During the past 2 decades, the grizzly bear (Ursus arctos) population in the Greater Yellowstone Ecosystem (GYE) has increased in numbers and expanded its range. Early efforts to model grizzly bear mortality were principally focused within the United States Fish and Wildlife Service Grizzly Bear Recovery Zone, which currently represents only about 61% of known bear distribution in the GYE. A more recent analysis that explored one spatial covariate that encompassed the entire GYE suggested that grizzly bear survival was highest in Yellowstone National Park, followed by areas in the grizzly bear Recovery Zone outside the park, and lowest outside the Recovery Zone. Although management differences within these areas partially explained differences in grizzly bear survival, these simple spatial covariates did not capture site-specific reasons why bears die at higher rates outside the Recovery Zone. Here, we model annual survival of grizzly bears in the GYE to 1) identify landscape features (i.e., foods, land management policies, or human disturbances factors) that best describe spatial heterogeneity among bear mortalities, 2) spatially depict the differences in grizzly bear survival across the GYE, and 3) demonstrate how our spatially explicit model of survival can be linked with demographic parameters to identify source and sink habitats. We used recent data from radiomarked bears to estimate survival (1983–2003) using the known-fate data type in Program MARK. Our top models suggested that survival of independent (age ≥2 yr) grizzly bears was best explained by the level of human development of the landscape within the home ranges of bears. Survival improved as secure habitat and elevation increased but declined as road density, number of homes, and site developments increased. Bears living in areas open to fall ungulate hunting suffered higher rates of mortality than bears living in areas closed to hunting. Our top model strongly supported previous research that identified roads and developed sites as hazards to grizzly bear survival. We also demonstrated that rural homes and ungulate hunting negatively affected survival, both new findings. We illustrate how our survival model, when linked with estimates of reproduction and survival of dependent young, can be used to identify demographically the source and sink habitats in the GYE. Finally, we discuss how this demographic model constitutes one component of a habitat-based framework for grizzly bear conservation. Such a framework can spatially depict the areas of risk in otherwise good habitat, providing a focus for resource management in the GYE.
","language":"English","publisher":"The Wildlife Society","doi":"10.2193/2009-206","usgsCitation":"Schwartz, C.C., Haroldson, M.A., and White, G., 2010, Hazards affecting grizzly bear survival in the Greater Yellowstone Ecosystem: Journal of Wildlife Management, v. 74, no. 4, p. 654-667, https://doi.org/10.2193/2009-206.","productDescription":"14 p.","startPage":"654","endPage":"667","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":398123,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.03857421875,\n 43.54854811091286\n ],\n [\n -109.248046875,\n 43.54854811091286\n ],\n [\n -109.248046875,\n 45.36758436884978\n ],\n [\n -112.03857421875,\n 45.36758436884978\n ],\n [\n -112.03857421875,\n 43.54854811091286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"74","issue":"4","noUsgsAuthors":false,"publicationDate":"2010-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Schwartz, Charles C.","contributorId":55950,"corporation":false,"usgs":true,"family":"Schwartz","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":839660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":839661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Gary C.","contributorId":287795,"corporation":false,"usgs":false,"family":"White","given":"Gary C.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":839662,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70198310,"text":"70198310 - 2010 - Permeability of the continental crust: Dynamic variations inferred from seismicity and metamorphism","interactions":[],"lastModifiedDate":"2021-04-07T13:34:34.159404","indexId":"70198310","displayToPublicDate":"2010-05-01T08:45:04","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1765,"text":"Geofluids","active":true,"publicationSubtype":{"id":10}},"title":"Permeability of the continental crust: Dynamic variations inferred from seismicity and metamorphism","docAbstract":"The variation of permeability with depth can be probed indirectly by various means, including hydrologic models that use geothermal data as constraints and the progress of metamorphic reactions driven by fluid flow. Geothermal and metamorphic data combine to indicate that mean permeability (k) of tectonically active continental crust decreases with depth (z) according to log k ≈ −14–3.2 log z, where k is in m2 and z in km. Other independently derived, crustal‐scale k–z relations are generally similar to this power‐law curve. Yet there is also substantial evidence for local‐to‐regional‐scale, transient, permeability‐generation events that entail permeabilities much higher than these mean k–z relations would suggest. Compilation of such data yields a fit to these elevated, transient values of log k ≈ −11.5–3.2 log z, suggesting a functional form similar to that of tectonically active crust, but shifted to higher permeability at a given depth. In addition, it seems possible that, in the absence of active prograde metamorphism, permeability in the deeper crust will decay toward values below the mean k–z curves. Several lines of evidence suggest geologically rapid (years to 103 years) decay of high‐permeability transients toward background values. Crustal‐scale k–zcurves may reflect a dynamic competition between permeability creation by processes such as fluid sourcing and rock failure, and permeability destruction by processes such as compaction, hydrothermal alteration, and retrograde metamorphism.
","language":"English","publisher":"Wiley","doi":"10.1111/j.1468-8123.2010.00278.x","usgsCitation":"Ingebritsen, S.E., and Manning, C.E., 2010, Permeability of the continental crust: Dynamic variations inferred from seismicity and metamorphism: Geofluids, v. 10, no. 1-2, p. 193-205, https://doi.org/10.1111/j.1468-8123.2010.00278.x.","productDescription":"13 p.","startPage":"193","endPage":"205","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":356041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"1-2","noUsgsAuthors":false,"publicationDate":"2010-05-07","publicationStatus":"PW","scienceBaseUri":"5b98b794e4b0702d0e844eaf","contributors":{"authors":[{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":740985,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, C. E.","contributorId":16987,"corporation":false,"usgs":true,"family":"Manning","given":"C.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":740986,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230185,"text":"70230185 - 2010 - Secular variation in economic geology","interactions":[],"lastModifiedDate":"2022-04-04T13:44:31.632535","indexId":"70230185","displayToPublicDate":"2010-05-01T08:40:54","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Secular variation in economic geology","docAbstract":"The temporal pattern of ore deposits on a constantly evolving Earth reflects the complex interplay between the evolving global tectonic regime, episodic mantle plume events, overall changes in global heat flow, atmospheric and oceanic redox states, and even singular impact and glaciation events. Within this framework, a particular ore deposit type will tend to have a time-bound nature. In other words, there are times in Earth history when particular deposit types are absent, times when these deposits are present but scarce, times when they are abundant, and still other times for which we lack sufficient data. Understanding of such secular variation provides a critical first-order tool for exploration targeting, because rocks that have formed or were deformed during a certain time slice may be very permissive for a given deposit type, whereas identification of rocks of less favorable ages would help eliminate large areas during exploration programs. Secular analysis, therefore, is potentially a powerful tool for mineral resource assessment in poorly known terranes, providing a quick filter for favorability of a given deposit type using age of host rocks.
Factors bearing on the known age distribution of a particular type of deposit include the following: (1) uneven preservation, (2) data gaps, (3) contingencies of plate motions, and (4) long-term secular changes in the Earth System. The present special issue of Economic Geology is focused on the latter factor, although all of these are interrelated. The selective preservation of certain mineral deposit types and the greater susceptibility for shallowly formed ores in tectonically active environments to be lost to erosion define a pattern that is superimposed on the secular formational trends (e.g., Groves et al., 2005a, b; Kerrich et al., 2005). With improved geochronological methods and the availability of information on important mineral deposits from most parts of the world, data gaps for defining broad temporal distributions of ore types are becoming smaller. It has been increasingly recognized that ore deposit formation is also correlated with plate tectonic setting. Nevertheless, a complex Earth history of supercontinent assembly and breakup has led to the fragmentation of many Paleozoic and Precambrian mineral provinces. The use of plate reconstructions in economic geology, although extremely controversial and conjectural before the late Paleozoic, is critical for defining these provinces prior to the added complications resulting from post-ore plate motions.
It is now well established that the temporal patterns of many types of mineral deposits (Fig. 11) reflect the formation or break-up of supercontinents and the preservation potential of deposits formed during these periods (Barley and Groves, 1992; Titley, 1993; Kerrich et al., 2005; Goldfarb et al., 2009). Approximate time periods for such formation and break-up, respectively, include 2800–2500 and 2450–2100 Ma for Kenorland, 2100–1800 and 1600–1300 Ma for Nuna/Columbia, 1300–1100 and 850–600 Ma for Rodinia, and 600–300 Ma and 200–60 Ma for Gondwanaland-Pangea. A new supercontinent, Amasia, has begun to form during the past 250 m.y., thus overlapping Pangea break-up. Many of the formation-preservation patterns are themselves controlled by progressive cooling of Earth, the change from a mantle-plume buoyancy style to subduction-dominated tectonics, a decreasing buoyancy of the subcontinental lithospheric mantle, and depth of ore formation. In general, orogenic Au, volcanogenic massive sulfide (VMS), epithermal Au-Ag, and porphyry Cu±Au and Mo porphyry deposits form in active margins during periods of supercontinent assembly. Numerous other ore deposit types show an association with supercontinent formation, but develop inland of the active margin. These include many of the MVT Pb-Zn deposits and unconformity-type U deposits. The Tertiary Carlin-type deposits within the deformed shelf sequences along the North American craton margin also appear to have formed during the ongoing growth of Amasia. Those ores associated with periods of supercontinent breakup or attempted breakup are more difficult to define. They probably include diamond, Bushveld-type Ni-Cu-PGE, IOCG, and clastic-dominated Pb-Zn (or SEDEX) deposits in intracontinental areas of failed rifting, and other clastic-dominated Pb-Zn deposits in areas of actual breakup. In all cases, however, these temporal/spatial distributions are ultimately controlled by the secular character of Earth history.
The United States Environmental Protection Agency has identified estrogens from animal feeding operations as a major environmental concern, but few data are available to quantify the excretion of estrogenic compounds by dairy cattle. The objectives of this study were to quantify variation in estrogenic activity in feces and urine due to increased dietary inclusion of phytoestrogens. Ten Holstein heifers were assigned to 2 groups balanced for age and days pregnant; groups were randomly assigned to treatment sequence in a 2-period crossover design. Dietary treatments consisted of grass hay or red clover hay, and necessary supplements. Total collection allowed for sampling of feed refusals, feces, and urine during the last 4 d of each period. Feces and urine samples were pooled by heifer and period, and base extracts were analyzed for estrogenic activity (estrogen equivalents) using the yeast estrogen screen bioassay. Feces and urine samples collected from 5 heifers were extracted and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify excretion of 7 phytoestrogenic compounds. Excretion of 17-β estradiolequivalents in urine was higher and tended to be higher in feces for heifers fed red clover hay (84.4 and 120.2 mg/d for feces and urine, respectively) compared with those fed grasshay (57.4 and 35.6 mg/d). Analysis by LC-MS/MS indicated greater fecal excretion of equol, genistein, daidzein, coumestrol, and formononetin by heifers fed red clover hay (1634, 29.9, 96.3, 27.8, and 163 mg/d, respectively) than heifers fed grass hay (340, 3.0, 46.2, 8.8, and 18.3 mg/d, respectively). Diet had no effect on fecal biochanin A or 2-carbethoxy-5, 7-dihydroxy-4’-methoxyisoflavone. Four phytoestrogens were detected in urine (2-carbethoxy-5, 7-dihydroxy-4’-methoxyisoflavone, daidzein, equol, and formononetin) and their excretion was not affected by diet. Identifying sources of variation in estrogenic activity of manure will aid in the development of practices to reduce environmental estrogen accumulation.
