Nutrient and sediment fluxes and changes in fluxes over time are key indicators that water resource managers can use to assess the progress being made in improving the structure and function of the Chesapeake Bay ecosystem. The U.S. Geological Survey collects annual nutrient (nitrogen and phosphorus) and sediment flux data and computes trends that describe the extent to which water-quality conditions are changing within the major Chesapeake Bay tributaries. Two regression-based approaches were compared for estimating annual nutrient and sediment fluxes and for characterizing how these annual fluxes are changing over time. The two regression models compared are the traditionally used ESTIMATOR and the newly developed Weighted Regression on Time, Discharge, and Season (WRTDS). The model comparison focused on answering three questions: (1) What are the differences between the functional form and construction of each model? (2) Which model produces estimates of flux with the greatest accuracy and least amount of bias? (3) How different would the historical estimates of annual flux be if WRTDS had been used instead of ESTIMATOR? One additional point of comparison between the two models is how each model determines trends in annual flux once the year-to-year variations in discharge have been determined. All comparisons were made using total nitrogen, nitrate, total phosphorus, orthophosphorus, and suspended-sediment concentration data collected at the nine U.S. Geological Survey River Input Monitoring stations located on the Susquehanna, Potomac, James, Rappahannock, Appomattox, Pamunkey, Mattaponi, Patuxent, and Choptank Rivers in the Chesapeake Bay watershed.
\nTwo model characteristics that uniquely distinguish ESTIMATOR and WRTDS are the fundamental model form and the determination of model coefficients. ESTIMATOR and WRTDS both predict water-quality constituent concentration by developing a linear relation between the natural logarithm of observed constituent concentration and three explanatory variables—the natural log of discharge, time, and season. ESTIMATOR uses two additional explanatory variables—the square of the log of discharge and time-squared. Both models determine coefficients for variables for a series of estimation windows. ESTIMATOR establishes variable coefficients for a series of 9-year moving windows; all observed constituent concentration data within the 9-year window are used to establish each coefficient. Conversely, WRTDS establishes variable coefficients for each combination of discharge and time using only observed concentration data that are similar in time, season, and discharge to the day being estimated. As a result of these distinguishing characteristics, ESTIMATOR reproduces concentration-discharge relations that are closely approximated by a quadratic or linear function with respect to both the log of discharge and time. Conversely, the linear model form of WRTDS coupled with extensive model windowing for each combination of discharge and time allows WRTDS to reproduce observed concentration-discharge relations that are more sinuous in form.
\nAnother distinction between ESTIMATOR and WRTDS is the reporting of uncertainty associated with the model estimates of flux and trend. ESTIMATOR quantifies the standard error of prediction associated with the determination of flux and trends. The standard error of prediction enables the determination of the 95-percent confidence intervals for flux and trend as well as the ability to test whether the reported trend is significantly different from zero (where zero equals no trend). Conversely, WRTDS is unable to propagate error through the many (over 5,000) models for unique combinations of flow and time to determine a total standard error. As a result, WRTDS flux estimates are not reported with confidence intervals and a level of significance is not determined for flow-normalized fluxes.
\nThe differences between ESTIMATOR and WRTDS, with regard to model form and determination of model coefficients, have an influence on the determination of nutrient and sediment fluxes and associated changes in flux over time as a result of management activities. The comparison between the model estimates of flux and trend was made for combinations of five water-quality constituents at nine River Input Monitoring stations.
\nThe major findings with regard to nutrient and sediment fluxes are as follows: (1)WRTDS produced estimates of flux for all combinations that were more accurate, based on reduction in root mean squared error, than flux estimates from ESTIMATOR; (2) for 67 percent of the combinations, WRTDS and ESTIMATOR both produced estimates of flux that were minimally biased compared to observed fluxes(flux bias = tendency to over or underpredict flux observations); however, for 33 percent of the combinations, WRTDS produced estimates of flux that were considerably less biased (by at least 10 percent) than flux estimates from ESTIMATOR; (3) the average percent difference in annual fluxes generated by ESTIMATOR and WRTDS was less than 10 percent at 80 percent of the combinations; and (4) the greatest differences related to flux bias and annual fluxes all occurred for combinations where the pattern in observed concentration-discharge relation was sinuous (two points of inflection) rather than linear or quadratic (zero or one point of inflection).
\nThe major findings with regard to trends are as follows: (1) both models produce water-quality trends that have factored in the year-to-year variations in flow; (2) trends in water-quality condition are represented by ESTIMATOR as a trend in flow-adjusted concentration and by WRTDS as a flow normalized flux; (3) for 67 percent of the combinations with trend estimates, the WRTDS trends in flow-normalized flux are in the same direction and magnitude to the ESTIMATOR trends in flow-adjusted concentration, and at the remaining 33 percent the differences in trend magnitude and direction are related to fundamental differences between concentration and flux; and (4) the majority (85 percent) of the total nitrogen, nitrate, and orthophosphorus combinations exhibited long-term (1985 to 2010) trends in WRTDS flow-normalized flux that indicate improvement or reduction in associated flux and the majority (83 percent) of the total phosphorus (from 1985 to 2010) and suspended sediment (from 2001 to 2010) combinations exhibited trends in WRTDS flow-normalized flux that indicate degradation or increases in the flux delivered.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125244","isbn":"978-1-4113-3525-7","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality, Maryland Department of Natural Resources, and the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Moyer, D., Hirsch, R.M., and Hyer, K., 2012, Comparison of two regression-based approaches for determining nutrient and sediment fluxes and trends in the Chesapeake Bay watershed: U.S. Geological Survey Scientific Investigations Report 2012-5244, x, 118 p., https://doi.org/10.3133/sir20125244.","productDescription":"x, 118 p.","numberOfPages":"132","costCenters":[{"id":614,"text":"Virginia Water Science 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Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1269","title":"Implications of NGA for NEHRP site coefficients","docAbstract":"Three proposals are provided to update tables 11.4-1 and 11.4-2 of Minimum Design Loads for Buildings and Other Structures (7-10), by the American Society of Civil Engineers (2010) (ASCE/SEI 7-10), with site coefficients implied directly by NGA (Next Generation Attenuation) ground motion prediction equations (GMPEs). Proposals include a recommendation to use straight-line interpolation to infer site coefficients at intermediate values of ̅vs (average shear velocity). Site coefficients are recommended to ensure consistency with ASCE/SEI 7-10 MCER (Maximum Considered Earthquake) seismic-design maps and simplified site-specific design spectra procedures requiring site classes with associated tabulated site coefficients and a reference site class with unity site coefficients. Recommended site coefficients are confirmed by independent observations of average site amplification coefficients inferred with respect to an average ground condition consistent with that used for the MCER maps. The NGA coefficients recommended for consideration are implied directly by the NGA GMPEs and do not require introduction of additional models.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121269","usgsCitation":"Borcherdt, R.D., 2012, Implications of NGA for NEHRP site coefficients: U.S. Geological Survey Open-File Report 2012-1269, iv, 25 p., https://doi.org/10.3133/ofr20121269.","productDescription":"iv, 25 p.","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":265558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1269.gif"},{"id":265556,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1269/"},{"id":265557,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1269/of2012-1269.