During the 2018 eruption of Kīlauea Volcano, Hawai'i, scientists relied heavily on a conceptual model of explosive eruptions triggered when lava‐lake levels drop below the water table. Numerical modeling of multiphase groundwater flow and heat transport revealed that, contrary to expectations, liquid water inflow to the drained magma conduit would likely be delayed by months to years, owing to the inability of liquid water to transit a zone of very hot rock. The summit of Kīlauea subsequently experienced an ∼2‐month period of consistent repeated collapses, and the crater now extends below the equilibrium position of the water table. Liquid water first emerged into the deepened crater in late July 2019. The timing of first appearance of liquid water (about 14 months postcollapse) and the rate of crater lake filling (currently ∼27 kg/s) were well‐predicted by the numerical modeling done in late spring 2018, which forecast liquid inflow after 3 to 24 months at rates of 10 to 100 kg/s. A second‐generation groundwater model, reflecting the current crater geometry, forecasts lake filling over the next several years. The successful 2018 to present forecasts with both models are based on unadjusted in situ permeability estimates (1 to 6 × 10−14 m2) and water‐table elevations (600 to 800 m) from a nearby research drillhole and geophysical surveys. Important unknowns that affect the reliability of longer‐term forecasts include the equilibrium water‐table geometry, the rate of evaporation from the hot and growing crater lake (currently ∼29,000 m2 at 70‐80 °C), and heterogenous permeability changes caused by the 2018 collapse.
Glacial aquifers are an important source of groundwater in the United States and require accurate characterization to make informed management decisions. One parameter that is crucial for understanding the movement of groundwater is hydraulic conductivity, K. Nuclear magnetic resonance (NMR) logging measures the NMR response associated with the water in geological materials. By utilizing an external magnetic field to manipulate the nuclear spins associated with 1H, the time‐varying decay of the nuclear magnetization is measured. This logging method could provide an effective way to estimate K at submeter vertical resolution, but the models that relate NMR measurements to K require calibration. At two field sites in a glacial aquifer in central Wisconsin, we collected a total of four NMR logs and obtained measurements of K in their immediate vicinity with a direct‐push permeameter (DPP). Using a bootstrap algorithm to calibrate the Schlumberger‐Doll Research (SDR) NMR‐K model, we estimated K to within a factor of 5 of the DPP measurements. The lowest levels of accuracy occurred in the lower‐K (K < 10−4 m/s) intervals. We also evaluated the applicability of prior SDR model calibrations. We found the NMR calibration parameters varied with K, suggesting the SDR model does not incorporate all the properties of the pore space that control K. Thus, the expected range of K in an aquifer may need to be considered during calibration of NMR‐K models. This study is the first step toward establishing NMR logging as an effective method for estimating K in glacial aquifers.
The migration of larval fish from spawning to rearing habitat in rivers is not well understood. This paper describes a methodology to predict larval drift using a Lagrangian particle-tracking (LPT) model with passive and active behavioural components loosely coupled to a quasi-three-dimensional hydraulic model. In the absence of measured larval drift, a heuristic approach is presented for the larval drift of two species of interest, white sturgeon (Acipenser transmontanus) and burbot (Lota lota), in the Kootenai River, Idaho. Previous studies found that many fish species prefer certain vertical zones within the water column; sturgeon tend to be found near the bottom and burbot close to the water surface. Limiting the vertical movement of larvae is incorporated into the active component of the LPT model. The results illustrate a pattern of drift where secondary flow in meander bends and other zones of flow curvature redistributes particles toward the outside of the bend for surface drifters and toward the inside of the bend for bottom drifters. This pattern periodically reinforces the intersection of drifting larvae with channel margins in meander bends. In the absence of measured larval drift data, the model provides a tool for hypothesis testing and a guide to both field and laboratory experiments to further define the role of active behaviour in drifting larvae.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/24705357.2019.1709102","usgsCitation":"McDonald, R.R., and Nelson, J.M., 2021, A Lagrangian particle-tracking approach to modelling larval drift in rivers: Journal of Ecohydraulics, v. 6, no. 1, p. 17-35, https://doi.org/10.1080/24705357.2019.1709102.","productDescription":"19 p.","startPage":"17","endPage":"35","ipdsId":"IP-102070","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":502662,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":436681,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K1U4O0","text":"USGS data release","linkHelpText":"fluvial-particle, U.S. Geological Survey software release"},{"id":415416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.1478357988205,\n 48.69782150172384\n ],\n [\n -116.12731836897655,\n 48.73211652626813\n ],\n [\n -116.32565352413204,\n 48.71948423713994\n ],\n [\n -116.34753878263186,\n 48.78261395455223\n ],\n [\n -116.35574575456924,\n 48.936497382156176\n ],\n [\n -116.41319455813164,\n 48.99935415693781\n ],\n [\n -116.58964445478699,\n 49.00025153683018\n ],\n [\n -116.55544873838093,\n 48.94907507626053\n ],\n [\n -116.45012593185005,\n 48.894249080754236\n ],\n [\n -116.43644764528761,\n 48.82135431506677\n ],\n [\n -116.4255050160379,\n 48.72038664873716\n ],\n [\n -116.33112483875708,\n 48.669826665231625\n ],\n [\n -116.1478357988205,\n 48.69782150172384\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":868972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":868973,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223732,"text":"70223732 - 2021 - Factors influencing Cinnamon Teal nest attendance patterns","interactions":[],"lastModifiedDate":"2021-09-07T13:22:44.296825","indexId":"70223732","displayToPublicDate":"2020-04-18T07:38:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"Factors influencing Cinnamon Teal nest attendance patterns","docAbstract":"Patterns of nest attendance in birds result from complex behaviours and influence the success of reproductive events. Incubation behaviours vary based on individual body condition, energy requirements and environmental factors. We assessed nest attendance patterns in Cinnamon Teal Spatula cyanoptera breeding in the San Luis Valley of Colorado in 2016–2017 using trail and video cameras to observe behaviours throughout incubation. We evaluated the effect of temporal, life-history and environmental covariates on the frequency and duration of incubation recesses as well as the incubation constancy. There was considerable model uncertainty among the models used to evaluate recess frequency. Recess duration varied according to the interaction between nest age and a quadratic effect of time of day, with hens on older nests taking longer recesses in the afternoon and hens on nests earlier in incubation taking longer recesses in the morning and evening. Incubation constancy decreased with higher ambient temperatures in the study area. This study provides evidence that Cinnamon Teal modify their behaviour during incubation according to the age of the nest and the time of day. These results improve our knowledge of Cinnamon Teal breeding ecology and shed light on the behaviours that fast-lived species may use to cope with environmental factors during nesting.
