Data collection, along with hydraulic and fluvial egg transport modeling, was completed along a 70.9-mile reach of the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam in Kentucky and Indiana. Water-quality data collected in this reach included surface measurements and vertical profiles of water temperature, specific conductance, pH, dissolved oxygen, turbidity, relative chlorophyll, and relative phycocyanin. Data were collected during two surveys: October 27–November 4, 2016, and June 26–29, 2017. Streamflow and velocity data were collected simultaneously with the water-quality data at cross sections and along longitudinal lines (corresponding to the water-quality surface measurements) and at selected stationary locations (corresponding to the water-quality vertical profiles). The data were collected to understand variability of flow and water-quality conditions relative to simulated reaches of the Ohio River and to aid in identifying parts of the reach that may provide conditions favorable to spawning and recruitment habitat for Hypophthalmichthys nobilis (bighead carp).
A copy of an existing step-backwater model of Ohio River flows was obtained from the National Weather Service and used to simulate hydraulic conditions for four different streamflows. Streamflows were selected to represent typical conditions ranging from a high-streamflow event to a seasonal dry-weather event, with two streamflows between these extremes for this reach of the Ohio River. Outputs from the hydraulic model, a range of five water temperatures observed in water-quality data, and four potential spawning locations were used as input to the Fluvial Egg Drift Simulator to simulate the extents and quantile positions of developing bighead carp, from egg hatching to the gas bladder inflation stage, under each scenario. A total of 80 simulations were run.
Results from the Fluvial Egg Drift Simulator scenarios (which include only the hydraulic influences on survival that result from settling, irrespective of mortality from other physical or biological factors such as excess turbulence, fertilization failure, predation, or starvation) indicate that most eggs will hatch, about half will die, and a quarter of the surviving larvae will reach the gas bladder inflation stage within the model reach. The overall mean percentage of embryos surviving to the gas bladder inflation stage was 13.1 percent. Individual simulations have embryo survival percentages as high as 49.1 percent. The highest embryo survival percentages occurred for eggs spawned at a streamflow of 38,100 cubic feet per second and water temperatures of 24 to 30 degrees Celsius. Conversely, embryo survival percentages were lowest for the lowest and highest streamflows regardless of water temperature or spawn location. Under low water temperature and high-streamflow conditions, some of the eggs did not hatch nor did the larvae reach the gas bladder inflation stage until passing beyond the downstream model domain. Although the final quantile positions of the eggs and larvae beyond the downstream model domain are unknown, the outcomes still provide useful information about conditions favorable to spawning and recruitment habitat for bighead carp in the Ohio River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215005","collaboration":"Biological Threats and Invasive Species Research Program","usgsCitation":"Ostheimer, C.J., Boldt, J.A., and Buszka, P.M., 2021, Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator: U.S. Geological Survey Scientific Investigations Report 2021–5005, 30 p., https://doi.org/10.3133/sir20215005.","productDescription":"Report: v, 30 p.; 2 Data Releases","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-116266","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":384390,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5005/coverthb.jpg"},{"id":384391,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5005/sir20215005.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5005"},{"id":384392,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MQHEPU","text":"USGS data release","linkHelpText":"Velocity and water-quality surveys in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, October 27–November 4, 2016, and June 26–29, 2017"},{"id":384393,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JHLGZL","text":"USGS data release","linkHelpText":"Geospatial data and models for the simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator"}],"country":"United States","state":"Indiana, Kentucky","otherGeospatial":"Ohio River, Markland Locks and Dam, McAlpine Locks and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.660400390625,\n 38.40194908237822\n ],\n [\n -84.935302734375,\n 38.40194908237822\n ],\n [\n -84.935302734375,\n 38.85682013474361\n ],\n [\n -85.660400390625,\n 38.85682013474361\n ],\n [\n -85.660400390625,\n 38.40194908237822\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd., Suite 100
Columbus, OH 43229–1737
Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.
