Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 °N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000–2009.
After a major disaster, a satellite image or a collection of aerial photographs of the event is frequently the fastest, most effective way to determine its scope and severity. The U.S. Geological Survey (USGS) Emergency Operations Portal provides emergency first responders and support personnel with easy access to imagery and geospatial data, geospatial Web services, and a digital library focused on emergency operations. Imagery and geospatial data are accessed through the Hazards Data Distribution System (HDDS). HDDS historically provided data access and delivery services through nongraphical interfaces that allow emergency response personnel to select and obtain pre-event baseline data and (or) event/disaster response data. First responders are able to access full-resolution GeoTIFF images or JPEG images at medium- and low-quality compressions through ftp downloads. USGS HDDS home page: http://hdds.usgs.gov/hdds2/
","language":"ENGLISH","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20103053","usgsCitation":"Jones, B., and Lamb, R.M., 2010, The Hazards Data Distribution System update (2.0): U.S. Geological Survey Fact Sheet 2010-3053, 1 p., https://doi.org/10.3133/fs20103053.","productDescription":"1 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":116130,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2010_3053.jpg"},{"id":13896,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2010/3053/","linkFileType":{"id":5,"text":"html"}}],"edition":"2.0","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ac9e4b07f02db67c530","contributors":{"authors":[{"text":"Jones, Brenda K. 0000-0003-4941-5349","orcid":"https://orcid.org/0000-0003-4941-5349","contributorId":60739,"corporation":false,"usgs":true,"family":"Jones","given":"Brenda K.","affiliations":[],"preferred":false,"id":305575,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Rynn M. 0000-0001-6054-4139 lamb@usgs.gov","orcid":"https://orcid.org/0000-0001-6054-4139","contributorId":4038,"corporation":false,"usgs":true,"family":"Lamb","given":"Rynn","email":"lamb@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":305574,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":98506,"text":"sir20105045 - 2010 - Alluvial Diamond Resource Potential and Production Capacity Assessment of Ghana","interactions":[],"lastModifiedDate":"2012-02-10T00:11:52","indexId":"sir20105045","displayToPublicDate":"2010-07-10T00:00:00","publicationYear":"2010","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":"2010-5045","title":"Alluvial Diamond Resource Potential and Production Capacity Assessment of Ghana","docAbstract":"In May of 2000, a meeting was convened in Kimberley, South Africa, and attended by representatives of the diamond industry and leaders of African governments to develop a certification process intended to assure that rough, exported diamonds were free of conflictual concerns. This meeting was supported later in 2000 by the United Nations in a resolution adopted by the General Assembly. By 2002, the Kimberley Process Certification Scheme (KPCS) was ratified and signed by both diamond-producing and diamond-importing countries. Over 70 countries were included as members at the end of 2007.\r\n\r\nTo prevent trade in 'conflict' diamonds while protecting legitimate trade, the KPCS requires that each country set up an internal system of controls to prevent conflict diamonds from entering any imported or exported shipments of rough diamonds. Every diamond or diamond shipment must be accompanied by a Kimberley Process (KP) certificate and be contained in tamper-proof packaging. \r\n\r\nThe objective of this study was to assess the alluvial diamond resource endowment and current production capacity of the alluvial diamond-mining sector in Ghana. A modified volume and grade methodology was used to estimate the remaining diamond reserves within the Birim and Bonsa diamond fields. The production capacity of the sector was estimated using a formulaic expression of the number of workers reported in the sector, their productivity, and the average grade of deposits mined. This study estimates that there are approximately 91,600,000 carats of alluvial diamonds remaining in both the Birim and Bonsa diamond fields: 89,000,000 carats in the Birim and 2,600,000 carats in the Bonsa. \r\n\r\nProduction capacity is calculated to be 765,000 carats per year, based on the formula used and available data on the number of workers and worker productivity. Annual production is highly dependent on the international diamond market and prices, the numbers of seasonal workers actively mining in the sector, and environmental conditions, which influence seasonal farming. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20105045","collaboration":"Prepared in cooperation with the Geological Survey Department,\r\nMinerals Commission, \r\nand Precious Minerals Marketing Company of Ghana\r\nunder the auspices of the U.S. Department of State","usgsCitation":"Chirico, P., Malpeli, K., Anum, S., and Phillips, E.C., 2010, Alluvial Diamond Resource Potential and Production Capacity Assessment of Ghana: U.S. Geological Survey Scientific Investigations Report 2010-5045, iv, 25 p. , https://doi.org/10.3133/sir20105045.","productDescription":"iv, 25 p. ","costCenters":[{"id":410,"text":"National Center","active":false,"usgs":true}],"links":[{"id":125931,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5045.jpg"},{"id":13895,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5045/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -20,5 ], [ -20,20 ], [ 13,20 ], [ 13,5 ], [ -20,5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4adee4b07f02db687526","contributors":{"authors":[{"text":"Chirico, Peter G.","contributorId":27086,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter G.","affiliations":[],"preferred":false,"id":305570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malpeli, Katherine C.","contributorId":55106,"corporation":false,"usgs":true,"family":"Malpeli","given":"Katherine C.","affiliations":[],"preferred":false,"id":305571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anum, Solomon","contributorId":91587,"corporation":false,"usgs":true,"family":"Anum","given":"Solomon","email":"","affiliations":[],"preferred":false,"id":305573,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phillips, Emily