Spatial information on the distribution of ecosystem patterns and processes can be a critical component of designing and implementing effective management programs in river‐floodplain ecosystems. For example, translating how flood pulses detected within a stream gauge record are spatially manifested across a river‐valley bottom can be used to evaluate whether the current distribution of physical conditions has the potential to support priority habitats or if intervention is needed to meet desired goals. The size and complexity of large river‐floodplain systems can make mapping inundation dynamics a challenging task. We used a geospatial model to simulate 40 years (1972–2011) of daily surface‐water inundation depths for 11,331 km2 of the Upper Mississippi River System floodplain. We identified discrete inundation events at each 4‐m × 4‐m pixel in the model as sequential days of submergence. We then quantified and mapped four aspects of inundation regime – event frequency, duration, magnitude, and timing – for each pixel. The spatial distribution of inundation regime attributes varied within and among multiple levels of river organization, including navigation pools and geomorphic reaches, but only event timing exhibited a strong down‐river trend. Non‐linear relations among inundation attributes and their geospatial distributions likely reflect complex interactions among topographic, hydrologic, and anthropogenic constraints on flooding dynamics. Together, our results reveal spatial gradients in inundation dynamics not captured by hydrologic data alone. Characterizing such diversity in inundation dynamics is important for testing hypotheses about ecological processes, developing models of ecosystem functions, and informing management actions.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3628","usgsCitation":"Van Appledorn, M., De Jager, N.R., and Rohweder, J.J., 2021, Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications: River Research and Applications, v. 37, no. 2, p. 241-255, https://doi.org/10.1002/rra.3628.","productDescription":"15 p.","startPage":"241","endPage":"255","ipdsId":"IP-113745","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":383139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.3955078125,\n 38.51378825951165\n ],\n [\n -88.154296875,\n 40.245991504199026\n ],\n [\n -86.572265625,\n 41.07935114946899\n ],\n [\n -86.7919921875,\n 41.50857729743935\n ],\n [\n -88.0224609375,\n 42.45588764197166\n ],\n [\n -89.3408203125,\n 44.213709909702054\n ],\n [\n -91.7578125,\n 45.85941212790755\n ],\n [\n -92.63671875,\n 46.195042108660154\n ],\n [\n -92.59277343749999,\n 47.724544549099676\n ],\n [\n -94.8779296875,\n 47.249406957888446\n ],\n [\n -95.9326171875,\n 47.30903424774781\n ],\n [\n -95.4052734375,\n 44.84029065139799\n ],\n [\n -93.6474609375,\n 42.13082130188811\n ],\n [\n -93.515625,\n 39.605688178320804\n ],\n [\n -90.3955078125,\n 38.51378825951165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810091,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218476,"text":"70218476 - 2021 - Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella)","interactions":[],"lastModifiedDate":"2021-03-01T15:28:00.787039","indexId":"70218476","displayToPublicDate":"2020-04-15T09:20:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella)","title":"Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella)","docAbstract":"Decisions about invasive species control and eradication can be difficult because of uncertainty in population demographics, movement ecology, and effectiveness of potential response actions. These decisions often include multiple stakeholders and management entities with potentially different objectives, management priorities, and jurisdictional authority. We provide a case study of using multi-party, collaborative decision analysis to aid decision makers in determining objectives and control actions for invasive grass carp (Ctenopharyngodon idella) in Lake Erie. Creating this process required binational (Canada-United States) and multi-state/provincial collaboration to craft a shared problem statement, establish objectives related to ecological, economic, and social concerns, determine potential response actions, and evaluate consequences and tradeoffs of these actions. We used participatory modeling and expert elicitation to evaluate the effectiveness of control scenarios that varied in action type (i.e., removal efforts and spawning barriers) and the temporal and spatial application of these actions. Using a matrix population model parameterized for western Lake Erie grass carp, we found that removal efforts concentrated in areas of high catchability, when paired with a spawning barrier on the Sandusky River, Ohio, USA, could effectively control grass carp in Lake Erie, if all assumptions are met. We determined a set of key uncertainties regarding gear catchability and current population size that have led to the transition to an adaptive management process. In addition, our work formed the basis for grass carp management plans for the states of Michigan and Ohio and has provided a means for collaboration among agencies for effective application of control efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.03.018","usgsCitation":"Robinson, K., DuFour, M.R., Jones, M., Herbst, S., Newcomb, T., Boase, J., Brenden, T.O., Chapman, D., Dettmers, J.M., Francis, J., Hartman, T., Kocovsky, P., Locke, B., Tyson, J., and Mayer, C., 2021, Using decision analysis to collaboratively respond to invasive species threats: A case study of Lake Erie grass carp (Ctenopharyngodon idella): Journal of Great Lakes Research, v. 47, no. 1, p. 108-119, https://doi.org/10.1016/j.jglr.2020.03.018.","productDescription":"12 p.","startPage":"108","endPage":"119","ipdsId":"IP-111132","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":383686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Ohio","otherGeospatial":"Lake Erie, Maumee River, River Raisin, Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.73779296875,\n 41.290189955885644\n ],\n [\n -82.430419921875,\n 41.290189955885644\n ],\n [\n -82.430419921875,\n 42.07783959017503\n ],\n [\n -83.73779296875,\n 42.07783959017503\n ],\n [\n -83.73779296875,\n 41.290189955885644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Kelly F.","contributorId":140157,"corporation":false,"usgs":false,"family":"Robinson","given":"Kelly F.","affiliations":[{"id":473,"text":"New York Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":13267,"text":"Warnell School of Forestry and Natural Resources, University of Georgia","active":true,"usgs":false},{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":811138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DuFour, Mark R.","contributorId":203270,"corporation":false,"usgs":false,"family":"DuFour","given":"Mark","email":"","middleInitial":"R.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":811139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, M.W.","contributorId":239977,"corporation":false,"usgs":false,"family":"Jones","given":"M.W.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":811140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herbst, Seth","contributorId":252926,"corporation":false,"usgs":false,"family":"Herbst","given":"Seth","affiliations":[{"id":50471,"text":"Michigan Department of Natural Resources, Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":811141,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newcomb, Tammy","contributorId":252928,"corporation":false,"usgs":false,"family":"Newcomb","given":"Tammy","affiliations":[{"id":50471,"text":"Michigan Department of Natural Resources, Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":811142,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boase, James C.","contributorId":38077,"corporation":false,"usgs":false,"family":"Boase","given":"James C.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":811143,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":811144,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":811145,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dettmers, John M.","contributorId":191256,"corporation":false,"usgs":false,"family":"Dettmers","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":811146,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Francis, James","contributorId":252929,"corporation":false,"usgs":false,"family":"Francis","given":"James","affiliations":[{"id":50473,"text":"Michigan Department of Natural Resources, Fisheries Division, Waterford Field Office, Waterford, MI","active":true,"usgs":false}],"preferred":false,"id":811147,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hartman, Travis","contributorId":66583,"corporation":false,"usgs":true,"family":"Hartman","given":"Travis","affiliations":[],"preferred":false,"id":811191,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":811148,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Locke, Brian","contributorId":252930,"corporation":false,"usgs":false,"family":"Locke","given":"Brian","affiliations":[{"id":50474,"text":"Ontario Ministry of Natural Resources and Forestry, Lake Erie Management Unit, Wheatley, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":811149,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tyson, Jeff","contributorId":147298,"corporation":false,"usgs":false,"family":"Tyson","given":"Jeff","affiliations":[],"preferred":false,"id":811151,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Mayer, Christine","contributorId":237769,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","affiliations":[{"id":47604,"text":"University of Toledo, Lake Erie Center","active":true,"usgs":false}],"preferred":false,"id":811150,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70216167,"text":"70216167 - 2021 - Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques","interactions":[],"lastModifiedDate":"2021-11-01T14:41:49.052895","indexId":"70216167","displayToPublicDate":"2020-04-01T09:21:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2375,"text":"Journal of Maps","active":true,"publicationSubtype":{"id":10}},"title":"Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques","docAbstract":"Increasing urbanization and suburban growth in cities globally has highlighted the importance of land planning using detailed geomorphologic maps that depict anthropogenic landform changes. Such mapping provides information crucial for land management, hazard identification, and the management of the challenges arising from urbanization. The development and use of quantitative and repeatable methods to map anthropogenic and natural processes are required to advance the science of urban geomorphological mapping. This study investigated the application of geospatial modeling, structure-from-motion (SfM) photogrammetric methods and DEM differencing as means of quantifying anthropogenic landform changes from archival aerial imagery. Anthropogenic landforms were incorporated into a detailed geomorphologic map in an urbanizing watershed located in the Washington, D.C. metropolitan suburb of Vienna, Virginia.