Barrier coasts, including barrier islands, beach-ridge plains, and associated landforms, can assume a broad spectrum of morphologies over multi-decadal scales that reflect conditions of sediment availability, accommodation, and relative sea-level rise. However, the quantitative thresholds of these controls on barrier-system behavior remain largely unexplored, even as modern sea-level rise and anthropogenic modification of sediment availability increasingly reshape the world's sandy coastlines. In this study, we conceptualize barrier coasts as sediment-partitioning frameworks, distributing sand delivered from the shoreface to the subaqueous and subaerial components of the coastal system. Using an idealized morphodynamic model, we explore thresholds of behavioral and morphologic change over decadal to centennial timescales, simulating barrier evolution within quasi-stratigraphic morphological cross sections. Our results indicate a wide diversity of barrier behaviors can be explained by the balance of fluxes delivered to the beach vs. the dune or backbarrier, including previously understudied forms of transgression that allow the subaerial system to continue accumulating sediment during landward migration. Most importantly, our results show that barrier state transitions between progradation, cross-shore amalgamation, aggradation, and transgression are controlled largely through balances within a narrow range of relative sea-level rise and sediment flux. This suggests that, in the face of rising sea levels, subtle changes in sediment fluxes could result in significant changes in barrier morphology. We also demonstrate that modeled barriers with reduced vertical sediment accommodation are highly sensitive to the magnitude and direction of shoreface fluxes. Therefore, natural barriers with limited sediment accommodation could allow for exploration of the future effects of sea-level rise and changing flux magnitudes over a period of years as opposed to the decades required for similar responses in sediment-rich barrier systems. Finally, because our model creates stratigraphy generated under different input parameters, we propose that it could be used in combination with stratigraphic data to hindcast the sensitivity of existing barriers and infer changes in prehistoric morphology, which we anticipate will provide a baseline to assess the reliability of forward modeling predictions.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/esurf-9-183-2021","usgsCitation":"Ciarletta, D.J., Miselis, J.L., Shawler, J.L., and Hein, C.J., 2021, Quantifying thresholds of barrier geomorphic change in a cross-shore sediment-partitioning model: Earth Surface Dynamics, v. 9, p. 183-203, https://doi.org/10.5194/esurf-9-183-2021.","productDescription":"21 p.","startPage":"183","endPage":"203","ipdsId":"IP-122455","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453052,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/esurf-9-183-2021","text":"Publisher Index Page"},{"id":436457,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O3D29V","text":"USGS data release","linkHelpText":"Python-based Subaerial Barrier Sediment Partitioning (pySBSP) model (ver. 1.0, February 2024)"},{"id":436456,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DE6QCL","text":"USGS data release","linkHelpText":"Subaerial Barrier Sediment Partitioning (SBSP) Model Version 1.0"},{"id":384718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ciarletta, Daniel J. 0000-0002-8555-2239","orcid":"https://orcid.org/0000-0002-8555-2239","contributorId":256700,"corporation":false,"usgs":true,"family":"Ciarletta","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":813078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":813079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shawler, Justin L.","contributorId":256701,"corporation":false,"usgs":false,"family":"Shawler","given":"Justin","email":"","middleInitial":"L.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":813080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hein, Christopher J.","contributorId":256702,"corporation":false,"usgs":false,"family":"Hein","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":813081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221345,"text":"70221345 - 2021 - Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses","interactions":[],"lastModifiedDate":"2021-06-11T12:10:19.688015","indexId":"70221345","displayToPublicDate":"2021-03-17T07:06:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses","docAbstract":"Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often requiring that practitioners have theoretical and practical understanding of several advanced topics. Worse, implementing these important analyses can take substantial time and effort. This additional effort is often cited as justification for postponing, or even forgoing, these analyses.
Herein, we present scripting tools to facilitate the efficient and repeatable construction of high-dimensional, geostatistical-based PEST interfaces, including uncertainty analyses. As demonstrated, these tools can be applied with minimal effort to a model with varied temporal and spatial discretization. Ultimately, these tools can enable low-cost access to valuable decision-support analyses earlier and more frequently during the modeling workflow.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105022","usgsCitation":"White, J., Hemmings, B., Fienen, M., and Knowling, M., 2021, Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses: Environmental Modelling & Software, v. 139, 105022, 9 p., https://doi.org/10.1016/j.envsoft.2021.105022.","productDescription":"105022, 9 p.","ipdsId":"IP-127193","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy","contributorId":260166,"corporation":false,"usgs":false,"family":"White","given":"Jeremy","affiliations":[{"id":52529,"text":"Interra","active":true,"usgs":false}],"preferred":false,"id":817388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hemmings, Brioch","contributorId":260167,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":817389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knowling, Matthew","contributorId":260168,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","affiliations":[{"id":36897,"text":"University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":817391,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228966,"text":"70228966 - 2021 - Filling knowledge gaps for a threatened species: Age and growth of Green Sturgeon of the southern distinct population segment","interactions":[],"lastModifiedDate":"2022-02-25T17:01:49.76587","indexId":"70228966","displayToPublicDate":"2021-03-16T10:56:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Filling knowledge gaps for a threatened species: Age and growth of Green Sturgeon of the southern distinct population segment","docAbstract":"The Green Sturgeon Acipenser medirostris is an anadromous, long-lived species that is distributed along the Pacific coast of North America. Green Sturgeon is vulnerable to global change because of its sensitive life history (e.g., delayed maturation) and few spawning locations. The persistence of Green Sturgeon is threatened by habitat modification, altered flows, and rising river temperatures. In 2006, because of persistent stressors, the U.S. Endangered Species Act listed the southern distinct population segment as threatened. Despite increased research efforts on this species after the listing, substantial gaps in basic population information for Green Sturgeon remain. We present the only published information on age structure and growth of a threatened population of Green Sturgeon. By analyzing archived fin rays collected from 1984 to 2016, we revealed highly variable growth among individuals. We detected several age classes from 0 to 26 y and found similar growth rates of southern distinct population segment Green Sturgeon compared with northern population Green Sturgeon. Although limited, this analysis is an important first step to understanding Green Sturgeon population dynamics and highlights critical research needs.