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50f1346ce4b0c982afefa871","contributors":{"authors":[{"text":"Borcherdt, Roger D. 0000-0002-8668-0849 borcherdt@usgs.gov","orcid":"https://orcid.org/0000-0002-8668-0849","contributorId":2373,"corporation":false,"usgs":true,"family":"Borcherdt","given":"Roger","email":"borcherdt@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":471797,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70042529,"text":"pp1796 - 2012 - An economic value of remote-sensing information—Application to agricultural production and maintaining groundwater quality","interactions":[],"lastModifiedDate":"2013-01-11T08:19:30","indexId":"pp1796","displayToPublicDate":"2013-01-11T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1796","title":"An economic value of remote-sensing information—Application to agricultural production and maintaining groundwater quality","docAbstract":"Does remote-sensing information provide economic benefits to society, and can a value be assigned to those benefits? Can resource management and policy decisions be better informed by coupling past and present Earth observations with groundwater nitrate measurements? Using an integrated assessment approach, the U.S. Geological Survey (USGS) applied an established conceptual framework to answer these questions, as well as to estimate the value of information (VOI) for remote-sensing imagery. The approach uses moderate-resolution land-imagery (MRLI) data from the Landsat and Advanced Wide Field Sensor satellites that has been classified by the National Agricultural Statistics Service into the Cropland Data Layer (CDL). Within the constraint of the U.S. Environmental Protection Agency's public health threshold for potable groundwater resources, the USGS modeled the relation between a population of the CDL's land uses and dynamic nitrate (NO3-) contamination of aquifers in a case study region in northeastern Iowa. Employing various multiscaled, multitemporal geospatial datasets with MRLI to maximize the value of agricultural production, the approach develops and uses multiple environmental science models to address dynamic nitrogen loading and transport at specified distances from specific sites (wells) and at landscape scales (for example, across 35 counties and two aquifers). In addition to the ecosystem service of potable groundwater, this effort focuses on the use of MRLI for the management of the major land uses in the study region-the production of corn and soybeans, which can impact groundwater quality. Derived methods and results include (1) economic and dynamic nitrate-pollution models, (2) probabilities of the survival of groundwater, and (3) a VOI for remote sensing. For the northeastern Iowa study region, the marginal benefit of the MRLI VOI (in 2010 dollars) is $858 million ±$197 million annualized, which corresponds to a net present value of $38.1 billion ±$8.8 billion for that flow of benefits in perpetuity. Given that these economic estimates are derived from one case study in a part of only one State, the estimates provide a lower estimate related to the potential value of the Landsat Data Continuity Mission.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1796","usgsCitation":"Forney, W.M., Raunikar, R.P., Bernknopf, R.L., and Mishra, S.K., 2012, An economic value of remote-sensing information—Application to agricultural production and maintaining groundwater quality: U.S. Geological Survey Professional Paper 1796, vii, 60 p., https://doi.org/10.3133/pp1796.","productDescription":"vii, 60 p.","numberOfPages":"72","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":265537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/pp_1796.gif"},{"id":265536,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1796/pp1796.pdf"},{"id":265535,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/pp/1796/"}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50f1345fe4b0c982afefa869","contributors":{"authors":[{"text":"Forney, William M.","contributorId":43490,"corporation":false,"usgs":true,"family":"Forney","given":"William","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":471705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raunikar, Ronald P.","contributorId":101535,"corporation":false,"usgs":true,"family":"Raunikar","given":"Ronald","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":471707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bernknopf, Richard L.","contributorId":97061,"corporation":false,"usgs":true,"family":"Bernknopf","given":"Richard","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":471706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mishra, Shruti K.","contributorId":21432,"corporation":false,"usgs":true,"family":"Mishra","given":"Shruti","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":471704,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042442,"text":"70042442 - 2012 - Laboratory triggering of stick-slip events by oscillatory loading in the presence of pore fluid with implications for physics of tectonic tremor","interactions":[],"lastModifiedDate":"2013-02-23T22:30:50","indexId":"70042442","displayToPublicDate":"2013-01-10T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Laboratory triggering of stick-slip events by oscillatory loading in the presence of pore fluid with implications for physics of tectonic tremor","docAbstract":"The physical mechanism by which the low-frequency earthquakes (LFEs) that make up portions of tectonic (also called non-volcanic) tremor are created is poorly understood. In many areas of the world, tectonic tremor and LFEs appear to be strongly tidally modulated, whereas ordinary earthquakes are not. Anomalous seismic wave speeds, interpreted as high pore fluid pressure, have been observed in regions that generate tremor. Here we build upon previous laboratory studies that investigated the response of stick-slip on artificial faults to oscillatory, tide-like loading. These previous experiments were carried out using room-dry samples of Westerly granite, at one effective stress. Here we augment these results with new experiments on Westerly granite, with the addition of varying effective stress using pore fluid at two pressures. We find that raising pore pressure, thereby lowering effective stress can significantly increase the degree of correlation of stick-slip to oscillatory loading. We also find other pore fluid effects that become important at higher frequencies, when the period of oscillation is comparable to the diffusion time of pore fluid into the fault. These results help constrain the conditions at depth that give rise to tidally modulated LFEs, providing confirmation of the effective pressure law for triggering and insights into why tremor is tidally modulated while earthquakes are at best only weakly modulated.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research B: Solid Earth","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1029/2012JB009452","usgsCitation":"Bartlow, N., Lockner, D.A., and Beeler, N.M., 2012, Laboratory triggering of stick-slip events by oscillatory loading in the presence of pore fluid with implications for physics of tectonic tremor: Journal of Geophysical Research B: Solid Earth, v. 117, no. B11, p. 1-11, https://doi.org/10.1029/2012JB009452.","productDescription":"B11411: 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-041164","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":474105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2012jb009452","text":"Publisher Index Page"},{"id":265521,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1029/2012JB009452"},{"id":265522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"B11","noUsgsAuthors":false,"publicationDate":"2012-11-28","publicationStatus":"PW","scienceBaseUri":"5129f331e4b04edf7e93f8ff","contributors":{"authors":[{"text":"Bartlow, Noel M.","contributorId":38868,"corporation":false,"usgs":true,"family":"Bartlow","given":"Noel M.","affiliations":[],"preferred":false,"id":471541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lockner, David A. 0000-0001-8630-6833 dlockner@usgs.gov","orcid":"https://orcid.org/0000-0001-8630-6833","contributorId":567,"corporation":false,"usgs":true,"family":"Lockner","given":"David","email":"dlockner@usgs.gov","middleInitial":"A.