ± 12 g C·m−2·year−1. A regression employing two measures of stability, the unvegetated‐vegetated marsh ratio (UVVR) and elevation, explained >95% of the variation in net fluxes. The regression indicates that marshes with lower elevation and UVVR import POM and POC while higher elevation marshes with high UVVR export POM and POC. We applied these relationships to marsh units within Barnegat Bay, New Jersey, USA, finding a net POM import of 2,355 ± 1,570 Mg OM/year (15 ± 10 g OM·m−2·year−1) and a net POC import of 1,263 ± 632 Mg C/year (8 ± 4 g C·m−2·year−1). The magnitude of this import was similar to an estimate of POM and POC export due to edge erosion (−2,535 Mg OM/year and − 1,291 Mg C/year), suggesting that this system may be neutral from a POM and POC perspective. In terms of a net budget, a disintegrating wetland should release organic material, while a stable wetland should trap material. This study quantifies that concept and demonstrates a linkage between POM/POC flux and geomorphic stability.
The Cornwall and Devon vein- and greisen-type copper and tin deposits of southwest England are spatially and genetically related to shallow-seated granitic intrusions. These late Variscan intrusions, collectively known as the Cornubian Batholith, extend over 200 km and form a continuous granitic spine from the Isles of Scilly Granite in the west to the Dartmoor Granite in the east. The granitic plutons of the Cornubian Batholith were intruded from ~ 295 to 270 Ma without a major hiatus. Twelve samples of cassiterite (SnO2) were obtained from tin deposits associated with seven different plutons within the Cornubian Batholith for in situ LA-ICPMS U–Pb dating. This study of cassiterite was undertaken to obtain the first results of direct dating of ore mineral to refine the geochronology of tin mineralization in this region. Of the cassiterite samples analyzed, the oldest ages were determined within the Kit Hill and Hingston–Gunnislake Granites in the central part of the Cornubian Batholith. The Hingston–Gunnislake cassiterite, from Drakewalls Mine, was the oldest sample dated at 291.8 ± 3.4 Ma. The next oldest dates, 290.5 ± 2.8 and 288.5 ± 2.9 Ma, were from two cassiterite samples extracted from the adjacent Kit Hill Consolidated Mines within the Kit Hill Granite. At the eastern end of the study area, two cassiterite samples within the Dartmoor Granite produced ages of 286.0 ± 1.8 and 284.1 ± 1.3 Ma. The youngest sample from this study, 275.4 ± 1.6 Ma, is from the Balleswidden Mine within the westernmost Land’s End Granite. The cassiterite dates do not reveal any readily observable relationship between ore ages and geographic relationship from west to east throughout the Cornubian Batholith. Incorporating the associated errors, the geochronology does indicate continuous mineralization within the granites for ~ 21 million years, from ca. 295 to 274 Ma. This span falls within the established period of granitic magmatism of ca. 295 to 270 Ma for the Cornubian Batholith and further confirms the reliability of in situ LA-ICPMS U–Pb dating of cassiterite.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-019-00870-y","usgsCitation":"Moscati, R.J., and Neymark, L., 2019, U-Pb geochronology of tin deposits associated with the Cornubian Batholith of southwest England: Direct dating of cassiterite by in situ LA-ICPMS: Mineralium Deposita, v. 55, p. 1-20, https://doi.org/10.1007/s00126-019-00870-y.","productDescription":"20 p.","startPage":"1","endPage":"20","ipdsId":"IP-102427","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":363209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"England","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -3.399367930011948,\n 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rmoscati@usgs.gov","orcid":"https://orcid.org/0000-0002-0818-4401","contributorId":2462,"corporation":false,"usgs":true,"family":"Moscati","given":"Richard","email":"rmoscati@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":761521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neymark, Leonid A. 0000-0003-4190-0278 lneymark@usgs.gov","orcid":"https://orcid.org/0000-0003-4190-0278","contributorId":140338,"corporation":false,"usgs":true,"family":"Neymark","given":"Leonid A.","email":"lneymark@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":761522,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202671,"text":"ofr20191026 - 2019 - Adaptive management of flows from R.L. Harris Dam (Tallapoosa River, Alabama)—Stakeholder process and use of biological monitoring data for decision making","interactions":[],"lastModifiedDate":"2019-11-22T06:49:08","indexId":"ofr20191026","displayToPublicDate":"2019-04-22T14:42:09","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1026","displayTitle":"Adaptive