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-073","usgsCitation":"Ulaski, M., and Quist, M.C., 2021, Filling knowledge gaps for a threatened species: Age and growth of Green Sturgeon of the southern distinct population segment: Journal of Fish and Wildlife Management, v. 12, no. 1, p. 234-240, https://doi.org/10.3996/JFWM-20-073.","productDescription":"7 p.","startPage":"234","endPage":"240","ipdsId":"IP-123529","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-073","text":"Publisher Index Page"},{"id":396498,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.826171875,\n 33.063924198120645\n ],\n [\n -115.48828125000001,\n 33.063924198120645\n ],\n [\n -115.48828125000001,\n 48.8936153614802\n ],\n [\n -126.826171875,\n 48.8936153614802\n ],\n [\n -126.826171875,\n 33.063924198120645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ulaski, Marta","contributorId":280108,"corporation":false,"usgs":false,"family":"Ulaski","given":"Marta","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":836042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836043,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236348,"text":"70236348 - 2021 - A probabilistic framework to model distributions of VS30","interactions":[],"lastModifiedDate":"2022-09-02T15:02:57.58682","indexId":"70236348","displayToPublicDate":"2021-03-16T09:56:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A probabilistic framework to model distributions of VS30","title":"A probabilistic framework to model distributions of VS30","docAbstract":"The time‐averaged shear‐wave velocity in the upper 30 m depth from the ground surface, or
Analysis of the water balance of the southwestern United States (SWUS) during 1900 through 2018 was used to evaluate the magnitude of the turn-of-the-century (TOC) drought in the SWUS. Results indicate that the warm season (April through September) soil moisture and runoff during the TOC drought were among the lowest values of the 1900 through 2018 period. Additionally, increases in temperature were identified as a significant driver of low soil moisture and runoff conditions during the warm season. In contrast, during the cool seasons (October through March) and the water year (October 1 through September 30) during the TOC drought, soil moisture and runoff did not indicate extremely dry conditions even though temperatures were the highest of the 1900 through 2018 period.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/abbfc1","usgsCitation":"McCabe, G.J., and Wolock, D.M., 2021, Water balance of the turn-of-the-century drought in the Southwestern United States: Environmental Research Letters, v. 16, 044015, 9 p., https://doi.org/10.1088/1748-9326/abbfc1.","productDescription":"044015, 9 p.","ipdsId":"IP-122582","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":453071,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abbfc1","text":"Publisher Index Page"},{"id":405904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Soputhwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100,\n 40\n ],\n [\n -108.08349609375,\n 40\n ],\n [\n -124,\n 40\n ],\n [\n -120.05859375,\n 34.161818161230386\n ],\n [\n -117.24609374999999,\n 32.39851580247402\n ],\n [\n -114.9609375,\n 32.76880048488168\n ],\n [\n -111.1376953125,\n 31.42866311735861\n ],\n [\n -108.2373046875,\n 31.316101383495624\n ],\n [\n -108.06152343749999,\n 31.765537409484374\n ],\n [\n -106.61132812499999,\n 31.765537409484374\n ],\n [\n -103.9306640625,\n 29.305561325527698\n ],\n [\n -103.0517578125,\n 29.075375179558346\n ],\n [\n -102.26074218749999,\n 29.954934549656144\n ],\n [\n -100.986328125,\n 29.6880527498568\n ],\n [\n -99.7998046875,\n 27.800209937418252\n ],\n [\n -99.5361328125,\n 27.68352808378776\n ],\n [\n -100,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolock, David M. 0000-0002-6209-938X","orcid":"https://orcid.org/0000-0002-6209-938X","contributorId":219213,"corporation":false,"usgs":true,"family":"Wolock","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":850206,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219181,"text":"70219181 - 2021 - American Woodcock singing-ground survey: Comparison of four models for trend in population size","interactions":[],"lastModifiedDate":"2021-08-03T13:58:58.411403","indexId":"70219181","displayToPublicDate":"2021-03-16T07:14:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"American Woodcock singing-ground survey: Comparison of four models for trend in population size","docAbstract":"Wildlife biologists monitor the status and trends of American woodcock Scolopax minor populations in the eastern and central United States and Canada via a singing-ground survey, conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, management regions, and survey-wide. In recent years, researchers have used a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S model) to model population change. Recently, researchers have proposed alternative models suitable for analysis of singing-ground survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey, has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross-validation to compare model fit for the present singing-ground survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t-distributed extra-Poisson overdispersion effects (H models) as opposed to normally distributed extra-Poisson overdispersion. Leave-one-out cross-validation results indicate that the Bayesian predictive information criterion favored the D model, but a pairwise t-test indicated that the D model was not significantly better-fitting to singing-ground survey data than the S model. The H models are not preferable to the alternatives with normally distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future singing-ground survey analyses.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-20-079","usgsCitation":"Sauer, J.R., Link, W., Seamans, M.E., and Rau, R.D., 2021, American Woodcock singing-ground survey: Comparison of four models for trend in population size: Journal of Fish and Wildlife Management, v. 12, no. 1, p. 83-97, https://doi.org/10.3996/JFWM-20-079.","productDescription":"15 p.","startPage":"83","endPage":"97","onlineOnly":"N","ipdsId":"IP-127453","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":453075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-079","text":"Publisher Index Page"},{"id":384754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Eastern and Central United States and Canada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.412109375,\n 50.17689812200107\n ],\n [\n -95.537109375,\n 51.069016659603896\n ],\n [\n -95.888671875,\n 48.922499263758255\n ],\n [\n -95.00976562499999,\n 43.58039085560784\n ],\n [\n -93.955078125,\n 39.027718840211605\n ],\n [\n -93.515625,\n 30.826780904779774\n ],\n [\n -86.748046875,\n 32.10118973232094\n ],\n [\n -82.6171875,\n 29.99300228455108\n ],\n [\n -77.783203125,\n 34.161818161230386\n ],\n [\n -75.76171875,\n 35.88905007936091\n ],\n [\n -60.29296874999999,\n 45.706179285330855\n ],\n [\n -60.29296874999999,\n 47.100044694025215\n ],\n [\n -67.412109375,\n 50.17689812200107\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":813142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, William 0000-0002-9913-0256","orcid":"https://orcid.org/0000-0002-9913-0256","contributorId":221718,"corporation":false,"usgs":true,"family":"Link","given":"William","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":813143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seamans, Mark E","contributorId":256724,"corporation":false,"usgs":false,"family":"Seamans","given":"Mark","email":"","middleInitial":"E","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rau, Rebecca D.","contributorId":256726,"corporation":false,"usgs":false,"family":"Rau","given":"Rebecca","email":"","middleInitial":"D.