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":471539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beeler, Nicholas M. 0000-0002-3397-8481 nbeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-3397-8481","contributorId":2682,"corporation":false,"usgs":true,"family":"Beeler","given":"Nicholas","email":"nbeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":471540,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042468,"text":"sir20125040 - 2012 - Status of groundwater quality in the California Desert Region, 2006-2008: California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2013-01-09T15:13:55","indexId":"sir20125040","displayToPublicDate":"2013-01-09T00:00:00","publicationYear":"2012","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":"2012-5040","title":"Status of groundwater quality in the California Desert Region, 2006-2008: California GAMA Priority Basin Project","docAbstract":"Groundwater quality in six areas in the California Desert Region (Owens, Antelope, Mojave, Coachella, Colorado River, and Indian Wells) was investigated as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The six Desert studies were designed to provide a spatially unbiased assessment of the quality of untreated groundwater in parts of the Desert and the Basin and Range hydrogeologic provinces, as well as a statistically consistent basis for comparing groundwater quality to other areas in California and across the Nation. Samples were collected by the USGS from September 2006 through April 2008 from 253 wells in Imperial, Inyo, Kern, Los Angeles, Mono, Riverside, and San Bernardino Counties. Two-hundred wells were selected using a spatially distributed, randomized grid-based method to provide a spatially unbiased representation of the study areas (grid wells), and fifty-three wells were sampled to provide additional insight into groundwater conditions (additional wells). The status of the current quality of the groundwater resource was assessed based on data from samples analyzed for volatile organic compounds (VOCs), pesticides, and inorganic constituents such as major ions and trace elements. Water-quality data from the California Department of Public Health (CDPH) database also were incorporated in the assessment. The status assessment is intended to characterize the quality of untreated groundwater resources within the primary aquifer systems of the Desert Region, not the treated drinking water delivered to consumers by water purveyors. The primary aquifer systems (hereinafter, primary aquifers) in the six Desert areas are defined as that part of the aquifer corresponding to the perforation intervals of wells listed in the CDPH database. Relative-concentrations (sample concentration divided by the benchmark concentration) were used as the primary metric for evaluating groundwater quality for those constituents that have Federal and (or) California benchmarks. A relative-concentration (RC) greater than (>) 1.0 indicates a concentration above a benchmark, and an RC less than or equal to (≤) 1.0 indicates a concentration equal to or below a benchmark. Organic and special-interest constituent RCs were classified as “low” (RC ≤ 0.1), “moderate” (0.1 < RC ≤ 1.0), or “high” (RC > 1.0). Inorganic constituent RCs were classified as “low” (RC ≤ 0.5), “moderate” (0.5 < RC ≤ 1.0), or “high” (RC > 1.0). A lower threshold value RC was used to distinguish between low and moderate RCs for organic constituents because these constituents are generally less prevalent and have smaller RCs than inorganic constituents. Aquifer-scale proportion was used as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion was defined as the percentage of the area of the primary aquifers with an RC greater than 1.0 for a particular constituent or class of constituents; percentage is based on an areal rather than a volumetric basis. Moderate and low aquifer-scale proportions were defined as the percentage of the primary aquifers with moderate and low RCs, respectively. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable in the Desert Region (within 90 percent confidence intervals). The status assessment determined that one or more inorganic constituents with health-based benchmarks had high RCs in 35.4 percent of the Desert Region’s primary aquifers, moderate RCs in 27.4 percent, and low RCs in 37.2 percent. The inorganic constituents with health-based benchmarks having the largest high aquifer-scale proportions were arsenic (17.8 percent), boron (11.4 percent), fluoride (8.9 percent), gross-alpha radioactivity (6.6 percent), molybdenum (5.7 percent), strontium (3.7 percent), vanadium (3.6 percent), uranium (3.2 percent), and perchlorate (2.4 percent). Inorganic constituents with non-health-based benchmarks were also detected at high RCs in 18.6 percent and at moderate RCs in 16.0 percent of the Desert Region’s primary aquifers. In contrast, organic constituents had high RCs in only 0.3 percent of the Desert Region’s primary aquifers, moderate in 2.0 percent, low in 48.0 percent, and were not detected in 49.7 percent of the primary aquifers in the Desert Region. Of 149 organic constituents analyzed for all six study areas, 42 constituents were detected. Six organic constituents, carbon tetrachloride, chloroform, 1,2-dichloropropane, dieldrin, 1,2-dichloroethane, and tetrachloroethene, were found at moderate RCs in one or more of the grid wells. One constituent, N-nitrosodimethylamine, a special-interest VOC, was detected at a high RC in one well. Thirty-nine organic constituents were detected only at low concentrations. Three organic constituents were frequently detected (in more than 10 percent of samples from grid wells): chloroform, simazine, and deethylatrazine.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125040","collaboration":"Prepared in cooperation with the California State Water Resources Control Board. A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. This report has related reports. Please see: FS 2012-3032, FS 2012-3033, FS 2012-3034, FS 2012-3035, FS 2012-3036, FS 2012-3098.","usgsCitation":"Dawson, B.J., and Belitz, K., 2012, Status of groundwater quality in the California Desert Region, 2006-2008: California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2012-5040, Report: viii, 110 p.; Related Reports: FS 2012-3032, FS 2012-3033, FS 2012-3034, FS 2012-3035, FS 2012-3036, FS 2012-3098, https://doi.org/10.3133/sir20125040.","productDescription":"Report: viii, 110 p.; Related Reports: FS 2012-3032, FS 2012-3033, FS 2012-3034, FS 2012-3035, FS 2012-3036, FS 2012-3098","numberOfPages":"122","additionalOnlineFiles":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":265481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5040.jpg"},{"id":265475,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3032"},{"id":265476,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3033"},{"id":265478,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3036"},{"id":265477,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3035"},{"id":265479,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3034"},{"id":265480,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/fs/2012/3098"},{"id":265473,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5040/"},{"id":265474,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5040/pdf/sir20125040.pdf"}],"projection":"Albers Equal Area Conic Projection","country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.0,32.5 ], [ -121.0,38.0 ], [ -114.0,38.0 ], [ -114.0,32.5 ], [ -121.0,32.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ee9176e4b0160a2d0ee347","contributors":{"authors":[{"text":"Dawson, Barbara J. Milby 0000-0002-0209-8158","orcid":"https://orcid.org/0000-0002-0209-8158","contributorId":57334,"corporation":false,"usgs":true,"family":"Dawson","given":"Barbara","email":"","middleInitial":"J. Milby","affiliations":[],"preferred":false,"id":471602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":471601,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70042410,"text":"ds709K - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kharnak-Kanjar mineral district in Afghanistan: Chapter K in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:13:12","indexId":"ds709K","displayToPublicDate":"2013-01-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"K","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kharnak-Kanjar mineral district in Afghanistan: Chapter K in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Kharnak-Kanjar mineral district, which has mercury deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2007,2008,2010), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 1,000-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Kharnak-Kanjar) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the Kharnak-Kanjar area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Kharnak-Kanjar study area, three subareas were designated for detailed field investigations (that is, the Koh-e-Katif Passaband, Panjshah-Mullayan, and Sahebdad-Khanjar subareas); these subareas were extracted from the area's image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709K","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter K in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kharnak-Kanjar mineral district in Afghanistan: Chapter K in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 3 Maps: 11 x 8.5 inches and 78.68 x 64.04 inches; 26 Image Files; 26 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709K.","productDescription":"Readme; 3 Maps: 11 x 8.5 inches and 78.68 x 64.04 inches; 26 Image Files; 26 