","language":"English","publisher":"Elsevier","doi":"10.3168/jds.2009-2657","usgsCitation":"Tucker, H., Knowlton, K., Meyer, M.T., Khunjar, W., and Love, N., 2010, Effect of diet on fecal and urinary estrogenic activity: Journal of Dairy Science, v. 93, no. 5, p. 2088-2094, https://doi.org/10.3168/jds.2009-2657.","productDescription":"7 p.","startPage":"2088","endPage":"2094","costCenters":[{"id":588,"text":"Toxic Hydrology Program","active":false,"usgs":true}],"links":[{"id":475726,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3168/jds.2009-2657","text":"Publisher Index Page"},{"id":358221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10c716e4b034bf6a7f50d5","contributors":{"authors":[{"text":"Tucker, H.A.","contributorId":208541,"corporation":false,"usgs":false,"family":"Tucker","given":"H.A.","email":"","affiliations":[],"preferred":false,"id":747606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knowlton, K.F.","contributorId":208543,"corporation":false,"usgs":false,"family":"Knowlton","given":"K.F.","email":"","affiliations":[],"preferred":false,"id":747607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Michael T. 0000-0001-6006-7985 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6006-7985","contributorId":866,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"T.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":747608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Khunjar, W.O","contributorId":208539,"corporation":false,"usgs":false,"family":"Khunjar","given":"W.O","email":"","affiliations":[],"preferred":false,"id":747609,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Love, N.G.","contributorId":93617,"corporation":false,"usgs":true,"family":"Love","given":"N.G.","email":"","affiliations":[],"preferred":false,"id":747610,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194389,"text":"70194389 - 2010 - Revisions of rump fat and body scoring indices for deer, elk, and moose","interactions":[],"lastModifiedDate":"2017-11-27T14:48:12","indexId":"70194389","displayToPublicDate":"2010-05-01T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Revisions of rump fat and body scoring indices for deer, elk, and moose","docAbstract":"Because they do not require sacrificing animals, body condition scores (BCS), thickness of rump fat (MAXFAT), and other similar predictors of body fat have advanced estimating nutritional condition of ungulates and their use has proliferated in North America in the last decade. However, initial testing of these predictors was too limited to assess their reliability among diverse habitats, ecotypes, subspecies, and populations across the continent. With data collected from mule deer (Odocoileus hemionus), elk (Cervus elaphus), and moose (Alces alces) during initial model development and data collected subsequently from free-ranging mule deer and elk herds across much of the western United States, we evaluated reliability across a broader range of conditions than were initially available. First, to more rigorously test reliability of the MAXFAT index, we evaluated its robustness across the 3 species, using an allometric scaling function to adjust for differences in animal size. We then evaluated MAXFAT, rump body condition score (rBCS), rLIVINDEX (an arithmetic combination of MAXFAT and rBCS), and our new allometrically scaled rump-fat thickness index using data from 815 free-ranging female Roosevelt and Rocky Mountain elk (C. e. roosevelti and C. e. nelsoni) from 19 populations encompassing 4 geographic regions and 250 free-ranging female mule deer from 7 populations and 2 regions. We tested for effects of subspecies, geographic region, and captive versus free-ranging existence. Rump-fat thickness, when scaled allometrically with body mass, was related to ingesta-free body fat over a 38–522-kg range of body mass (r2 = 0.87; P < 0.001), indicating the technique is remarkably robust among at least the 3 cervid species of our analysis. However, we found an underscoring bias with the rBCS for elk that had >12% body fat. This bias translated into a difference between subspecies, because Rocky Mountain elk tended to be fatter than Roosevelt elk in our sample. Effects of observer error with the rBCS also existed for mule deer with moderate to high levels of body fat, and deer body size significantly affected accuracy of the MAXFAT predictor. Our analyses confirm robustness of the rump-fat index for these 3 species but highlight the potential for bias due to differences in body size and to observer error with BCS scoring. We present alternative LIVINDEX equations where potential bias from rBCS and bias due to body size are eliminated or reduced. These modifications improve the accuracy of estimating body fat for projects intended to monitor nutritional status of herds or to evaluate nutrition's influence on population demographics.
","language":"English","publisher":"Wiley","doi":"10.2193/2009-031","usgsCitation":"Cook, R.C., Cook, J.G., Stephenson, T.R., Myers, W.L., Mccorquodale, S.M., Vales, D.J., Irwin, L.L., Hall, P.B., Spencer, R.D., Murphie, S.L., Schoenecker, K.A., and Miller, P.J., 2010, Revisions of rump fat and body scoring indices for deer, elk, and moose: Journal of Wildlife Diseases, v. 74, no. 4, p. 880-896, https://doi.org/10.2193/2009-031.","productDescription":"17 p.","startPage":"880","endPage":"896","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":349383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.20019531249999,\n 36.527294814546245\n ],\n [\n -103.0078125,\n 36.527294814546245\n ],\n [\n -103.0078125,\n 49.06666839558117\n ],\n [\n -125.20019531249999,\n 49.06666839558117\n ],\n [\n -125.20019531249999,\n 36.527294814546245\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"74","issue":"4","noUsgsAuthors":false,"publicationDate":"2010-12-13","publicationStatus":"PW","scienceBaseUri":"5a610abbe4b06e28e9c256cd","contributors":{"authors":[{"text":"Cook, Rachel C.","contributorId":19064,"corporation":false,"usgs":true,"family":"Cook","given":"Rachel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":723653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, John G.","contributorId":12903,"corporation":false,"usgs":true,"family":"Cook","given":"John","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":723654,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stephenson, Thomas R.","contributorId":64114,"corporation":false,"usgs":true,"family":"Stephenson","given":"Thomas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":723655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Myers, Woodrow L.","contributorId":200876,"corporation":false,"usgs":false,"family":"Myers","given":"Woodrow","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723656,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mccorquodale, Scott M.","contributorId":62921,"corporation":false,"usgs":true,"family":"Mccorquodale","given":"Scott","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":723657,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vales, David J.","contributorId":74662,"corporation":false,"usgs":true,"family":"Vales","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":723658,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Irwin, Larry L.","contributorId":105649,"corporation":false,"usgs":true,"family":"Irwin","given":"Larry","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723659,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hall, P. Briggs","contributorId":200877,"corporation":false,"usgs":false,"family":"Hall","given":"P.","email":"","middleInitial":"Briggs","affiliations":[],"preferred":false,"id":723660,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spencer, Rocky D.","contributorId":200878,"corporation":false,"usgs":false,"family":"Spencer","given":"Rocky","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":723661,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Murphie, Shannon L.","contributorId":200879,"corporation":false,"usgs":false,"family":"Murphie","given":"Shannon","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723662,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X schoeneckerk@usgs.gov","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":2001,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn","email":"schoeneckerk@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":723663,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Miller, Patrick J.","contributorId":200880,"corporation":false,"usgs":false,"family":"Miller","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":723664,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70157294,"text":"70157294 - 2010 - Supply of and demand for selected energy related mineral commodities","interactions":[],"lastModifiedDate":"2021-10-29T14:38:10.221787","indexId":"70157294","displayToPublicDate":"2010-04-29T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Supply of and demand for selected energy related mineral commodities","docAbstract":"In this report, subjects discussed include components of mineral supply, production, and consumption data, and information on selected mineral commodities in which the Energy Critical Elements Study Group has an interest, and U.S. Geological Survey (USGS) recycling studies, with some results of these studies.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Critical elements for new energy technologies: an MIT Energy Initiative Workshop report","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"MIT Energy Workshop on Critical Elements for New Energy Technologies","conferenceDate":"April 29, 2010","conferenceLocation":"Cambridge, Massachusetts","language":"English","publisher":"Massachusetts Institute of Technology","usgsCitation":"Sibley, S.F., 2010, Supply of and demand for selected energy related mineral commodities, in Critical elements for new energy technologies: an MIT Energy Initiative Workshop report, Cambridge, Massachusetts, April 29, 2010, p. 111-121.","productDescription":"11 p.","startPage":"111","endPage":"121","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-021295","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":308256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55fbe449e4b05d6c4e502905","contributors":{"authors":[{"text":"Sibley, Scott F.","contributorId":105426,"corporation":false,"usgs":true,"family":"Sibley","given":"Scott","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":572613,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":98351,"text":"sir20095250 - 2010 - Techniques for Estimating the Magnitude and Frequency of Peak Flows on Small Streams in Minnesota Based on Data through Water Year 2005","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"sir20095250","displayToPublicDate":"2010-04-28T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-5250","title":"Techniques for Estimating the Magnitude and Frequency of Peak Flows on Small Streams in Minnesota Based on Data through Water Year 2005","docAbstract":"Knowledge of the peak flow of floods of a given recurrence interval is essential for regulation and planning of water resources and for design of bridges, culverts, and dams along Minnesota's rivers and streams. Statistical techniques are needed to estimate peak flow at ungaged sites because long-term streamflow records are available at relatively few places. Because of the need to have up-to-date peak-flow frequency information in order to estimate peak flows at ungaged sites, the U.S. Geological Survey (USGS) conducted a peak-flow frequency study in cooperation with the Minnesota Department of Transportation and the Minnesota Pollution Control Agency.\r\n\r\nEstimates of peak-flow magnitudes for 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence intervals are presented for 330 streamflow-gaging stations in Minnesota and adjacent areas in Iowa and South Dakota based on data through water year 2005. The peak-flow frequency information was subsequently used in regression analyses to develop equations relating peak flows for selected recurrence intervals to various basin and climatic characteristics. Two statistically derived techniques-regional regression equation and region of influence regression-can be used to estimate peak flow on ungaged streams smaller than 3,000 square miles in Minnesota. Regional regression equations were developed for selected recurrence intervals in each of six regions in Minnesota: A (northwestern), B (north central and east central), C (northeastern), D (west central and south central), E (southwestern), and F (southeastern). The regression equations can be used to estimate peak flows at ungaged sites. The region of influence regression technique dynamically selects streamflow-gaging stations with characteristics similar to a site of interest. Thus, the region of influence regression technique allows use of a potentially unique set of gaging stations for estimating peak flow at each site of interest. Two methods of selecting streamflow-gaging stations, similarity and proximity, can be used for the region of influence regression technique.\r\n\r\nThe regional regression equation technique is the preferred technique as an estimate of peak flow in all six regions for ungaged sites. The region of influence regression technique is not appropriate for regions C, E, and F because the interrelations of some characteristics of those regions do not agree with the interrelations throughout the rest of the State. Both the similarity and proximity methods for the region of influence technique can be used in the other regions (A, B, and D) to provide additional estimates of peak flow. The peak-flow-frequency estimates and basin characteristics for selected streamflow-gaging stations and regional peak-flow regression equations are included in this report.