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":813145,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218843,"text":"70218843 - 2021 - A systematic review of potential habitat suitability for the jaguar Panthera onca in central Arizona and New Mexico, USA","interactions":[],"lastModifiedDate":"2021-03-17T12:09:21.458718","indexId":"70218843","displayToPublicDate":"2021-03-16T07:00:01","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":"A systematic review of potential habitat suitability for the jaguar Panthera onca in central Arizona and New Mexico, USA","docAbstract":"In April 2019, the U.S. Fish and Wildlife Service (USFWS) released its recovery plan for the jaguar Panthera onca after several decades of discussion, litigation and controversy about the status of the species in the USA. The USFWS estimated that potential habitat, south of the Interstate-10 highway in Arizona and New Mexico, had a carrying capacity of c. six jaguars, and so focused its recovery programme on areas south of the USA–Mexico border. Here we present a systematic review of the modelling and assessment efforts over the last 25 years, with a focus on areas north of Interstate-10 in Arizona and New Mexico, outside the recovery unit considered by the USFWS. Despite differences in data inputs, methods, and analytical extent, the nine previous studies found support for potential suitable jaguar habitat in the central mountain ranges of Arizona and New Mexico. Applying slightly modified versions of the USFWS model and recalculating an Arizona-focused model over both states provided additional confirmation. Extending the area of consideration also substantially raised the carrying capacity of habitats in Arizona and New Mexico, from six to 90 or 151 adult jaguars, using the modified USFWS models. This review demonstrates the crucial ways in which choosing the extent of analysis influences the conclusions of a conservation plan. More importantly, it opens a new opportunity for jaguar conservation in North America that could help address threats from habitat losses, climate change and border infrastructure.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0030605320000459","usgsCitation":"Sanderson, E.W., Fisher, K., Peters, R., Beckmann, J.P., Bird, B., Bradley, C., Bravo, J., Grigione, M.M., Hatten, J., Gonzalez, C., Menke, K., Miller, J., Miller, P., Mormorunni, C., Robinson, M., Thomas, R.E., and Wilcox, S., 2021, A systematic review of potential habitat suitability for the jaguar Panthera onca in central Arizona and New Mexico, USA: Oryx, p. 1-12, https://doi.org/10.1017/S0030605320000459.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-114595","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":453076,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/s0030605320000459","text":"Publisher Index 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Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health","docAbstract":"Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.
","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2021.620715","usgsCitation":"Sepulveda, A., Hoegh, A.B., Gage, J.A., Caldwell Eldridge, S.L., Birch, J.M., Stratton, C., Hutchins, P.R., and Barnhart, E.P., 2021, Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health: Frontiers in Ecology and Evolution, v. 9, 13 p., https://doi.org/10.3389/fevo.2021.620715.","productDescription":"13 p.","ipdsId":"IP-123750","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":453078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2021.620715","text":"Publisher Index Page"},{"id":384620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming, Montana","otherGeospatial":"Yellowstone River, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.400390625,\n 43.89789239125797\n ],\n [\n -107.666015625,\n 43.89789239125797\n ],\n [\n -107.666015625,\n 46.49839225859763\n ],\n [\n -115.400390625,\n 46.49839225859763\n ],\n [\n -115.400390625,\n 43.89789239125797\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoegh, Andrew B.","contributorId":166684,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":812862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gage, Joshua A.","contributorId":255726,"corporation":false,"usgs":false,"family":"Gage","given":"Joshua","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":812863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell Eldridge, Sara L. 0000-0001-8838-8940 seldridge@usgs.gov","orcid":"https://orcid.org/0000-0001-8838-8940","contributorId":4981,"corporation":false,"usgs":true,"family":"Caldwell Eldridge","given":"Sara","email":"seldridge@usgs.gov","middleInitial":"L.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":812864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Birch, James M.","contributorId":255728,"corporation":false,"usgs":false,"family":"Birch","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":812865,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stratton, Christian","contributorId":217711,"corporation":false,"usgs":false,"family":"Stratton","given":"Christian","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":812866,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hutchins, Patrick R. 0000-0001-5232-0821 phutchins@usgs.gov","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":198337,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick","email":"phutchins@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812867,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812868,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219207,"text":"70219207 - 2021 - Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah","interactions":[],"lastModifiedDate":"2021-05-18T14:07:03.389177","indexId":"70219207","displayToPublicDate":"2021-03-15T13:32:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3042,"text":"Petroleum Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah","docAbstract":"Geological carbon sequestration (GCS) is necessary to help meet emissions reduction goals, but groundwater contamination may occur if CO2 and/or brine were to leak out of deep storage formations into the shallow subsurface. For this study, a natural analogue was investigated: in the Virgin River Basin of southwest Utah, water with moderate salinity and high CO2 concentrations is leaking upward into shallow aquifers that contain heavy metal-bearing concretions. The aquifer system is comprised of the Navajo and Kayenta formations, which are pervasive across southern Utah and have been considered as a potential GCS injection unit where they are sufficiently deep. Numerical models of the site were constructed based on measured water chemistry and head distributions from previous studies. Simulations were used to improve understanding of the rate and distribution of the upwelling flow into the aquifers, and to assess the reactive transport processes that may occur if the upwelling fluids were to interact with a zone of iron oxide and other heavy metals, representing the concretions that are common in the area. Various mineralogies were tested, including one in which Pb+2 was adsorbed onto ferrihydrite, and another in which it was bound within a solid mixture of litharge (PbO) and hematite (Fe2O3). Results indicate that metal mobilization depends strongly on the source zone composition and that Pb+2 transport can be naturally attenuated by gas phase formation and carbonate mineral precipitation. These findings could be used to improve risk assessment and mitigation strategies at geological carbon sequestration sites.