C.","contributorId":65189,"corporation":false,"usgs":true,"family":"Phillips","given":"Emily","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":305572,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":98505,"text":"sir20105105 - 2010 - Simulated groundwater flow in the Ogallala and Arikaree aquifers, Rosebud Indian Reservation area, South Dakota – Revisions with data through water year 2008 and simulations of potential future scenarios","interactions":[],"lastModifiedDate":"2021-12-14T19:52:30.499727","indexId":"sir20105105","displayToPublicDate":"2010-07-09T00:00:00","publicationYear":"2010","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":"2010-5105","title":"Simulated groundwater flow in the Ogallala and Arikaree aquifers, Rosebud Indian Reservation area, South Dakota – Revisions with data through water year 2008 and simulations of potential future scenarios","docAbstract":"The Ogallala and Arikaree aquifers are important water resources in the Rosebud Indian Reservation area and are used extensively for irrigation, municipal, and domestic water supplies. Drought or increased withdrawals from the Ogallala and Arikaree aquifers in the Rosebud Indian Reservation area have the potential to affect water levels in these aquifers. This report documents revisions and recalibration of a previously published three-dimensional, numerical groundwater-flow model for this area. Data for a 30-year period (water years 1979 through 2008) were used in steady-state and transient numerical simulations of groundwater flow. In the revised model, revisions include (1) extension of the transient calibration period by 10 years, (2) the use of inverse modeling for steady-state calibration, (3) model calibration to base flow for an additional four surface-water drainage basins, (4) improved estimation of transient aquifer recharge, (5) improved delineation of vegetation types, and (6) reduced cell size near large capacity water-supply wells. In addition, potential future scenarios were simulated to assess the potential effects of drought and increased groundwater withdrawals.
The model comprised two layers: the upper layer represented the Ogallala aquifer and the lower layer represented the Arikaree aquifer. The model’s grid had 168 rows and 202 columns, most of which were 1,640 feet (500 meters) wide, with narrower rows and columns near large water-supply wells. Recharge to the Ogallala and Arikaree aquifers occurs from precipitation on the outcrop areas. The average recharge rates used for the steady-state simulation were 2.91 and 1.45 inches per year for the Ogallala aquifer and Arikaree aquifer, respectively, for a total rate of 255.4 cubic feet per second (ft3/s). Discharge from the aquifers occurs through evapotranspiration, discharge to streams as base flow and spring flow, and well withdrawals. Discharge rates for the steady-state simulation were 171.3 ft3/s for evapotranspiration, 74.4 ft3/s for net outflow to streams and springs, and 11.6 ft3/s for well withdrawals. Estimated horizontal hydraulic conductivity used for the numerical model ranged from 0.2 to 84.4 feet per day (ft/d) in the Ogallala aquifer and from 0.1 to 4.3 ft/d in the Arikaree aquifer. A uniform vertical hydraulic conductivity value of 4.2x10-4 ft/d was estimated for the Ogallala aquifer. Vertical hydraulic conductivity was estimated for five zones in the Arikaree aquifer and ranged from 8.8x10-5 to 3.7 ft/d. Average rates of recharge, maximum evapotranspiration, and well withdrawals were included in the steady-state simulation, whereas the time-varying rates were included in the transient simulation.
Inverse modeling techniques were used for steady-state model calibration. These methods were designed to estimate parameter values that are, statistically, the most likely set of values to result in the smallest differences between simulated and observed hydraulic heads and base-flow discharges. For the steady-state simulation, the root mean square error for simulated hydraulic heads for all 383 wells was 27.3 feet. Simulated hydraulic heads were within ±50 feet of observed values for 93 percent of the wells. The potentiometric surfaces of the two aquifers calculated by the steady-state simulation established initial conditions for the transient simulation. For the transient simulation, the difference between the simulated and observed means for hydrographs was within ±40 feet for 98 percent of 44 observation wells.
A sensitivity analysis was used to examine the response of the calibrated steady-state model to changes in model parameters including horizontal and vertical hydraulic conductivity, evapotranspiration, recharge, and riverbed conductance. The model was most sensitive to recharge and maximum evapotranspiration and least sensitive to riverbed and spring conductances.
To simulate a potential future drought scenario, a synthetic recharge record was created, the mean of which was equal to 64 percent of the average estimated recharge rate for the 30-year calibration period. This synthetic recharge record was used to simulate the last 20 years of the calibration period under drought conditions. Compared with results of the calibrated model, decreases in hydraulic-head values for the drought scenario at the end of the simulation period were as much as 39 feet for the Ogallala aquifer. To simulate the effects of potential increases in pumping, well withdrawal rates were increased by 50 percent from those estimated for the 30-year calibration period for the last 20 years of the calibration period. Compared with results of the calibrated model, decreases in hydraulic-head values for the scenario of increased pumping at the end of the simulation period were as much as 13 feet for the Ogallala aquifer.
This numerical model is suitable as a tool to help understand the flow system, to help confirm that previous estimates of aquifer properties were reasonable, and to estimate aquifer properties in areas without data. The model also is useful to help assess the effects of drought and increases in pumping by simulations of these scenarios, the results of which are not precise but may be considered when making water management decisions.