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095250","collaboration":"Prepared in cooperation with the Minnesota Department of Transportation and the Minnesota Pollution Control Agency","usgsCitation":"Lorenz, D.L., Sanocki, C.A., and Kocian, M.J., 2010, Techniques for Estimating the Magnitude and Frequency of Peak Flows on Small Streams in Minnesota Based on Data through Water Year 2005: U.S. Geological Survey Scientific Investigations Report 2009-5250, iv, 54 p. , https://doi.org/10.3133/sir20095250.","productDescription":"iv, 54 p. ","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":125896,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5250.jpg"},{"id":13600,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5250/","linkFileType":{"id":5,"text":"html"}}],"projection":"Universal Transverse Mercator","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98,43 ], [ -98,50 ], [ -89,50 ], [ -89,43 ], [ -98,43 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4adbe4b07f02db6860bb","contributors":{"authors":[{"text":"Lorenz, David L. 0000-0003-3392-4034 lorenz@usgs.gov","orcid":"https://orcid.org/0000-0003-3392-4034","contributorId":1384,"corporation":false,"usgs":true,"family":"Lorenz","given":"David","email":"lorenz@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":305057,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanocki, Christopher A. 0000-0001-6714-5421 sanocki@usgs.gov","orcid":"https://orcid.org/0000-0001-6714-5421","contributorId":3142,"corporation":false,"usgs":true,"family":"Sanocki","given":"Christopher","email":"sanocki@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":305058,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocian, Matthew J.","contributorId":19654,"corporation":false,"usgs":false,"family":"Kocian","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":305059,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":98349,"text":"sir20105003 - 2010 - Flood of April and May 2008 in Northern Maine","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"sir20105003","displayToPublicDate":"2010-04-28T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5003","title":"Flood of April and May 2008 in Northern Maine","docAbstract":"Severe flooding occurred in Aroostook and Penobscot Counties in northern Maine between April 28 and May 1, 2008, and was most extreme in the town of Fort Kent. Peak streamflows in northern Aroostook County were the result of a persistent heavy snowpack that caused high streamflows when it quickly melted during the third week of April 2008. Snowmelt was followed by from two to four inches of rainfall over a 2-day period in northern Maine. Peak water-surface elevations resulting from the flood were obtained from 13 continuous-record streamgages and 63 surveyed high-water marks in Aroostook and Penobscot Counties. Peak streamflows were obtained from 20 sites on 15 streams through stage/discharge rating curves or hydraulic flow models. Peak water-surface elevations and streamflows were the highest ever recorded at seven continuous-record streamgages, which had between 25 and 84 years of record in northern Aroostook County. The annual exceedance probability (the percent chance of exceeding the streamflow recorded during the April/May 2008 flood during any given year) at six streamgages in northern Maine was equal to or less than 1 percent. \r\n\r\nData from flood-insurance studies published by the Federal Emergency Management Agency were available for five of the locations analyzed for the April/May 2008 flood and were compared to streamflows and observed peak water-surface elevations from the 2008 flood. Water-surface elevations that would be expected given the observed flow as applied to the effective flood insurance studies ranged from between 1 and 4 feet from the water-surface elevations observed during the 2008 flood. Differences were likely the result of up to 30 years of additional data for the calculation of recurrence intervals and the fact that hydraulic models used for the models had not previously been calibrated to a flood of this magnitude. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20105003","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Lombard, P., 2010, Flood of April and May 2008 in Northern Maine: U.S. Geological Survey Scientific Investigations Report 2010-5003, iv, 17 p. , https://doi.org/10.3133/sir20105003.","productDescription":"iv, 17 p. ","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2008-04-28","temporalEnd":"2008-05-01","costCenters":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"links":[{"id":125900,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5003.jpg"},{"id":13598,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5003/","linkFileType":{"id":5,"text":"html"}}],"projection":"Universal Transverse Mercator","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -70.5,45 ], [ -70.5,48 ], [ -67,48 ], [ -67,45 ], [ -70.5,45 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4814e4b07f02db4dac15","contributors":{"authors":[{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":23899,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela J.","affiliations":[],"preferred":false,"id":305055,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70216852,"text":"70216852 - 2010 - Trends in groundwater levels in wells in the active management areas of Arizona, USA","interactions":[],"lastModifiedDate":"2020-12-10T13:27:59.552004","indexId":"70216852","displayToPublicDate":"2010-04-27T15:42:25","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Trends in groundwater levels in wells in the active management areas of Arizona, USA","docAbstract":"In 1980, the Arizona legislature passed the Groundwater Management Act (GMA), creating the active management areas (AMAs) to protect shared groundwater resources and to control severe overdrafts occurring in many parts of the state. With the 30-year anniversary of the GMA approaching, this article addresses the question: Have there been notable changes in the trends in observed groundwater levels in the AMAs from before enactment of the GMA until present? New tools developed for the US Geological Survey’s National Water Availability and Use Pilot Program are used to analyze and present trends in observed groundwater level data. Trends in groundwater levels in the AMAs were investigated for 10-year time periods from 1970 through 1999 and an 9-year period from 2000–2008. Results indicate that the number of wells with rising trends in water levels increased and the number of wells with falling trends in water levels decreased during the early decades after passage of the GMA in the most-populated Phoenix and heavily agricultural Pinal AMAs. However, these trends in water levels are reversed during the 1995–2004 time period. The value of trend analyses would be improved by consistent groundwater-level monitoring in both developed and undeveloped areas of the region.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-010-0603-3","usgsCitation":"Tillman, F.D., and Leake, S.A., 2010, Trends in groundwater levels in wells in the active management areas of Arizona, USA: Hydrogeology Journal, v. 18, p. 1515-1524, https://doi.org/10.1007/s10040-010-0603-3.","productDescription":"10 p.","startPage":"1515","endPage":"1524","numberOfPages":"10","ipdsId":"IP-014532","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":381179,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":\"4\",\"properties\":{\"name\":\"Arizona\",\"nation\":\"USA 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Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leake, Stanley A. 0000-0003-3568-2542 saleake@usgs.gov","orcid":"https://orcid.org/0000-0003-3568-2542","contributorId":1846,"corporation":false,"usgs":true,"family":"Leake","given":"Stanley","email":"saleake@usgs.gov","middleInitial":"A.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806642,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202252,"text":"70202252 - 2010 - Seasonal H2O and CO2 ice cycles at the Mars Phoenix landing site: 1. Prelanding CRISM and HiRISE observations","interactions":[],"lastModifiedDate":"2019-02-18T12:54:17","indexId":"70202252","displayToPublicDate":"2010-04-27T12:52:31","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2317,"text":"Journal of Geophysical Research E: Planets","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal H2O and CO2 ice cycles at the Mars Phoenix landing site: 1. Prelanding CRISM and HiRISE observations","docAbstract":"The condensation, evolution, and sublimation of seasonal water and carbon dioxide ices were characterized at the Mars Phoenix landing site from Martian northern midsummer to midspring (Ls ∼ 142° – Ls ∼ 60°) for the year prior to the Phoenix landing on 25 May 2008. Ice relative abundances and grain sizes were estimated using data from the Compact Reconnaissance Imaging Spectrometer for Mars and High Resolution Imaging Science Experiment aboard Mars Reconnaissance Orbiter and a nonlinear mixing model. Water ice first appeared at the Phoenix landing site during the afternoon in late summer (Ls ∼ 167°) as an optically thin layer on top of soil. CO2 ice appeared after the fall equinox. By late winter (Ls∼ 344°), the site was covered by relatively pure CO2 ice (∼30 cm thick), with a small amount of ∼100 μm diameter water ice and soil. As spring progressed, CO2 ice grain sizes gradually decreased, a change interpreted to result from granulation during sublimation losses. The combined effect of CO2 sublimation and decreasing H2O ice grain sizes allowed H2O ice to dominate spectra during the spring and significantly brightened the surface. CO2 ice disappeared by early spring (Ls ∼ 34°) and H2O ice by midspring (Ls ∼ 59°). Spring defrosting was not uniform and occurred more rapidly over the centers of polygons and geomorphic units with relatively higher thermal inertia values.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1029/2009JE003340","usgsCitation":"Cull, S., Arvidson, R.E., Mellon, M.T., Wiseman, S.M., Clark, R.N., Titus, T.N., Morris, R., and McGuire, P.E., 2010, Seasonal H2O and CO2 ice cycles at the Mars Phoenix landing site: 1. Prelanding CRISM and HiRISE observations: Journal of Geophysical Research E: Planets, v. 115, no. E4, 14 p., https://doi.org/10.1029/2009JE003340.","productDescription":"14 p.","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":361319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"115","issue":"E4","noUsgsAuthors":false,"publicationDate":"2010-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Cull, Selby","contributorId":19100,"corporation":false,"usgs":true,"family":"Cull","given":"Selby","affiliations":[],"preferred":false,"id":757506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arvidson, Raymond E.","contributorId":106626,"corporation":false,"usgs":false,"family":"Arvidson","given":"Raymond","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":757507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mellon, Michael T.","contributorId":8603,"corporation":false,"usgs":false,"family":"Mellon","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":7037,"text":"Southwest Research Institute, Boulder, Colorado","active":true,"usgs":false}],"preferred":false,"id":757508,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiseman, Sandra M.","contributorId":212719,"corporation":false,"usgs":false,"family":"Wiseman","given":"Sandra","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":757509,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Roger N. 0000-0002-7021-1220 rclark@usgs.gov","orcid":"https://orcid.org/0000-0002-7021-1220","contributorId":515,"corporation":false,"usgs":true,"family":"Clark","given":"Roger","email":"rclark@usgs.gov","middleInitial":"N.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":757510,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":757511,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morris, Richard V.","contributorId":167513,"corporation":false,"usgs":false,"family":"Morris","given":"Richard V.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":757512,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McGuire, Patrick E.","contributorId":71008,"corporation":false,"usgs":false,"family":"McGuire","given":"Patrick","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":757513,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70230290,"text":"70230290 - 2010 - Mg isotope constraints on soil pore-fluid chemistry: Evidence from Santa Cruz, California","interactions":[],"lastModifiedDate":"2022-04-06T16:17:22.640131","indexId":"70230290","displayToPublicDate":"2010-04-27T10:02:58","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Mg isotope constraints on soil pore-fluid chemistry: Evidence from Santa Cruz, California","docAbstract":"Mg isotope ratios (26Mg/24Mg) are reported in soil pore-fluids, rain and seawater, grass and smectite from a 90 kyr old soil, developed on an uplifted marine terrace from Santa Cruz, California. Rain water has an invariant 26Mg/24Mg ratio (expressed as δ26Mg>δ26Mg) at −0.79 ± 0.05‰, identical to seawater δ26Mg>δ26Mg. Detrital smectite (from the base of the soil profile, and therefore unweathered) has a δ26Mg\">δ26Mg value of 0.11‰, potentially enriched in 26Mg by up to 0.3‰ compared to the bulk silicate Earth Mg isotope composition (although within the range of all terrestrial silicates). The soil pore-waters show a continuous profile with depth for δ26Mg>δ26Mg, ranging from −0.99‰ near the surface to −0.43‰ at the base of the profile. Shallow pore-waters (<1 m) have δ26Mg>δ26Mg values that are similar to, or slightly lower than the rain waters. This implies that the degree of biological cycling of Mg in the pore-waters is relatively small and is quantified as <32%, calculated using the average Mg isotope enrichment factor between grass and rain (δ26Mggrass-δ26Mgrain>δ26Mggrass-δ26Mgrain) of 0.21‰. The deep pore-waters (1–15 m deep) have δ26Mg>δ26Mg values that are intermediate between the smectite and rain, ranging from −0.76‰ to −0.43‰, and show a similar trend with depth compared to Sr isotope ratios. The similarity between Sr and Mg isotope ratios confirms that the Mg in the pore-waters can be explained by a mixture between rain and smectite derived Mg, despite the fact that Mg and Sr concentrations may be buffered by the exchangeable reservoir. However, whilst Sr isotope ratios in the pore-waters span almost the complete range between mineral and rain inputs, Mg isotopes compositions are much closer to the rain inputs. If Mg and Sr isotope ratios are controlled uniquely by a mixture, the data can be used to estimate the mineral weathering inputs to the pore-waters, by correcting for the rain inputs. This isotopic correction is compared to the commonly used chloride correction for precipitation inputs. A consistent interpretation is only possible if Mg isotope ratios are fractionated either by the precipitation of a secondary Mg bearing phase, not detected by conventional methods, or selective leaching of 24Mg from smectite. There is therefore dual control on the Mg isotopic composition of the pore-waters, mixing of two inputs with distinct isotopic compositions, modified by fractionation. The data provide (1) further evidence for Mg isotope fractionation at the surface of the Earth and (2) the first field evidence of Mg isotope fractionation during uptake by natural plants. The coherent behaviour of Mg isotope ratios in soil environments is encouraging for the development of Mg isotope ratios as a quantitative tracer of both weathering inputs of Mg to waters, and the physicochemical processes that cycle Mg, a major cation linked to the carbon cycle, during continental weathering.