","language":"English","publisher":"The Geological Society of London","doi":"10.1144/petgeo2020-109","usgsCitation":"Plampin, M.R., Blondes, M., Sonnenthal, E., and Craddock, W.H., 2021, Potential Pb+2 mobilization, transport, and sequestration in shallow aquifers impacted by multiphase CO2 leakage: A natural analogue study from the Virgin River Basin in Southwest Utah: Petroleum Geoscience, v. 27, no. 3, petgeo2020-109, 15 p., https://doi.org/10.1144/petgeo2020-109.","productDescription":"petgeo2020-109, 15 p.","ipdsId":"IP-120620","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":384769,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":\"47\",\"properties\":{\"name\":\"Utah\",\"nation\":\"USA 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Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":813215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":813216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sonnenthal, Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":813217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Craddock, William H. 0000-0002-4181-4735 wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":813218,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218647,"text":"sir20215004 - 2021 - Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","interactions":[],"lastModifiedDate":"2021-03-16T11:43:44.760872","indexId":"sir20215004","displayToPublicDate":"2021-03-15T08:46:16","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5004","displayTitle":"Numerical Simulation of the Effects of Groundwater Withdrawal and Injection of High-Salinity Water on Salinity and Groundwater Discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","title":"Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi","docAbstract":"Kaloko-Honokōhau National Historical Park (KAHO) is located on the west coast of the island of Hawaiʻi and contains water resources exposed in fishponds, anchialine pools, and marine waters that are cultural resources and that provide habitat for threatened, endangered, and other culturally important native species. KAHO’s water resources are sustained by and dependent on groundwater discharge. In 1978, the year of KAHO authorization, the lands immediately surrounding KAHO were undeveloped and zoned for conservation purposes; at present, most surrounding lands are either developed or zoned for industrial, commercial, or residential use. Urbanization of the North Kona District has increased the need for additional drinking and nonpotable (irrigation) water. Because KAHO’s water resources may be affected by existing and proposed groundwater withdrawals and injections of high-salinity water in the surrounding area, the U.S. Geological Survey, in cooperation with the National Park Service, undertook this study to refine the understanding of how groundwater withdrawals and injection of high-salinity water may affect KAHO’s water resources.
Changes in KAHO water resources, in terms of changes in salinity and groundwater discharge, were modeled for selected scenarios of groundwater withdrawal and high-salinity water injection in the aquifer. The numerical model was developed using the model code SUTRA, which accounts for density-dependent flow and salinity transport, and included the coastal-confined groundwater system beneath the coastal freshwater-lens system. Model results indicate that withdrawal of additional groundwater from the coastal freshwater-lens system will affect the salinity of KAHO’s anchialine pools, which provide habitat for the endangered orangeblack Hawaiian damselfly (Megalagrion xanthomelas). The magnitude of the effect is dependent on the amount and location of the withdrawal. Greater withdrawal rates cause greater increases in salinity in KAHO, other factors being equal. For a given withdrawal rate, the greatest increase in salinity in KAHO is associated with wells withdrawing groundwater in an area inland of KAHO and the least increase in salinity is associated with wells near the coast. Model results also indicate that withdrawal of additional groundwater from the coastal freshwater-lens system will affect the groundwater discharge, in terms of the freshwater component (water with zero salinity) of the discharge, through KAHO. Greater withdrawal rates cause greater reductions in freshwater discharge through KAHO. For a given withdrawal rate, the greatest reduction in freshwater discharge through KAHO is associated with wells near the north boundary of KAHO and the least reduction is associated with wells near the coast to the north and south of KAHO.