Compositional data carry relative information. Hence, their statistical analysis has to be performed on coordinates with respect to a log-ratio basis. Frequently, the modeler is required to back-transform the estimates obtained with the modeling to have them in the original units such as euros, kg or mg/liter. Approaches for recovering original units need to be formally introduced and its properties explored. Here, we formulate and analyze the properties of two procedures: a simple approach consisting of adding a residual part to the composition and an approach based on the use of an auxiliary variable. Both procedures are illustrated using a geochemical data set where the original units are recovered when spatial models are applied.
The long-standing ore-genesis model for world-class deposits of the Butte mining district, Montana, is of deep pre-Main Stage porphyry Cu-Mo and overlying Main Stage Ag-Zn-Cu-zoned lode veinsformed from discrete hydrothermal systems related to rhyolite dikes. The lode-specific model describes metals zones that formed in the lode veins as hydrothermal processes diminished in intensity (changing temperature and chemical characteristics) outward from the district center. New geologic and multi-method geochronologic studies pro- vide new timing constraints on the lode veins and reevaluation of geologic relations (Lund and others, 2018), leading to a new model for formation of the lode veins and their relations to stockwork Cu-Mo deposits and igneous events.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Montana Mining and Mineral Symposium 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Montana Mining and Mineral Symposium 2019","conferenceDate":"October 9-11, 2019","language":"English","publisher":"Montana Bureau of Mines and Geology","usgsCitation":"Lund, K., McAleer, R.J., Aleinikoff, J.N., and Cosca, M., 2021, Two-event genesis of Butte lode veins: Geologic and geochronologic evidence from ore veins, dikes, and host plutons, in Proceedings of the Montana Mining and Mineral Symposium 2019, October 9-11, 2019, p. 71-73.","productDescription":"3 p.","startPage":"71","endPage":"73","ipdsId":"IP-112266","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":386095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385671,"type":{"id":15,"text":"Index Page"},"url":"https://www.mbmg.mtech.edu/mbmgcat/public/ListCitation.asp?pub_id=32264&"}],"country":"United States","state":"Montana","otherGeospatial":"Butte mining district","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.76916503906249,\n 45.882360730184025\n ],\n [\n -112.37640380859375,\n 45.882360730184025\n ],\n [\n -112.37640380859375,\n 46.086566879725034\n ],\n [\n -112.76916503906249,\n 46.086566879725034\n ],\n [\n -112.76916503906249,\n 45.882360730184025\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lund, Karen 0000-0002-4249-3582 klund@usgs.gov","orcid":"https://orcid.org/0000-0002-4249-3582","contributorId":1235,"corporation":false,"usgs":true,"family":"Lund","given":"Karen","email":"klund@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":815738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":815739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aleinikoff, John N. 0000-0003-3494-6841 jaleinikoff@usgs.gov","orcid":"https://orcid.org/0000-0003-3494-6841","contributorId":1478,"corporation":false,"usgs":true,"family":"Aleinikoff","given":"John","email":"jaleinikoff@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cosca, Michael 0000-0002-0600-7663","orcid":"https://orcid.org/0000-0002-0600-7663","contributorId":33043,"corporation":false,"usgs":true,"family":"Cosca","given":"Michael","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":815741,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216098,"text":"70216098 - 2021 - Making Recursive Bayesian inference accessible","interactions":[],"lastModifiedDate":"2021-05-19T12:08:37.54648","indexId":"70216098","displayToPublicDate":"2019-11-04T13:50:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":747,"text":"American Statistician","active":true,"publicationSubtype":{"id":10}},"title":"Making Recursive Bayesian inference accessible","docAbstract":"Bayesian models provide recursive inference naturally because they can formally reconcile new data and existing scientific information. However, popular\nuse of Bayesian methods often avoids priors that are based on exact posterior distributions resulting from former studies. Two existing Recursive Bayesian methods\nare: Prior- and Proposal-Recursive Bayes. Prior-Recursive Bayes uses Bayesian\nupdating, fitting models to partitions of data sequentially, and provides a way\nto accommodate new data as they become available using the posterior from the\nprevious stage as the prior in the new stage based on the latest data. ProposalRecursive Bayes is intended for use with hierarchical Bayesian models and uses a\nset of transient priors in first stage independent analyses of the data partitions.\nThe second stage of Proposal-Recursive Bayes uses the posteriors from the first\nstage as proposals in an MCMC algorithm to fit the full model. We combine\nPrior- and Proposal-Recursive concepts to fit any Bayesian model, and often with\ncomputational improvements. We demonstrate our method with two case studies.\nOur approach has implications for big data, streaming data, and optimal adaptive\ndesign situations.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00031305.2019.1665584","usgsCitation":"Hooten, M., Johnson, D., and Brost, B., 2021, Making Recursive Bayesian inference accessible: American Statistician, v. 75, no. 2, p. 185-194, https://doi.org/10.1080/00031305.2019.1665584.","productDescription":"10 p.","startPage":"185","endPage":"194","ipdsId":"IP-101580","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":454561,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://arxiv.org/abs/1807.10981","text":"External Repository"},{"id":380171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"75","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-10-16","publicationStatus":"PW","contributors":{"authors":[{"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":804074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Devin S.","contributorId":244505,"corporation":false,"usgs":false,"family":"Johnson","given":"Devin S.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":804076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brost, Brian M.","contributorId":244504,"corporation":false,"usgs":false,"family":"Brost","given":"Brian M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":804075,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241471,"text":"70241471 - 2021 - Surrogate rearing a keystone species to enhance population and ecosystem restoration","interactions":[],"lastModifiedDate":"2023-03-21T12:11:33.253252","indexId":"70241471","displayToPublicDate":"2019-09-20T07:08:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2968,"text":"Oryx","active":true,"publicationSubtype":{"id":10}},"title":"Surrogate rearing a keystone species to enhance population and ecosystem restoration","docAbstract":"Translocation and rehabilitation programs are critical tools for wildlife conservation. These methods achieve greater impact when integrated in a combined strategy for enhancing population or ecosystem restoration. From 2002-2016, we reared 37 orphaned southern sea otter (Enhydra lutris nereis) pups, using captive sea otters as surrogate mothers, then released them into a degraded coastal estuary. As a keystone species, observed increases in the local sea otter population unsurprisingly brought many ecosystem benefits. The role that surrogate-reared otters played in this success story, however, remained uncertain. To resolve this question, we developed an individual-based model (IBM) of the local population using surveyed individual fates (survival and reproduction) of surrogate-reared and wild-captured otters, and modeled estimates of immigration. Estimates derived from a decade of population monitoring indicated that surrogate-reared and wild sea otters experienced similar reproductive and survival rates. This was true for males and females, across all ages (1-13 years) and locations evaluated. The IBM simulations indicated that reconstructed counts of the wild population are best explained by surrogate-reared otters combined with low levels of unassisted immigration. In addition, the model shows that 55% of observed population growth over this period is attributable to surrogate-reared otters and their wild progeny. Together, our results indicate that the integration of surrogacy methods and reintroduction of juvenile sea otters helped establish a biologically successful population and restore a once-impaired ecosystem.","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0030605319000346","usgsCitation":"Mayer, K.A., Tinker, M., Nicholson, T.E., Murray, M.J., Johnson, A.B., Staedler, M.M., Fujii, J.A., and Van Houtan, K.S., 2021, Surrogate rearing a keystone species to