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2010.04.021","usgsCitation":"Tipper, E.T., Gaillardet, J., Louvat, P., Capmas, F., and White, A.F., 2010, Mg isotope constraints on soil pore-fluid chemistry: Evidence from Santa Cruz, California: Geochimica et Cosmochimica Acta, v. 74, no. 14, p. 3883-3896, https://doi.org/10.1016/j.gca.2010.04.021.","productDescription":"14 p.","startPage":"3883","endPage":"3896","costCenters":[],"links":[{"id":398221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Santa Cruz","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.10891723632812,\n 36.94495296068268\n ],\n [\n -121.93038940429688,\n 36.94495296068268\n ],\n [\n -121.93038940429688,\n 37.04092825594592\n ],\n [\n -122.10891723632812,\n 37.04092825594592\n ],\n [\n -122.10891723632812,\n 36.94495296068268\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"74","issue":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tipper, Edward T.","contributorId":289842,"corporation":false,"usgs":false,"family":"Tipper","given":"Edward","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":839881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaillardet, Jerome","contributorId":184199,"corporation":false,"usgs":false,"family":"Gaillardet","given":"Jerome","email":"","affiliations":[],"preferred":false,"id":839882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Louvat, Pascale","contributorId":289843,"corporation":false,"usgs":false,"family":"Louvat","given":"Pascale","email":"","affiliations":[],"preferred":false,"id":839883,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Capmas, Francoise","contributorId":289844,"corporation":false,"usgs":false,"family":"Capmas","given":"Francoise","email":"","affiliations":[],"preferred":false,"id":839884,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, Arthur F. afwhite@usgs.gov","contributorId":3718,"corporation":false,"usgs":true,"family":"White","given":"Arthur","email":"afwhite@usgs.gov","middleInitial":"F.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":839885,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":98345,"text":"ofr20101089 - 2010 - Long-Billed Curlew Breeding Success on Mid-Columbia River National Wildlife Refuges, South-Central Washington and North-Central Oregon, 2007-08","interactions":[],"lastModifiedDate":"2012-02-02T00:14:35","indexId":"ofr20101089","displayToPublicDate":"2010-04-27T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-1089","title":"Long-Billed Curlew Breeding Success on Mid-Columbia River National Wildlife Refuges, South-Central Washington and North-Central Oregon, 2007-08","docAbstract":"Long-billed curlew (Numenius americanus) reproductive success was evaluated on the Mid-Columbia River National Wildlife Refuges of south-central Washington and north-central Oregon during the 2007 and 2008 breeding seasons. Additionally, we assisted the U.S. Fish and Wildlife Service in collecting information on distribution, abundance, and brood habitat for this shorebird species of conservation concern. A total of 32 breeding pairs were located on the refuges in 2007 and 35 pairs were located in 2008. We monitored 17 nests in 2007 and 23 nests in 2008. Curlew pairs were most abundant on Hanford Reach National Monument in 2007 but more nests were located on Umatilla National Wildlife Refuge in both years, with Columbia National Wildlife Refuge supporting few pairs. Nest success was 23.6 percent in 2007 and 32.9 percent in 2008 after taking into account exposure time and combining data for all the refuges. We were unable to detect any relationship between nest success and habitat type or habitat variables measured. However, our study was the first to document use of agricultural fields on the refuge as curlew nest habitat. We collected 39 and 28 brood locations in 2007 and 2008, respectively, and many observations were likely resightings of the same brood. Broods used a similar variety of habitats as nesting curlew and no clear habitat use pattern was detected.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101089","usgsCitation":"Stocking, J., Elliott-Smith, E., Holcomb, N., and Haig, S.M., 2010, Long-Billed Curlew Breeding Success on Mid-Columbia River National Wildlife Refuges, South-Central Washington and North-Central Oregon, 2007-08: U.S. Geological Survey Open-File Report 2010-1089, iv, 28 p.; Appendices, https://doi.org/10.3133/ofr20101089.","productDescription":"iv, 28 p.; Appendices","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2007-01-01","temporalEnd":"2008-12-31","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":193696,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":13594,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1089/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a6de4b07f02db63ece1","contributors":{"authors":[{"text":"Stocking, Jessica","contributorId":104167,"corporation":false,"usgs":true,"family":"Stocking","given":"Jessica","affiliations":[],"preferred":false,"id":305048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elliott-Smith, Elise eelliott-smith@usgs.gov","contributorId":3645,"corporation":false,"usgs":true,"family":"Elliott-Smith","given":"Elise","email":"eelliott-smith@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":305046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holcomb, Neil","contributorId":10887,"corporation":false,"usgs":true,"family":"Holcomb","given":"Neil","email":"","affiliations":[],"preferred":false,"id":305047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":305045,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":98342,"text":"ofr20101072 - 2010 - Thermal Maturity Data Used by the U.S. Geological Survey for the U.S. Gulf Coast Region Oil and Gas Assessment","interactions":[],"lastModifiedDate":"2012-02-02T00:14:44","indexId":"ofr20101072","displayToPublicDate":"2010-04-24T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-1072","title":"Thermal Maturity Data Used by the U.S. Geological Survey for the U.S. Gulf Coast Region Oil and Gas Assessment","docAbstract":"The U.S. Geological Survey is currently assessing the oil and natural gas resources of the U.S. Gulf of Mexico region using a total petroleum system approach. An essential part of this geologically based method is evaluating the effectiveness of potential source rocks in the petroleum system. The purpose of this report is to make available to the public RockEval and vitrinite reflectance data from more than 1,900 samples of Mesozoic and Tertiary rock core and coal samples in the Gulf of Mexico area in a format that facilitates inclusion into a geographic information system. These data provide parameters by which the thermal maturity, type, and richness of potential sources of oil and gas in this region can be evaluated. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101072","usgsCitation":"Dennen, K., Warwick, P.D., and McDade, E.C., 2010, Thermal Maturity Data Used by the U.S. Geological Survey for the U.S. Gulf Coast Region Oil and Gas Assessment: U.S. Geological Survey Open-File Report 2010-1072, Report: iii, 7 p.; Appendix (xls), https://doi.org/10.3133/ofr20101072.","productDescription":"Report: iii, 7 p.; Appendix (xls)","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":118644,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1072.jpg"},{"id":13590,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1072/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b15e4b07f02db6a4c32","contributors":{"authors":[{"text":"Dennen, Kristin O.","contributorId":61437,"corporation":false,"usgs":true,"family":"Dennen","given":"Kristin O.","affiliations":[],"preferred":false,"id":305041,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":305039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDade, Elizabeth Chinn","contributorId":59899,"corporation":false,"usgs":true,"family":"McDade","given":"Elizabeth","email":"","middleInitial":"Chinn","affiliations":[],"preferred":false,"id":305040,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":98340,"text":"sir20105061 - 2010 - Quality of groundwater at and near an aquifer storage and recovery site, Bexar, Atascosa, and Wilson Counties, Texas, June 2004-August 2008","interactions":[],"lastModifiedDate":"2022-12-15T21:04:55.299248","indexId":"sir20105061","displayToPublicDate":"2010-04-24T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5061","title":"Quality of groundwater at and near an aquifer storage and recovery site, Bexar, Atascosa, and Wilson Counties, Texas, June 2004-August 2008","docAbstract":"The U.S. Geological Survey, in cooperation with the San Antonio Water System, did a study during 2004–08 to characterize the quality of native groundwater from the Edwards aquifer and pre- and post-injection water from the Carrizo aquifer at and near an aquifer storage and recovery (ASR) site in Bexar, Atascosa, and Wilson Counties, Texas. Groundwater samples were collected and analyzed for selected physical properties and constituents to characterize the quality of native groundwater from the Edwards aquifer and pre- and post-injection water from the Carrizo aquifer at and near the ASR site. Geochemical and isotope data indicated no substantial changes in major-ion, trace-element, and isotope chemistry occurred as the water from the Edwards aquifer was transferred through a 38-mile pipeline to the aquifer storage and recovery site. The samples collected from the four ASR recovery wells were similar in major-ion and stable isotope chemistry compared to the samples collected from the Edwards aquifer source wells and the ASR injection well. The similarity could indicate that as Edwards aquifer water was injected, it displaced native Carrizo aquifer water, or, alternatively, if mixing of Edwards and Carrizo aquifer waters was occurring, the major-ion and stable isotope signatures for the Carrizo aquifer water might have been obscured by the signatures of the injected Edwards aquifer water. Differences in the dissolved iron and dissolved manganese concentrations indicate that either minor amounts of mixing occurred between the waters from the two aquifers, or as Edwards aquifer water displaced Carrizo aquifer water it dissolved the iron and manganese directly from the Carrizo Sand. Concentrations of radium-226 in the samples collected at the ASR recovery wells were smaller than the concentrations in samples collected from the Edwards aquifer source wells and from the ASR injection well. The smaller radium-226 concentrations in the samples collected from the ASR recovery wells likely indicate some degree of mixing of the two waters occurred rather than continued decay of radium-226 in the injected water. Geochemical and isotope data measured in samples collected in May 2005 from two Carrizo aquifer monitoring wells and in July 2008 from the three ASR production-only wells in the northern section of the ASR site indicate that injected Edwards aquifer water had not migrated to these five sites. Geochemical and isotope data measured in samples collected from Carrizo aquifer wells in 2004, 2005, and 2008 were graphically analyzed to determine if changes in chemistry could be detected. Major-ion, trace element, and isotope chemistry varied spatially in the samples collected from the Carrizo aquifer. With the exception of a few samples, major-ion concentrations measured in samples collected in Carrizo aquifer wells in 2004, 2005, and 2008 were similar. A slightly larger sulfate concentration and a slightly smaller bicarbonate concentration were measured in samples collected in 2005 and 2008 from well NC1 compared to samples collected at well NC1 in 2004. Larger sodium concentrations and smaller calcium, magnesium, bicarbonate, and sulfate concentrations were measured in samples collected in 2008 from well WC1 than in samples collected at this well in 2004 and 2005. Larger calcium and magnesium concentrations and a smaller sodium concentration were measured in the samples collected in 2008 at well EC2 compared to samples collected at this well in 2004 and 2005. While in some cases the computed percent differences (compared to concentrations from June 2004) in dissolved iron and dissolved manganese concentrations in 11 wells sampled in the Carrizo aquifer in 2005 and 2008 were quite large, no trends that might have been caused by migration of injected Edwards aquifer water were observed. Because of the natural variation in geochemical data in the Carrizo aquifer and the small data set collected for this study, differences in major-ion and trace element data among the samples collected in 2004, 2005 and 2008 cannot be directly attributed to the ASR site operations. When the data were analyzed graphically, no appreciable differences in isotope concentrations were observed between samples collected in 2004 and 2008 from Carrizo aquifer wells, indicating that the Edwards aquifer source water might not have affected the isotope chemistry of the native Carrizo aquifer water near the sampled Carrizo wells by July 2008.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Virginia","doi":"10.3133/sir20105061","collaboration":"In cooperation with the San Antonio Water System","usgsCitation":"Otero, C.L., and Petri, B.L., 2010, Quality of groundwater at and near an aquifer storage and recovery site, Bexar, Atascosa, and Wilson Counties, Texas, June 2004-August 2008: U.S. Geological Survey Scientific Investigations Report 2010-5061, vii, 34 p., https://doi.org/10.3133/sir20105061.","productDescription":"vii, 34 p.","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2004-06-01","temporalEnd":"2008-08-31","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":410578,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_93056.htm","linkFileType":{"id":5,"text":"html"}},{"id":118640,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5061.jpg"},{"id":13588,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5061/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Texas","county":"Atascosa County, Bexar County, Wilson County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.75,\n 28.905\n ],\n [\n -98.75,\n 29.4742\n ],\n [\n -98,\n 29.4742\n ],\n [\n -98,\n 28.905\n ],\n [\n -98.75,\n 28.905\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a6de4b07f02db63f23d","contributors":{"authors":[{"text":"Otero, Cassi L.","contributorId":100469,"corporation":false,"usgs":true,"family":"Otero","given":"Cassi","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":305037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petri, Brian L.","contributorId":64712,"corporation":false,"usgs":true,"family":"Petri","given":"Brian","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":305036,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":98339,"text":"sir20095264 - 2010 - Field Surveys of Rare Plants on Santa Cruz Island, California, 2003-2006: Historical Records and Current Distributions","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"sir20095264","displayToPublicDate":"2010-04-24T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-5264","title":"Field Surveys of Rare Plants on Santa Cruz Island, California, 2003-2006: Historical Records and Current Distributions","docAbstract":"Santa Cruz Island is the largest of the northern Channel Islands located off the coast of California. It is owned and managed as a conservation reserve by The Nature Conservancy and the Channel Islands National Park. The island is home to nine plant taxa listed in 1997 as threatened or endangered under the federal Endangered Species Act, because of declines related to nearly 150 years of ranching on the island. Feral livestock were removed from the island as a major conservation step, which was part of a program completed in early 2007 with the eradication of pigs and turkeys. For the first time in more than a century, the rare plants of Santa Cruz Island have a chance to recover in the wild. This study provides survey information and living plant materials needed for recovery management of the listed taxa. We developed a database containing information about historical collections of the nine taxa and used it to plan a survey strategy. Our objectives were to relocate as many of the previously known populations as possible, with emphasis on documenting sites not visited in several decades, sites that were poorly documented in the historical record, and sites spanning the range of environmental conditions inhabited by the taxa. From 2003 through 2006, we searched for and found 39 populations of the taxa, indicating that nearly 80 percent of the populations known earlier in the 1900s still existed. Most populations are small and isolated, occupying native-dominated habitat patches in a highly fragmented and invaded landscape; they are still at risk of declining through population losses. Most are not expanding beyond the edges of their habitat patches. However, most taxa appeared to have good seed production and a range of size classes in populations, indicating a good capacity for plant recruitment and population growth in these restricted sites. For these taxa, seed collection and outplanting might be a good strategy to increase numbers of populations for species recovery. Several taxa have particular problems evidenced by lack of fruit set, very small population sizes, or unstable habitats. We collected seeds of all but two taxa for seed banking, and live cuttings of two clonal shrubs for cultivation at the Santa Barbara Botanic Garden. The survey data, seeds and cuttings provide a baseline and a foundation for planning, conducting, and tracking recovery of the nine federally listed plant taxa of Santa Cruz Island.