Winds and temperature gradients greatly affect the long-range propagation of infrasound. The spatio-temporal variability of these parameters must therefore be accurately characterized to correctly interpret recorded infrasound at long distances, specifically to differentiate between source and propagation effects. Here we present the first results of an open source reanalysis model, termed Alaska Volcano Observatory Ground-to-Space (AVO-G2S), constructed to accurately characterize the atmosphere and model long-range infrasound propagation from volcanic eruptions in Alaska. We select a number of case studies to examine recent eruptions of Alaskan volcanoes whose ash emissions posed a threat to air traffic, including the two most recent eruptions of Pavlof Volcano and two typical explosions from Cleveland Volcano. Strong tropospheric ducting and low noise at the station during the 21 July 2015 explosion of Cleveland Volcano led to an automated detection of the explosion at an infrasound array 992 km away, whereas low signal-to-noise ratio for the 6 November 2014 Cleveland Volcano explosion helps explain the non-detection in real-time of a predicted strong stratospheric arrival. For the November 2014 Pavlof eruption, discrepancies between local seismic data and a distal infrasound array 460 km away cannot be solely explained by changes in atmospheric conditions, though some features of the complex propagation predictions follow the trends in long-range infrasound signals. The most recent eruption of Pavlof Volcano in March 2016 shows minimal changes in propagation conditions throughout the eruption and therefore indicates that the signals detected at long-range primarily reflect source processes. These results show how detailed examination of the acoustic propagation conditions provides insight into detection capability and eruption dynamics. Future work will implement AVO-G2S and high-resolution long-range infrasound propagation modeling in real-time for Alaskan volcanoes of interest.
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Fairbanks","active":true,"usgs":false}],"preferred":false,"id":838180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":838181,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205109,"text":"70205109 - 2019 - A new indicator framework for quantifying the intensity of the terrestrialwater cycle","interactions":[],"lastModifiedDate":"2019-09-03T15:14:53","indexId":"70205109","displayToPublicDate":"2018-04-02T15:10:30","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A new indicator framework for quantifying the intensity of the terrestrialwater cycle","docAbstract":"A quantitative framework for characterizing the intensity of the water cycle over land is presented, and illustrated using a spatially distributed water-balance model of the conterminous United States (CONUS). We approach water cycle intensity (WCI) from a landscape perspective; WCI is defined as the sum of precipitation (P) and actual evapotranspiration (AET) over a spatially explicit landscape unit of interest, averaged over a specified time period (step) of interest. The time step may be of any length for which data or simulation results are available (e.g., sub-daily to multi-decadal). We define the storage-adjusted runoff (Q0) as the sum of actual runoff (Q) and the rate of change in soil moisture storage (DS/Dt, positive or negative) during the time step of interest. The Q0 indicator is demonstrated to be mathematically complementary to WCI, in a manner that allows graphical interpretation of their relationship. For the purposes of this study, the indicators were demonstrated using long-term, spatially distributed model simulations with an annual time step. WCI was found to increase over most of the CONUS between the 1945 to 1974 and 1985 to 2014 periods, driven primarily by increases in P. In portions of the western and southeastern CONUS, Q0 decreased because of decreases in Q and soil moisture storage. Analysis of WCI and Q0 at temporal scales ranging from sub-daily to multi-decadal could improve understanding of the wide spectrum of hydrologic responses that have been attributed to water cycle intensification, as well as trends in those responses.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.02.048","usgsCitation":"Huntington, T.G., Weiskel, P., Wolock, D.M., and McCabe, G.J., 2019, A new indicator framework for quantifying the intensity of the terrestrialwater cycle: Journal of Hydrology, v. 559, p. 361-372, https://doi.org/10.1016/j.jhydrol.2018.02.048.","productDescription":"12 p.","startPage":"361","endPage":"372","ipdsId":"IP-070433","costCenters":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science 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dwolock@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":540,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"dwolock@usgs.gov","middleInitial":"M.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":770058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":770059,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196326,"text":"70196326 - 2019 - Gene flow connects coastal populations of a habitat specialist, the Clapper Rail Rallus crepitans","interactions":[],"lastModifiedDate":"2019-01-28T09:56:08","indexId":"70196326","displayToPublicDate":"2018-04-02T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Gene flow connects coastal populations of a habitat specialist, the Clapper Rail Rallus crepitans","title":"Gene flow connects coastal populations of a habitat specialist, the Clapper Rail Rallus crepitans","docAbstract":"Examining population genetic structure can reveal patterns of reproductive isolation or population mixing and inform conservation management. Some avian species are predicted to exhibit minimal genetic differentiation among populations as a result of the species high mobility, with habitat specialists tending to show greater fine‐scale genetic structure. To explore the relationship between habitat specialization and gene flow, we investigated the genetic structure of a saltmarsh specialist with high potential mobility across a wide geographic range of fragmented habitat. Little variation among mitochondrial sequences (620 bp from ND2) was observed among 149 individual Clapper Rails Rallus crepitans sampled along the Atlantic coast of North America, with the majority of individuals at all sampling sites sharing a single haplotype. Genotyping of nine microsatellite loci across 136 individuals revealed moderate genetic diversity, no evidence of bottlenecks, and a weak pattern of genetic differentiation that increased with geographic distance. Multivariate analyses, Bayesian clustering and an AMOVA all suggested a lack of genetic structuring across the North American Atlantic coast, with all individuals grouped into a single interbreeding population. Spatial autocorrelation analyses showed evidence of weak female philopatry and a lack of male philopatry. We conclude that high gene flow connecting populations of this habitat specialist may result from the interaction of ecological and behavioral factors that promote dispersal and limit natal philopatry and breeding‐site fidelity. As climate change threatens saltmarshes, the genetic diversity and population connectivity of Clapper Rails may promote resilience of their populations. This finding helps inform about potential fates of other similarly behaving saltmarsh specialists on the Atlantic coast.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12599","usgsCitation":"Coster, S.S., Welsh, A.B., Costanzo, G.R., Harding, S.R., Anderson, J.T., and Katzner, T., 2019, Gene flow connects coastal populations of a habitat specialist, the Clapper Rail Rallus crepitans: Ibis, v. 161, no. 1, p. 66-78, https://doi.org/10.1111/ibi.12599.","productDescription":"13 p.","startPage":"66","endPage":"78","ipdsId":"IP-095851","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":468131,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ibi.12599","text":"Publisher Index Page"},{"id":353068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"161","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-06","publicationStatus":"PW","scienceBaseUri":"5afee6eae4b0da30c1bfbf5d","contributors":{"authors":[{"text":"Coster, Stephanie S. 0000-0002-5170-4548","orcid":"https://orcid.org/0000-0002-5170-4548","contributorId":203794,"corporation":false,"usgs":false,"family":"Coster","given":"Stephanie","email":"","middleInitial":"S.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":732332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Amy B.","contributorId":192239,"corporation":false,"usgs":false,"family":"Welsh","given":"Amy","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":732333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Costanzo, Gary R.","contributorId":198907,"corporation":false,"usgs":false,"family":"Costanzo","given":"Gary","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":732334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harding, Sergio R.","contributorId":198906,"corporation":false,"usgs":false,"family":"Harding","given":"Sergio","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":732335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, James T.","contributorId":28071,"corporation":false,"usgs":false,"family":"Anderson","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":732336,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":732331,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215594,"text":"70215594 - 2019 - The influence of land-cover changes on the variability of saturated hydraulic conductivity in tropical peatlands","interactions":[],"lastModifiedDate":"2020-10-25T18:10:30.770026","indexId":"70215594","displayToPublicDate":"2018-03-29T13:03:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7177,"text":"Mitigation and Adaption Strategies for Global Change","active":true,"publicationSubtype":{"id":10}},"title":"The influence of land-cover changes on the variability of saturated hydraulic conductivity in tropical peatlands","docAbstract":"Understanding the movement of water through peat is essential for effective conservation and management strategies for peatlands. Saturated hydraulic conductivity, Ks, describes water movement through the peat profile. However, the spatial variability of Ks in tropical peatlands and the effects of land conversion on peat characteristics are poorly understood. Utilizing the slug test method, we estimated hydraulic conductivity in tropical peatlands in West Kalimantan, Indonesia, at three depths (0.75, 3.5, and 5.5 m) across four different land-cover types (undrained forests, recently burned forests, early seral communities, and oil palm (Elaeis guineensis Jacq.) plantations). We found strong spatial autocorrelation among measurements collected at our 19 study sites and evaluated the relationship between hydraulic conductivity and land-cover types, peat properties, and depth of measurement with a hierarchical linear model. Hydraulic conductivity varied greatly (c. 0.001–13.9 m d−1). The best approximating model for estimating Ks contained depth, forest cover, a depth and forest cover interaction, and the von Post degree of decomposition (Ks ~ depth + forest + depth × forest + von Post). Parameter estimates indicated that Ks was greater in forested than non-forested sites and decreased with increasing depth and decomposition stage. There was no evidence that Ks differed among the non-forested sites or was related to other physical and chemical peat properties. Our results suggest that Ks should be measured directly in tropical peatlands rather than estimated as a function of peat properties. Additionally, the strong spatial dependence suggests that similar research designs should examine the sample data for spatial dependence and, if necessary, incorporate hierarchical models.