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-01-24","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":265362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_k.jpg"},{"id":265354,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/k/index_maps/Kharnak-Kanjar_Area-of-Interest_Index_Map.pdf"},{"id":265355,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/k/index_maps/Kharnak-Kanjar_Image_Index_Map.pdf"},{"id":265356,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/k/index_maps/Kharnak-Kanjar_Subarea_Image_Index_Map.pdf"},{"id":265357,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/k/index_maps/index_maps.html"},{"id":265358,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/k/image_files/image_files.html"},{"id":265359,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/k/metadata/metadata.html"},{"id":265360,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/k/shapefiles/shapefiles.html"},{"id":265361,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"},{"id":265352,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/k/"},{"id":265353,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/k/1_readme.txt"}],"country":"Afghanistan","state":"Farah;Ghor;Daykundi","otherGeospatial":"Kharnak-kanjar Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 64.0,33.0 ], [ 64.0,34.25 ], [ 65.75,34.25 ], [ 65.75,33.0 ], [ 64.0,33.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ebee6ce4b07f1501afcfb4","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":471485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":471486,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042404,"text":"ds709I - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dusar-Shaida mineral district in Afghanistan: Chapter I in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:13:56","indexId":"ds709I","displayToPublicDate":"2013-01-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"I","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dusar-Shaida mineral district in Afghanistan: Chapter I in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Dusar-Shaida mineral district, which has copper and tin deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’ picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’ local zone (41 for Dusar-Shaida) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the Dusar-Shaida area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Dusar-Shaida study area, three subareas were designated for detailed field investigations (that is, the Dahana-Misgaran, Kaftar VMS, and Shaida subareas); these subareas were extracted from the area’ image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709I","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter I in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Dusar-Shaida mineral district in Afghanistan: Chapter I in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 3 Maps: 11 x 8.5 inches and 63.42 x 42.75 inches; 28 Image Files; 28 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709I.","productDescription":"Readme; 3 Maps: 11 x 8.5 inches and 63.42 x 42.75 inches; 28 Image Files; 28 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-01-24","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":265341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_i.jpg"},{"id":265333,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/i/index_maps/Dusar-Shaida_Area-of-Interest_Index_Map.pdf"},{"id":265334,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/i/index_maps/Dusar-Shaida_Image_Index_Map.pdf"},{"id":265335,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/i/index_maps/Dusar-Shaida_Subarea_Image_Index_Map.pdf"},{"id":265338,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/i/metadata/metadata.html"},{"id":265339,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/i/shapefiles/shapefiles.html"},{"id":265336,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/i/index_maps/index_maps.html"},{"id":265337,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/i/image_files/image_files.html"},{"id":265340,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"},{"id":265331,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/i/"},{"id":265332,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/i/1_readme.txt"}],"country":"Afghanistan","state":"Farah;Herat","otherGeospatial":"Dusar-shaida Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 61.0,33.2 ], [ 61.0,34.0 ], [ 62.5,34.0 ], [ 62.5,33.2 ], [ 61.0,33.2 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ebee6be4b07f1501afcfb0","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":471470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":471471,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042329,"text":"70042329 - 2012 - Understanding the role of ecohydrological feedbacks in ecosystem state change in drylands","interactions":[],"lastModifiedDate":"2013-01-10T15:47:55","indexId":"70042329","displayToPublicDate":"2013-01-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the role of ecohydrological feedbacks in ecosystem state change in drylands","docAbstract":"Ecohydrological feedbacks are likely to be critical for understanding the mechanisms by which changes in exogenous forces result in ecosystem state change. We propose that in drylands, the dynamics of ecosystem state change are determined by changes in the type (stabilizing vs amplifying) and strength of ecohydrological feedbacks following a change in exogenous forces. Using a selection of five case studies from drylands, we explore the characteristics of ecohydrological feedbacks and resulting dynamics of ecosystem state change. We surmise that stabilizing feedbacks are critical for the provision of plant-essential resources in drylands. Exogenous forces that break these stabilizing feedbacks can alter the state of the system, although such changes are potentially reversible if strong amplifying ecohydrological feedbacks do not develop. The case studies indicate that if amplifying ecohydrological feedbacks do develop, they are typically associated with abiotic processes such as runoff, erosion (by wind and water), and fire. These amplifying ecohydrological feedbacks progressively modify the system in ways that are long-lasting and possibly irreversible on human timescales.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecohydrology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","publisherLocation":"Hoboken, NJ","doi":"10.1002/eco.265","usgsCitation":"Turnbull, L., Wilcox, B., Belnap, J., Ravi, S., D’Odorico, P., Childers, D., Gwenzi, W., Okin, G., Wainwright, J., Caylor, K., and Sankey, T., 2012, Understanding the role of ecohydrological feedbacks in ecosystem state change in drylands: Ecohydrology, v. 5, no. 2, p. 174-183, https://doi.org/10.1002/eco.265.","productDescription":"10 p.","startPage":"174","endPage":"183","numberOfPages":"10","ipdsId":"IP-029337","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":474106,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.265","text":"Publisher Index Page"},{"id":265524,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/eco.265"},{"id":265526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2011-11-25","publicationStatus":"PW","scienceBaseUri":"53cd7a32e4b0b2908510d537","contributors":{"authors":[{"text":"Turnbull, L.","contributorId":74649,"corporation":false,"usgs":true,"family":"Turnbull","given":"L.","email":"","affiliations":[],"preferred":false,"id":471293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilcox, B.P.","contributorId":83490,"corporation":false,"usgs":true,"family":"Wilcox","given":"B.P.","email":"","affiliations":[],"preferred":false,"id":471294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belnap, J. 0000-0001-7471-2279","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":23872,"corporation":false,"usgs":true,"family":"Belnap","given":"J.","affiliations":[],"preferred":false,"id":471288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ravi, S.","contributorId":45977,"corporation":false,"usgs":true,"family":"Ravi","given":"S.","affiliations":[],"preferred":false,"id":471290,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"D’Odorico, P.","contributorId":56528,"corporation":false,"usgs":true,"family":"D’Odorico","given":"P.","email":"","affiliations":[],"preferred":false,"id":471291,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Childers, D.","contributorId":86654,"corporation":false,"usgs":true,"family":"Childers","given":"D.","email":"","affiliations":[],"preferred":false,"id":471295,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gwenzi, W.","contributorId":43242,"corporation":false,"usgs":true,"family":"Gwenzi","given":"W.","email":"","affiliations":[],"preferred":false,"id":471289,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Okin, G.","contributorId":64963,"corporation":false,"usgs":true,"family":"Okin","given":"G.","affiliations":[],"preferred":false,"id":471292,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wainwright, J.","contributorId":19046,"corporation":false,"usgs":true,"family":"Wainwright","given":"J.","email":"","affiliations":[],"preferred":false,"id":471287,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Caylor, K.K.","contributorId":15820,"corporation":false,"usgs":true,"family":"Caylor","given":"K.K.","email":"","affiliations":[],"preferred":false,"id":471285,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sankey, T.","contributorId":16287,"corporation":false,"usgs":true,"family":"Sankey","given":"T.","email":"","affiliations":[],"preferred":false,"id":471286,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70042409,"text":"ds709J - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Tourmaline mineral district in Afghanistan: Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:10:57","indexId":"ds709J","displayToPublicDate":"2013-01-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"J","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Tourmaline mineral district in Afghanistan: Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Tourmaline mineral district, which has tin deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Tourmaline) and the WGS84 datum. The final image mosaics were subdivided into four overlapping tiles or quadrants because of the large size of the target area. The four image tiles (or quadrants) for the Tourmaline area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709J","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Cagney, L.E., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Tourmaline mineral district in Afghanistan: Chapter J in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 Maps: 11 x 8.5 inches and 26.58 x 24.67 inches; 8 Image Files; 8 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709J.","productDescription":"Readme; 2 Maps: 11 x 8.5 inches and 26.58 x 24.67 inches; 8 Image Files; 8 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-01-24","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":265351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_j.jpg"},{"id":265344,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/j/index_maps/Tourmaline_Area-of-Interest_Index_Map.pdf"},{"id":265345,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/j/index_maps/Tourmaline_Image_Index_Map.pdf"},{"id":265346,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/j/index_maps/index_maps.html"},{"id":265347,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/j/image_files/image_files.html"},{"id":265348,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/j/metadata/metadata.html"},{"id":265349,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/j/shapefiles/shapefiles.html"},{"id":265350,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"},{"id":265342,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/j/"},{"id":265343,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/j/1_readme.txt"}],"country":"Afghanistan","state":"Farah;Herat","otherGeospatial":"Tourmaline Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 61.5,32.8 ], [ 61.5,33.25 ], [ 62.0,33.25 ], [ 62.0,32.8 ], [ 61.5,32.8 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ebee6de4b07f1501afcfbc","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":471481,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":471482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":471483,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042403,"text":"ds709H - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kundalyan mineral district in Afghanistan: Chapter H in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:12:57","indexId":"ds709H","displayToPublicDate":"2013-01-07T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"H","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kundalyan mineral district in Afghanistan: Chapter H in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Kundalyan mineral district, which has porphyry copper and gold deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Kundalyan) and the WGS84 datum. The final image mosaics were subdivided into five overlapping tiles or quadrants because of the large size of the target area. The five image tiles (or quadrants) for the Kundalyan area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Kundalyan study area, three subareas were designated for detailed field investigations (that is, the Baghawan-Garangh, Charsu-Ghumbad, and Kunag Skarn subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709H","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter H in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Cagney, L.E., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kundalyan mineral district in Afghanistan: Chapter H in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 3 Maps: 11 x 8.5 inches and 41.22 x 49.43 inches; 16 Image Files; 16 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709H.","productDescription":"Readme; 3 Maps: 11 x 8.5 inches and 41.22 x 49.43 inches; 16 Image Files; 16 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2006-01-24","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":265330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_h.jpg"},{"id":265320,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/h/"},{"id":265321,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/h/1_readme.txt"},{"id":265322,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/h/index_maps/Kundalyan_Area-of-Interest_Index_Map.pdf"},{"id":265323,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/h/index_maps/Kundalyan_Image_Index_Map.pdf"},{"id":265324,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/h/index_maps/Kundalyan_Subarea_Image_Index_Map.pdf"},{"id":265325,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/h/index_maps/index_maps.html"},{"id":265326,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/h/image_files/image_files.html"},{"id":265327,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/h/metadata/metadata.html"},{"id":265328,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/h/shapefiles/shapefiles.html"},{"id":265329,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/"}],"country":"Afghanistan","otherGeospatial":"Kundalyan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 66.0,31.75 ], [ 66.0,33.0 ], [ 67.0,33.0 ], [ 67.0,31.75 ], [ 66.0,31.75 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ebee6de4b07f1501afcfb8","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":471466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":471467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":471469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":471468,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042379,"text":"fs20123138 - 2012 - Assessing the vulnerability of public-supply wells to contamination: Rio Grande aquifer system in Albuquerque, New Mexico","interactions":[],"lastModifiedDate":"2013-01-06T12:14:53","indexId":"fs20123138","displayToPublicDate":"2013-01-06T00:00:00","publicationYear":"2012","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":"2012-3138","title":"Assessing the vulnerability of public-supply wells to contamination: Rio Grande aquifer system in Albuquerque, New Mexico","docAbstract":"This fact sheet highlights findings from the vulnerability study of a public-supply well in Albuquerque, New Mexico (hereafter referred to as “the study well”). The study well produces about 3,000 gallons of water per minute from the Rio Grande aquifer system. Water samples were collected at the study well, at two other nearby public-supply wells, and at monitoring wells installed in or near the simulated zone of contribution to the study well. Untreated water samples from the study well contained arsenic at concentrations exceeding the Maximum Contaminant Level (MCL) of 10 micrograms per liter (µg/L) established by the U.S. Environmental Protection Agency for drinking water. Volatile organic compounds (VOCs) and nitrate also were detected, although at concentrations at least an order of magnitude less than established drinking-water standards, where such standards exist. Overall, study findings point to four primary influences on the movement and (or) fate of contaminants and the vulnerability of the public-supply well in Albuquerque: (1) groundwater age (how long ago water entered, or recharged, the aquifer), (2) groundwater development (introduction of manmade recharge and discharge sources), (3) natural geochemical conditions of the aquifer, and (4) seasonal pumping stresses. Concentrations of the isotope carbon-14 indicate that groundwater from most sampled wells in the local study area is predominantly water that entered, or recharged, the aquifer more than 6,000 years ago. However, the additional presence of the age tracer tritium in several groundwater samples at concentrations above 0.3 tritium units indicates that young (post-1950) recharge is reaching the aquifer across broad areas beneath Albuquerque. This young