","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/sir20105105","collaboration":"Prepared in cooperation with the Rosebud Sioux Tribe","usgsCitation":"Long, A.J., and Putnam, L.D., 2010, Simulated groundwater flow in the Ogallala and Arikaree aquifers, Rosebud Indian Reservation area, South Dakota – Revisions with data through water year 2008 and simulations of potential future scenarios: U.S. Geological Survey Scientific Investigations Report 2010-5105, viii, 54 p., https://doi.org/10.3133/sir20105105.","productDescription":"viii, 54 p.","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2007-10-01","temporalEnd":"2008-09-30","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":118481,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5105.jpg"},{"id":392872,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_93504.htm"},{"id":13894,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5105/","linkFileType":{"id":5,"text":"html"}}],"scale":"1","projection":"Universal Transverse Mercator","country":"United States","state":"South Dakota","otherGeospatial":"Arikaree aquifer, Ogallala aquifer, Rosebud Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.2881,\n 42.96\n ],\n [\n -100.1711,\n 42.96\n ],\n [\n -100.1711,\n 43.6456\n ],\n [\n -101.2881,\n 43.6456\n ],\n [\n -101.2881,\n 42.96\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4acce4b07f02db67e568","contributors":{"authors":[{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":305568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Putnam, Larry D. ldputnam@usgs.gov","contributorId":990,"corporation":false,"usgs":true,"family":"Putnam","given":"Larry","email":"ldputnam@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":true,"id":305569,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":98502,"text":"ofr20101095 - 2010 - A Review of Aeromagnetic Anomalies in the Sawatch Range, Central Colorado","interactions":[],"lastModifiedDate":"2012-02-10T00:11:53","indexId":"ofr20101095","displayToPublicDate":"2010-07-09T00:00:00","publicationYear":"2010","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":"2010-1095","title":"A Review of Aeromagnetic Anomalies in the Sawatch Range, Central Colorado","docAbstract":"This report contains digital data and image files of aeromagnetic anomalies in the Sawatch Range of central Colorado. The primary product is a data layer of polygons with linked data records that summarize previous interpretations of aeromagnetic anomalies in this region. None of these data files and images are new; rather, they are presented in updated formats that are intended to be used as input to geographic information systems, standard graphics software, or map-plotting packages.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101095","usgsCitation":"Bankey, V., 2010, A Review of Aeromagnetic Anomalies in the Sawatch Range, Central Colorado: U.S. Geological Survey Open-File Report 2010-1095, iii, 5 p.; Downloads Directory, https://doi.org/10.3133/ofr20101095.","productDescription":"iii, 5 p.; Downloads Directory","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":266,"text":"Environmental Resources Science Center","active":false,"usgs":true}],"links":[{"id":118483,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1095.jpg"},{"id":13890,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1095/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -107,38 ], [ -107,39.75 ], [ -105.75,39.75 ], [ -105.75,38 ], [ -107,38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd4966e4b0b290850ef21b","contributors":{"authors":[{"text":"Bankey, Viki viki@usgs.gov","contributorId":1238,"corporation":false,"usgs":true,"family":"Bankey","given":"Viki","email":"viki@usgs.gov","affiliations":[],"preferred":true,"id":305545,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":98503,"text":"ofr20101144 - 2010 - Public Review Draft: A Method for Assessing Carbon Stocks, Carbon Sequestration, and Greenhouse-Gas Fluxes in Ecosystems of the United States Under Present Conditions and Future Scenarios","interactions":[{"subject":{"id":98503,"text":"ofr20101144 - 2010 - Public Review Draft: A Method for Assessing Carbon Stocks, Carbon Sequestration, and Greenhouse-Gas Fluxes in Ecosystems of the United States Under Present Conditions and Future Scenarios","indexId":"ofr20101144","publicationYear":"2010","noYear":false,"title":"Public Review Draft: A Method for Assessing Carbon Stocks, Carbon Sequestration, and Greenhouse-Gas Fluxes in Ecosystems of the United States Under Present Conditions and Future Scenarios"},"predicate":"SUPERSEDED_BY","object":{"id":98900,"text":"sir20105233 - 2010 - A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios","indexId":"sir20105233","publicationYear":"2010","noYear":false,"title":"A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios"},"id":1}],"supersededBy":{"id":98900,"text":"sir20105233 - 2010 - A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios","indexId":"sir20105233","publicationYear":"2010","noYear":false,"title":"A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios"},"lastModifiedDate":"2012-02-02T00:15:01","indexId":"ofr20101144","displayToPublicDate":"2010-07-09T00:00:00","publicationYear":"2010","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":"2010-1144","title":"Public Review Draft: A Method for Assessing Carbon Stocks, Carbon Sequestration, and Greenhouse-Gas Fluxes in Ecosystems of the United States Under Present Conditions and Future Scenarios","docAbstract":"The Energy Independence and Security Act of 2007 (EISA), Section 712, authorizes the U.S. Department of the Interior to develop a methodology and conduct an assessment of the Nation's ecosystems focusing on carbon stocks, carbon sequestration, and emissions of three greenhouse gases (GHGs): carbon dioxide, methane, and nitrous oxide. The major requirements include (1) an assessment of all ecosystems (terrestrial systems, such as forests, croplands, wetlands, shrub and grasslands; and aquatic ecosystems, such as rivers, lakes, and estuaries), (2) an estimation of annual potential capacities of ecosystems to increase carbon sequestration and reduce net GHG emissions in the context of mitigation strategies (including management and restoration activities), and (3) an evaluation of the effects of controlling processes, such as climate change, land use and land cover, and wildlfires. The purpose of this draft methodology for public review is to propose a technical plan to conduct the assessment. \r\nWithin the methodology, the concepts of ecosystems, carbon pools, and GHG fluxes used for