enhance population and ecosystem restoration: Oryx, v. 55, no. 4, p. 535-545, https://doi.org/10.1017/S0030605319000346.","productDescription":"11 p.","startPage":"535","endPage":"545","ipdsId":"IP-107393","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":454566,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/s0030605319000346","text":"Publisher Index Page"},{"id":414427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Elkhorn Slough","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.89242949642858,\n 36.96553755127172\n ],\n [\n -121.89242949642858,\n 36.656893027349454\n ],\n [\n -121.62983524058207,\n 36.656893027349454\n ],\n [\n -121.62983524058207,\n 36.96553755127172\n ],\n [\n -121.89242949642858,\n 36.96553755127172\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"55","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Mayer, Karl A.","contributorId":203504,"corporation":false,"usgs":false,"family":"Mayer","given":"Karl","email":"","middleInitial":"A.","affiliations":[{"id":36639,"text":"University of Wisconsin Zoological Museum, 250 North Mills Street, Madison, WI 53706 (PMH) Sea Otter Research and Conservation Program, Monterey Bay Aquarium, 886 Cannery Row, Monterey, CA 93940","active":true,"usgs":false}],"preferred":false,"id":866935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinker, M Tim","contributorId":303260,"corporation":false,"usgs":false,"family":"Tinker","given":"M Tim","affiliations":[{"id":65732,"text":"former USGS WERC PI; University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":866936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nicholson, Teri E.","contributorId":213741,"corporation":false,"usgs":false,"family":"Nicholson","given":"Teri","email":"","middleInitial":"E.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":866937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murray, Michael J.","contributorId":206852,"corporation":false,"usgs":false,"family":"Murray","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":37418,"text":"Monterey Bay Aquarium, Monterey, CA","active":true,"usgs":false}],"preferred":false,"id":866938,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Andrew B.","contributorId":127459,"corporation":false,"usgs":false,"family":"Johnson","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":866939,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staedler, Michelle M. 0000-0002-1101-6580","orcid":"https://orcid.org/0000-0002-1101-6580","contributorId":213742,"corporation":false,"usgs":false,"family":"Staedler","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":866940,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fujii, Jessica A. 0000-0003-4794-479X","orcid":"https://orcid.org/0000-0003-4794-479X","contributorId":196602,"corporation":false,"usgs":false,"family":"Fujii","given":"Jessica","email":"","middleInitial":"A.","affiliations":[],"preferred":true,"id":866941,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Van Houtan, Kyle S.","contributorId":213743,"corporation":false,"usgs":false,"family":"Van Houtan","given":"Kyle","email":"","middleInitial":"S.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":866942,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70202002,"text":"70202002 - 2021 - Perspectives on the paleolimnology of the late Eocene Florissant lake from diatom and sedimentary evidence at Clare’s Quarry, Teller County, Colorado, USA","interactions":[{"subject":{"id":70202002,"text":"70202002 - 2021 - Perspectives on the paleolimnology of the late Eocene Florissant lake from diatom and sedimentary evidence at Clare’s Quarry, Teller County, Colorado, USA","indexId":"70202002","publicationYear":"2021","noYear":false,"chapter":"10","title":"Perspectives on the paleolimnology of the late Eocene Florissant lake from diatom and sedimentary evidence at Clare’s Quarry, Teller County, Colorado, USA"},"predicate":"IS_PART_OF","object":{"id":70225733,"text":"70225733 - 2021 - From saline to freshwater: The diversity of western lakes in space and time","indexId":"70225733","publicationYear":"2021","noYear":false,"title":"From saline to freshwater: The diversity of western lakes in space and time"},"id":1}],"isPartOf":{"id":70225733,"text":"70225733 - 2021 - From saline to freshwater: The diversity of western lakes in space and time","indexId":"70225733","publicationYear":"2021","noYear":false,"title":"From saline to freshwater: The diversity of western lakes in space and time"},"lastModifiedDate":"2021-11-08T18:11:04.142208","indexId":"70202002","displayToPublicDate":"2019-01-01T10:58:16","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"10","title":"Perspectives on the paleolimnology of the late Eocene Florissant lake from diatom and sedimentary evidence at Clare’s Quarry, Teller County, Colorado, USA","docAbstract":"The late Eocene Florissant Formation in central Colorado is a rich and diverse continental Lagerstätte yielding well-preserved fossil assemblages from lacustrine and fluvial facies. This investigation focused on the lacustrine facies at Clare’s Quarry and used biotic and abiotic evidence to characterize aspects of the lake and processes that resulted in the accumulation and preservation of the host rock and its fossils. Autecology of modern analogs representing the fossil diatom taxa was used to augment sedimentary data in characterizing the lake, propose peripheral habitats within the catchment area, and suggest a terrestrial source for mudstone units.
The sedimentary and stratigraphic record at the study site reveals a lake with sufficient depth to allow bottom waters to remain isolated and anoxic for long periods. Sediments that accumulated in the lake produced distinct lacustrine lithofacies that are interpreted as representing at least three modes of origin: stable lake, pyroclastic, and mud turbidite sedimentation. Slow, suspension settling of fine clays and volcanic ash into a moderately deep, stable lake resulted in laminated shales. These laminated shales contain frustules of diatoms from planktic and benthic lake habitats; diatoms transported into the lake from streams and wetlands; fish, mollusks, ostracods, and insects; and plants from marginal and upslope environments. Intermittent volcanic eruptions produced air-fall ash and granular tuff that accumulated as interbeds within the lake shales. Periods of stable lake sedimentation were frequently interrupted by rapid influxes of suspended fine clays, perhaps as mud-dominated turbidites that prograded into the lake at intervals of high runoff triggered by climatic, volcanic, or tectonic events.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From saline to freshwater: The diversity of western lakes in space and time","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2018.2536(10)","usgsCitation":"Benson, M., Smith, D.M., and Spaulding, S.A., 2021, Perspectives on the paleolimnology of the late Eocene Florissant lake from diatom and sedimentary evidence at Clare’s Quarry, Teller County, Colorado, USA, chap. 10 of From saline to freshwater: The diversity of western lakes in space and time, v. 536, 26 p., https://doi.org/10.1130/2018.2536(10).","productDescription":"26 p.","ipdsId":"IP-077030","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":361012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","county":"Teller County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-105.3232,39.1307],[-105.274,39.1309],[-105.1607,39.1306],[-105.0503,39.1312],[-105.032,39.1311],[-105.026,39.0413],[-105.0296,38.8668],[-105.0502,38.8665],[-105.0674,38.8666],[-105.0671,38.7946],[-104.939,38.7949],[-104.9386,38.7808],[-104.9399,38.6938],[-104.9428,38.6938],[-104.9427,38.6648],[-104.9427,38.6621],[-104.9429,38.6503],[-104.9429,38.6467],[-104.9806,38.6479],[-104.9989,38.649],[-105.0507,38.6507],[-105.0696,38.6473],[-105.0755,38.646],[-105.0885,38.646],[-105.1657,38.6461],[-105.1845,38.6458],[-105.2222,38.6461],[-105.2387,38.6462],[-105.239,38.677],[-105.2394,38.6965],[-105.2741,38.6971],[-105.2765,38.6972],[-105.3119,38.6969],[-105.3319,38.697],[-105.3294,38.779],[-105.3292,38.867],[-105.3296,38.9535],[-105.3297,39.0116],[-105.3297,39.1308],[-105.3232,39.1307]]]},\"properties\":{\"name\":\"Teller\",\"state\":\"CO\"}}]}","volume":"536","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benson, Mary Ellen 0000-0002-4424-0730","orcid":"https://orcid.org/0000-0002-4424-0730","contributorId":212794,"corporation":false,"usgs":true,"family":"Benson","given":"Mary Ellen","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":756608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Dena M. 0000-0002-1689-7188","orcid":"https://orcid.org/0000-0002-1689-7188","contributorId":212795,"corporation":false,"usgs":false,"family":"Smith","given":"Dena","email":"","middleInitial":"M.","affiliations":[{"id":12642,"text":"National Science Foundation","active":true,"usgs":false}],"preferred":false,"id":756609,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":212796,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":756610,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217687,"text":"70217687 - 2021 - Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data","interactions":[],"lastModifiedDate":"2021-02-08T18:00:13.597366","indexId":"70217687","displayToPublicDate":"2018-12-31T11:58:10","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data","docAbstract":"Airborne electromagnetic (AEM) dataare usedto estimate large-scale model structural geometry, i.e. the spatial distribution of different lithological units based on assumed or estimated resistivity-lithology relationships, and the uncertainty in those structures given imperfect measurements. Geophysically derived estimates of model structural uncertainty are then combined with hydrologic observations to assess the impact of model structural error on hydrologic calibration and prediction errors. Using a synthetic numerical model, we describe a sequential hydrogeophysical approach that: (1) uses Bayesian Markov chain Monte Carlo (McMC) methods to produce a robust estimate of uncertainty in electrical resistivity parameters, (2) combines geophysical parameter uncertainty estimates with borehole observations of lithology to produce probabilistic estimates of model structural uncertainty over the entire AEM survey area using geostatistical sequential indicator simulation algorithms, and (3) uses model structural estimates along with hydrologic observations to quantify both hydrologic parameter and prediction uncertainty using a second McMC sampling algorithm. Results of simulations will be presented that illustrate the complete workflow from geophysical parameter uncertainty analysis to the impact of model structural uncertainty on hydrologic parameter estimates.
","conferenceTitle":"7th International Workshop on Airborne Electromagnetics","conferenceDate":"June 17-20, 2018","conferenceLocation":"Kolding, Denmark","language":"English","publisher":"Aarhus University","usgsCitation":"Minsley, B.J., Christensen, N.K., Christensen, S., and Ley-Cooper, Y., 2021, Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data, 7th International Workshop on Airborne Electromagnetics, Kolding, Denmark, June 17-20, 2018, 4 p.","productDescription":"4 p.","ipdsId":"IP-095925","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":383107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383106,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.conferencemanager.dk/aem2018"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809258,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, Nikolaj K","contributorId":199736,"corporation":false,"usgs":false,"family":"Christensen","given":"Nikolaj","email":"","middleInitial":"K","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":809259,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Steen","contributorId":199737,"corporation":false,"usgs":false,"family":"Christensen","given":"Steen","email":"","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":809260,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ley-Cooper, Yusen","contributorId":248494,"corporation":false,"usgs":false,"family":"Ley-Cooper","given":"Yusen","email":"","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":809261,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221393,"text":"70221393 - 2021 - Streamflow, sediment transport, and geomorphic change during the 2011 flood on the Missouri River near Bismarck-Mandan, ND","interactions":[],"lastModifiedDate":"2021-06-15T10:36:19.944894","indexId":"70221393","displayToPublicDate":"2018-08-27T07:47:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2126,"text":"JAWRA","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow, sediment transport, and geomorphic change during the 2011 flood on the Missouri River near Bismarck-Mandan, ND","docAbstract":"Geomorphic change from extreme events in large managed rivers has implications for river management. A steady-state, quasi-three-dimensional hydrodynamic model was applied to a 29-km reach of the Missouri River using 2011 flood data. Model results for an extreme flow (500-year recurrence interval [RI]) and an elevated managed flow (75-year RI) were used to assess sediment mobility through examination of the spatial distribution of boundary or bed shear stress (τb) and longitudinal patterns of average τb, velocity, and kurtosis of τb. Kurtosis of τb was used as an indicator of planform channel complexity and can be applied to other river systems. From differences in longitudinal patterns of sediment mobility for the two flows we can infer: (1) under extreme flow, the channel behaves as a single-thread channel controlled primarily by flow, which enhances the meander pattern; (2) under elevated managed flows, the channel behaves as multithread channel controlled by the interaction of flow with bed and channel topography, resulting in a more complex channel; and (3) for both flows, the model reach lacks a consistent pattern of deposition or erosion, which indicates migration of areas of erosion and deposition within the reach. Despite caveats and limitations, the analysis provides useful information about geomorphic change under extreme flow and potential implications for river management. Although a 500-year RI is rare, extreme hydrologic events such as this are predicted to increase in frequency.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12678","usgsCitation":"Nustad, R.A., Benthem, A.J., Skalak, K., McDonald, R.R., Schenk, E., and Galloway, J.M., 2021, Streamflow, sediment transport, and geomorphic change during the 2011 flood on the Missouri River near Bismarck-Mandan, ND: JAWRA, v. 54, no. 5, p. 1151-1167, https://doi.org/10.1111/1752-1688.12678.","productDescription":"17 p.","startPage":"1151","endPage":"1167","ipdsId":"IP-075678","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":454576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12678","text":"Publisher Index Page"},{"id":386466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","city":"Bismarck","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.0137939453125,\n 45.94351068030587\n ],\n [\n -100.3436279296875,\n 45.94351068030587\n ],\n [\n -100.3436279296875,\n 46.98774725646568\n ],\n [\n -101.0137939453125,\n 46.98774725646568\n ],\n [\n -101.0137939453125,\n 45.94351068030587\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"5","noUsgsAuthors":false,"publicationDate":"2018-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Nustad, Rochelle A. 0000-0002-4713-5944 ranustad@usgs.gov","orcid":"https://orcid.org/0000-0002-4713-5944","contributorId":1811,"corporation":false,"usgs":true,"family":"Nustad","given":"Rochelle","email":"ranustad@usgs.gov","middleInitial":"A.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benthem, Adam J. 0000-0003-2372-0281","orcid":"https://orcid.org/0000-0003-2372-0281","contributorId":220000,"corporation":false,"usgs":true,"family":"Benthem","given":"Adam","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":817500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":817501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schenk, Edward R.","contributorId":202017,"corporation":false,"usgs":false,"family":"Schenk","given":"Edward R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":817554,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817555,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254945,"text":"70254945 - 2020 - Statistical implementations of agent-based demographic models","interactions":[],"lastModifiedDate":"2024-06-11T19:15:50.661658","indexId":"70254945","displayToPublicDate":"2024-08-03T13:41:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17811,"text":"International Statistical Review","onlineIssn":"1751-5823","printIssn":"0306-7734","active":true,"publicationSubtype":{"id":10}},"title":"Statistical implementations of agent-based demographic models","docAbstract":"A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing\ndue to imperfect detection of individuals, the associated measurement error can\nbe accommodated under certain study designs (e.g., those that involve multiple\nsurveys or replication). However, the interaction of the measurement error and\nthe underlying dynamic process can complicate the implementation of statistical\nagent-based models (ABMs) for population demography. In a Bayesian setting,\ntraditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of\napproaches for fitting statistical ABMs to data and demonstrate how to use multistage recursive Bayesian computing and statistical emulators to fit models in such\na way that alleviates the need to have analytical knowledge of the ABM likelihood.