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20095264","collaboration":"Prepared in cooperation with La Luna Biological Consulting","usgsCitation":"McEachern, A.K., Chess, K., and Niessen, K., 2010, Field Surveys of Rare Plants on Santa Cruz Island, California, 2003-2006: Historical Records and Current Distributions: U.S. Geological Survey Scientific Investigations Report 2009-5264, vi, 34 p., https://doi.org/10.3133/sir20095264.","productDescription":"vi, 34 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":118637,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2009_5264.jpg"},{"id":13587,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2009/5264/","linkFileType":{"id":5,"text":"html"}}],"projection":"UniversalTransverse Mercator","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.95,33.916666666666664 ], [ -119.95,34.083333333333336 ], [ -119.5,34.083333333333336 ], [ -119.5,33.916666666666664 ], [ -119.95,33.916666666666664 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49fce4b07f02db5f59e7","contributors":{"authors":[{"text":"McEachern, A. Kathryn","contributorId":30165,"corporation":false,"usgs":true,"family":"McEachern","given":"A.","email":"","middleInitial":"Kathryn","affiliations":[],"preferred":false,"id":305033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chess, Katherine A.","contributorId":76778,"corporation":false,"usgs":true,"family":"Chess","given":"Katherine A.","affiliations":[],"preferred":false,"id":305034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niessen, Ken","contributorId":93590,"corporation":false,"usgs":true,"family":"Niessen","given":"Ken","email":"","affiliations":[],"preferred":false,"id":305035,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":98338,"text":"ofr20101063 - 2010 - Digital tabulation of geologic and hydrologic data from wells in the northern San Francisco Bay region, northern California","interactions":[],"lastModifiedDate":"2022-06-28T21:40:25.068848","indexId":"ofr20101063","displayToPublicDate":"2010-04-22T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-1063","title":"Digital tabulation of geologic and hydrologic data from wells in the northern San Francisco Bay region, northern California","docAbstract":"Downhole lithologic information and aquifer pumping test data are reported from 464 wells from a broad area of the northern part of the Coast Ranges in California. These data were originally published in paper form as numerous tables within three USGS Water-Supply Papers describing geology and groundwater conditions in Napa and Sonoma Valleys, the Santa Rosa and Petaluma Valley areas, and in the Russian River Valley and areas in Sonoma and Mendocino Counties, Calif. The well data are compiled in this report in digital form suitable for use in a digital mapping environment. These data, although mostly from relatively shallow water wells, provide important subsurface information that displays the disposition and facies transition of lithologic units throughout this broad area. Well lithologic data themselves and simple three-dimensional interpolation of those data show distinct spatial patterns that are linked to subsurface stratigraphy and structure and can be used to aid in the assessment of the groundwater resources.","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101063","usgsCitation":"Sweetkind, D.S., and Taylor, E.M., 2010, Digital tabulation of geologic and hydrologic data from wells in the northern San Francisco Bay region, northern California: U.S. Geological Survey Open-File Report 2010-1063, Report: iv, 17.; Appendixes; 1 Plate: 42.0 x 33.0 inches, https://doi.org/10.3133/ofr20101063.","productDescription":"Report: iv, 17.; Appendixes; 1 Plate: 42.0 x 33.0 inches","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":402654,"rank":2,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_92518.htm","linkFileType":{"id":5,"text":"html"}},{"id":13586,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1063/","linkFileType":{"id":5,"text":"html"}},{"id":125894,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1063.jpg"}],"country":"United States","state":"California","otherGeospatial":"northern San Francisco Bay region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 37.97018468810549\n ],\n [\n -121.5087890625,\n 37.97018468810549\n ],\n [\n -121.5087890625,\n 39.26628442213066\n ],\n [\n -122.958984375,\n 39.26628442213066\n ],\n [\n -122.958984375,\n 37.97018468810549\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a9ae4b07f02db65d5c7","contributors":{"authors":[{"text":"Sweetkind, D. S.","contributorId":61507,"corporation":false,"usgs":true,"family":"Sweetkind","given":"D.","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":305032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, E. M.","contributorId":55842,"corporation":false,"usgs":true,"family":"Taylor","given":"E.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":305031,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":98336,"text":"ofr20101081 - 2010 - Nitrogen Loads in Groundwater Entering Back Bays and Ocean from Fire Island National Seashore, Long Island, New York","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"ofr20101081","displayToPublicDate":"2010-04-22T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-1081","title":"Nitrogen Loads in Groundwater Entering Back Bays and Ocean from Fire Island National Seashore, Long Island, New York","docAbstract":"Fire Island is a barrier island that lies south of central Long Island, N.Y. It is about 60 km (37 mi) long and 0.5 km (1/4 mi) wide and is bounded by the Great South Bay, Narrow Bay, and Moriches Bay estuaries to the north; by the Atlantic Ocean to the south; by Fire Island Inlet to the west; and by Moriches Inlet to the east (fig. 1). Fire Island National Seashore (FIIS) encompasses a 42-km (26-mi) length of Fire Island that is bordered by Robert Moses State Park to the west and Smith Point County Park to the east (fig. 2). Interspersed throughout FIIS are 17 residential beach communities that together contain about 4,100 homes.\r\n\r\nThe barrier island's summer population increases 50-fold through the arrival of summer residents and vacationers. The National Park Service (NPS) has established several facilities on the island to accommodate visitors to FIIS. About 2.2 million people visit at least one of the 17 communities and (or) Smith Point County Park, the waterways surrounding Fire Island, or a FIIS facility annually (National Park Service, 2007). Combined visitation on a peak-season weekend day can be as high as 100,000 (National Park Service, 2002).\r\n\r\nMost homes and businesses in the 17 barrier-island communities discharge untreated wastewater directly to the shallow (water-table) aquifer through private septic systems and cesspools; the NPS facilities discharge wastewater to this aquifer through leach fields and cesspools. (The community of Ocean Beach (fig. 2) has a treatment plant that discharges to tidewater.) Contaminants in sewage entering the shallow groundwater move through the flow system and are ultimately discharged to adjacent marine surface waters, where they can pose a threat to coastal habitats. A contaminant of major concern is nitrogen, which is derived from fertilizers and human waste. The continuous inflow of nitrogen to surface-water bodies can lead to increased production of phytoplankton and macroalgae, which in turn can cause oxygen depletion, decreases in size of estuarine fish and shellfish communities, and loss of submerged seagrass habitat through light limitation (Valiela and others, 1992).\r\n\r\nThe FIIS boundary extends roughly 1.2 km (0.8 mi) into the back-barrier estuaries of Great South Bay, Narrow Bay, and Moriches Bay (fig. 1). Within this estuarine zone are extensive areas of seagrass, shellfish, and finfish habitat, as well as intense recreational activity (Bokuniewicz and others, 1993). Management strategies for protection of these habitats require data on (1) concentrations and movement of nutrients and other human-derived contaminants that enter the groundwater system from on-site septic systems, and (2) aquifer characteristics and groundwater flow patterns. These data can then be used in three-dimensional flow models of the shallow aquifer system to predict the rates of groundwater discharge to the marine surface waters that bound Fire Island and the concentrations of nitrogen entering these water bodies from the aquifer's discharge zones.\r\n\r\nIn 2004, the U.S. Geological Survey (USGS), in cooperation with the NPS, began a 3-year investigation to (1) measure groundwater levels within four local study areas at FIIS, (2) collect groundwater samples from these areas for nutrient (nitrogen) analysis, (3) develop a three-dimensional model of the hydrologic system and adjacent saltwater bodies for groundwater-flow delineation and particle tracking, and (4) apply the results of groundwater-discharge simulations to calculate the annual nitrogen loads in these discharges, particularly those entering Great South Bay, which together with the other back bays receives an estimated 80 percent of the total groundwater discharge from Fire Island.\r\n\r\nThe four areas on which the investigation focused were the communities of Kismet and Robbins Rest, the NPS Visitor Center at Watch Hill, and the undeveloped Otis Pike Fire Island High Dune Wilderness (shown in panels A, B, C, and D in fig. 2); these were","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101081","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Schubert, C., deVries, M.P., and Finch, A.J., 2010, Nitrogen Loads in Groundwater Entering Back Bays and Ocean from Fire Island National Seashore, Long Island, New York: U.S. Geological Survey Open-File Report 2010-1081, 16 p., https://doi.org/10.3133/ofr20101081.","productDescription":"16 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":125893,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1081.jpg"},{"id":13584,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1081/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.33333333333333,40.53333333333333 ], [ -73.33333333333333,40.85 ], [ -72.76666666666667,40.85 ], [ -72.76666666666667,40.53333333333333 ], [ -73.33333333333333,40.53333333333333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a51e4b07f02db629c59","contributors":{"authors":[{"text":"Schubert, Christopher 0000-0003-0705-3933 schubert@usgs.gov","orcid":"https://orcid.org/0000-0003-0705-3933","contributorId":1243,"corporation":false,"usgs":true,"family":"Schubert","given":"Christopher","email":"schubert@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":false,"id":305026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"deVries, M. Peter pdevries@usgs.gov","contributorId":1555,"corporation":false,"usgs":true,"family":"deVries","given":"M.","email":"pdevries@usgs.gov","middleInitial":"Peter","affiliations":[],"preferred":true,"id":305027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finch, Anne J.","contributorId":102494,"corporation":false,"usgs":true,"family":"Finch","given":"Anne","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":305028,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":98337,"text":"sir20105034 - 2010 - Method for Estimating Annual Atrazine Use for Counties in the Conterminous United States, 1992-2007","interactions":[],"lastModifiedDate":"2012-03-08T17:16:29","indexId":"sir20105034","displayToPublicDate":"2010-04-22T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5034","title":"Method for Estimating Annual Atrazine Use for Counties in the Conterminous United States, 1992-2007","docAbstract":"A method was developed to estimate annual atrazine use during 1992 to 2007 on sixteen crops and four agricultural land uses. For each year, atrazine use was estimated for all counties in the conterminous United States (except California) by combining (1) proprietary data from the Doane Marketing Research-Kynetec (DMRK) AgroTrak database on the mass of atrazine applied to agricultural crops, (2) county harvested crop acreage, by county, from the 1992, 1997, 2002, and 2007 Censuses of Agriculture, and (3) annual harvested crop acreage from National Agriculture Statistics Service (NASS) for non-Census years. DMRK estimates of pesticide use on individual crops were derived from surveys of major field crops and selected specialty crops in multicounty areas referred to as Crop Reporting Districts (CRD). The CRD-level atrazine-use estimates were disaggregated to obtain county-level application rates by dividing the mass (pounds) of pesticides applied to a crop by the acreage of that crop in the CRD to yield a rate per harvested acre. When atrazine-use estimates were not available for a CRD, crop, or year, an estimated rate was developed following a hierarchy of decision rules that checked first for the availability of a crop application rate from surveyed atrazine application rate(s) for adjacent CRDs for a specific year, and second, the rates from surveyed CRDs within for U.S. Department of Agriculture Farm Production Regions for a specific year or multiple years. The estimation method applied linear interpolation to estimate crop acreage for years when harvested acres for a crop and county were not reported in either the Census of Agriculture or the NASS database, but were reported by these data sources for other years for that crop and county. Data for atrazine use for the counties in California was obtained from farmers' reports of pesticide use collected and published by the California Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) because these data are more complete than DMRK survey data. National and state annual atrazine-use totals derived by this method were compared with other published pesticide-use estimates and were highly correlated. The method developed is designed to be applicable to other pesticides for which there are similar data; however, for some pesticides that are applied to specialty crops, fewer surveys are usually available to estimate application rates and there are a greater number of years with unreported crop acreage, potentially resulting in greater uncertainty in use ","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20105034","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Thelin, G.P., and Stone, W.W., 2010, Method for Estimating Annual Atrazine Use for Counties in the Conterminous United States, 1992-2007: U.S. Geological Survey Scientific Investigations Report 2010-5034, viii, 29 p.; Tables; Appendixes, https://doi.org/10.3133/sir20105034.","productDescription":"viii, 29 p.; Tables; Appendixes","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":125895,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5034.jpg"},{"id":13585,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5034/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a4ae4b07f02db624848","contributors":{"authors":[{"text":"Thelin, Gail P.","contributorId":75178,"corporation":false,"usgs":true,"family":"Thelin","given":"Gail","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":305030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stone, Wesley W. 0000-0003-0239-2063 wwstone@usgs.gov","orcid":"https://orcid.org/0000-0003-0239-2063","contributorId":1496,"corporation":false,"usgs":true,"family":"Stone","given":"Wesley","email":"wwstone@usgs.gov","middleInitial":"W.