the assessment follow conventional definitions in use by major national and international assessment or inventory efforts. In order to estimate current ecosystem carbon stocks and GHG fluxes and to understand the potential capacity and effects of mitigation strategies, the method will use two time periods for the assessment: 2001 through 2010, which establishes a current ecosystem GHG baseline and will be used to validate the models; and 2011 through 2050, which will be used to assess future potential conditions based on a set of projected scenarios. The scenario framework is constructed using storylines of the Intergovernmental Panel on Climate Change (IPCC) Special Report Emission Scenarios (SRES), along with initial reference land-use and land-cover (LULC) and land-management scenarios. An additional three LULC and land-management mitigation scenarios will be constructed for each storyline to enhance carbon sequestration and reduce GHG fluxes in ecosystems. Input from regional experts and stakeholders will be solicited to construct realistic and meaningful scenarios. \r\nThe methods for mapping the current LULC and ecosystem disturbances will require the extensive use of both remote-sensing data and in-situ (for example, forest inventory data) to capture and characterize landscape-change events. For future potential LULC and ecosystem disturbances, key drivers such as socioeconomic, policy, and climate assumptions will be used in addition to biophysical data. The product of these analyses will be a series of maps for each future year for each scenario. These annual maps will form the basis for estimating carbon storage and GHG emissions. For terrestrial ecosystems, carbon storage, carbon-sequestration capacities, and GHG emissions under the current and projected future conditions will be assessed using the LULC and ecosystem-disturbance estimates in map format with a spatially explicit biogeochemical ensemble modeling system that incorporates properties of management activities (such as tillage or harvesting) and properties of individual ecosystems (such as elevation, vegetation characteristics, and soil attributes). For aquatic ecosystems, carbon burial in sediments and GHG fluxes are functions of the current and projected future stream flow and sediment transports, and therefore will be assessed using empirical modeling methods. Validation and uncertainty analysis methods described in the methodology will follow established guidelines to assess the quality of the assessment results. \r\nThe U.S. Environmental Protection Agency's Level II ecoregions map (which delineates 24 ecoregions for the Nation) will be the practical instrument for developing and delivering assessment results. Consequently, the ecoregion will be the reporting unit of the assessment because the mitigation scenarios, assessment results, validation, and uncertainty analysis will be","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101144","usgsCitation":"Bergamaschi, B., Bernknopf, R., Clow, D., Dye, D., Faulkner, S., Forney, W., Gleason, R., Hawbaker, T., Liu, J., Liu, S., Prisley, S., Reed, B., Reeves, M., Rollins, M., Sleeter, B., Sohl, T., Stackpoole, S., Stehman, S., Striegl, R.G., Wein, A., and Zhu, Z., 2010, Public Review Draft: A Method for Assessing Carbon Stocks, Carbon Sequestration, and Greenhouse-Gas Fluxes in Ecosystems of the United States Under Present Conditions and Future Scenarios: U.S. Geological Survey Open-File Report 2010-1144, xviii, 196 p.; Appendices, https://doi.org/10.3133/ofr20101144.","productDescription":"xviii, 196 p.; 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bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":1448,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian A.","email":"bbergama@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":305548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernknopf, Richard","contributorId":51701,"corporation":false,"usgs":true,"family":"Bernknopf","given":"Richard","affiliations":[],"preferred":false,"id":305557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clow, David","contributorId":21920,"corporation":false,"usgs":true,"family":"Clow","given":"David","affiliations":[],"preferred":false,"id":305552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dye, 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Alaska","interactions":[],"lastModifiedDate":"2012-02-10T00:10:06","indexId":"ofr20101135","displayToPublicDate":"2010-07-08T00:00:00","publicationYear":"2010","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":"2010-1135","title":"Initial Results from a Study of Climatic Changes and the Effect on Wild Sheep Habitat in Selected Study Areas of Alaska","docAbstract":"Climate change theorists have projected striking changes in local weather on earth due to increases in temperature. These predicted changes may cause melting glaciers and ice caps, rising sea levels, increasing desertification and other environmental changes which seem likely to affect presumed indicator species as harbingers of more significant changes. Wild sheep, even though they are one of the more successful mammalian taxa since Pleistocene times, exhibit a suite of adaptations to glacier driven environments which may be presumed to render them sensitive to environmental changes. The authors began investigation with these assumptions by comparing changes, as determined by satellite imagery, in glacier extent in our study areas in Denali National Park, Alaska, during the last 30 years. Our findings showed the extent of glacial retreat in Alaska during this time period was approximately 40-50 percent as measured by ablation zone and retreat of terminal moraines. During the first half of this 30-year period, Dall sheep (Ovis dalli dalli) populations were stable at historically recorded highs. In the early to mid-1990s, Dall sheep populations in Alaska declined from an historical estimated high of 75,000 sheep to the presently estimated 40-50,000. The declines seemed to be weather related, on the basis of the presumption that lamb survival rates are primarily weather-mediated in Alaska. Changes in local weather appear, at this point, to be correlated with oscillation in the Pacific Current in the Northern Pacific ocean. Of course, changes in local weather affect forage abundance and quality seasonally. In investigating a possible linkage of weather to seasonal forage abundance and quality, we also investigated changes in snow and ice extent and distribution, as well as increased water runoff associated with permafrost and depleted glaciers. Databases