\nUsing two examples, a demographic model for survival and a compartment model\nfor COVID-19, we illustrate statistical procedures for implementing ABMs. The\napproaches we describe are intuitive and accessible for practitioners and can be\nparallelized easily for additional computational eciency.","language":"English","publisher":"Wiley","doi":"10.1111/insr.12399","usgsCitation":"Hooten, M., Wikle, C., and Schwob, M., 2020, Statistical implementations of agent-based demographic models: International Statistical Review, v. 88, no. 2, p. 441-461, https://doi.org/10.1111/insr.12399.","productDescription":"21 p,","startPage":"441","endPage":"461","ipdsId":"IP-120052","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":454581,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/insr.12399","text":"Publisher Index Page"},{"id":429907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"88","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-08-03","publicationStatus":"PW","contributors":{"authors":[{"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":902944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wikle, Christopher K.","contributorId":338088,"corporation":false,"usgs":false,"family":"Wikle","given":"Christopher K.","affiliations":[{"id":81080,"text":"umo","active":true,"usgs":false}],"preferred":false,"id":902945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwob, Michael R.","contributorId":338089,"corporation":false,"usgs":false,"family":"Schwob","given":"Michael R.","affiliations":[{"id":81083,"text":"un","active":true,"usgs":false}],"preferred":false,"id":902946,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213160,"text":"ofr20191023B - 2020 - Focus areas for data acquisition for potential domestic resources of 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico—Aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, rare earth elements, tantalum, tin, titanium, and tungsten","interactions":[],"lastModifiedDate":"2026-03-25T16:54:19.281618","indexId":"ofr20191023B","displayToPublicDate":"2022-07-14T10:31:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1023","chapter":"B","displayTitle":"Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in the Conterminous United States, Hawaii, and Puerto Rico—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum-Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten","title":"Focus areas for data acquisition for potential domestic resources of 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico—Aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, rare earth elements, tantalum, tin, titanium, and tungsten","docAbstract":"In response to a need for information on potential domestic sources of critical minerals, the Earth Mapping Resources Initiative (Earth MRI) was established to identify and prioritize areas for acquisition of new geologic mapping, geophysical data, and elevation data to improve our knowledge of the geologic framework of the United States. Phase 1 of Earth MRI concentrated on those geologic terranes favorable for hosting the rare earth elements (REEs). Phase 2 continued to address the REEs and also identified focus areas for potential domestic sources of 10 more of the 35 critical minerals on the U.S. critical minerals list (aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, tantalum, tin, titanium, tungsten). This report describes the methodology, data sources, and summary results for mineral systems that host these 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico; Alaska is covered in a separate report. The mineral systems framework adopted for this study links critical mineral commodities to families of genetically related mineral deposit types. The mineral systems approach is an efficient approach, providing a simultaneous evaluation of geologic terranes through aggregation of genetically related mineral deposit types that are much larger than individual ore deposits. Geologic, geochemical, topographic, and geophysical mapping provided by Earth MRI will document geologic features that reflect the extent of individual mineral systems and provide information about critical mineral deposits that may not have been recognized previously.
Each critical mineral commodity is discussed in terms of importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types along with maps and tables listing examples of focus areas for each critical mineral. Important mineral systems for these critical minerals include chemical weathering systems for aluminum (bauxite); placer systems for titanium and REEs; metamorphic systems for graphite; mafic magmatic systems for platinum-group elements and cobalt; lacustrine evaporite and porphyry tin systems for lithium; and copper-molybdenum-gold (Cu-Mo-Au) systems for tungsten. REEs occur in many different mineral systems. Focus areas were developed by scientists from the U.S. Geological Survey in collaboration with scientists from State geological surveys and other institutions. This first national-scale compilation of focus areas represents an initial step in addressing the Nation’s critical mineral needs by screening areas for acquisition of new data to provide the geologic framework necessary for identifying domestic sources of critical minerals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023B","collaboration":"Prepared in cooperation with American Association of State Geologists","usgsCitation":"Hammarstrom, J., Dicken, C., Day, W., Hofstra, A., Drenth, B., Shah, A., McCafferty, A., Woodruff, L., Foley, N., Ponce, D., Frost, T., and Stillings, L., 2020, Focus areas for data acquisition for potential domestic resources of 11 critical minerals in the conterminous United States, Hawaii, and Puerto Rico—Aluminum, cobalt, graphite, lithium, niobium, platinum-group elements, rare earth elements, tantalum, tin, titanium, and tungsten (ver. 1.1, July 2022), chap. B of U.S. Geological Survey, Focus areas for data acquisition for potential domestic sources of critical minerals: U.S. Geological Survey Open-File Report 2019–1023, 67 p., https://doi.org/10.3133/ofr20191023B.","productDescription":"xiii, 67 p.","numberOfPages":"67","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119187","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":436687,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6SODG","text":"USGS data release","linkHelpText":"GIS for focus areas of potential domestic resources of 11 critical minerals-aluminum, cobalt, graphite, lithium, niobium, platinum group elements, rare earth elements, tantalum, tin, titanium, and tungsten (version 2.0, August 2020)"},{"id":436686,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95CO8LR","text":"USGS data release","linkHelpText":"GIS for focus areas of potential domestic resources of 11 critical minerals - aluminum, cobalt, graphite, lithium, niobium, platinum group elements, rare earth elements, tantalum, tin, titanium, and tungsten"},{"id":403732,"rank":7,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023E","text":"Open-File Report 2019-1023-E","linkHelpText":"- Alaska Focus Area Definition for Data Acquisition for Potential Domestic Sources of Critical Minerals in Alaska for Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Magnesium, Manganese, Uranium, Vanadium, and 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Resources Program
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913 National Center
Reston, VA 20192
The lesser prairie-chicken (Tympanuchus pallidicinctus) occurs in the semiarid southern Great Plains, a region prone to periods of drought. Researchers generally believe that lesser prairie-chickens are able to satisfy their water requirements through preformed water and metabolic processes, but also know that they experience low survival and reproductive success during periods of drought. We used motion-sensing cameras to assess lesser prairie-chicken visits to man-made free water sources over a 48-month period from March 2009 to February 2013 in west Texas. Our objective was to examine temporal patterns of water use by lesser prairie-chickens, and to explore life history phenology and environmental conditions that may influence the species' use of free water. We documented 1,439 visits to water sources by lesser prairie-chickens. Their use of water sources was high during the winter months (December–February; 92 visits per 100 trap days) but the highest average visit rate to water sources occurred during the lekking-nesting life stage (March–May; 146 visits per 100 trap days). Water use was lower during the brood-rearing stage (June–August; 71 visits per 100 trap days) and lowest during the brood dispersal and independence stage (September–November; 19 visits per 100 trap days). Water use was strongly associated with dew point (P < 0.0001) and temperature (P = 0.0002) but was not associated with precipitation (P = 0.1037). These data indicate life-cycle stage (e.g., lekking-nesting) and reduced availability of preformed water may influence use of free water sources by lesser prairie-chickens. Current climate models predict the region of the study area will experience increases in temperature and decreases in frequency of precipitation. The combined effect of this would be reduced environmental moisture. If the prediction of increasing aridity in the region holds true, man-made water sources may become a tool for conservation of the species.