In the upper Chattahoochee River basin, where some populations of shoal bass, Micropterus cataractae Williams & Burgess, are imperilled, age and growth data are lacking. Age and growth of shoal bass in this basin were assessed with non‐lethal means using scales and mark–recapture. Mark–recapture data allowed for estimation of accuracy and determination of effects of any scale‐based inaccuracies on growth models. Scale‐based age estimates were accurate for 57% of the samples, and errors of 1 to 3 years included equal numbers of over‐ and underestimates of age. von Bertalanffy growth models based on scale ages were similar to those based on mark–recapture ages for ages 3–8 but noticeably divergent for younger and older fish. Scales provided estimates of longevity up to 12 years of age, and growth models produced from mark–recapture suggest scale ages underestimated age, especially for older fish. These populations of shoal bass live longer and grow slower than other populations, suggesting regional management strategies may be needed.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12274","usgsCitation":"Long, J.M., Holley, C.T., and Taylor, A.T., 2019, Evaluation of ageing accuracy with complementary non‐lethal methods for slow‐growing, northern populations of shoal bass: Fisheries Management and Ecology, v. 25, no. 2, p. 150-157, https://doi.org/10.1111/fme.12274.","productDescription":"7 p.","startPage":"150","endPage":"157","ipdsId":"IP-080326","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":365774,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Gerogia","otherGeospatial":"Chattahoochee River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.91357421875,\n 34.939985151560435\n ],\n [\n -85.95703125,\n 33.63291573870479\n ],\n [\n -86.02294921875,\n 32.175612478499325\n ],\n [\n -85.62744140625,\n 30.619004797647808\n ],\n [\n -84.74853515625,\n 29.611670115197377\n ],\n [\n -83.75976562499999,\n 29.859701442126756\n ],\n [\n -83.95751953125,\n 30.600093873550072\n ],\n [\n -84.52880859375,\n 32.30570601389429\n ],\n [\n -84.83642578125,\n 33.137551192346145\n ],\n [\n -82.96875,\n 34.59704151614417\n ],\n [\n -83.91357421875,\n 34.939985151560435\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":766516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holley, C. T.","contributorId":217373,"corporation":false,"usgs":false,"family":"Holley","given":"C.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":766517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, A. T.","contributorId":217377,"corporation":false,"usgs":false,"family":"Taylor","given":"A.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":766670,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204369,"text":"70204369 - 2019 - Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements","interactions":[],"lastModifiedDate":"2019-07-22T13:00:12","indexId":"70204369","displayToPublicDate":"2018-02-05T12:54:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements","title":"Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements","docAbstract":"Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground‐based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi‐state mark–recapture methods using a hierarchical N‐mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state‐space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N‐mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1692","usgsCitation":"Ketz, A.C., Johnson, T.L., Monello, R.J., Mack, J.A., George, J.L., Hooten, M., Kraft, B.R., Wild, M.A., and Hobbs, N.T., 2019, Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements: Ecological Applications, v. 28, no. 3, p. 816-825, https://doi.org/10.1002/eap.1692.","productDescription":"10 p.","startPage":"816","endPage":"825","ipdsId":"IP-082132","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":365802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Ketz, Alison C.","contributorId":217310,"corporation":false,"usgs":false,"family":"Ketz","given":"Alison","email":"","middleInitial":"C.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":766559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Therese L.","contributorId":217311,"corporation":false,"usgs":false,"family":"Johnson","given":"Therese","email":"","middleInitial":"L.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monello, Ryan J.","contributorId":217312,"corporation":false,"usgs":false,"family":"Monello","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mack, John A.","contributorId":217313,"corporation":false,"usgs":false,"family":"Mack","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"George, Janet L.","contributorId":217314,"corporation":false,"usgs":false,"family":"George","given":"Janet","email":"","middleInitial":"L.","affiliations":[{"id":36246,"text":"CPW","active":true,"usgs":false}],"preferred":false,"id":766563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":766558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kraft, Benjamin R.","contributorId":217315,"corporation":false,"usgs":false,"family":"Kraft","given":"Benjamin","email":"","middleInitial":"R.","affiliations":[{"id":36246,"text":"CPW","active":true,"usgs":false}],"preferred":false,"id":766564,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wild, Margaret A.","contributorId":217316,"corporation":false,"usgs":false,"family":"Wild","given":"Margaret","email":"","middleInitial":"A.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766565,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hobbs, N. Thompson","contributorId":217317,"corporation":false,"usgs":false,"family":"Hobbs","given":"N.","email":"","middleInitial":"Thompson","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":766566,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70194829,"text":"sir20185003 - 2019 - Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California","interactions":[],"lastModifiedDate":"2019-02-04T09:40:36","indexId":"sir20185003","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2019","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":"2018-5003","title":"Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California","docAbstract":"Beginning in the 1970s, Alameda County Water District began infiltrating imported water through ponds in repurposed gravel quarries at the Quarry Lakes Regional Park, in the Niles Cone groundwater subbasin, to recharge groundwater and to minimize intrusion of saline, San Francisco Bay water into freshwater aquifers. Hydraulic connection between distinct aquifers underlying Quarry Lakes allows water to recharge the upper aquifer system to depths of 400 feet below land surface, and the Deep aquifer to depths of more than 650 feet. Previous studies of the Niles Cone and southern East Bay Plain groundwater subbasins suggested that these two subbasins may be hydraulically connected. Characterization of storage capacities and hydraulic properties of the complex aquifers and the structural and stratigraphic controls on groundwater movement aids in optimal storage and recovery of recharged water and provides information on the ability of aquifers shared by different water management agencies to fulfill competing storage and extraction demands. The movement of recharge water through the Niles Cone groundwater subbasin from Quarry Lakes and the possible hydraulic connection between the Niles Cone and the southern East Bay Plain groundwater subbasins were investigated using interferometric synthetic aperture radar (InSAR), water-chemistry, and isotopic data, including tritium/helium-3, helium-4, and carbon-14 age-dating techniques.