were assembled from a wide variety of remotely sensed satellite data, ground-based observations, and historical data bases relating to Dall sheep habitats in selected study areas. Alaska's sheep habitats are typified by long, narrow bands of mountainous uplifts generally arrayed west-to-east, and perpendicular to prevailing south-to-north weather-front movements. Classic Dall sheep habitat occurs on snow-shadowed slopes within these narrow mountainous habitats. On the basis of these data, we offer an explanatory hypothesis relating Dall sheep welfare to weather and climate-influenced nutrition and a monitoring scheme, which should produce data sufficient to test the robustness of this hypothesis. If correlated with population changes, the methods used in our comparative observations may provide long-term monitoring tools for wildlife managers and be applicable in other widely-dispersed wild sheep habitats. If no significant correlations emerge from our modeling exercises, the notion that wild sheep are a sufficiently sensitive species to be seen as an indicator species will have to be reexamined. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101135","usgsCitation":"Pfeifer, E., Ruhlman, J., Middleton, B., Dye, D., and Acosta, A., 2010, Initial Results from a Study of Climatic Changes and the Effect on Wild Sheep Habitat in Selected Study Areas of Alaska: U.S. Geological Survey Open-File Report 2010-1135, iv, 39 p.; Appendices, https://doi.org/10.3133/ofr20101135.","productDescription":"iv, 39 p.; Appendices","onlineOnly":"Y","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":125930,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1135.jpg"},{"id":13886,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1135/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -150.33333333333334,63.166666666666664 ], [ -150.33333333333334,63.666666666666664 ], [ -149,63.666666666666664 ], [ -149,63.166666666666664 ], [ -150.33333333333334,63.166666666666664 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4acce4b07f02db67e886","contributors":{"authors":[{"text":"Pfeifer, Edwin epfeifer@usgs.gov","contributorId":569,"corporation":false,"usgs":true,"family":"Pfeifer","given":"Edwin","email":"epfeifer@usgs.gov","affiliations":[],"preferred":true,"id":305534,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruhlman, Jana","contributorId":93013,"corporation":false,"usgs":true,"family":"Ruhlman","given":"Jana","email":"","affiliations":[],"preferred":false,"id":305538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Middleton, Barry","contributorId":38119,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","affiliations":[],"preferred":false,"id":305535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dye, Dennis","contributorId":54159,"corporation":false,"usgs":true,"family":"Dye","given":"Dennis","affiliations":[],"preferred":false,"id":305536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Acosta, Alex aacosta@usgs.gov","contributorId":73557,"corporation":false,"usgs":true,"family":"Acosta","given":"Alex","email":"aacosta@usgs.gov","affiliations":[],"preferred":false,"id":305537,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":98497,"text":"sir20105092 - 2010 - A Geochemical Mass-Balance Method for Base-Flow Separation, Upper Hillsborough River Watershed, West-Central Florida, 2003-2005 and 2009","interactions":[],"lastModifiedDate":"2012-02-10T00:11:53","indexId":"sir20105092","displayToPublicDate":"2010-07-07T00:00:00","publicationYear":"2010","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":"2010-5092","title":"A Geochemical Mass-Balance Method for Base-Flow Separation, Upper Hillsborough River Watershed, West-Central Florida, 2003-2005 and 2009","docAbstract":"Geochemical mass-balance (GMB) and conductivity mass-balance (CMB) methods for hydrograph separation were used to determine the contribution of base flow to total stormflow at two sites in the upper Hillsborough River watershed in west-central Florida from 2003-2005 and at one site in 2009. The chemical and isotopic composition of streamflow and precipitation was measured during selected local and frontal low- and high-intensity storm events and compared to the geochemical and isotopic composition of groundwater. Input for the GMB method included cation, anion, and stable isotope concentrations of surface water and groundwater, whereas input for the CMB method included continuous or point-sample measurement of specific conductance. \r\n\r\nThe surface water is a calcium-bicarbonate type water, which closely resembles groundwater geochemically, indicating that much of the surface water in the upper Hillsborough River basin is derived from local groundwater discharge. This discharge into the Hillsborough River at State Road 39 and at Hillsborough River State Park becomes diluted by precipitation and runoff during the wet season, but retains the calcium-bicarbonate characteristics of Upper Floridan aquifer water. \r\n\r\nField conditions limited the application of the GMB method to low-intensity storms but the CMB method was applied to both low-intensity and high-intensity storms. The average contribution of base flow to total discharge for all storms ranged from 31 to 100 percent, whereas the contribution of base flow to total discharge during peak discharge periods ranged from less than 10 percent to 100 percent. \r\n\r\nAlthough calcium, magnesium, and silica were consistent markers of Upper Floridan aquifer chemistry, their use in calculating base flow by the GMB method was limited because the frequency of point data collected in this study was not sufficient to capture the complete hydrograph from pre-event base-flow to post-event base-flow concentrations. In this study, pre-event water represented somewhat diluted groundwater. \r\n\r\nStreamflow conductivity integrates the concentrations of the major ions, and the logistics of acquiring specific conductance at frequent time intervals are less complicated than data collection, sample processing, shipment, and analysis of water samples in a laboratory. The acquisition of continuous specific conductance data reduces uncertainty associated with less-frequently collected geochemical point data. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20105092","collaboration":"Prepared in cooperation with\r\nSouthwest Florida Water Management District","usgsCitation":"Kish, G.R., Stringer, C., Stewart, M., Rains, M., and Torres, A.E., 2010, A Geochemical Mass-Balance Method for Base-Flow Separation, Upper Hillsborough River Watershed, West-Central Florida, 2003-2005 and 2009: U.S. Geological Survey Scientific Investigations Report 2010-5092, viii, 33 p. , https://doi.org/10.3133/sir20105092.","productDescription":"viii, 33 p. ","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2003-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":125557,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5092.jpg"},{"id":13885,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5092/","linkFileType":{"id":5,"text":"html"}}],"projection":"Albers Equal-Area Conic","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -82.75,27.833333333333332 ], [ -82.75,28.5 ], [ -81.83333333333333,28.5 ], [ -81.83333333333333,27.833333333333332 ], [ -82.75,27.833333333333332 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd4956e4b0b290850ef127","contributors":{"authors":[{"text":"Kish, G. R.","contributorId":65118,"corporation":false,"usgs":true,"family":"Kish","given":"G.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":305530,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stringer, C.E.","contributorId":73311,"corporation":false,"usgs":true,"family":"Stringer","given":"C.E.","email":"","affiliations":[],"preferred":false,"id":305531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, M.T.","contributorId":6487,"corporation":false,"usgs":true,"family":"Stewart","given":"M.T.","email":"","affiliations":[],"preferred":false,"id":305529,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rains, M.C.","contributorId":78046,"corporation":false,"usgs":true,"family":"Rains","given":"M.C.","email":"","affiliations":[],"preferred":false,"id":305532,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Torres, A. E.","contributorId":94350,"corporation":false,"usgs":true,"family":"Torres","given":"A.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":305533,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70156102,"text":"70156102 - 2010 - Sub-weekly to interannual variability of a high-energy shoreline","interactions":[],"lastModifiedDate":"2021-03-17T12:20:47.309201","indexId":"70156102","displayToPublicDate":"2010-07-07T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Sub-weekly to interannual variability of a high-energy shoreline","docAbstract":"Sixty-one Global Positioning System (GPS), sub-aerial beach surveys were completed at 7 km long Ocean Beach, San Francisco, CA (USA), between April 2004 and March 2009. The five-year time series contains over 1 million beach elevation measurements and documents detailed changes in beach morphology over a variety of spatial, temporal, and physical forcing scales. Results show that seasonal processes dominate at Ocean Beach, with the seasonal increase and decrease in wave height being the primary driver of shoreline change. Storm events, while capable of causing large short-term changes in the shoreline, did not singularly account for a large percentage of the overall observed change. Empirical orthogonal function (EOF) analysis shows that the first two modes account for approximately three-quarters of the variance in the data set and are represented by the seasonal onshore/offshore movement of sediment (60%) and the multi-year trend of shoreline rotation (14%). The longer-term trend of shoreline rotation appears to be related to larger-scale bathymetric change. An EOF-based decomposition technique is developed that is capable of estimating the shoreline position to within one standard deviation of the range of shoreline positions observed at most locations along the beach. The foundation of the model is the observed relationship between the temporal amplitudes of the first EOF mode and seasonally-averaged offshore wave height as well as the linear trend of shoreline rotation. This technique, while not truly predictive because of the requirement of real-time wave data, is useful because it can predict shoreline position to within reasonable confidence given the absence of field data once the model is developed at a particular site.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2010.05.011","usgsCitation":"Hansen, J., and Barnard, P.L., 2010, Sub-weekly to interannual variability of a high-energy shoreline: Coastal Engineering, v. 57, no. 11-12, p. 959-972, https://doi.org/10.1016/j.coastaleng.2010.05.011.","productDescription":"13 p.","startPage":"959","endPage":"972","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-011159","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":306838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Ocean Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51815795898436,\n 37.68708070686609\n ],\n [\n -122.49412536621094,\n 37.68708070686609\n ],\n [\n -122.49412536621094,\n 37.78102667641841\n ],\n [\n -122.51815795898436,\n 37.78102667641841\n ],\n [\n -122.51815795898436,\n 37.68708070686609\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"11-12","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55d45734e4b0518e354694f5","contributors":{"authors":[{"text":"Hansen, Jeff E.","contributorId":146437,"corporation":false,"usgs":false,"family":"Hansen","given":"Jeff E.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":567872,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":567871,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70156577,"text":"70156577 - 2010 - Assimilating models and data to enhance predictions of shoreline evolution","interactions":[],"lastModifiedDate":"2021-10-26T15:55:17.530257","indexId":"70156577","displayToPublicDate":"2010-07-05T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Assimilating models and data to enhance predictions of shoreline evolution","docAbstract":"A modeling system that considers both long- and short-term process-driven shoreline change is presented. The modeling system is integrated into a data assimilation framework that uses sparse observations of shoreline change to correct a model forecast and to determine unobserved model variables and free parameters. Application of the assimilation algorithm also provides quantitative statistical estimates of uncertainty that can be applied to coastal hazard and vulnerability assessments. Significant attention is given to the estimation of four non-observable quantities using the data assimilation framework that utilizes only one observable process (i.e. ,shoreline change). The general framework discussed here can be applied to many other geophysical processes by simply changing the model component to one applicable to the processes of interest.