","language":"English","publisher":"Southwestern Association of Naturalists","doi":"10.1894/0038-4909-65.3-4.197","usgsCitation":"Gicklhorn, T.S., Boal, C.W., and Borsdorf, P.K., 2020, Lesser prairie-chicken (Tympanuchus pallidicinctus) use of man-made water sources: Southwestern Naturalist, v. 65, no. 3-4, p. 197-204, https://doi.org/10.1894/0038-4909-65.3-4.197.","productDescription":"8 p.","startPage":"197","endPage":"204","ipdsId":"IP-083938","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":402264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Cochran County, Hockley County, Terry County, Yoakum County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.03802490234375,\n 33.01557297778958\n ],\n [\n -102.36785888671875,\n 33.01557297778958\n ],\n [\n -102.36785888671875,\n 33.73347670599252\n ],\n [\n -103.03802490234375,\n 33.73347670599252\n ],\n [\n -103.03802490234375,\n 33.01557297778958\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"3-4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gicklhorn, Trevor S.","contributorId":166698,"corporation":false,"usgs":false,"family":"Gicklhorn","given":"Trevor","email":"","middleInitial":"S.","affiliations":[{"id":24740,"text":"Department of Natural Resources Management, Texas Tech University, Lubbock, TX, 79409, USA","active":true,"usgs":false}],"preferred":false,"id":844733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":844734,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borsdorf, Philip K.","contributorId":93386,"corporation":false,"usgs":false,"family":"Borsdorf","given":"Philip","email":"","middleInitial":"K.","affiliations":[{"id":24740,"text":"Department of Natural Resources Management, Texas Tech University, Lubbock, TX, 79409, USA","active":true,"usgs":false}],"preferred":false,"id":844735,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70214145,"text":"70214145 - 2020 - Seismic monitoring & response for the Trans-Alaska Pipeline System","interactions":[],"lastModifiedDate":"2024-02-21T15:50:09.404918","indexId":"70214145","displayToPublicDate":"2021-12-01T11:22:40","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Seismic monitoring & response for the Trans-Alaska Pipeline System","docAbstract":"The 800-mile Trans Alaska Pipeline System (TAPS) passes through extremely remote regions, where there is a high potential for seismic activity. Alyeska Pipeline Service Company, the TAPS operator, has been on the forefront of seismic engineering and situational awareness, and continues to enhance its capabilities. TAPS has used earthquake monitoring since the pipeline was constructed in 1977 and recently upgraded to a fourth-generation of its monitoring system. This upgrade includes recent technology to improve accuracy and increase system redundancy, and it incorporates lessons learned during the 2018 M6.3 Kaktovik and the 2018 M7.1 Anchorage earthquakes. The modernized earthquake monitoring system includes strong-motion accelerograph stations installed at key locations along the pipeline tied into the control system to provide real-time detection of seismic events. The accelerometers also telemeter data to provide local constraints in ShakeMap so that they not only provide site-specific shaking values, but also contribute openly to constraining ground motions elsewhere so shaking at locations without stations can be better inferred. Alyeska then employs U. S. Geological Survey’s ShakeCast system to automatically ingest the ShakeMap to provide near real-time alerts of shaking as well as inspection priorities across the system, both for pipeline assets and infrastructure. TAPS stakeholders who receive ShakeCast alerts via email and text messages include controllers, engineers, and emergency managers. As part of our standard post-earthquake protocol, damage assessment checklists have been pre-deployed at multiple locations to guide these teams as they determine the integrity of TAPS following an event. This unprecedented level of situational awareness allows for rapid prioritization and deployment of damage assessment teams. The purpose of this manuscript is to expand on the details of these systems.","conferenceTitle":"17th World Conference on Earthquake Engineering","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Strait, S., and Wald, D.J., 2020, Seismic monitoring & response for the Trans-Alaska Pipeline System, 17th World Conference on Earthquake Engineering, Sendai, Japan, September 13-18, 2020, 12 p.","productDescription":"12 p.","ipdsId":"IP-116224","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":378710,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/","linkFileType":{"id":5,"text":"html"}},{"id":425800,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -144.3699605637841,\n 60.231305314797595\n ],\n [\n -144.3699605637841,\n 70.37934050762061\n ],\n [\n -152.86285792275456,\n 70.37934050762061\n ],\n [\n -152.86285792275456,\n 60.231305314797595\n ],\n [\n -144.3699605637841,\n 60.231305314797595\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Strait, S","contributorId":241100,"corporation":false,"usgs":false,"family":"Strait","given":"S","email":"","affiliations":[{"id":48206,"text":"Alyeska Pipeline Service Company","active":true,"usgs":false}],"preferred":false,"id":799561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799562,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70214144,"text":"70214144 - 2020 - An update of USGS bear-real-time earthquake shaking and impact products","interactions":[],"lastModifiedDate":"2024-02-21T15:49:50.02989","indexId":"70214144","displayToPublicDate":"2021-12-01T11:11:54","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"An update of USGS bear-real-time earthquake shaking and impact products","docAbstract":"We report on advancements in both hazard and consequence modeling that form the core of the U.S. Geological Survey’s (USGS) strategy to improve rapid earthquake shaking and loss estimates. Whereas our primary goal is to improve our operational capabilities of the USGS National Earthquake Information Center, the science, software, and datasets behind these systems continue to advance uses and studies of earthquake shaking and impact by the seismological, engineering, financial, and risk modeling communities. Several important updates to our integrated shaking and impact products are outlined and we introduce new earthquake information products that have recently been brought online, including rapid ground failure estimates and more spatially refined loss estimates domestically (in the U.S). We continue to compile, develop, and refine key openly available models and datasets that contribute to calibrating these systems and report on the collection and storage of new inventories. We also describe some of the basic operational considerations in the current generation of these shaking and loss-estimation systems. A key aspect of the product integration and development is leveraging earthquake-hazard and loss-modeling science done internally (within the USGS) and by external researchers and collaborators. Lastly, we outline new opportunities for further research and development by emphasizing scientific, data, and application gaps and challenges that must be solved in order to improve our shaking and impact information tools.","conferenceTitle":"17th World Conference on Earthquake Engineering","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Wald, D.J., Jaiswal, K.S., Marano, K., Hearne, M., Lin, K., Slosky, D., Allstadt, K.E., Thompson, E.M., Worden, C., Hayes, G.P., and Quitoriano, V., 2020, An update of USGS bear-real-time earthquake shaking and impact products, 17th World Conference on Earthquake Engineering, Sendai, Japan, September 13-18, 2020, 12 p.","productDescription":"12 p.","ipdsId":"IP-116227","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":378709,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/"},{"id":425799,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marano, Kristin 0000-0002-0420-2748 kmarano@usgs.gov","orcid":"https://orcid.org/0000-0002-0420-2748","contributorId":207906,"corporation":false,"usgs":true,"family":"Marano","given":"Kristin","email":"kmarano@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hearne, Mike 0000-0002-8225-2396 mhearne@usgs.gov","orcid":"https://orcid.org/0000-0002-8225-2396","contributorId":4659,"corporation":false,"usgs":true,"family":"Hearne","given":"Mike","email":"mhearne@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lin, Kuo-wan 0000-0002-7520-8151 klin@usgs.gov","orcid":"https://orcid.org/0000-0002-7520-8151","contributorId":1539,"corporation":false,"usgs":true,"family":"Lin","given":"Kuo-wan","email":"klin@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799554,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Slosky, Daniel 0000-0001-7407-3606 dslosky@usgs.gov","orcid":"https://orcid.org/0000-0001-7407-3606","contributorId":194954,"corporation":false,"usgs":true,"family":"Slosky","given":"Daniel","email":"dslosky@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799555,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799556,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799557,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Worden, Charles 