InSAR data collected during refilling of the Quarry Lakes recharge ponds show corresponding ground-surface displacement. Maximum uplift was about 0.8 inches, reasonable for elastic expansion of sedimentary materials experiencing an increase in hydraulic head that resulted from pond refilling. Sodium concentrations increase while calcium and magnesium concentrations in groundwater decrease along groundwater flowpaths from the Niles Cone groundwater subbasin through the Deep aquifer to the northwest toward the southern East Bay Plain groundwater subbasin. Residual effects of pre-1970s intrusion of saline water from San Francisco Bay, including high chloride concentrations in groundwater, are evident in parts of the Niles Cone subbasin. Noble gas recharge temperatures indicate two primary recharge sources (Quarry Lakes and Alameda Creek) in the Niles Cone groundwater subbasin. Although recharge at Quarry Lakes affects hydraulic heads as far as the transition zone between the Niles Cone and East Bay Plain groundwater subbasins (about 5 miles), the effect of recharged water on water quality is only apparent in wells near (less than 2 miles) recharge sources. Groundwater chemistry from upper aquifer system wells near Quarry Lakes showed an evaporated signal (less negative oxygen and hydrogen isotopic values) relative to surrounding groundwater and a tritium concentration (2 tritium units) consistent with recently recharged water from a surface-water impoundment.
Uncorrected carbon-14 activities measured in water sampled from wells in the Niles Cone groundwater subbasin range from 16 to 100 percent modern carbon (pmC). The geochemical reaction modeling software NETPATH was used to interpret carbon-14 ages along a flowpath from Quarry Lakes toward the East Bay Plain groundwater subbasin. Model results indicate that changes in groundwater chemistry are controlled by cation exchange on clay minerals and weathering of primary silicate minerals. Old groundwater (lower carbon-14 activities) is characterized by high dissolved silica and pH. Interpreted carbon-14 ages ranged from 830 to more than 7,000 years before present and are less than helium-4 ages that range from 2,000 to greater than 11,000 years before present. The average horizontal groundwater velocity along the studied flowpath, as calculated using interpreted carbon-14 ages, through the Deep aquifer of the Niles Cone groundwater subbasin is between 3 and 12 feet per year. The groundwater velocity decreases near the boundary of the transition zone to the southern East Bay Plain groundwater subbasin to about 0.5 feet per year. These changes may result from water recharged from different sources converging in flowpaths north of the transition zone, or a boundary to flow between the Niles Cone and southern East Bay Plain groundwater subbasins, likely owing to changes in lithology caused by depositional patterns.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185003","collaboration":"Prepared in cooperation with the East Bay Municipal Utility District, City of Hayward, and Alameda County Water District","usgsCitation":"Teague, Nick, Izbicki, John, Borchers, Jim, Kulongoski, Justin, and Jurgens, Bryant, 2018, Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2018–5003, 62 p., https://doi.org/10.3133/sir20185003.","productDescription":"x, 62 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-043410","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":360934,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5003/versionHist.txt"},{"id":351228,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5003/coverthb.jpg"},{"id":351229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5003/sir20185003_v1.1.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5003"}],"country":"United States","state":"California","county":"Alameda County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.3333,\n 37.5\n ],\n [\n -121.9167,\n 37.5\n ],\n [\n -121.9167,\n 37.8333\n ],\n [\n -122.3333,\n 37.8333\n ],\n [\n -122.3333,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Ver. 1.0: February 2018; Ver. 1.1: February 2019","contact":"Director,
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
We investigated the direct and indirect influence of tides on net ecosystem exchange (NEE) of carbon dioxide (CO2) in a temperate brackish tidal marsh. NEE displayed a tidally driven pattern with obvious characteristics at the multiday scale, with greater net CO2uptake during spring tides than neap tides. Based on the relative mutual information between NEE and biophysical variables, this was driven by a combination of higher water table depth (WTD), cooler air temperature, and lower vapor pressure deficit (VPD) during spring tides relative to neap tides, as the fortnightly tidal cycle not only influenced water levels but also strongly modulated water and air temperature and VPD. Tides also influenced NEE at shorter timescales, with a reduction in nighttime fluxes during growing season spring tides when the higher of the two semidiurnal tides caused inundation at the site. WTD significantly influenced ecosystem respiration (Reco), with lower Reco during spring tides than neap tides. While WTD did not appear to affect ecosystem photosynthesis (gross ecosystem production, GPP) directly, the impact of tides on temperature and VPD influenced GPP, with higher daily light‐use efficiency and photosynthetic activity during spring tides than neap tides when temperature and VPD were lower. The strong direct and indirect influence of tides on NEE across the diel and multiday timescales has important implications for modeling NEE in tidal wetlands and can help inform the timing and frequency of chamber measurements as annual or seasonal net CO2 uptake may be underestimated if measurements are only taken during nonflooded periods.
","language":"English","publisher":"Wiley","doi":"10.1002/2017JG004048","usgsCitation":"Knox, S., Windham-Myers, L., Frank Anderson, Sturtevant, C., and Bergamaschi, B.A., 2019, Direct and indirect effects of tides on ecosystem-scale CO2 exchange in a brackish tidal marsh in Northern California: Journal of Geophysical Research: Biogeosciences, v. 123, no. 3, p. 787-806, https://doi.org/10.1002/2017JG004048.","productDescription":"20 p.","startPage":"787","endPage":"806","ipdsId":"IP-094185","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":365630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay National Estuarine Research Reserve, Suisun Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.2235870361328,\n 38.065932950547484\n ],\n [\n -122.22427368164064,\n 38.05944549633448\n ],\n [\n -122.1906280517578,\n 38.053498158026564\n ],\n [\n -122.17758178710939,\n 38.03619406237626\n ],\n [\n -122.15217590332031,\n 38.02267239225365\n ],\n [\n -122.13294982910156,\n 38.028622234587964\n ],\n [\n -122.11509704589845,\n 38.038357297980816\n ],\n [\n -122.09587097167967,\n 38.04322434446539\n ],\n [\n -122.08694458007812,\n 38.04755033643351\n ],\n [\n -122.0684051513672,\n 38.05403884511629\n ],\n [\n -122.04574584960938,\n 38.05566088242076\n ],\n [\n -122.02583312988281,\n 38.058364198044636\n ],\n [\n -121.99493408203125,\n 38.05403884511629\n ],\n [\n -121.96609497070312,\n 38.04755033643351\n ],\n [\n -121.94892883300781,\n 38.050794662666895\n ],\n [\n -121.93450927734375,\n 38.05457952821193\n ],\n [\n -121.9207763671875,\n 38.059986139487975\n ],\n [\n -121.91734313964844,\n 38.07025760046315\n ],\n [\n -121.91116333007811,\n 38.067554724225275\n ],\n [\n -121.90910339355467,\n 38.077284611299554\n ],\n [\n -121.91665649414062,\n 38.08268954483802\n ],\n [\n -121.9379425048828,\n 38.07944663265489\n ],\n [\n -121.95236206054688,\n 38.077284611299554\n ],\n [\n -121.96060180664062,\n 38.07674409597339\n ],\n [\n -121.97158813476561,\n 38.07458199472\n ],\n [\n -121.97845458984375,\n 38.07998712800633\n ],\n [\n -122.00042724609374,\n 38.08647276060778\n ],\n [\n -122.02308654785156,\n 38.09674050228651\n ],\n [\n -122.00111389160155,\n 38.10268432504872\n ],\n [\n -121.98875427246092,\n 38.11024849111732\n ],\n [\n -121.9921875,\n 38.131856078273124\n ],\n [\n -122.00935363769531,\n 38.1399572748485\n ],\n [\n -122.03407287597655,\n 38.13725697591337\n ],\n [\n -122.06085205078125,\n 38.135096664819784\n ],\n [\n -122.06634521484374,\n 38.12159327165922\n ],\n [\n -122.06634521484374,\n 38.109708219514644\n ],\n [\n -122.11990356445312,\n 38.05944549633448\n ],\n [\n -122.13020324707033,\n 38.04538737239996\n ],\n [\n -122.135009765625,\n 38.04268357749736\n ],\n [\n -122.15011596679688,\n 38.04322434446539\n ],\n [\n -122.16110229492186,\n 38.043765107439675\n ],\n [\n -122.17071533203125,\n 38.05403884511629\n ],\n [\n -122.17483520507811,\n 38.05944549633448\n ],\n [\n -122.1844482421875,\n 38.065932950547484\n ],\n [\n -122.18994140624999,\n 38.069176461951876\n ],\n [\n -122.1954345703125,\n 38.06052677864718\n ],\n [\n -122.20573425292967,\n 38.06809530746208\n ],\n [\n -122.2235870361328,\n 38.065932950547484\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Knox, Sara 0000-0003-2255-5835","orcid":"https://orcid.org/0000-0003-2255-5835","contributorId":216996,"corporation":false,"usgs":false,"family":"Knox","given":"Sara","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":766227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":766226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frank Anderson 0000-0002-1418-4678","orcid":"https://orcid.org/0000-0002-1418-4678","contributorId":216997,"corporation":false,"usgs":false,"family":"Frank Anderson","affiliations":[{"id":39554,"text":"USGS CA WSC","active":true,"usgs":false}],"preferred":false,"id":766228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sturtevant, Cove 0000-0002-0341-3228","orcid":"https://orcid.org/0000-0002-0341-3228","contributorId":216998,"corporation":false,"usgs":false,"family":"Sturtevant","given":"Cove","email":"","affiliations":[{"id":39555,"text":"NSF NEON","active":true,"usgs":false}],"preferred":false,"id":766229,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":766230,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204760,"text":"70204760 - 2019 - Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King","interactions":[],"lastModifiedDate":"2019-09-03T08:16:10","indexId":"70204760","displayToPublicDate":"2017-12-31T12:46:33","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King","docAbstract":"We genotyped 7,588 brook trout representing 406 collections from across the State of North Carolina (Figure 1) at 12 microsatellite loci (King et al. 2012). The vast majority of