Injection of high-salinity water that is denser than ocean water can affect the salinity of damselfly habitat in KAHO, with the magnitude of the effect dependent on the location of the injection. Model results indicate that salinity may either increase or decrease in the anchialine pools that provide damselfly habitat in KAHO, depending on the site of injection. Injection inland of KAHO and at sites immediately north and south of KAHO causes a simulated decrease in salinity at the damselfly habitat, whereas injection farther north and south of KAHO causes an increase in salinity. Injection of high-salinity water also causes a reduction in freshwater discharge through KAHO, with the greatest reduction associated with distant injection wells to the north and south of KAHO and the least reduction associated with wells located near and immediately inland from KAHO.
The numerical groundwater models developed for this study have a number of limitations. Lack of understanding of the subsurface geology constrains the ability to accurately model the groundwater-flow system. The models developed for this study are nonunique, cannot account for local-scale heterogeneities in the aquifer, and contain uncertainties related to recharge, boundary conditions, assigned parameter values in the model, and representations of the different hydrogeological features. Confidence in model results can be improved by addressing these and other limitations. In spite of these limitations, the three-dimensional numerical model developed for this study provides a useful conceptual understanding of the potential effects of additional withdrawals and injections on groundwater resources in KAHO. Further evaluation of the ecologic effects of the simulated changes in groundwater quality and quantity in KAHO is needed but is beyond the scope of this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215004","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Oki, D.S., 2021, Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2021–5004, 59 p., https://doi.org/10.3133/sir20215004.","productDescription":"Report: viii, 59 p.; Data Release","numberOfPages":"59","ipdsId":"IP-119308","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":383763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZ3EVJ","linkHelpText":"SUTRA Model Used to Evaluate the Effects of Groundwater Withdrawal and Injection, Kaloko-Honokōhau National Historical Park, Hawaiʻi"},{"id":383762,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5004/sir20215004.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383761,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5004/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaloko-Honokōhau National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.03761672973633,\n 19.66829132832601\n ],\n [\n -156.01186752319336,\n 19.66829132832601\n ],\n [\n -156.01186752319336,\n 19.69350614042769\n ],\n [\n -156.03761672973633,\n 19.69350614042769\n ],\n [\n -156.03761672973633,\n 19.66829132832601\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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The Osage Nation of northeastern Oklahoma, conterminous with Osage County, covers about 2,900 square miles. The area is primarily rural with 62 percent of the land being native prairie grass, and much of the area is used for cattle ranching and extraction of petroleum and natural gas. Protection of water rights are important to the Osage Nation because of its reliance on cattle ranching and the potential for impairment of water quality by petroleum extraction. Additionally, the potential for future population increases, demands for water from neighboring areas such as the Tulsa metropolitan area, and expansion of petroleum and natural-gas extraction on water resources of this area further the need for the Osage Nation to better understand its water availability. Therefore, the U.S. Geological Survey, in cooperation with the Osage Nation, completed a hydrologic investigation to assess the status and availability of surface-water and groundwater resources in the Osage Nation.
A transient integrated hydrologic-flow model was constructed using the U.S. Geological Survey fully integrated hydrologic-flow model called the MODFLOW One-Water Hydrologic Model. The integrated hydrologic-flow model, called the Osage Nation Integrated Hydrologic Model (ONIHM), was constructed and uses an orthogonal grid of 276 rows and 289 columns, and each grid cell measures 1,312.34 feet (ft; 400 meters) per side, with eight variably thick vertical layers that represented the alluvial and bedrock aquifers within the study area, including the alluvial aquifer, the Vamoosa-Ada aquifer, and the minor Pennsylvanian bedrock aquifers, and the confining units. Landscape and groundwater-flow processes were simulated for two periods: (1) the 1950–2014 period from January 1950 through September 2014 and (2) the forecast period from October 2014 through December 2099. The 1950–2014 period ONIHM simulated past conditions using measured or estimated inputs, and the forecast-period ONIHM simulated three separate potential forecast conditions under constant dry, average, or wet climate conditions using calibrated input values from the 1950–2014 period ONIHM.
The 1950–2014 period ONIHM was calibrated by linking the Parameter Estimation software (PEST) with the MODFLOW One-Water Hydrologic Model. PEST uses statistical parameter estimation techniques to identify the best set of parameter values to minimize the difference between measured or estimated calibration targets and their simulated equivalent values (residuals). Tikhonov regularization and singular-value decomposition-assist features of PEST were used during the calibration process. The 1950–2014 period ONIHM was calibrated to 713 measured groundwater levels at 195 wells; 95,636 estimated monthly mean groundwater levels at 124 wells; 5,307 measured streamflows at 13 streamgages; and 8,679 simulated mean monthly streamflows at 10 streamgages extracted from a surface-water model by adjusting 231 parameters. The estimated groundwater-level observations and streamflows were included as observations to improve the spatial and temporal density of observation targets during calibration. The best set of parameter values obtained during the calibration process of the 1950–2014 model was then used as the input parameter values for the forecast model simulations. A comparison of the calibration targets to their corresponding simulated values indicated that the model adequately reproduced streamflows and groundwater levels for some streamgages and wells and underestimated streamflows and groundwater levels at other locations. Measured and simulated streamflows correlated adequately with a coefficient of determination of 0.938, as did water levels with a coefficient of determination of 0.795. The 1950–2014 period ONIHM underestimated certain groundwater levels and streamflows, but generally measured or estimated calibration targets correlated well with simulated equivalents, which indicated that the model can adequately simulate the response of the hydrologic system to stresses in the 1950–2014 and forecast periods.