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":305029,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":98333,"text":"sir20105014 - 2010 - Potentiometric Surfaces and Water-Level Trends in the Cockfield (Upper Claiborne) and Wilcox (Lower Wilcox) Aquifers of Southern and Northeastern Arkansas, 2009","interactions":[],"lastModifiedDate":"2012-02-10T00:11:53","indexId":"sir20105014","displayToPublicDate":"2010-04-21T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5014","title":"Potentiometric Surfaces and Water-Level Trends in the Cockfield (Upper Claiborne) and Wilcox (Lower Wilcox) Aquifers of Southern and Northeastern Arkansas, 2009","docAbstract":"Eocene-age sand beds near the base of the Cockfield Formation of Claiborne Group constitute the aquifer known locally as the Cockfield aquifer. Upper-Paleocene age sand beds within the lower parts of the Wilcox Group constitute the aquifer known locally as the Wilcox aquifer. In 2005, reported water withdrawals from the Cockfield aquifer in Arkansas totaled 16.1 million gallons per day, while reported water withdrawals from the Wilcox aquifer in Arkansas totaled 27.0 million gallons per day. Major withdrawals from these units were for industrial and public water supplies with lesser but locally important withdrawals for commercial, domestic, and agricultural uses. \r\n\r\nDuring February 2009, 56 water-level measurements were made in wells completed in the Cockfield aquifer and 57 water-level measurements were made in wells completed in the Wilcox aquifer. The results from the 2009 water-level measurements are presented in potentiometric-surface maps and in combination with previous water-level measurements. \r\n\r\nTrends in water-level change over time within the two aquifers are investigated using water-level difference maps and well hydrographs. Water-level difference maps were constructed for each aquifer using the difference between depth to water measurements made in 2003 to 2009. Well hydrographs for each aquifer were constructed for wells with 20 or more years of historical water-level data. The hydrographs were evaluated individually using linear regression to calculate the annual rise or decline in water levels, and by aggregating the regression results by county and statistically summarizing for the range, mean, and median water-level change in each county.\r\n\r\nThe 2009 potentiometric surface of the Cockfield aquifer map indicates the regional direction of groundwater flow generally towards the east and southeast, except in two areas of intense groundwater withdrawals that have developed into cones of depression. The lowest water-level altitude measured was 43 feet and the highest water-level altitude measured was 351 feet. \r\n\r\nA water-level difference map was constructed from 54 wells completed in the Cockfield aquifer within Arkansas. The largest rise in water level was 14.9 feet and the largest decline was 27.4 feet. Seven wells had a rise in water level, and the remaining 47 wells had a decline in water level. \r\n\r\nHydrographs for 33 wells completed in the Cockfield aquifer were developed. Hydrographs indicate water-level changes in individual wells ranged from rises of 0.33 feet per year to declines of 1.21 feet per year over the 20-year period (1990-2009). County summaries of the linear regression analysis indicate Cleveland and Columbia Counties have mean annual rises. Arkansas, Ashley, Bradley, Calhoun, Chicot, Desha, Drew, Lincoln, and Union Counties have mean annual declines. \r\n\r\nThe potentiometric surface for the Wilcox aquifer is presented using two maps, one for a southern area and another for a northeastern area, because of the absence of water-level data in the central part of the State. The direction of groundwater flow in the southern area is generally the east, except around two cones of depression and around two mounds of elevated water levels. Water-level altitudes in the southern area range from 147 feet to 400 feet. The direction of groundwater flow in the northeastern area is generally to the south and southeast except in an area of intense groundwater withdrawals that has altered the flow to a westerly direction.\r\n\r\nTwo water-level difference maps were constructed using water-level altitudes measured in 2003 to 2009 from 53 wells completed in the Wilcox aquifer within southern and northeastern Arkansas. In the southern area the largest rise in water level was 16.0 feet and the largest decline was 17.7 feet. Eight wells in the southern area had rising water levels and the remaining five wells had declining water levels. In the northeastern area, the largest rise in water level was 1.3 feet and the larg","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sir20105014","collaboration":"Prepared in cooperation with the Arkansas Natural Resources Commission and the Arkansas Geological Survey","usgsCitation":"Pugh, A., 2010, Potentiometric Surfaces and Water-Level Trends in the Cockfield (Upper Claiborne) and Wilcox (Lower Wilcox) Aquifers of Southern and Northeastern Arkansas, 2009: U.S. Geological Survey Scientific Investigations Report 2010-5014, v, 47 p. , https://doi.org/10.3133/sir20105014.","productDescription":"v, 47 p. ","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"2003-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"links":[{"id":118633,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5014.jpg"},{"id":13582,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5014/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.7,33 ], [ -94.7,36.5 ], [ -89.68333333333334,36.5 ], [ -89.68333333333334,33 ], [ -94.7,33 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a1ae4b07f02db606677","contributors":{"authors":[{"text":"Pugh, Aaron L. apugh@usgs.gov","contributorId":2480,"corporation":false,"usgs":true,"family":"Pugh","given":"Aaron L.","email":"apugh@usgs.gov","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":305019,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":98331,"text":"cir1347 - 2010 - Water-the Nation's Fundamental Climate Issue A White Paper on the U.S. Geological Survey Role and Capabilities","interactions":[],"lastModifiedDate":"2012-03-02T17:16:07","indexId":"cir1347","displayToPublicDate":"2010-04-17T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1347","title":"Water-the Nation's Fundamental Climate Issue A White Paper on the U.S. Geological Survey Role and Capabilities","docAbstract":"Of all the potential threats posed by climatic variability and change, those associated with water resources are arguably the most consequential for both society and the environment (Waggoner, 1990). Climatic effects on agriculture, aquatic ecosystems, energy, and industry are strongly influenced by climatic effects on water. Thus, understanding changes in the distribution, quantity and quality of, and demand for water in response to climate variability and change is essential to planning for and adapting to future climatic conditions. A central role of the U.S. Geological Survey (USGS) with respect to climate is to document environmental changes currently underway and to develop improved capabilities to predict future changes. Indeed, a centerpiece of the USGS role is a new Climate Effects Network of monitoring sites. Measuring the climatic effects on water is an essential component of such a network (along with corresponding effects on terrestrial ecosystems).\r\n\r\nThe USGS needs to be unambiguous in communicating with its customers and stakeholders, and with officials at the Department of the Interior, that although modeling future impacts of climate change is important, there is no more critical role for the USGS in climate change science than that of measuring and describing the changes that are currently underway. One of the best statements of that mission comes from a short paper by Ralph Keeling (2008) that describes the inspiration and the challenges faced by David Keeling in operating the all-important Mauna Loa Observatory over a period of more than four decades. Ralph Keeling stated: 'The only way to figure out what is happening to our planet is to measure it, and this means tracking changes decade after decade and poring over the records.'\r\n\r\nThere are three key ideas that are important to the USGS in the above-mentioned sentence. First, to understand what is happening requires measurement. While models are a tool for learning and testing our understanding, they are not a substitute for observations. The second key idea is that measurement needs to be done over a period of many decades. When viewing hydrologic records over time scales of a few years to a few decades, trends commonly appear. However, when viewed in the context of many decades to centuries, these short-term trends are recognized as being part of much longer term oscillations. Thus, while we might want to initiate monitoring of important aspects of our natural resources, the data that will prove to be most useful in the next few years are those records that already have long-term continuity. USGS streamflow and groundwater level data are excellent examples of such long-term records. These measured data span many decades, follow standard protocols for collection and quality assurance, and are stored in a database that provides access to the full period of record.\r\n\r\nThe third point from the Keeling quote relates to the notion of ?poring over the records.? Important trends will not generally jump off the computer screen at us. Thoughtful analyses are required to get past a number of important but confounding influences in the record, such as the role of seasonal variation, changes in water management, or influences of quasi-periodic phenomena, such as El Ni?o-Southern Oscillation (ENSO) or the Pacific Decadal Oscillation (PDO). No organization is better situated to pore over the records than the USGS because USGS scientists know the data, quality-assure the data, understand the factors that influence the data, and have the ancillary information on the watersheds within which the data are collected.\r\n\r\nTo fulfill the USGS role in understanding climatic variability and change, we need to continually improve and strengthen two of our key capabilities: (1) preserving continuity of long-term water data collection and (2) analyzing and interpreting water data to determine how the Nation's water resources are changing.\r\n\r\nUnderstanding change in water resources","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/cir1347","usgsCitation":"Lins, H.F., Hirsch, R.M., and Kiang, J., 2010, Water-the Nation's Fundamental Climate Issue A White Paper on the U.S. Geological Survey Role and Capabilities: U.S. Geological Survey Circular 1347, iv, 9 p., https://doi.org/10.3133/cir1347.","productDescription":"iv, 9 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":125362,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/cir_1347.jpg"},{"id":13580,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/circ/1347/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49e2e4b07f02db5e4bcb","contributors":{"authors":[{"text":"Lins, Harry F. 0000-0001-5385-9247 hlins@usgs.gov","orcid":"https://orcid.org/0000-0001-5385-9247","contributorId":1505,"corporation":false,"usgs":true,"family":"Lins","given":"Harry","email":"hlins@usgs.gov","middleInitial":"F.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":305014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":305015,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiang, Julie","contributorId":45804,"corporation":false,"usgs":true,"family":"Kiang","given":"Julie","affiliations":[],"preferred":false,"id":305016,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208556,"text":"70208556 - 2010 - Advances in estimation methods of vegetation water content based on optical remote sensing techniques","interactions":[],"lastModifiedDate":"2020-02-20T10:04:56","indexId":"70208556","displayToPublicDate":"2010-04-15T15:28:03","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5930,"text":"Science China Technological Sciences","printIssn":"1674-7321","active":true,"publicationSubtype":{"id":10}},"title":"Advances in estimation methods of vegetation water content based on optical remote sensing techniques","docAbstract":"Quantitative estimation of vegetation water content (VWC) using optical remote sensing techniques is helpful in forest fire assessment, agricultural drought monitoring and crop yield estimation. This paper reviews the research advances of VWC retrieval using spectral reflectance, spectral water index and radiative transfer model (RTM) methods. It also evaluates the reliability of VWC estimation using spectral water index from the observation data and the RTM. Focusing on two main definitions of VWC—the fuel moisture content (FMC) and the equivalent water thickness (EWT), the retrieval accuracies of FMC and EWT using vegetation water indices are analyzed. Moreover, the measured information and the dataset are used to estimate VWC, the results show there are significant correlations among three kinds of vegetation water indices (i.e., WSI, NDII, NDWI1640, WI/NDVI) and canopy FMC of winter wheat (n=45). Finally, the future development directions of VWC detection based on optical remote sensing techniques are also summarized.