collections appeared to represent single populations, based on general conformance to HardyWeinberg equilibrium and limited evidence for linkage-disequilibrium. Allelic diversity was low to moderate relative to Brook Trout Salvelinus fontinalis populations endemic to higher latitudes. Effective population sizes varied widely among populations, but were often very small and indicate that many populations are at risk of losing diversity through genetic drift. Remarkable levels of genetic differentiation exist among populations, which suggests that little, if any, gene flow occurs among most populations. Analysis of molecular variance (AMOVA) revealed that a substantial portion of the observed genetic variation was attributed to differences among patches (44.8%), and there was some variation (11.2%) even among collections within a single patch. These results, taken in conjunction with high levels of genetic differentiation among populations, suggest that the fundamental unit of management for Brook Trout should be the
population. Interestingly, despite extensive stocking across the state, the vast majority of wild populations show limited evidence of introgression by northern origin hatchery strains. These results represent a valuable baseline for management and restoration efforts, and can be used to (a) select suitable donor streams for translocation efforts, (b) identify streams with low effective population sizes that may be vulnerable to extirpation, and (c) target stocking efforts into watersheds where extensive introgression has already occurred. All data associated with this manuscript has been publicly released (Kazyak et al. 2017).
In 2014, the U.S. Geological Survey, in cooperation with the U.S. Department of Defense’s Strategic Environmental Research and Development Program, initiated a project to evaluate the potential impacts of projected climate-change on Department of Defense installations that rely on Guam’s water resources. A major task of that project was to develop a watershed model of southern Guam and a water-balance model for the Fena Valley Reservoir. The southern Guam watershed model provides a physically based tool to estimate surface-water availability in southern Guam. The U.S. Geological Survey’s Precipitation Runoff Modeling System, PRMS-IV, was used to construct the watershed model. The PRMS-IV code simulates different parts of the hydrologic cycle based on a set of user-defined modules. The southern Guam watershed model was constructed by updating a watershed model for the Fena Valley watersheds, and expanding the modeled area to include all of southern Guam. The Fena Valley watershed model was combined with a previously developed, but recently updated and recalibrated Fena Valley Reservoir water-balance model.
Two important surface-water resources for the U.S. Navy and the citizens of Guam were modeled in this study; the extended model now includes the Ugum River watershed and improves upon the previous model of the Fena Valley watersheds. Surface water from the Ugum River watershed is diverted and treated for drinking water, and the Fena Valley watersheds feed the largest surface-water reservoir on Guam. The southern Guam watershed model performed “very good,” according to the criteria of Moriasi and others (2007), in the Ugum River watershed above Talofofo Falls with monthly Nash-Sutcliffe efficiency statistic values of 0.97 for the calibration period and 0.93 for the verification period (a value of 1.0 represents perfect model fit). In the Fena Valley watershed, monthly simulated streamflow volumes from the watershed model compared reasonably well with the measured values for the gaging stations on the Almagosa, Maulap, and Imong Rivers—tributaries to the Fena Valley Reservoir—with Nash-Sutcliffe efficiency values of 0.87 or higher. The southern Guam watershed model simulated the total volume of the critical dry season (January to May) streamflow for the entire simulation period within –0.54 percent at the Almagosa River, within 6.39 percent at the Maulap River, and within 6.06 percent at the Imong River.
The recalibrated water-balance model of the Fena Valley Reservoir generally simulated monthly reservoir storage volume with reasonable accuracy. For the calibration and verification periods, errors in end-of-month reservoir-storage volume ranged from 6.04 percent (284.6 acre-feet or 92.7 million gallons) to –5.70 percent (–240.8 acre-feet or –78.5 million gallons). Monthly simulation bias ranged from –0.48 percent for the calibration period to 0.87 percent for the verification period; relative error ranged from –0.60 to 0.88 percent for the calibration and verification periods, respectively. The small bias indicated that the model did not consistently overestimate or underestimate reservoir storage volume.
In the entirety of southern Guam, the watershed model has a “satisfactory” to “very good” rating when simulating monthly mean streamflow for all but one of the gaged watersheds during the verification period. The southern Guam watershed model uses a more sophisticated climate-distribution scheme than the older model to make use of the sparse climate data, as well as includes updated land-cover parameters and the capability to simulate closed depression areas.
The new Fena Valley Reservoir water-balance model is useful as an updated tool to forecast short-term changes in the surface-water resources of Guam. Furthermore, the now spatially complete southern Guam watershed model can be used to evaluate changes in streamflow and recharge owing to climate or land-cover changes. These are substantial improvements to the previous models of the Fena Valley watershed and Reservoir. Datasets associated with this report are available as a U.S. Geological Survey data release (Rosa and Hay, 2017; DOI:10.5066/F7HH6HV4).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175093","collaboration":"Prepared in cooperation with the U.S. Department of Defense Strategic Environmental Research and Development Program (SERDP)","usgsCitation":"Rosa, S.N., and Hay, L.E., 2019, Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2017–5093, 64 p., https://doi.org/10.3133/sir20175093.","productDescription":"Report: viii, 64 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-081743","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":349631,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5093/coverthb2.jpg"},{"id":349632,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5093/sir20175093.pdf","text":"Report","size":"22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5093 v1.1"},{"id":361066,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5093/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2017-5093 Version History"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.6240234375,\n 13.230587802102518\n ],\n [\n 144.96047973632812,\n 13.230587802102518\n ],\n [\n 144.96047973632812,\n 13.652659349024093\n ],\n [\n 144.6240234375,\n 13.652659349024093\n ],\n [\n 144.6240234375,\n 13.230587802102518\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 2017; Version 1.1: February 2019","contact":"Director,
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Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network‐based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row‐standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals (Phoca vitulina) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row‐standardization. We include several take‐home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical models, and not only in explicit spatial settings, but also for more general connectivity models.
Innovative, open, and rapid methods to map crop types over large areas are needed for long-term cropland monitoring. We developed two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification. The classification approaches use similarities and dissimilarities in crop type phenologyderived from NDVI time-series data for the two approaches. The year-specific approach uses the training samples from one year and classifies crop types for that year only, whereas the generalized classification approach uses above-average, average, and below-average precipitation years for training to produce crop type maps for one or multiple years more robustly. We produced annual crop type maps using the generalized classification approach for 2001–2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies > 78%, while the generalized classifier had accuracies > 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year. The resulting crop type maps for years prior to 2007 are new and especially important for long-term cropland monitoring and food security analysis because no other map products are currently available for 2001–2007.