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of 32nd International Conference on Coastal Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"32nd International Conference on Coastal Engineering","conferenceDate":"June 30-July 5 2010","conferenceLocation":"Shanghai, China","language":"English","publisher":"International Conference on Coastal Engineering","doi":"10.9753/icce.v32.sediment.91","usgsCitation":"Long, J.W., and Plant, N.G., 2010, Assimilating models and data to enhance predictions of shoreline evolution, in Proceedings of 32nd International Conference on Coastal Engineering, Shanghai, China, June 30-July 5 2010, 6 p., https://doi.org/10.9753/icce.v32.sediment.91.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-024580","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":475692,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.9753/icce.v32.sediment.91","text":"Publisher Index Page"},{"id":307339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2011-01-31","publicationStatus":"PW","scienceBaseUri":"55dc402be4b0518e354d10d9","contributors":{"editors":[{"text":"Smith, Jane McKee","contributorId":146956,"corporation":false,"usgs":false,"family":"Smith","given":"Jane","email":"","middleInitial":"McKee","affiliations":[],"preferred":false,"id":569562,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Lynett, Patrick","contributorId":24298,"corporation":false,"usgs":true,"family":"Lynett","given":"Patrick","affiliations":[],"preferred":false,"id":569563,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Long, Joseph W. 0000-0003-2912-1992 jwlong@usgs.gov","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":3303,"corporation":false,"usgs":true,"family":"Long","given":"Joseph","email":"jwlong@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":569560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":569561,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":98494,"text":"sir20105120 - 2010 - Bromide, Chloride, and Sulfate Concentrations and Loads at U.S. Geological Survey Streamflow-Gaging Stations 07331600 Red River at Denison Dam, 07335500 Red River at Arthur City, and 07336820 Red River near DeKalb, Texas, 2007-09","interactions":[],"lastModifiedDate":"2019-12-30T14:24:32","indexId":"sir20105120","displayToPublicDate":"2010-07-03T00:00:00","publicationYear":"2010","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":"2010-5120","title":"Bromide, Chloride, and Sulfate Concentrations and Loads at U.S. Geological Survey Streamflow-Gaging Stations 07331600 Red River at Denison Dam, 07335500 Red River at Arthur City, and 07336820 Red River near DeKalb, Texas, 2007-09","docAbstract":"The U.S. Geological Survey, in cooperation with the City of Dallas Water Utilities Division, did a study to characterize bromide, chloride, and sulfate concentrations and loads at three U.S. Geological Survey streamflow-gaging stations on the reach of the Red River from Denison Dam, which impounds Lake Texoma, to the U.S. Highway 259 bridge near DeKalb, Texas. Bromide, chloride, and sulfate concentrations and loads were computed for streamflow-gaging stations on the study reach of the Red River. Continuous streamflow and specific conductance data and discrete samples for bromide, chloride, sulfate, and specific conductance were collected at three main-stem streamflow-gaging stations on the Red River: 07331600 Red River at Denison Dam near Denison, Texas (Denison Dam gage), 07335500 Red River at Arthur City, Texas (Arthur City gage), and 07336820 Red River near DeKalb, Texas (DeKalb gage). At each of these streamflow-gaging stations, discrete water-quality data were collected during January 2007-February 2009; continuous water-quality data were collected during March 2007-February 2009. Two periods of high flow resulted from floods during the study; floods during June-July 2007 resulted in elevated flow during June-September 2007 and smaller floods during March-April 2008 resulted in elevated flow during March-April 2008. Bromide, chloride, and sulfate concentrations in samples collected at the three gages decreased downstream. Median bromide concentrations ranged from 0.32 milligram per liter at the Denison Dam gage to 0.19 milligram per liter at the DeKalb gage. Median chloride concentrations ranged from 176 milligrams per liter at the Denison Dam gage to 108 milligrams per liter at the DeKalb gage, less than the 300-milligrams per liter secondary maximum contaminant level established by the Texas Commission on Environmental Quality. Median sulfate concentrations ranged from 213 milligrams per liter at the Denison Dam gage to 117 milligrams per liter at the DeKalb gage, also less than the 300-milligrams per liter secondary maximum contaminant level. Kruskal-Wallis analyses indicated statistically significant differences among bromide, chloride, and sulfate concentrations at the three gages. Regression equations to estimate bromide, chloride, and sulfate loads were developed for each of the three gages. The largest loads were estimated for a period of relatively large streamflow, June-September 2007, when about 50 percent of the load for the study period occurred at each gage. Adjusted R-squared values were largest for regression equations for the DeKalb gage, ranging from .957 for sulfate to .976 for chloride. Adjusted R-squared values for all regression equations developed to estimate loads of bromide, chloride, and sulfate at the three gages were .899 or larger.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Virginia","doi":"10.3133/sir20105120","collaboration":"In cooperation with the City of Dallas Water Utilities Division","usgsCitation":"Baldys, S., Churchill, C.J., Mobley, C.A., and Coffman, D.K., 2010, Bromide, Chloride, and Sulfate Concentrations and Loads at U.S. Geological Survey Streamflow-Gaging Stations 07331600 Red River at Denison Dam, 07335500 Red River at Arthur City, and 07336820 Red River near DeKalb, Texas, 2007-09: U.S. Geological Survey Scientific Investigations Report 2010-5120, vi, 30 p., https://doi.org/10.3133/sir20105120.","productDescription":"vi, 30 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":125848,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5120.jpg"},{"id":13880,"rank":100,"type":{"id":15,"text":"Index 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Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-1109","title":"Neosho madtom and other ictalurid populations in relation to hydrologic characteristics of an impounded Midwestern warmwater stream: Update","docAbstract":"The Neosho madtom, Noturus placidus, is a small (less than 75 millimeters in total length) ictalurid that is native to the main stems of the Neosho and Cottonwood