","language":"English","publisher":"Springer","doi":"10.1007/s11431-010-0131-3","usgsCitation":"Zhang, J., Xu, Y., Yao, F., Wang, P., Guo, W., Li, L., and Yang, L., 2010, Advances in estimation methods of vegetation water content based on optical remote sensing techniques: Science China Technological Sciences, v. 53, no. 5, p. 1159-1167, https://doi.org/10.1007/s11431-010-0131-3.","productDescription":"9 p.","startPage":"1159","endPage":"1167","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":372369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"5","noUsgsAuthors":false,"publicationDate":"2010-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Jiahua","contributorId":35479,"corporation":false,"usgs":true,"family":"Zhang","given":"Jiahua","email":"","affiliations":[],"preferred":false,"id":782458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Yun","contributorId":222535,"corporation":false,"usgs":false,"family":"Xu","given":"Yun","email":"","affiliations":[],"preferred":false,"id":782459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yao, Fengmei","contributorId":107927,"corporation":false,"usgs":true,"family":"Yao","given":"Fengmei","email":"","affiliations":[],"preferred":false,"id":782460,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, PeiJuan","contributorId":222536,"corporation":false,"usgs":false,"family":"Wang","given":"PeiJuan","email":"","affiliations":[],"preferred":false,"id":782461,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guo, WenJuan","contributorId":222537,"corporation":false,"usgs":false,"family":"Guo","given":"WenJuan","email":"","affiliations":[],"preferred":false,"id":782462,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Li","contributorId":222539,"corporation":false,"usgs":false,"family":"Li","given":"Li","email":"","affiliations":[],"preferred":true,"id":782468,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782469,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":98328,"text":"ds501 - 2010 - Seasonal and Spatial Distribution of Freshwater Flow and Salinity in the Ten Thousand Islands Estuary, Florida, 2007-2009","interactions":[],"lastModifiedDate":"2019-11-08T06:32:08","indexId":"ds501","displayToPublicDate":"2010-04-15T00:00:00","publicationYear":"2010","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":"501","title":"Seasonal and Spatial Distribution of Freshwater Flow and Salinity in the Ten Thousand Islands Estuary, Florida, 2007-2009","docAbstract":"The watershed of the Ten Thousand Islands (TTI) estuary has been substantially altered through the construction of canals and roads for the Southern Golden Gate Estates (SGGE), Barron River Canal, and U.S. 41 (Tamiami Trail). Two restoration projects designed to improve freshwater delivery to the estuary are the Picayune Strand Restoration Project, which includes the Southern Golden Gate Estates, and the Tamiami Trail Culverts Project; both are part of the Comprehensive Everglades Restoration Plan. To address hydrologic information needs critical for monitoring the effects of these restoration projects, the U.S. Geological Survey initiated a study in October 2006 to characterize freshwater outflows from the rivers, internal circulation and mixing within the estuary, and surface-water exchange between the estuary and Gulf of Mexico. The effort is conducted in cooperation with the South Florida Water Management District and complemented by monitoring performed by the Rookery Bay National Estuarine Research Reserve. \r\n\r\nSurface salinity was measured during moving boat surveys using a flow-through system that operated at planing speeds averaging 20 miles per hour. The data were logged every 10 seconds by a data recorder that simultaneously logged location information from a Global Positioning System. The major rivers, bays, and nearshore Gulf of Mexico region of the TTI area were surveyed in approximately 5 hours by two boats traversing about 200 total miles. Salinity and coordinate data were processed using inverse distance weighted interpolation to create salinity contour maps of the entire TTI region. \r\n\r\nTen maps were created from salinity surveys performed between May 2007 and May 2009 and illustrate the dry season, transitional, and wet season salinity patterns of the estuarine rivers, inner bays, mangrove islands, and Gulf of Mexico boundary. The effects of anthropogenic activities are indicated by exceptionally low salinities associated with point discharge into the estuary from the Faka Union Canal and Barron River during the wet season. Low salinities in Faka Union Bay may cause reduced diversity and density of submerged aquatic vegetation, fish, and benthic organisms compared with neighboring Fakahatchee Bay. The Faka Union Canal System reduced the size of the watershed for the western TTI estuary, resulting in increased wet season salinities compared to those for the eastern TTI estuary, the watershed of which is composed of the relatively pristine Fakahatchee Strand Preserve State Park. Minimal river discharge and high evaporation caused hypersaline conditions to develop throughout the entire TTI region during the dry season. The 2007-2008 drought and passage of Tropical Storm Fay on August 18-19, 2008, demonstrated the effects of seasonal rainfall on salinity patterns, with substantially higher salinities observed during the 2007 wet season compared to those for the 2008 wet season. The salinity maps, coupled with data from the monitoring stations, provide baseline information of seasonal and spatial distribution of freshwater flow and salinity in the TTI estuary, and a means of monitoring the effects of restoration in improving freshwater delivery to the estuary. \r\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds501","collaboration":"Prepared in cooperation with South Florida Water Management District","usgsCitation":"Soderqvist, L.E., and Patino, E., 2010, Seasonal and Spatial Distribution of Freshwater Flow and Salinity in the Ten Thousand Islands Estuary, Florida, 2007-2009: U.S. Geological Survey Data Series 501, vi, 24 p., https://doi.org/10.3133/ds501.","productDescription":"vi, 24 p.","onlineOnly":"N","temporalStart":"2007-05-01","temporalEnd":"2009-05-31","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":118621,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_501.jpg"},{"id":13577,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/501/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.62155151367188,\n 25.977181684362176\n ],\n [\n -81.69261932373047,\n 25.857060917861336\n ],\n [\n -81.42345428466797,\n 25.759082934951692\n ],\n [\n -81.35890960693358,\n 25.90185031509369\n ],\n [\n -81.62155151367188,\n 25.977181684362176\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a0ce4b07f02db5fc42b","contributors":{"authors":[{"text":"Soderqvist, Lars E.","contributorId":92358,"corporation":false,"usgs":true,"family":"Soderqvist","given":"Lars","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":305007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Eduardo 0000-0003-1016-3658 epatino@usgs.gov","orcid":"https://orcid.org/0000-0003-1016-3658","contributorId":1743,"corporation":false,"usgs":true,"family":"Patino","given":"Eduardo","email":"epatino@usgs.gov","affiliations":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true},{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"preferred":true,"id":305006,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":98330,"text":"sir20105018 - 2010 - Mercury assessment and monitoring protocol for the Bear Creek Watershed, Colusa County, California","interactions":[],"lastModifiedDate":"2019-12-30T14:11:55","indexId":"sir20105018","displayToPublicDate":"2010-04-15T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5018","title":"Mercury assessment and monitoring protocol for the Bear Creek Watershed, Colusa County, California","docAbstract":"This report summarizes the known information on the occurrence and distribution of mercury (Hg) in physical/chemical and biological matrices within the Bear Creek watershed. Based on these data, a matrix-specific monitoring protocol for the evaluation of the effectiveness of activities designed to remediate Hg contamination in the Bear Creek watershed is presented. The monitoring protocol documents procedures for collecting and processing water, sediment, and biota for estimation of total Hg (TotHg) and monomethyl mercury (MMeHg) in the Bear Creek watershed. The concurrent sampling of TotHg and MMeHg in biota as well as water and sediment from 10 monitoring sites is designed to assess the relative bioavailability of Hg released from Hg sources in the watershed and identify environments conducive to Hg methylation. These protocols are designed to assist landowners, land managers, water quality regulators, and scientists in determining whether specific restoration/mitigation actions lead to significant progress toward achieving water quality goals to reduce Hg in Bear and Sulphur Creeks.\r\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105018","collaboration":"Prepared for the Bureau of Land Management","usgsCitation":"Suchanek, T.H., Hothem, R.L., Rytuba, J.J., and Yee, J.L., 2010, Mercury assessment and monitoring protocol for the Bear Creek Watershed, Colusa County, California: U.S. Geological Survey Scientific Investigations Report 2010-5018, vi, 34 p., https://doi.org/10.3133/sir20105018.","productDescription":"vi, 34 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":118630,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2010_5018.jpg"},{"id":13579,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5018/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"Colusa County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-122.7389,39.3834],[-122.6896,39.3847],[-122.5813,39.3865],[-122.4544,39.3851],[-122.4353,39.3855],[-122.1947,39.3855],[-122.1382,39.3862],[-122.1386,39.4148],[-122.0832,39.4142],[-122.0034,39.4131],[-122.0005,39.3991],[-122.0015,39.3945],[-122.0048,39.3863],[-122.0048,39.3845],[-121.8881,39.3849],[-121.8873,39.3809],[-121.8864,39.3691],[-121.8933,39.3626],[-121.8972,39.3557],[-121.8953,39.3526],[-121.8944,39.3422],[-121.8995,39.333],[-121.9027,39.3239],[-121.9067,39.3188],[-121.9086,39.3052],[-121.9149,39.2974],[-121.9211,39.2864],[-121.9232,39.2768],[-121.9341,39.2675],[-121.9381,39.2629],[-121.9432,39.2546],[-121.9454,39.2482],[-121.944,39.2415],[-121.9426,39.2356],[-121.9406,39.2311],[-121.9356,39.2217],[-121.9355,39.2185],[-121.9292,39.21],[-121.926,39.201],[-121.9288,39.1964],[-121.937,39.1926],[-121.9426,39.1839],[-121.9413,39.1794],[-121.9394,39.1758],[-121.9381,39.1727],[-121.9337,39.1655],[-121.9197,39.1549],[-121.9154,39.1495],[-121.9169,39.1436],[-121.9258,39.1416],[-121.9335,39.141],[-121.9358,39.1401],[-121.9368,39.1355],[-121.9331,39.1306],[-121.9265,39.1289],[-121.921,39.124],[-121.9139,39.1224],[-121.9102,39.1206],[-121.9089,39.1157],[-121.9065,39.1148],[-121.9052,39.1126],[-121.9031,39.1022],[-121.8946,39.0955],[-121.8915,39.0924],[-121.8938,39.0901],[-121.8901,39.0843],[-121.8916,39.0784],[-121.8862,39.0739],[-121.8837,39.0717],[-121.8717,39.0679],[-121.8575,39.0686],[-121.8475,39.0701],[-121.842,39.0653],[-121.8384,39.0626],[-121.837,39.0567],[-121.838,39.0522],[-121.836,39.0441],[-121.8305,39.0387],[-121.8286,39.0342],[-121.8243,39.0307],[-121.8235,39.0252],[-121.8227,39.0176],[-121.8158,39.0055],[-121.8043,38.998],[-121.7983,38.