Steep declines in North American monarch butterfly (Danaus plexippus) populations have prompted continent-wide conservation efforts. While monarch monitoring efforts have existed for years, we lack a comprehensive approach to monitoring population vital rates integrated with habitat quality to inform adaptive management and effective conservation strategies. Building a geographically and ecologically representative dataset of monarchs and their habitat will improve these efforts. These data will help track long-term changes in the distribution and abundance of monarchs and their habitats, refine population and habitat models, and illuminate how conservation activities affect monarchs and their habitats. The Monarch Conservation Science Partnership developed the Integrated Monarch Monitoring Program (IMMP) to profile breeding habitats and their use by monarchs in North America. A spatially balanced random sampling framework guides site selection, while also allowing opportunistic inclusion of sites chosen by participants, such as conservation areas. The IMMP weaves new protocols together with those from existing monitoring programs to improve data compatibility for assessing milkweed (Asclepias spp.) density, nectar resources, monarch reproduction and survival, and adult monarch habitat use. Participants may select a protocol subset according to interests or local monitoring objectives, thereby maximizing contributions. Conservation partners, including public and private land managers, academic researchers, and citizen scientists contribute data to a national dataset available for analyses at multiple scales. We describe the program and its development, implementation elements that make the program robust and feasible, participation to date, and how IMMP data can advance research and conservation for monarchs, pollinators, and their habitats.
","language":"English","publisher":"Frontiers Media SA","doi":"10.3389/fevo.2019.00167","usgsCitation":"Cariveau, A.B., Holt, H.L., Ward, J.P., Lukens, L., Kasten, K., Thieme, J., Caldwell, W., Tuerk, K., Baum, K.A., Drobney, P., Drum, R.G., Grundel, R., Hamilton, K., Hoang, C., Kinkead, K., McIntyre, J., Thogmartin, W.E., Turner, T., Weiser, E.L., and Oberhauser, K., 2019, The integrated monarch monitoring program: From design to implementation: Frontiers in Ecology and Evolution, v. 29, https://doi.org/10.3389/fevo.2019.00167.","ipdsId":"IP-106644","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00167","text":"Publisher Index Page"},{"id":364415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364294,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00167"}],"country":"United States","otherGeospatial":"North America","volume":"29","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cariveau, Alison B","contributorId":215961,"corporation":false,"usgs":false,"family":"Cariveau","given":"Alison","email":"","middleInitial":"B","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":763543,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holt, Holly L","contributorId":215962,"corporation":false,"usgs":false,"family":"Holt","given":"Holly","email":"","middleInitial":"L","affiliations":[{"id":39337,"text":"Oak Ridge Associated 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Minnesota","active":true,"usgs":false}],"preferred":false,"id":763547,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thieme, Jennifer","contributorId":215966,"corporation":false,"usgs":false,"family":"Thieme","given":"Jennifer","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":763548,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Caldwell, Wendy","contributorId":215967,"corporation":false,"usgs":false,"family":"Caldwell","given":"Wendy","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":763549,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tuerk, Karen","contributorId":215968,"corporation":false,"usgs":false,"family":"Tuerk","given":"Karen","email":"","affiliations":[{"id":6626,"text":"University of 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Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":763555,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Kinkead, Karen","contributorId":215972,"corporation":false,"usgs":false,"family":"Kinkead","given":"Karen","email":"","affiliations":[{"id":39338,"text":"Iowa DNR","active":true,"usgs":false}],"preferred":false,"id":763556,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"McIntyre, Julie","contributorId":215973,"corporation":false,"usgs":false,"family":"McIntyre","given":"Julie","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":763557,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":763558,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Turner, Tenlea","contributorId":215974,"corporation":false,"usgs":false,"family":"Turner","given":"Tenlea","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":763559,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":763560,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Oberhauser, Karen","contributorId":191431,"corporation":false,"usgs":false,"family":"Oberhauser","given":"Karen","affiliations":[],"preferred":false,"id":763561,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70227841,"text":"70227841 - 2018 - Predicting spatial factors associated with cattle depredations by the Mexican wolf (Canis lupus baileyi) with recommendations for depredation risk modeling","interactions":[],"lastModifiedDate":"2022-02-01T17:56:59.268014","indexId":"70227841","displayToPublicDate":"2021-06-21T11:53:14","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Predicting spatial factors associated with cattle depredations by the Mexican wolf (Canis lupus baileyi) with recommendations for depredation risk modeling","title":"Predicting spatial factors associated with cattle depredations by the Mexican wolf (Canis lupus baileyi) with recommendations for depredation risk modeling","docAbstract":"Predation on livestock is one of the primary concerns for Mexican wolf (Canis lupus baileyi) recovery because it causes economic losses and negative attitudes toward wolves. Our objectives were to develop a spatial risk model of cattle depredation by Mexican wolves in the USA portion of their recovery area to help reduce the potential for future depredations.
Arizona and New Mexico, USA.
We used a presence-only maximum entropy modeling approach (Maxent) to develop a risk model based on confirmed depredation incidents on public lands. In addition to landscape and human variables, we developed a model for annual livestock density using linear regression analysis of Animal Unit Month (AUM), and models for abundance of elk (Cervus canadensis), mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginiana) using Maxent, to include them as biotic variables in the risk model. We followed current recommendations for controlling model complexity and other sources of bias.
The primary factors associated with increased risk of depredation by Mexican wolf were higher canopy cover variation and higher relative abundance of elk. Additional factors with increased risk but smaller effect were gentle and open terrain, and greater distances from roads and developed areas.
The risk map revealed areas with relatively high potential for cattle depredations that can inform future expansion of Mexican wolf distribution (e.g., by avoiding hotspots) and prioritize areas for depredation risk mitigation including the implementation of active non-lethal methods in depredation hotspots. We suggest that livestock be better protected in or moved from potential hotspots, especially during periods when they are vulnerable to depredation (e.g. calving season). Our approach to create natural prey and livestock abundance variables can facilitate the process of spatial risk modeling when limitations in availability of abundance data are a challenge, especially in large-scale studies.
Proxy species, which represent suites of organisms with similar habitat requirements, are common in conservation. Landscape Capability (LC) models aim to quantify the spatially-explicit capability of landscapes to support proxy species that represent suites of forest birds.
We evaluated the North Atlantic Landscape Conservation Cooperative (NALCC) proxy models of LC and represented species framework across 13 states in the northeastern United States from Virginia to Maine. We validated a suite of questions related to co-occurrence of proxy and represented species with a compilation of independent datasets.
We tested proxy species LC models ability to explain represented species’ occurrences, including using multiple proxies together, and benchmarked against empirical data and land cover type classifications. We tested effect of several factors on predictive ability including relative range overlap and ecological and taxonomic dissimilarity between proxy and represented species.
LC models performed variably, but represented species occurrences were rarely predicted as accurately as proxy species. Models improved predictions over macrohabitat classifications. Using multiple proxies together occasionally improved predictions of represented species. Considerable range overlap was needed for models to be predictive of represented species. Ecological and taxonomic similarity had no effect on predictive ability. LC models worked similarly to using empirical observations, suggesting shortcomings were because of imperfect surrogacy.
Conservation proxies as representatives of species groups that are associated with macrohabitats are useful, but empirical data are necessary to evaluate proxy species’ effectiveness. Habitat-based models can provide similar predictive ability as empirical observations of proxies and represent a useful tool in conservation planning.