0000-0003-1181-685X cbworden@usgs.gov","orcid":"https://orcid.org/0000-0003-1181-685X","contributorId":152042,"corporation":false,"usgs":true,"family":"Worden","given":"Charles","email":"cbworden@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799558,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799559,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Quitoriano, Vince 0000-0003-4157-1101 vinceq@usgs.gov","orcid":"https://orcid.org/0000-0003-4157-1101","contributorId":2582,"corporation":false,"usgs":true,"family":"Quitoriano","given":"Vince","email":"vinceq@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799560,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70208800,"text":"70208800 - 2020 - An exploration of parametric earthquake risk transfer solutions that dynamically adapt to seismicity changes","interactions":[],"lastModifiedDate":"2024-02-21T15:49:29.156746","indexId":"70208800","displayToPublicDate":"2021-12-01T10:47:58","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"An exploration of parametric earthquake risk transfer solutions that dynamically adapt to seismicity changes","docAbstract":"(Re)insurance companies rely on earthquake risk models to estimate the frequency and severity of their potential financial losses. To protect themselves, they sometimes use parametric risk transfer solutions, which are derivative-form agreements that provide compensation as a function of routine measurable earthquake characteristics. These mechanisms typically remain in force for one to three years and assume seismic conditions—and our estimates of them—remain unchanged during this period. However, seismic risk estimates evolve continuously due to changes in nearby seismicity, sudden ruptures, slower redistributions of stress, or improvements in our own understanding of these phenomena. As a consequence, the likelihood of some loss-causing events might decrease and make the protection superfluous (wasted money), or, more problematically, it might increase and render the protection insufficient (increased risk). This paper explores the construction of parametric earthquake risk transfer mechanisms that adapt efficiently (i.e., near real-time) to changes in seismicity throughout the lifetime of the transaction. The mechanism proposes the periodic adjustment of the payment conditions of the parametric agreement in harmony with the evolving probabilities of event occurrence. This, we hypothesize, may result in a more efficient allocation of premiums that reflects the changing nature of seismic risk. To build the proposed dynamic risk transfer mechanism, we first employ one of the earthquake models commonly used in the (re)insurance industry to assess the risk of a portfolio of assets. The modeling exercise yields the expected frequency distribution of loss, which a standard (re)insurance transaction would typically consider constant for the entire coverage period. Here, we use these results simply as a baseline for the initial time step of reference. Next, we construct a retrospective update loop, which consists of two parts: (1) we obtain the earthquake occurrence rate conditions at a previous time step taking into account the changes in seismicity observed in the interim period; and (2) we use the modeled losses and adjusted frequencies at the new time step to build a parametric risk transfer solution. This parametric solution remains in force until it is updated at the next iteration. We also track the effects on the efficiency of the risk transfer solution and its premium if these continuous updates were not implemented.
We apply the proposed mechanism to California and find that changes in seismicity can cause swings in the frequency of parametric payments (which is related to the premium paid for the cover) in average of 16% and up to 36% in any three-year period from 1986 to 2020. We also find that avoiding an update of the parametric solution on a yearly basis to match the new risk profile can decrease the efficiency of the cover (measured as the relative contribution to the average annual loss of the events covered) in the same time period by 13% on average and up to 35%.
","conferenceTitle":"17th World Conference on Earthquake Engineering, 17WCEE","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Franco, G., Guidotti, R., Field, E., Milner, K., Lee, Y., and Stein, R.S., 2020, An exploration of parametric earthquake risk transfer solutions that dynamically adapt to seismicity changes, 17th World Conference on Earthquake Engineering, 17WCEE, Sendai, Japan, September 13-18, 2020, 12 p.","productDescription":"12 p.","ipdsId":"IP-117006","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":425797,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/","linkFileType":{"id":5,"text":"html"}},{"id":425798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Franco, Guillermo","contributorId":194951,"corporation":false,"usgs":false,"family":"Franco","given":"Guillermo","email":"","affiliations":[],"preferred":false,"id":783436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guidotti, R","contributorId":222891,"corporation":false,"usgs":false,"family":"Guidotti","given":"R","email":"","affiliations":[{"id":40620,"text":"Guy Carpenter","active":true,"usgs":false}],"preferred":false,"id":783437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":783435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Milner, K.R.","contributorId":222892,"corporation":false,"usgs":false,"family":"Milner","given":"K.R.","email":"","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":783438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lee, Y.J.","contributorId":222893,"corporation":false,"usgs":false,"family":"Lee","given":"Y.J.","affiliations":[{"id":40621,"text":"ImageCat Inc.","active":true,"usgs":false}],"preferred":false,"id":783439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stein, R. S.","contributorId":222894,"corporation":false,"usgs":false,"family":"Stein","given":"R.","email":"","middleInitial":"S.","affiliations":[{"id":40622,"text":"Temblor","active":true,"usgs":false}],"preferred":false,"id":783440,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228603,"text":"70228603 - 2020 - Decision context as an essential component of population viability analysis","interactions":[],"lastModifiedDate":"2022-02-14T14:59:02.146641","indexId":"70228603","displayToPublicDate":"2021-09-30T08:41:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Decision context as an essential component of population viability analysis","docAbstract":"Population viability analysis (PVA) is a widely used tool that applies demographic data in simulation frameworks to assess extinction risk for species or populations. It is used in diverse conservation applications, including evaluating management effectiveness, relative risk of threats, and potential changes to protective status (Beissinger & McCullough, 2002), and can be a critical tool for making decisions with imperfect knowledge of the system state, often on limited timelines (Meine et al., 2006).
Chaudhary and Oli (2020) recently developed a framework to appraise the quality of PVAs based on the presence of essential background, model, and analysis components. They evaluated 160 published PVAs and reported a decline in the quality of PVAs over time (1990−2017). We agree PVA studies should report unambiguous descriptions of their essential components (Table 1 in Chaudhary and Oli) and explicitly state the model's biological and statistical assumptions. The need for increased transparency in PVAs is evident. Morrison et al. (2016) reported that only 50% of PVAs published in peer-reviewed and gray literature were both reproducible and repeatable. Further, in an examination of 67 studies that used matrix population models (widely used in PVAs), Kendall et al. (2019) reported that models frequently contained misspecification errors. Given the rapid advancement of simulation techniques, updated guidance for PVA construction is warranted.
However, we believe the essential PVA components identified by Chaudhary and Oli contain a critical omission: the decision context in which the PVA was created and its usefulness in that context. Quality and utility are not mutually exclusive; however, some models that do not meet idealized quality standards might still be valuable because they are useful and represent the best available science for a given decision context (hereafter, decision-support models). The definition of quality for decision-support models should be different than models developed for the purpose of learning (hereafter, heuristic models) and should incorporate how useful the model was, despite information gaps. We further argue that assessment questions should be used prospectively to guide modeling projects, rather than for retrospective comparison of model quality.
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