recharge is mixing with the thousands-of-years-old water, is migrating to depths as great as 245 feet below the water table, and is traveling to some (but not all) of the public-supply wells sampled. Most groundwater samples containing a fraction of young water also contain manmade VOCs, including chloroform (a byproduct of drinking-water chlorination), which indicates that the source of young recharge is, at least in part, infiltration of chlorinated municipal-supply water from leaking waterlines and sewerlines or from turf watering. Other likely manmade, urban recharge sources are seepage from constructed ponds and unlined portions of a stormwater diversion channel. A regional-scale computer-model simulation of groundwater flow and transport to the public-supply well shows that manmade sources of recharge and discharge that were added after about 1930 have greatly altered directions of groundwater flow near Albuquerque and have caused water levels to decline by as much as 120 feet. Local-scale simulations show that seasonal changes in the pumping schedule of the study well affect the age and quality of water produced by the well. Increased pumping during the summer causes significant volumes of water to flow downward from the shallow to the intermediate zones of the aquifer, causing a higher fraction of young water to be produced by the well in the summer than in the winter months and a corresponding increase in VOC detections in the summer relative to the winter. During the winter when the study-well pump is idle for several hours each day, old, high-arsenic water from the deep zone of the aquifer travels up the wellbore and exits into the intermediate zone of the aquifer. When the pump is activated in the winter (for a relatively short time each day), some of the leaked, high-arsenic water is recaptured by the well. This results in a higher arsenic concentration (commonly more than 12 µg/L) in water produced in the winter than in the summer, and a smaller fraction of young water being produced by the well in the winter than in the summer (6 percent in the winter, compared to 11 percent in the summer). Knowledge of the vertical flow direction (both natural and pumping-enhanced) in the vicinity of a long-screened well, coupled with understanding of variations in contaminant concentrations with depth in the aquifer, can help water managers predict the positive or negative effect that wellbore flow will have on water quality and can lead to development of strategies to mitigate contamination (such as changes in pumping schedules or development of devices to inhibit wellbore flow when the pump is off).","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123138","collaboration":"National Water-Quality Assessment, Transport of Anthropogenic and Natural Contaminants (TANC) to Public-Supply Wells","usgsCitation":"Jagucki, M.L., Bexfield, L.M., Heywood, C.E., and Eberts, S., 2012, Assessing the vulnerability of public-supply wells to contamination: Rio Grande aquifer system in Albuquerque, New Mexico: U.S. Geological Survey Fact Sheet 2012-3138, 6 p., https://doi.org/10.3133/fs20123138.","productDescription":"6 p.","additionalOnlineFiles":"N","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":265301,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3138/"},{"id":265302,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2012/3138/pdf/fs2012-3138.pdf"},{"id":265303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3138.gif"}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.666667,35.041667 ], [ -106.666667,35.1 ], [ -106.608333,35.1 ], [ -106.608333,35.041667 ], [ -106.666667,35.041667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ea9cebe4b02dd6076fad87","contributors":{"authors":[{"text":"Jagucki, Martha L. 0000-0003-3798-8393 mjagucki@usgs.gov","orcid":"https://orcid.org/0000-0003-3798-8393","contributorId":1794,"corporation":false,"usgs":true,"family":"Jagucki","given":"Martha","email":"mjagucki@usgs.gov","middleInitial":"L.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":true,"id":471421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":471420,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heywood, Charles E. cheywood@usgs.gov","contributorId":2043,"corporation":false,"usgs":true,"family":"Heywood","given":"Charles","email":"cheywood@usgs.gov","middleInitial":"E.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":471422,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eberts, Sandra M. smeberts@usgs.gov","contributorId":2264,"corporation":false,"usgs":true,"family":"Eberts","given":"Sandra M.","email":"smeberts@usgs.gov","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":471423,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042074,"text":"70042074 - 2012 - Variance partitioning of stream diatom, fish, and invertebrate indicators of biological condition","interactions":[],"lastModifiedDate":"2014-05-14T12:41:22","indexId":"70042074","displayToPublicDate":"2013-01-05T11:15:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Variance partitioning of stream diatom, fish, and invertebrate indicators of biological condition","docAbstract":"Stream indicators used to make assessments of biological condition are influenced by many possible sources of variability. To examine this issue, we used multiple-year and multiple-reach diatom, fish, and invertebrate data collected from 20 least-disturbed and 46 developed stream segments between 1993 and 2004 as part of the US Geological Survey National Water Quality Assessment Program. We used a variance-component model to summarize the relative and absolute magnitude of 4 variance components (among-site, among-year, site × year interaction, and residual) in indicator values (observed/expected ratio [O/E] and regional multimetric indices [MMI]) among assemblages and between basin types (least-disturbed and developed). We used multiple-reach samples to evaluate discordance in site assessments of biological condition caused by sampling variability. Overall, patterns in variance partitioning were similar among assemblages and basin types with one exception. Among-site variance dominated the relative contribution to the total variance (64–80% of total variance), residual variance (sampling variance) accounted for more variability (8–26%) than interaction variance (5–12%), and among-year variance was always negligible (0–0.2%). The exception to this general pattern was for invertebrates at least-disturbed sites where variability in O/E indicators was partitioned between among-site and residual (sampling) variance (among-site = 36%, residual = 64%). This pattern was not observed for fish and diatom indicators (O/E and regional MMI). We suspect that unexplained sampling variability is what largely remained after the invertebrate indicators (O/E predictive models) had accounted for environmental differences among least-disturbed sites. The influence of sampling variability on discordance of within-site assessments was assemblage or basin-type specific. Discordance among assessments was nearly 2× greater in developed basins (29–31%) than in least-disturbed sites (15–16%) for invertebrates and diatoms, whereas discordance among assessments based on fish did not differ between basin types (least-disturbed = 16%, developed = 17%). Assessments made using invertebrate and diatom indicators from a single reach disagreed with other samples collected within the same stream segment nearly ⅓ of the time in developed basins, compared to ⅙ for all other cases.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Freshwater Science","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Society for Freshwater Science","doi":"10.1899/11-040.1","usgsCitation":"Zuellig, R.E., Carlisle, D.M., Meador, M., and Potapova, M., 2012, Variance partitioning of stream diatom, fish, and invertebrate indicators of biological condition: Freshwater Science, v. 31, no. 1, p. 182-190, https://doi.org/10.1899/11-040.1.","productDescription":"9 p.","startPage":"182","endPage":"190","ipdsId":"IP-011898","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":474107,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.1899/11-040.1","text":"External Repository"},{"id":287130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":287129,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1899/11-040.1"}],"country":"United States","volume":"31","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5374907ae4b0870f4d23d007","contributors":{"authors":[{"text":"Zuellig, Robert E. 0000-0002-4784-2905 rzuellig@usgs.gov","orcid":"https://orcid.org/0000-0002-4784-2905","contributorId":1620,"corporation":false,"usgs":true,"family":"Zuellig","given":"Robert","email":"rzuellig@usgs.gov","middleInitial":"E.