Streambank erosion in areas of past glacial deposition has been shown to be a dominant source of sediment to streams. Water resource managers are faced with the challenge of developing long and short term (emergency) stream restoration efforts that rely on the most suitable channel geometry for project design. A geomorphic dataset of new (2016, n=5) and previous (1999–2006, n=96) estimates of bankfull discharge and channel dimensions at U.S. Geological Survey streamflow-gaging stations was compiled to present and contrast the glaciated and unglaciated noncarbonate settings of southern New York and Pennsylvania that included selected areas of Maryland. Empirical models were developed by using simple linear regressions that relate bankfull discharge and channel geometry to drainage area (regional curves). Significant relations (p<0.05) were able to explain variability with coefficient of determination (R2 ) values of 0.89 for bankfull discharge, 0.94 for cross-sectional area, 0.87 for bankfull width, and 0.83 for bankfull depth. These regression relations for the glaciated noncarbonate settings of northern Pennsylvania and southern New York were able to provide a slightly better fit than regional curve models developed previously for the entire noncarbonate region of Pennsylvania. Although, the analysis of covariance (ANCOVA) results for comparison between regression equations for the glaciated and unglaciated settings showed that except for the significant intercept of bankfull discharge versus drainage area (F=8.26, p-value<0.005), the regression equations are not significantly different between the glaciated and unglaciated setting of Pennsylvania and southern New York. Therefore, data stratification by glaciation does not improve regional curves relations developed previously for the noncarbonate (glaciated and unglaciated) and carbonate settings of Pennsylvania and Maryland. Further analysis that incorporates data stratification or multivariate approaches based on mean annual runoff, precipitation, slope, stream classification, or other relevant parameters may optimize the accuracy and utility of statewide models. The new estimates of bankfull discharge and channel dimensions at streamflowgaging sites and updated drainage areas from StreamStats were incorporated into previously developed regional curves to produce an updated set of regression relations of bankfull discharge and channel geometry for the noncarbonate and carbonate settings of Pennsylvania and Maryland.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185066","collaboration":"Prepared in cooperation with the Bradford County Conservation District","usgsCitation":"Clune, J.W., Chaplin, J.J., and White, K.E., 2018, Comparison of regression relations of bankfull discharge and channel geometry for the glaciated and nonglaciated settings of Pennsylvania and southern New York (ver. 1.1, July 2020): U.S. Geological Survey Scientific Investigations Report 2018–5066, 20 p., https://doi.org/10.3133/sir20185066.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091919","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":355942,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5066/sir20185066.pdf","text":"Report","size":"13.8 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York\",\"nation\":\"USA \"}}]}","edition":"Version 1.1: July 2020; Version 1.0: July 2018","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
The quality of stormwater runoff from bridge decks (hereafter referred to as “bridge-deck runoff”) was characterized in a field study from August 2014 through August 2016 in which concentrations of suspended sediment (SS) and total nutrients were monitored. These new data were collected to supplement existing highway-runoff data collected in Massachusetts which were deficient in bridge-deck runoff concentration data. Monitoring stations were installed at three bridges maintained by the Massachusetts Department of Transportation in eastern Massachusetts (State Route 2A in the city of Boston, Interstate 90 in the town of Weston, and State Route 20 near Quinsigamond Village in the city of Worcester). The bridges had annual average daily traffic volumes from 21,200 to 124,000 vehicles per day; the land use surrounding the monitoring stations was 25 to 67 percent impervious.
Automatic-monitoring techniques were used to collect more than 160 flow-proportional composite samples of bridge-deck runoff. Samples were analyzed for concentrations of SS, loss on ignition of suspended solids (LOI), particulate carbon (PC), total phosphorus (TP), total dissolved nitrogen (DN), and particulate nitrogen (PN). The distribution of particle size of SS also was determined for composite samples. Samples of bridge-deck runoff were collected year round during rain, mixed precipitation, and snowmelt runoff and with different dry antecedent periods throughout the 2-year sampling period.
At the three bridge-deck-monitoring stations, median concentrations of SS in composite samples of bridge-deck runoff ranged from 1,490 to 2,020 milligrams per liter (mg/L); however, the range of SS in individual composites was vast at 44 to 142,000 mg/L. Median concentrations of SS were similar in composite samples collected from the State Route 2A and Interstate 90 bridge (2,010 and 2,020 mg/L, respectively), and lowest at the State Route 20 bridge (1,490 mg/L). Concentrations of coarse sediment (greater than 0.25 millimeters in diameter) dominated the SS matrix by more than an order of magnitude. Concentrations of LOI and PC in composite samples ranged from 15 to 1,740 mg/L and 6.68 to 1,360 mg/L, respectively, and generally represented less than 10 and 3 percent of the median mass of SS, respectively. Concentrations of TP in composite samples ranged from 0.09 to 7.02 mg/L; median concentrations of TP ranged from 0.505 to 0.69 mg/L and were highest on the bridge on State Route 2A in Boston. Concentrations of total nitrogen (TN) (sum DN and PN) in composite samples were variable (0.36 to 29 mg/L). Median DN (0.64 to 0.90 mg/L) concentrations generally represented about 40 percent of the TN concentration at each bridge and were similar to annual volume-weighted mean concentrations of nitrogen in precipitation in Massachusetts.
Nonparametric statistical methods were used to test for differences between sample constituent concentrations among the three bridges. These results indicated that there are no statistically significant differences for concentrations of SS, LOI, PC, and TP among the three bridges (one-way analysis of variance test on rank-transformed data, 95-percent confidence level). Test results for concentrations of TN in composite samples indicated that concentrations of TN collected on State Route 20 near Quinsigamond Village were significantly higher than those concentrations collected on State Route 2A in Boston and Interstate 90 near Weston. Median concentrations of TN were about 93 and 55 percent lower at State Route 2A and at Interstate 90, respectively, compared to the median concentrations of TN at State Route 20.
Samples of sediment were collected from five fixed locations on each bridge on three occasions during dry weather to calculate semiquantitative distributions of sediment yields on the bridge surface relative to the monitoring location. Mean yields of bridge-deck sediment during this study for State Route 2A in Boston, Interstate 90 near Weston, and State Route 20 near Quinsigamond Village were 1,500, 250, and 5,700 pounds per curb-mile, respectively. Sediment yields at each sampling location varied widely (26 to 25,000 pounds per curb-mile) but were similar to yields reported elsewhere in Massachusetts and the United States. Yields calculated for each sampling location indicated that the sediment was not evenly distributed across each bridge in this study for plausible reasons such as bridge slope, vehicular tracking, and bridge deterioration.
Bridge-deck sediment quality was largely affected by the distribution of sediment particle size. Concentrations of TP in the fine sediment-size fraction (less than 0.0625 millimeter in diameter) of samples of bridge-deck sediment were about 6 times greater than in the coarse size fraction. Concentrations for many total-recoverable metals were 2 to 17 times greater in the fine size fraction compared to concentrations in the coarse size fraction (greater than or equal to 0.25 millimeter in diameter), and concentrations of total-recoverable copper and lead in the fine size fraction were 2 to 65 times higher compared to concentrations in the intermediate (greater than or equal to 0.0625 to 0.25 millimeter in diameter) or the coarse size fraction. However, the proportion of sediment particles less than 0.0625 millimeter in diameter in composite samples of bridge-deck runoff was small (median values range from 4 to 8 percent at each bridge) compared to the larger sediment particle-size mass. As a result, more than 50 percent of the sediment-associated TP, aluminum, chromium, manganese, and nickel was estimated to be associated with the coarse size fraction of the SS load. In contrast, about 95 percent of the estimated sediment-associated copper concentration was associated with the fine size fraction of the SS load.