In the 1950–2014 period ONIHM, the calibrated mean horizontal hydraulic conductivity for layer 1 alluvial aquifer was 30.7 feet per day, and the seven lower layers had a calibrated mean horizontal hydraulic conductivity of less than 3.3 feet per day. The mean calibrated groundwater-level residual was 16.6 ft, and the mean calibrated streamflow residual of the Arkansas River at Ralston, Oklahoma, streamgage (U.S. Geological Survey station 07152500) was within 6 percent (373 cubic feet per second) of mean measured streamflow for the 1950–2014 period ONIHM.
The ONIHM simulated landscape fluxes of precipitation; groundwater applied by irrigation wells; evapotranspiration from precipitation, groundwater, and irrigation; runoff from precipitation; and deep percolation from precipitation. The largest loss of water from the landscape was evapotranspiration from precipitation with a calibrated mean annual outflow of 32 inches (in.): mean annual precipitation was about 36 in. Calibrated mean annual runoff and deep percolation (recharge to the water table) rates were 4.7 inches per year (in/yr) and 0.70 in/yr, respectively, for the 1950–2014 period ONIHM.
The calibrated 1950–2014 period ONIHM groundwater fluxes included net farm net recharge (calculated as the difference between the inflow of recharge to the water table and the outflow of evapotranspiration from the water table such that negative values indicate that evapotranspiration from the water table was greater than deep percolation [recharge to the water table] and vice versa). Net farm net recharge was the largest flux from the groundwater system with a mean annual net outflow of 153.4 cubic feet per second. Stream leakage was the largest flux to the groundwater system with a mean annual net inflow of 152.5 cubic feet per second, indicating that, on average, the groundwater/surface-water interaction was a “losing” system where stream water leaked into the subsurface and recharged the water table. Simulated monthly trends demonstrated that net stream leakage was the largest inflow to the groundwater-flow system for 10 of the 12 months; for the other 2 months (January and March), farm net recharge (January) and net storage (March) were the largest inflow to the groundwater-flow system.
A saline groundwater interface map was created for the study and compared to the water levels from the final stress period of the 1950–2014 model to identify the presence of fresh/marginal groundwater throughout the study area. Fresh/marginal groundwater was characterized as groundwater with less than 1,500 milligrams per liter of total dissolved solids. Fresh/marginal groundwater thickness ranged from 0 to 438.2 ft within the study area. The thickest regions of fresh/marginal groundwater were in the eastern part of the study area near Sand Creek, Bird Creek, and Hominy Creek and in the Arkansas River alluvial aquifer in the region downstream from the Arkansas River at Ralston, Okla.
Like the 1950–2014 model, forecast model results for the landscape indicated that transpiration from precipitation was the largest flux out of the landscape for all three forecasts, constituting 77, 73, and 58 percent of precipitation for the dry, average, and wet forecasts, respectively. The dry and average forecast landscape fluxes demonstrated similar trends and magnitudes, whereas the wet forecast landscape fluxes indicated the largest changes compared to the average forecast fluxes. Most notably, runoff increased from a mean of 1.1 and 1.6 in/yr for the dry and average forecasts, respectively, to 10 in/yr for the wet forecast. Similar changes occurred for the other wet forecast landscape fluxes.
The calibrated 1950–2014 period ONIHM simulated three forecasts to assess the effects of potential climatic changes on the hydrologic system from October 2014 to December 2099. The three forecasts simulated theoretical dry, average, and wet conditions using precipitation and potential evapotranspiration datasets from selected years in the calibrated 1950–2014 period ONIHM. Annual precipitation amounts were 26.89, 35.47, and 50.73 in. for the dry, average, and wet forecasts, respectively. Groundwater-flow component forecast results indicated that stream leakage is always a net inflow to the groundwater-flow system for dry, average, and wet conditions, meaning the study area stream network is always predominantly a “losing” regime where stream water infiltrates into the underlying aquifer. Storage was only a net outflow from the groundwater-flow system and indicated a replenishment to groundwater storage that resulted in an increase in groundwater levels only during the wet forecast. Further, these gains in groundwater storage for the wet forecast occurred only during February through June.