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":470743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":470741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meador, Michael R. mrmeador@usgs.gov","contributorId":615,"corporation":false,"usgs":true,"family":"Meador","given":"Michael R.","email":"mrmeador@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":470742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Potapova, Marina","contributorId":89274,"corporation":false,"usgs":true,"family":"Potapova","given":"Marina","email":"","affiliations":[],"preferred":false,"id":470744,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70042366,"text":"ofr20121261 - 2012 - Should ground-motion records be rotated to fault-normal/parallel or maximum direction for response history analysis of buildings?","interactions":[],"lastModifiedDate":"2013-01-06T12:07:11","indexId":"ofr20121261","displayToPublicDate":"2013-01-05T00:00:00","publicationYear":"2012","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":"2012-1261","title":"Should ground-motion records be rotated to fault-normal/parallel or maximum direction for response history analysis of buildings?","docAbstract":"In the United States, regulatory seismic codes (for example, California Building Code) require at least two sets of horizontal ground-motion components for three-dimensional (3D) response history analysis (RHA) of building structures. For sites within 5 kilometers (3.1 miles) of an active fault, these records should be rotated to fault-normal and fault-parallel (FN/FP) directions, and two RHAs should be performed separately—when FN and then FP direction are aligned with transverse direction of the building axes. This approach is assumed to lead to two sets of responses that envelope the range of possible responses over all nonredundant rotation angles. The validity of this assumption is examined here using 3D computer models of single-story structures having symmetric (torsionally stiff) and asymmetric (torsionally flexible) layouts subjected to an ensemble of near-fault ground motions with and without apparent velocity pulses. In this parametric study, the elastic vibration period is varied from 0.2 to 5 seconds, and yield-strength reduction factors, R, are varied from a value that leads to linear-elastic design to 3 and 5. Further validations are performed using 3D computer models of 9-story structures having symmetric and asymmetric layouts subjected to the same ground-motion set. The influence of the ground-motion rotation angle on several engineering demand parameters (EDPs) is examined in both linear-elastic and nonlinear-inelastic domains to form benchmarks for evaluating the use of the FN/FP directions and also the maximum direction (MD). The MD ground motion is a new definition for horizontal ground motions for use in site-specific ground-motion procedures for seismic design according to provisions of the American Society of Civil Engineers/Seismic Engineering Institute (ASCE/SEI) 7-10. The results of this study have important implications for current practice, suggesting that ground motions rotated to MD or FN/FP directions do not necessarily provide the most critical EDPs in nonlinear-inelastic domain; however, they tend to produce larger EDPs than as-recorded (arbitrarily oriented) motions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121261","usgsCitation":"Reyes, J.C., and Kalkan, E., 2012, Should ground-motion records be rotated to fault-normal/parallel or maximum direction for response history analysis of buildings?: U.S. Geological Survey Open-File Report 2012-1261, xii, 81 p., https://doi.org/10.3133/ofr20121261.","productDescription":"xii, 81 p.","numberOfPages":"93","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-040285","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":265293,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1261.gif"},{"id":265291,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1261/"},{"id":265292,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1261/of2012-1261.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50eaac31e4b02dd6076fadba","contributors":{"authors":[{"text":"Reyes, Juan C.","contributorId":30731,"corporation":false,"usgs":true,"family":"Reyes","given":"Juan","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":471382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":471381,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70042382,"text":"sir20105070F - 2012 - Occurrence model for volcanogenic beryllium deposits","interactions":[],"lastModifiedDate":"2022-04-22T20:13:40.290191","indexId":"sir20105070F","displayToPublicDate":"2013-01-05T00:00:00","publicationYear":"2012","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":"2010-5070","chapter":"F","title":"Occurrence model for volcanogenic beryllium deposits","docAbstract":"Current global and domestic mineral resources of beryllium (Be) for industrial uses are dominated by ores produced from deposits of the volcanogenic Be type. Beryllium deposits of this type can form where hydrothermal fluids interact with fluorine and lithophile-element (uranium, thorium, rubidium, lithium, beryllium, cesium, tantalum, rare earth elements, and tin) enriched volcanic rocks that contain a highly reactive lithic component, such as carbonate clasts. Volcanic and hypabyssal high-silica biotite-bearing topaz rhyolite constitutes the most well-recognized igneous suite associated with such Be deposits. The exemplar setting is an extensional tectonic environment, such as that characterized by the Basin and Range Province, where younger topaz-bearing igneous rock sequences overlie older dolomite, quartzite, shale, and limestone sequences. Mined deposits and related mineralized rocks at Spor Mountain, Utah, make up a unique economic deposit of volcanogenic Be having extensive production and proven and probable reserves. Proven reserves in Utah, as reported by the U.S. Geological Survey National Mineral Information Center, total about 15,900 tons of Be that are present in the mineral bertrandite (Be4Si2O7(OH)2). At the type locality for volcanogenic Be, Spor Mountain, the tuffaceous breccias and stratified tuffs that host the Be ore formed as a result of explosive volcanism that brought carbonate and other lithic fragments to the surface through vent structures that cut the underlying dolomitic Paleozoic sedimentary rock sequences. The tuffaceous sediments and lithic clasts are thought to make up phreatomagmatic base surge deposits. Hydrothermal fluids leached Be from volcanic glass in the tuff and redeposited the Be as bertrandite upon reaction of the hydrothermal fluid with carbonate clasts in lithic-rich sections of tuff. The localization of the deposits in tuff above fluorite-mineralized faults in carbonate rocks, together with isotopic evidence for the involvement of magmatic water in an otherwise meteoric water-dominated hydrothermal system, indicate that magmatic volatiles contributed to mineralization. At the type locality, hydrothermal alteration of dolomite clasts formed layered nodules of calcite, opal, fluorite, and bertrandite, the latter occurring finely intergrown with fluorite. Alteration assemblages and elemental enrichments in the tuff and surrounding volcanic rocks include regional diagenetic clays and potassium feldspar and distinctive hydrothermal halos of anomalous fluorine, lithium, molybdenum, niobium, tin, and tantalum, and intense potassium feldspathization with sericite and lithium-smectite in the immediate vicinity of Be ore. Formation of volcanogenic Be deposits is due to the coincidence of multiple factors that include an appropriate Be-bearing source rock, a subjacent pluton that supplied volatiles and heat to drive convection of meteoric groundwater, a depositional site characterized by the intersection of normal faults with permeable tuff below a less permeable cap rock, a fluorine-rich ore fluid that facilitated Be transport (for example, BeF42- complex), and the existence of a chemical trap that caused fluorite and bertrandite to precipitate at the former site of carbonate lithic clasts in the tuff.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit models for resource assessment (Scientific Investigations Report 2010-5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070F","usgsCitation":"Foley, N.K., Hofstra, A.H., Lindsey, D.A., Seal, R., Jaskula, B.W., and Piatak, N., 2012, Occurrence model for volcanogenic beryllium deposits: U.S. Geological Survey Scientific Investigations Report 2010-5070, vi, 43 p., https://doi.org/10.3133/sir20105070F.","productDescription":"vi, 43 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":265312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5070_F.gif"},{"id":399523,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_98030.htm"},{"id":265310,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5070/f/"},{"id":265311,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/f/SIR10-5070F.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50eaabf2e4b02dd6076fadb0","contributors":{"authors":[{"text":"Foley, Nora K. 0000-0003-0124-3509 nfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-0124-3509","contributorId":4010,"corporation":false,"usgs":true,"family":"Foley","given":"Nora","email":"nfoley@usgs.gov","middleInitial":"K.