Management of Flows from R.L. Harris Dam (Tallapoosa River, Alabama)—Stakeholder Process and Use of Biological Monitoring Data for Decision Making","title":"Adaptive management of flows from R.L. Harris Dam (Tallapoosa River, Alabama)—Stakeholder process and use of biological monitoring data for decision making","docAbstract":"Adaptive management has been applied to problems with multiple conflicting objectives in various natural resources settings to learn how management actions affect divergent values regarding system response. Hydropower applications have only recently begun to emerge in the field, yet in the specific example reported herein, stakeholders invested in determining the best management alternatives for attainment of a suite of objectives outlined in a long-term adaptive management program below R.L. Harris Dam, a large, privately owned dam in Alabama. Stakeholders convened an objective-setting workshop to engage a governance structure and developed a decision support model to determine appropriate actions that optimized stakeholder values. The process led to implemented change in dam operation inclusive of incorporating hypothetical responses in system parameters to management. To account for the iterative loop of adaptive management, yearly monitoring of state variables that approximated many stakeholder objectives was performed from 2005 to 2016 and data collected were incorporated into the decision model. Specific analysis of fish and macroinvertebrate population responses indicated a less than satisfactory response for some stakeholders to the flow-management changes at the dam. Uncertainty regarding the best management to provide adequate hydrologic and thermal habitats for fauna and boatable days for recreationists still exists. The project led to a Federal Energy Regulatory Commission process for renewing the license to operate the dam (beginning in 2018); adaptive management could be a viable path forward to ensure stakeholder satisfaction related to new management options.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191026","collaboration":"Prepared in cooperation with the Alabama Department of Conservation and Natural Resources, Alabama Power Company, U.S. Fish and Wildlife Service, and R.L. Harris Dam Adaptive Management Stakeholders","usgsCitation":"Irwin, E.R., ed., 2019, Adaptive management of flows from R.L. Harris Dam (Tallapoosa River, Alabama)—Stakeholder process and use of biological monitoring data for decision making: U.S. Geological Survey Open-File Report 2019–1026, 93 p., https://doi.org/10.3133/ofr20191026.","productDescription":"Report: x, 93 p.; 4 Appendixes; 1 Table","numberOfPages":"108","onlineOnly":"Y","ipdsId":"IP-096592","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":363058,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A2.pdf","text":"Appendix A2","size":"302 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A2","linkHelpText":"– Initial Bayesian Belief Network (2005), Training Cases and Learned Networks (2005–16)"},{"id":363057,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A1.pdf","text":"Appendix A1","size":"1.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A1","linkHelpText":"– Transcripts from the Adaptive Management Workshop, April 30–May 1, 2003"},{"id":363061,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_table_C2.1.pdf","text":"Table C2.1","size":"198 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Table C2.1","linkHelpText":"– Sum of total observations for each macroinvertebrate taxon at all sites, listed alphabetically by class, order, family and taxon"},{"id":363060,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_B.pdf","text":"Appendix B","size":"296 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix B","linkHelpText":"– R code used to conduct metapopulation analyses"},{"id":363056,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026.pdf","text":"Report","size":"5.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026"},{"id":363053,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1026/coverthb3.jpg"},{"id":363059,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A3.pdf","text":"Appendix A3","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A3","linkHelpText":"– Charter of the R.L. Harris Stakeholders Board"}],"country":"United States","state":"Alabama","otherGeospatial":"Tallapoosa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.7208251953125,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 32.93953889877841\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alabama Cooperative Fish and Wildlife Research Unit
School of Forestry and Wildlife Sciences
Auburn University
602 Duncan Dr.
Auburn, AL 36849–5418