Version 1.0.2 of the Stochastic Empirical Loading and Dilution Model was used to simulate long-term (29–30-year) concentrations and annual yields of SS, TP, and TN in bridge-deck runoff and in discharges from a hypothetical stormwater treatment best-management practice structure. Three methods (traditional statistics, robust statistics, and L-moments) were used to calculate statistics for stochastic simulations because the high variability in measured concentration values during the field study resulted in extreme simulated concentrations. Statistics of each dataset, including the average, standard deviation, and skew of the common (base 10) logarithms, for each of the three bridges, and for a lumped dataset, were calculated and used for simulations; statistics representing the median of statistics calculated for the three bridges also were used for simulations. These median statistics were selected for the interpretive simulations so that the simulations could be used to estimate concentrations and yields from other, unmonitored bridges in Massachusetts. Comparisons of the standard and robust statistics indicated that simulation results with either method would be similar, which indicated that the large variability in simulated results was not caused by a few outliers. Comparison to statistics calculated by the L-moments methods indicated that L-moments do not produce extreme concentrations; however, they also do not produce results that represent the bulk of concentration data.
The runoff-quality risk analysis indicated that bridge-deck runoff would exceed discharge standards commonly used for large, advanced wastewater treatment plants, but that commonly used stormwater best-management practices may reduce the percentage of exceedances by one-half. Results of simulations indicated that long-term average yields of TN, TP, and SS may be about 21.4, 6.44, and 40,600 pounds per acre per year, respectively. These yields are about 1.3, 3.4, and 16 times simulated ultra-urban highway yields in Massachusetts; however, simulations indicated that use of a best-management practice structure to treat bridge-deck runoff may reduce discharge yields to about 10, 2.8, and 4,300, pounds per acre per year, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185033","isbn":"978-1-4113-4222-4","usgsCitation":"Smith, K.P., Sorenson, J.R., and Granato, G.E., 2018, Characterization of stormwater runoff from bridge decks in eastern Massachusetts, 2014–16: U.S. Geological Survey Scientific Investigations Report 2018–5033, 73 p., https://doi.org/10.3133/sir20185033.","productDescription":"xiii, 73 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-088034","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":374915,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5033/sir20185033.pdf","text":"Report","size":"4.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5033"},{"id":353906,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5033/coverthb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.98516845703125,\n 41.97582726102573\n ],\n [\n -70.7904052734375,\n 41.97582726102573\n ],\n [\n -70.7904052734375,\n 42.827638636242284\n ],\n [\n -71.98516845703125,\n 42.827638636242284\n ],\n [\n -71.98516845703125,\n 41.97582726102573\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The Challenge—Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer.
The Aim of Geostatistics—The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps.
Geostatistical Methods—Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods.
Differences with Time Series—On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications.
Historical Remarks—As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields.
Pioneers—Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology
Calculations—Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations.
Software—For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics.
Organization of the Primer—The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics.
Course Objectives—This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20091103","usgsCitation":"Olea, R., 2018, A practical primer on geostatistics (Version 1.0: Originally posted July 6, 2009; Version 1.1: January 2010; Version 1.2: July 2017, Version 1.3: November 2017; Version 1.4: December 2018): U.S. Geological Survey Open-File Report 2009-1103, ii, 346 p., https://doi.org/10.3133/ofr20091103.","productDescription":"ii, 346 p.","numberOfPages":"348","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":344191,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2009/1103/versionHist_1_4.txt","size":"4.74 KB","linkFileType":{"id":2,"text":"txt"}},{"id":344186,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2009/1103/ofr20091103.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2009-1103"},{"id":125462,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2009/1103/coverthb4.jpg"}],"edition":"Version 1.0: Originally posted July 6, 2009; Version 1.1: January 2010; Version 1.2: July 2017, Version 1.3: November 2017; Version 1.4: December 2018","contact":"Eastern Energy Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Lakes, reservoirs, and other ponded waters are ubiquitous features of the aquatic landscape, yet their cumulative role in nitrogen removal in large river basins is often unclear. Here we use predictive modeling, together with comprehensive river water quality, land use, and hydrography datasets, to examine and explain the influences of more than 18,000 ponded waters on nitrogen removal through river networks of the Northeastern United States. Thresholds in pond density where ponded waters become important features to regional nitrogen removal are identified and shown to vary according to a ponded waters’ relative size, network position, and degree of connectivity to the river network, which suggests worldwide importance of these new metrics. Consideration of the interacting physical and biological factors, along with thresholds in connectivity, reveal where, why, and how much ponded waters function differently than streams in removing nitrogen, what regional water quality outcomes may result, and in what capacity management strategies could most effectively achieve desired nitrogen loading reduction.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-018-05156-x","usgsCitation":"Schmadel, N.M., Harvey, J., Alexander, R., Schwarz, G., Moore, R., Eng, K., Gomez-Velez, J., Boyer, E.W., and Scott, D., 2018, Thresholds of lake and reservoir connectivity in river networks control nitrogen removal: Nature Communications, v. 9, 2779, 10 p., https://doi.org/10.1038/s41467-018-05156-x.","productDescription":"2779, 10 p.","ipdsId":"IP-093046","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468151,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-018-05156-x","text":"Publisher Index Page"},{"id":367536,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, District of Columbia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode island, Vermont, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.884765625,\n 44.37098696297173\n ],\n [\n -66.884765625,\n 44.99588261816546\n ],\n [\n -67.67578124999999,\n 45.82879925192134\n ],\n [\n -67.8515625,\n 47.368594345213374\n ],\n [\n -69.2138671875,\n 47.517200697839414\n ],\n [\n -71.19140625,\n 45.398449976304086\n ],\n [\n -71.8505859375,\n 44.99588261816546\n ],\n [\n -74.3994140625,\n 45.182036837015886\n ],\n [\n -76.81640625,\n 42.74701217318067\n ],\n [\n -77.6953125,\n 40.81380923056958\n ],\n [\n -79.40917968749999,\n 39.40224434029275\n ],\n [\n -79.27734374999999,\n 37.50972584293751\n ],\n [\n -78.9697265625,\n 36.914764288955936\n ],\n [\n -75.234375,\n 36.38591277287651\n ],\n [\n -73.125,\n 40.111688665595956\n ],\n [\n -69.2578125,\n 41.11246878918088\n ],\n [\n -66.884765625,\n 44.37098696297173\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah M.","contributorId":219098,"corporation":false,"usgs":false,"family":"Schmadel","given":"Noah","email":"","middleInitial":"M.","affiliations":[{"id":39961,"text":"USGS Post-Doc","active":true,"usgs":false}],"preferred":false,"id":771257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson","contributorId":219097,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":771256,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard 0000-0001-9166-0626 ralex@usgs.gov","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":219099,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","email":"ralex@usgs.gov","affiliations":[],"preferred":true,"id":771258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":219100,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[],"preferred":false,"id":771259,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, Richard","contributorId":219101,"corporation":false,"usgs":true,"family":"Moore","given":"Richard","affiliations":[],"preferred":true,"id":771260,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eng, Ken 0000-0001-6838-5849 keng@usgs.gov","orcid":"https://orcid.org/0000-0001-6838-5849","contributorId":3580,"corporation":false,"usgs":true,"family":"Eng","given":"Ken","email":"keng@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":771261,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gomez-Velez, Jesus D.","contributorId":219103,"corporation":false,"usgs":false,"family":"Gomez-Velez","given":"Jesus D.","affiliations":[{"id":39962,"text":"Department of Earth & Environmental Science, New Mexico Institute of Mining and Technology, Socorro, New Mexico, USA","active":true,"usgs":false}],"preferred":false,"id":771262,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":771263,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scott, Durelle","contributorId":219088,"corporation":false,"usgs":false,"family":"Scott","given":"Durelle","affiliations":[{"id":39959,"text":"Virginia Tech.","active":true,"usgs":false}],"preferred":false,"id":771264,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70204196,"text":"70204196 - 2018 - Drain tiles and groundwater resources: Understanding the relations","interactions":[],"lastModifiedDate":"2019-08-02T10:52:42","indexId":"70204196","displayToPublicDate":"2019-06-01T11:14:22","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Drain tiles and groundwater resources: Understanding the relations","docAbstract":"Executive Summary
Drainage for agricultural production over the past 150 years has been an integral component of human-driven change to Minnesota’s rural landscapes.