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":471436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":471434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindsey, David A. 0000-0002-9466-0899 dlindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-9466-0899","contributorId":773,"corporation":false,"usgs":true,"family":"Lindsey","given":"David","email":"dlindsey@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":471433,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Seal, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":397,"corporation":false,"usgs":true,"family":"Seal","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[],"preferred":false,"id":471432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaskula, Brian W. bjaskula@usgs.gov","contributorId":1935,"corporation":false,"usgs":true,"family":"Jaskula","given":"Brian","email":"bjaskula@usgs.gov","middleInitial":"W.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":471435,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Piatak, Nadine M.","contributorId":23621,"corporation":false,"usgs":true,"family":"Piatak","given":"Nadine M.","affiliations":[],"preferred":false,"id":471437,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70042337,"text":"ds736 - 2012 - Land Capability Potential Index (LCPI) and geodatabase for the Lower Missouri River Valley","interactions":[],"lastModifiedDate":"2017-05-24T12:54:21","indexId":"ds736","displayToPublicDate":"2013-01-04T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"736","title":"Land Capability Potential Index (LCPI) and geodatabase for the Lower Missouri River Valley","docAbstract":"The Land Capacity Potential Index (LCPI) is a coarse-scale index intended to delineate broad land-capability classes in the Lower Missouri River valley bottom from the Gavins Point Dam near Yankton, South Dakota to the mouth of the Missouri River near St. Louis, Missouri (river miles 811–0). The LCPI provides a systematic index of wetness potential and soil moisture-retention potential of the valley-bottom lands by combining the interactions among water-surface elevations, land-surface elevations, and the inherent moisture-retention capability of soils. A nine-class wetness index was generated by intersecting a digital elevation model for the valley bottom with sloping water-surface elevation planes derived from eight modeled discharges. The flow-recurrence index was then intersected with eight soil-drainage classes assigned to soils units in the digital Soil Survey Geographic (SSURGO) Database (Soil Survey Staff, 2010) to create a 72-class index of potential flow-recurrence and moisture-retention capability of Missouri River valley-bottom lands. The LCPI integrates the fundamental abiotic factors that determine long-term suitability of land for various uses, particularly those relating to vegetative communities and their associated values. Therefore, the LCPI provides a mechanism allowing planners, land managers, landowners, and other stakeholders to assess land-use capability based on the physical properties of the land, in order to guide future land-management decisions. This report documents data compilation for the LCPI in a revised and expanded, 72-class version for the Lower Missouri River valley bottom, and inclusion of additional soil attributes to allow users flexibility in exploring land capabilities.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds736","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service, Nebraska Game and Parks Commission, and the Nature Conservancy","usgsCitation":"Chojnacki, K.A., Struckhoff, M.A., and Jacobson, R.B., 2012, Land Capability Potential Index (LCPI) and geodatabase for the Lower Missouri River Valley: U.S. Geological Survey Data Series 736, Report: iv, 18 p.; Downloads Directory, https://doi.org/10.3133/ds736.","productDescription":"Report: iv, 18 p.; Downloads Directory","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-037780","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":265277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_736.gif"},{"id":265276,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/736/downloads/"},{"id":265274,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/736/"},{"id":265275,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/736/ds736.pdf"}],"scale":"2000000","datum":"North American Datum 1983","country":"United States","state":"Iowa, Kansas, Missouri, Nebraska, South Dakota","otherGeospatial":"Missouri River Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98.0,38.0 ], [ -98.0,43.5 ], [ -90.0,43.5 ], [ -90.0,38.0 ], [ -98.0,38.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e7f9ebe4b033ce2d2433e9","contributors":{"authors":[{"text":"Chojnacki, Kimberly A. kchojnacki@usgs.gov","contributorId":1978,"corporation":false,"usgs":true,"family":"Chojnacki","given":"Kimberly","email":"kchojnacki@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":471329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Struckhoff, Matthew A. 0000-0002-4911-9956 mstruckhoff@usgs.gov","orcid":"https://orcid.org/0000-0002-4911-9956","contributorId":2095,"corporation":false,"usgs":true,"family":"Struckhoff","given":"Matthew","email":"mstruckhoff@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":471330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jacobson, Robert B. 0000-0002-8368-2064 rjacobson@usgs.gov","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":1289,"corporation":false,"usgs":true,"family":"Jacobson","given":"Robert","email":"rjacobson@usgs.gov","middleInitial":"B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":471328,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70042372,"text":"ds709G - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Zarkashan mineral district in Afghanistan: Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:10:42","indexId":"ds709G","displayToPublicDate":"2013-01-04T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"G","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Zarkashan mineral district in Afghanistan: Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Zarkashan mineral district, which has copper and gold deposits. ALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2006,2007, 2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. The selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 315-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Zarkashan) and the WGS84 datum. The final image mosaics were subdivided into two overlapping tiles or quadrants because of the large size of the target area. The two image tiles (or quadrants) for the Zarkashan area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Zarkashan study area, three subareas were designated for detailed field investigations (that is, the Mine Area, Bolo Gold Prospect, and Luman-Tamaki Gold Prospect subareas); these subareas were extracted from the area’s image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709G","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey. This report is Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., and Cagney, L.E., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Zarkashan mineral district in Afghanistan: Chapter G in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 Maps: 11 x 8.5 inches and 37.63 x 40.38 inches; 10 Images; 10 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709G.","productDescription":"Readme; 2 Maps: 11 x 8.5 inches and 37.63 x 40.38 inches; 10 Images; 10 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":265290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":265281,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/g/"},{"id":265283,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/g/index_maps/Zarkashan_Area-of-Interest_Index_Map.pdf"},{"id":265282,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/g/1_readme.txt"},{"id":265284,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/g/index_maps/Zarkashan_Image_Index_Map.pdf"},{"id":265285,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/g/index_maps/index_maps.html"},{"id":265286,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/g/image_files/image_files.html"},{"id":265287,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/g/metadata/metadata.html"},{"id":265288,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/ds/709/g/shapefiles/shapefiles.html"},{"id":265289,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"}],"country":"Afghanistan","otherGeospatial":"Zarkashan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 67.0,32.5 ], [ 67.0,33.5 ], [ 68.0,33.5 ], [ 68.0,32.5 ], [ 67.0,32.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e7f9ede4b033ce2d2433ed","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":471401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":471402,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}