9963],[-121.7971,38.994],[-121.797,38.9908],[-121.8004,38.988],[-121.8063,38.987],[-121.8146,38.9864],[-121.8188,38.9873],[-121.8204,38.9827],[-121.8202,38.9763],[-121.8253,38.9704],[-121.8324,38.9698],[-121.8304,38.9608],[-121.8367,38.9561],[-121.8365,38.9493],[-121.8339,38.943],[-121.8303,38.9394],[-121.8347,38.9303],[-121.8351,38.9248],[-122.0233,38.9252],[-122.2717,38.924],[-122.3138,38.9249],[-122.3386,38.9248],[-122.3423,38.9274],[-122.3483,38.9286],[-122.3555,38.9321],[-122.3622,38.9365],[-122.3676,38.9391],[-122.3803,38.9469],[-122.3937,38.9548],[-122.4002,38.956],[-122.4051,38.96],[-122.4094,38.963],[-122.409,38.968],[-122.4087,38.9739],[-122.4135,38.9765],[-122.4185,38.9846],[-122.4177,38.9928],[-122.4121,39.0015],[-122.4077,39.0102],[-122.4116,39.0192],[-122.4224,39.0217],[-122.4384,39.0227],[-122.4506,39.0305],[-122.4575,39.039],[-122.4709,39.0491],[-122.4782,39.0521],[-122.4907,39.0545],[-122.4847,39.0669],[-122.4898,39.0754],[-122.485,39.0896],[-122.4888,39.0954],[-122.4909,39.1017],[-122.4959,39.1083],[-122.4938,39.1147],[-122.4999,39.1205],[-122.4957,39.1333],[-122.493,39.1388],[-122.4878,39.143],[-122.4805,39.1391],[-122.4782,39.1391],[-122.4753,39.141],[-122.4748,39.1442],[-122.4805,39.1523],[-122.4788,39.1555],[-122.4808,39.159],[-122.4815,39.1636],[-122.4787,39.1668],[-122.4747,39.1701],[-122.4766,39.1736],[-122.4826,39.1744],[-122.4915,39.1747],[-122.4931,39.1837],[-122.5004,39.189],[-122.5088,39.1915],[-122.5144,39.1968],[-122.5154,39.2076],[-122.5346,39.2104],[-122.5445,39.2069],[-122.5557,39.2053],[-122.564,39.2033],[-122.5693,39.2022],[-122.5723,39.2031],[-122.576,39.2062],[-122.5869,39.2113],[-122.6019,39.2141],[-122.6254,39.2231],[-122.6378,39.22],[-122.6506,39.2147],[-122.6602,39.2158],[-122.6796,39.2262],[-122.6768,39.2295],[-122.6901,39.2473],[-122.6997,39.2507],[-122.715,39.2598],[-122.7181,39.2638],[-122.7224,39.265],[-122.7292,39.273],[-122.7346,39.2729],[-122.7541,39.2828],[-122.758,39.2904],[-122.7663,39.3025],[-122.7746,39.3158],[-122.7714,39.3241],[-122.7648,39.3374],[-122.7634,39.3438],[-122.7505,39.3482],[-122.7484,39.3546],[-122.7444,39.3597],[-122.741,39.3634],[-122.7442,39.3674],[-122.7521,39.3708],[-122.7589,39.377],[-122.7631,39.3774],[-122.7719,39.3749],[-122.7798,39.3792],[-122.7849,39.3845],[-122.7389,39.3834]]]},\"properties\":{\"name\":\"Colusa\",\"state\":\"CA\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a2de4b07f02db614d23","contributors":{"authors":[{"text":"Suchanek, Thomas H.","contributorId":69235,"corporation":false,"usgs":true,"family":"Suchanek","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":305013,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hothem, Roger L. roger_hothem@usgs.gov","contributorId":1721,"corporation":false,"usgs":true,"family":"Hothem","given":"Roger","email":"roger_hothem@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":305010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rytuba, James J. jrytuba@usgs.gov","contributorId":3043,"corporation":false,"usgs":true,"family":"Rytuba","given":"James","email":"jrytuba@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":305011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":305012,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":98329,"text":"fs20103001 - 2010 - Groundwater Quality in the Central Eastside San Joaquin Valley, California","interactions":[],"lastModifiedDate":"2012-03-08T17:16:30","indexId":"fs20103001","displayToPublicDate":"2010-04-15T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-3001","title":"Groundwater Quality in the Central Eastside San Joaquin Valley, California","docAbstract":"The Central Eastside study unit is located in California's San Joaquin Valley. The 1,695 square mile study unit includes three groundwater subbasins: Modesto, Turlock, and Merced (California Department of Water Resources, 2003). The primary water-bearing units consist of discontinuous lenses of gravel, sand, silt, and clay, which are derived largely from the Sierra Nevada Mountains to the east. Public-supply wells provide most of the drinking water supply in the Central Eastside. Consequently, the primary aquifer in the Central Eastside study unit is defined as that part of the aquifer corresponding to the perforated interval of wells listed in the California Department of Public Health database. Public-supply wells are typically drilled to depths of 200 to 350 feet, consist of solid casing from the land surface to a depth of about 100 to 200 feet, and they are perforated below the solid casing. Water quality in the shallower and deeper parts of the aquifer system may differ from that in the primary aquifer.\r\n\r\nThe Central Eastside study unit has hot and dry summers and cool, moist, winters. Average annual rainfall ranges from 11 to 15 inches. The Stanislaus, Tuolumne, and Merced Rivers, with headwaters in the Sierra Nevada Mountains, are the primary streams traversing the study unit.\r\n\r\nLand use in the study unit is approximately 59 percent (%) agricultural, 34% natural (primarily grassland), and 7% urban. The primary crops are almonds, walnuts, peaches, grapes, grain, corn, and alfalfa. The largest urban areas (2003 population in parentheses) are the cities of Modesto (206,872), Turlock (63,467), and Merced (69,512).\r\n\r\nMunicipal water use accounts for about 5% of the total water use in the Central Eastside study unit, with the remainder used for irrigated agriculture. Groundwater accounts for about 75% of the municipal supply, and surface water accounts for about 25%. Recharge to the groundwater flow system is primarily from percolation of irrigation return, precipitation, seepage from reservoirs and rivers, and urban return (Burow and others, 2004; Phillips and others, 2007). The primary sources of discharge are pumping for irrigation and municipal supply, evaporation from areas with a shallow depth to water, and discharge to streams. Recharge at shallow depths and pumping from wells at greater depths causes downward movement of groundwater in the aquifer in the Central Eastside. This vertical movement of water has the potential to carry chemical constituents from shallow depths to the greater depths where supply wells commonly are perforated.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/fs20103001","collaboration":"U.S. Geological Survey and the California State Water Resources Control Board","usgsCitation":"Belitz, K., and Landon, M.K., 2010, Groundwater Quality in the Central Eastside San Joaquin Valley, California: U.S. Geological Survey Fact Sheet 2010-3001, 4 p., https://doi.org/10.3133/fs20103001.","productDescription":"4 p.","onlineOnly":"N","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":118624,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2010-3001.jpg"},{"id":13578,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2010/3001/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4acce4b07f02db67e991","contributors":{"authors":[{"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":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":305009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":305008,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":98325,"text":"ofr20091250 - 2010 - Geomorphology and depositional subenvironments of Gulf Islands National Seashore, Mississippi","interactions":[],"lastModifiedDate":"2023-12-06T15:30:36.673536","indexId":"ofr20091250","displayToPublicDate":"2010-04-14T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2009-1250","title":"Geomorphology and depositional subenvironments of Gulf Islands National Seashore, Mississippi","docAbstract":"The U.S. Geological Survey (USGS) is studying coastal hazards and coastal change to improve our understanding of coastal ecosystems and to develop better capabilities of predicting future coastal change. One approach to understanding the dynamics of coastal systems is to monitor changes in barrier-island subenvironments through time. This involves examining morphological and topographic change at temporal scales ranging from millennia to years and spatial scales ranging from tens of kilometers to meters. Of particular interest are the processes that produce those changes and the determination of whether or not those processes are likely to persist into the future. In these analyses of hazards and change, both natural and anthropogenic influences are considered. Quantifying past magnitudes and rates of coastal change and knowing the principal factors that govern those changes are critical to predicting what changes are likely to occur under different scenarios, such as short-term impacts of extreme storms or long-term impacts of sea-level rise. Gulf Islands National Seashore was selected for detailed mapping of barrier-island morphology and topography because the islands offer a diversity of depositional subenvironments and the islands' areas and positions have changed substantially in historical time. The geomorphologic and subenvironmental maps emphasize the processes that formed the surficial features and also serve as a basis for documenting which subenvironments are relatively stable, such as the beach ridge complex, and those which are highly dynamic, such as the beach and active overwash zones.\r\n\r\nThe primary mapping procedures used supervised functions within a Geographic Information System (GIS) that classified depositional subenvironments and features (map units) and delineated boundaries of the features (shapefiles). The GIS classified units on the basis of tonal patterns of a feature in contrast to adjacent features observed on georeferenced aerial photographs. Land elevations from recent lidar surveys served as supplementary data to assist in delineating the map-unit boundaries.","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20091250","collaboration":"Prepared in cooperation with the National Park Service (NPS).","usgsCitation":"Morton, R., and Rogers, B.E., 2010, Geomorphology and depositional subenvironments of Gulf Islands National Seashore, Mississippi: U.S. Geological Survey Open-File Report 2009-1250, 4 Plates: 34.00 x 44.00 inches; Metadata, https://doi.org/10.3133/ofr20091250.","productDescription":"4 Plates: 34.00 x 44.00 inches; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":423274,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_94715.htm","linkFileType":{"id":5,"text":"html"}},{"id":13574,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2009/1250/","linkFileType":{"id":5,"text":"html"}},{"id":199412,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Cat Island, Horn Island, Petit Bois Island, Ship Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.40293885515848,\n 30.27097270274332\n ],\n [\n -89.16125785215971,\n 30.27097270274332\n ],\n [\n -89.16125785215971,\n 30.150027436564756\n ],\n [\n -88.40293885515848,\n 30.150027436564756\n ],\n [\n -88.40293885515848,\n 30.27097270274332\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ac9e4b07f02db67c4d9","contributors":{"authors":[{"text":"Morton, Robert A.","contributorId":88333,"corporation":false,"usgs":true,"family":"Morton","given":"Robert A.","affiliations":[],"preferred":false,"id":304996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Bryan E.","contributorId":67368,"corporation":false,"usgs":true,"family":"Rogers","given":"Bryan","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":304995,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}