Benefits of drainage
Historically, poorly drained soils across much of the State would often remain saturated or flooded after spring snowmelt, preventing timely farm operations such as tilling and planting crops (Arneman, 1963). Installation of agricultural drainage, both surface ditches and subsurface drainage, accelerated transport of water off farm fields and imparted producers higher crop yields (Beauchamp, 1987; Stoner and others, 1993). Agricultural drainage offered many other benefits such as preventing crop drown out, aerating the soil profile for improved plant growth, limiting surface runoff and soil erosion, and allowing farmers better access to croplands (Fausey and others, 1987). Without agricultural drainage on much of Minnesota’s croplands, it would have been difficult to realize high enough crop yields to remain economically viable.
Environmental concerns
While drainage of Minnesota croplands provided the benefits mentioned above, several environmental concerns result. These include wetland loss, degradation of downstream water quality, and reduced [potential for] recharge.
Early agricultural drainage efforts (pre-20th century) led to the disappearance of much of Minnesota’s natural wetlands. Increased focus on preventing or mitigating wetland loss over the last 50 years has helped curtail further losses, even as agricultural drainage proceeds. Prior to establishment of Minnesota statehood, wetlands accounted for more than 10 million acres in Minnesota, including prairie wetlands, peatlands, and forest wetlands that comprised approximately 19 percent of the total land area (Palmer, 1915; King, 1980). In 2018, only half of Minnesota’s pre-settlement wetlands remain, mostly in parts of the State that have not experienced widespread drainage, such as northern Minnesota.
Water-quality monitoring has shown that agricultural drainage, in particular the practice of subsurface drainage, provides a direct flow path for nutrient (nitrogen and soluble phosphorus) losses to surface water resources. The negative consequences of agricultural drainage on surface water quality are well documented (for example, Dinnes and others, 2002; Kladivko and others, 2004; Richards and others, 2008; Rozemeijer and others, 2010; Schottler and others, 2013). Agricultural basins with a high percentage of agricultural drainage have been implicated as part of the cause of the Gulf of Mexico hypoxia zone due to excessive nitrogen export (Goolsby and Battaglin, 2001; Randall and Mulla, 2001).
The connection of hydrological effects of agricultural subsurface drainage on groundwater recharge and aquifers, on the other hand, has not been well-established. Agricultural subsurface drainage intercepts infiltrating water below croplands and directly discharges the water to nearby surface waters. However, the size of the water balance shift from drained water that would have evapotranspired or run off the land to drained water that would recharge underlying aquifers has been poorly characterized (Schuh, 2008).
Drain Tiles and Groundwater
Given the poor accounting of subsurface drainage effects on groundwater resources, the Minnesota Ground Water Association (MGWA) deemed it imperative that we document these effects so that groundwater resources in agricultural regions with substantial drainage can be effectively managed. This white paper documents the relations of drain tiles and groundwater resources and discusses the historical significance of agricultural drainage practices, the recognized positive benefits and potential negative consequences of agricultural drainage practices, and the gaps in understanding of the connections between agricultural drainage and groundwater resources.
The major messages emerged from the findings of this white paper are:
Near Moku‘āweoweo, Mauna Loa’s summit caldera, there are three fans of explosive deposits. The fans, located to the west, northwest, and east, are strongly arcuate in map view. Along ‘Āinapō Trail, 2.8–3.5 km southeast of the caldera, there are several small kīpuka that expose a fourth explosive deposit. Although these explosive deposits have been known for some time, no study bearing on the nature of the explosive activity that formed them has been done. By analyzing cosmogenic exposure age data and the physical properties of the debris fans—lithology, size distributions, and clast dispersal—we conclude that the lithic deposits are the result of five separate phreatic events. The lithic ejecta consist of fragments of ponded lavas, pāhoehoe, gabbroic xenoliths, and “bread-crust” fragments. The exposure ages indicate that the explosive deposit on the west caldera rim was erupted 868 ± 57 yr B.P.; for the northwest fan, the age determination is 829 ± 51 yr B.P.; and on the east rim, ejecta deposits are younger, with ages of 150 ± 20 and 220 ± 20 yr B.P. Lavas underlying these deposits have exposure ages of 960–1020 yr B.P., consistent with the stratigraphy. Near ‘Āinapō Trail, the explosive deposit is much older, overlain by flows dated with a pooled mean age of 1507 ± 19 yr B.P. From the cosmogenic dating, we have three reliable and unambiguous dates. At a much earlier time, a fourth explosive eruption created the ‘Āinapō Trail deposit. We conclude there were at least five explosive episodes around the summit caldera. These deposits, along with recent work done on Kīlauea’s explosive activity, further discredit the notion that Hawaiian volcanoes are strictly effusive in nature. The evidence from the summit of Mauna Loa indicates that it, too, has erupted explosively in recent history.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Field volcanology: A tribute to the distinguished career of Don Swanson","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2018.2538(15)","usgsCitation":"Trusdell, F., Hungerford, J., Stone, J., Fifield, K., McCann, K., Wershow, H., Zaarur, S., and Dimeo Boyd, M., 2018, Explosive eruptions at the summit of Mauna Loa: Lithology, modeling, and dating, chap. of Field volcanology: A tribute to the distinguished career of Don Swanson, v. 538, p. 325-349, https://doi.org/10.1130/2018.2538(15).","productDescription":"25 p.","startPage":"325","endPage":"349","ipdsId":"IP-089763","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":395128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.6297492980957,\n 19.42013505603468\n ],\n [\n -155.55353164672852,\n 19.42013505603468\n ],\n [\n -155.55353164672852,\n 19.502842244396035\n ],\n [\n -155.6297492980957,\n 19.502842244396035\n ],\n [\n -155.6297492980957,\n 19.42013505603468\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"538","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Trusdell, Frank A. 0000-0002-0681-0528 trusdell@usgs.gov","orcid":"https://orcid.org/0000-0002-0681-0528","contributorId":754,"corporation":false,"usgs":true,"family":"Trusdell","given":"Frank A.","email":"trusdell@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":832274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hungerford, Jefferson","contributorId":243584,"corporation":false,"usgs":false,"family":"Hungerford","given":"Jefferson","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":832275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stone, John","contributorId":199224,"corporation":false,"usgs":false,"family":"Stone","given":"John","email":"","affiliations":[],"preferred":false,"id":832276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fifield, Keith","contributorId":272639,"corporation":false,"usgs":false,"family":"Fifield","given":"Keith","email":"","affiliations":[{"id":37791,"text":"Australia National University, Canberra","active":true,"usgs":false}],"preferred":false,"id":832277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCann, Kaitlin","contributorId":272640,"corporation":false,"usgs":false,"family":"McCann","given":"Kaitlin","email":"","affiliations":[{"id":37174,"text":"Volunteer","active":true,"usgs":false}],"preferred":false,"id":832278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wershow, Harold","contributorId":272641,"corporation":false,"usgs":false,"family":"Wershow","given":"Harold","email":"","affiliations":[{"id":56394,"text":"Everett Community College, Everett, WA","active":true,"usgs":false}],"preferred":false,"id":832279,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zaarur, Shikma","contributorId":272642,"corporation":false,"usgs":false,"family":"Zaarur","given":"Shikma","email":"","affiliations":[{"id":56395,"text":"Hebrew University Insitute of Earh Sciences, Jerusalem","active":true,"usgs":false}],"preferred":false,"id":832280,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dimeo Boyd, Melissa","contributorId":272643,"corporation":false,"usgs":false,"family":"Dimeo Boyd","given":"Melissa","email":"","affiliations":[{"id":56396,"text":"Yeh and Associates, Denver CO","active":true,"usgs":false}],"preferred":false,"id":832281,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}