Rivers in Kansas and Oklahoma and the Spring River in Kansas and Missouri. The Neosho madtom was federally listed as threatened by the U.S. Fish and Wildlife Service in May 1990. The U.S. Fish and Wildlife Service has been monitoring Neosho madtoms since 1991, and questioned whether or not Neosho madtom densities were affected by other catfish species, reservoirs, and hydrologic characteristics. Using the first 8 years of U.S. Fish and Wildlife Service monitoring data, Wildhaber and others (2000) analyzed whether or not Neosho madtom densities were related to these environmental characteristics. The goal of this report is to update these results with data from 1999 to 2008. The trends of Neosho madtom densities in respect to John Redmond Reservoir and other catfish species remains consistent with the previous report. In both the Neosho and Spring Rivers, Neosho madtoms had a significant positive association with all catfish species. Of those species tested, only in the population of Neosho madtoms were significantly different in density above verses below the John Redmond Reservoir after accounting for the yearly variation. The average density of Neosho madtoms at the streamgage immediately below the reservoir had the second lowest density compared to the other streamgages. The positive associations with Neosho madtoms that remained consistent from the previous report included the 1-, 3-, and 7-day minima discharges and the annual minimum discharge from the previous water year (water year prior to when the fish were sampled) and the 1-, 3-, 7-, and 30-day minima discharges from the current water year (same water year fish were sampled).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20101109","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Bryan, J.L., Wildhaber, M.L., Leeds, W.B., and Dey, R., 2010, Neosho madtom and other ictalurid populations in relation to hydrologic characteristics of an impounded Midwestern warmwater stream: Update: U.S. Geological Survey Open-File Report 2010-1109, v, 20 p., https://doi.org/10.3133/ofr20101109.","productDescription":"v, 20 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":125853,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1109.jpg"},{"id":341600,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2010/1109/pdf/OFR2010-1109.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}},{"id":13881,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1109/","linkFileType":{"id":5,"text":"html"}},{"id":405504,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_93391.htm","linkFileType":{"id":5,"text":"html"}}],"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 -96.8333,\n 36.5\n ],\n [\n -94,\n 36.5\n ],\n [\n -94,\n 38.6667\n ],\n [\n -96.8333,\n 38.6667\n ],\n [\n -96.8333,\n 36.5\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4afee4b07f02db697952","contributors":{"authors":[{"text":"Bryan, Janice L.","contributorId":58589,"corporation":false,"usgs":true,"family":"Bryan","given":"Janice","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":305525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":305523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leeds, William B.","contributorId":45563,"corporation":false,"usgs":true,"family":"Leeds","given":"William","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":305524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dey, Rima","contributorId":81210,"corporation":false,"usgs":true,"family":"Dey","given":"Rima","email":"","affiliations":[],"preferred":false,"id":305526,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":98493,"text":"fs20103047 - 2010 - Estuaries of the Greater Everglades Ecosystem: Laboratories of Long-term Change","interactions":[],"lastModifiedDate":"2012-02-02T00:14:44","indexId":"fs20103047","displayToPublicDate":"2010-07-03T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-3047","title":"Estuaries of the Greater Everglades Ecosystem: Laboratories of Long-term Change","docAbstract":"Restoring the greater Everglades ecosystem of south Florida is arguably the largest ecosystem restoration effort to date. A critical goal is to return more natural patterns of flow through south Florida wetlands and into the estuaries, but development of realistic targets requires acknowledgement that ecosystems are constantly evolving and changing in response to a variety of natural and human-driven stressors.\r\n\r\nExamination of ecosystems over long periods of time requires analysis of sedimentary records, such as those deposited in the wetlands and estuaries of south Florida. As sediment accumulates, it preserves information about the animals and plants that lived in the environment and the physical, chemical, and climatic conditions present. One of the methods used to interpret this information is paleoecology-the study of the ecology of previously living organisms. \r\n\r\nPaleoecologic investigations of south Florida estuaries provide quantitative data on historical variability of salinity and trends that may be applied to statistical models to estimate historical freshwater flow. These data provide a unique context to estimate future ecosystem response to changes related to restoration activities and predicted changes in sea level and temperature, thus increasing the likelihood of successful and sustainable ecosystem restoration.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/fs20103047","usgsCitation":"Wingard, G., Hudley, J., and Marshall, F., 2010, Estuaries of the Greater Everglades Ecosystem: Laboratories of Long-term Change: U.S. Geological Survey Fact Sheet 2010-3047, 4 p., https://doi.org/10.3133/fs20103047.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":125855,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2010_3047.jpg"},{"id":13879,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2010/3047/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4acce4b07f02db67eb16","contributors":{"authors":[{"text":"Wingard, G.L.","contributorId":79981,"corporation":false,"usgs":true,"family":"Wingard","given":"G.L.","email":"","affiliations":[],"preferred":false,"id":305517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudley, J.W.","contributorId":18872,"corporation":false,"usgs":true,"family":"Hudley","given":"J.W.","affiliations":[],"preferred":false,"id":305516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marshall, F.E.","contributorId":103380,"corporation":false,"usgs":true,"family":"Marshall","given":"F.E.","email":"","affiliations":[],"preferred":false,"id":305518,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}