Mean fresh/marginal groundwater saturated thicknesses were 125 and 126 ft for the dry and average forecast conditions, respectively, and wet forecast average thickness was 145 ft and ranged from 0 to 443 ft. The spatial extents of fresh/marginal groundwater at the end of the dry, average, and wet forecast model periods (December 2099) did not change substantially from the end of the 1950–2014 model period (September 2014).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205141","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Traylor, J.P., Mashburn, S.L., Hanson, R.T., and Peterson, S.M., 2021, Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model: U.S. Geological Survey Scientific Investigations Report 2020–5141, 96 p., https://doi.org/10.3133/sir20205141.","productDescription":"Report: xiii, 96 p.; 2 Interactive Figures; Data Release; Dataset","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102662","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":384320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5141/coverthb.jpg"},{"id":384321,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141"},{"id":384322,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure8.pdf","text":"Figure 8 (layered)","size":"626 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 8","linkHelpText":"— Supergroups for the Osage Nation Integrated Hydrologic Model (note: some supergroups are hidden; in order to see a given supergroup, the reader may need to turn off layers for the overlying supergroups)."},{"id":384324,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OKQ2C","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-One Water Hydrologic Model integrated hydrologic-flow model used to evaluate water availability in the Osage Nation"},{"id":384323,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure14.pdf","text":"Figure 14 (layered)","size":"711 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 14","linkHelpText":"— Simulated groundwater-level altitude contours for the final stress period of the calibrated Osage Nation Integrated Hydrologic Model (September 30, 2014), dry forecast (December 31, 2099), average forecast (December 31, 2099), and wet forecast (December 31, 2099). This figure is a layered PDF."},{"id":384325,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas, Oklahoma","otherGeospatial":"Osage Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 36.13565654678543\n ],\n [\n -95.99853515625,\n 37.00035919622158\n ],\n [\n -95.97930908203125,\n 37.081475648860525\n ],\n [\n -96.29241943359375,\n 37.13623498442895\n ],\n [\n -96.48193359375,\n 36.96306042436515\n ],\n [\n -96.9873046875,\n 36.94989178681327\n ],\n [\n -97.12188720703125,\n 36.6992553955527\n ],\n [\n -97.14385986328125,\n 36.36822190085111\n ],\n [\n -96.6412353515625,\n 36.213255233061844\n ],\n [\n -96.26220703125,\n 36.11125252076156\n ],\n [\n -95.99578857421875,\n 36.13565654678543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The 1973 Oklahoma Water Law (82 OK Stat § 82-1020.5) requires that the Oklahoma Water Resources Board (OWRB) conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin (hereinafter referred to as an “aquifer”). The maximum annual yield allocated per acre of land is known as the equal-proportionate-share (EPS) pumping rate. At present (2021), the OWRB has not yet established a maximum annual yield and EPS pumping rate for the Salt Fork Red River aquifer. To provide updated information to the OWRB that could support evaluation and determination of an appropriate maximum annual yield, the U.S. Geological Survey (USGS), in cooperation with the OWRB, conducted a hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater availability in the Salt Fork Red River aquifer.
The Salt Fork Red River aquifer in Greer, Harmon, and Jackson Counties of southwestern Oklahoma is composed of about 274.5 square miles of alluvium and terrace deposits associated with the Salt Fork Red River. The mean annual recharge rate to the Salt Fork Red River aquifer for the period 1980–2015 was estimated to be about 2.94 inches per year, or 10.0 percent of the mean annual precipitation for the same period (29.4 inches per year). This 1980–2015 mean annual recharge rate is equivalent to a mean annual recharge rate of about 38,000 acre-feet per year (acre-ft/yr) for the Salt Fork Red River aquifer excluding about 19,764 acres comprising the Mulberry Creek and Horse Creek terraces. The mean annual recharge rates upgradient and downgradient from USGS streamgage 07300500 Salt Fork Red River at Mangum, Okla. (hereinafter referred to as the “Mangum gage”), apportioned by aquifer area (41.5 and 58.5 percent, respectively), were about 16,000 and 22,000 acre-ft/yr, respectively. Mean annual groundwater use for the study period (1980–2015) was 3,532.7 acre-ft/yr; about 77 percent of that groundwater use was for irrigation, and about 23 percent was for public supply. Most groundwater use for irrigation was associated with wells in the Martha terrace.
A hydrogeologic framework was developed for the Salt Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of the numerical groundwater-flow model of the Salt Fork Red River aquifer described in this report. A conceptual model for the Salt Fork Red River aquifer that reasonably represents the groundwater-flow system was developed to constrain the construction and calibration of the numerical model. The conceptual-model water budget estimated mean annual inflows to, and outflows from, the Salt Fork Red River aquifer for the period 1980–2015 and included a subaccounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from the Mangum gage.
The numerical groundwater-flow model of the Salt Fork Red River aquifer was constructed by using MODFLOW-2005 with the Newton formulation solver. The model of the Salt Fork Red River aquifer was spatially discretized into 1,050 rows, 1,125 columns, about 170,000 active cells measuring 200 by 200 feet (ft), and a single convertible layer. The model was temporally discretized into 432 monthly transient stress periods (each with two time steps to improve model stability). An initial steady-state stress period represented mean annual inflows to, and outflows from, the aquifer and produced a solution that was used as the initial condition for subsequent transient stress periods as well as some groundwater-availability scenarios. The model was calibrated to water-table-altitude observations at selected wells and base-flow observations at selected streamgages.
The simulated saturated thickness of the Salt Fork Red River aquifer was determined by subtracting the altitude of the aquifer base from the simulated water-table altitude at the end of the numerical-model period (2015). The simulated saturated thickness was more than 75 ft in a paleochannel in the Dodson terrace near the Texas border. The mean aquifer thickness (sum of saturated and unsaturated) was 49.62 ft, and the mean saturated thickness was 28.55 ft. A simulated mean transmissivity of 1,024 feet squared per day was computed from the calibrated hydraulic conductivity and saturated thickness of each cell. The simulated available water in storage at the end of the numerical-model period (2015) was 526,117 acre-feet (acre-ft); about 42 percent of that total was available upgradient from the Mangum gage, and about 58 percent of that total was available downgradient from the Mangum gage (including the Mangum terrace).
Three types of groundwater-availability scenarios were run using the calibrated numerical model. These scenarios were used to (1) estimate the EPS pumping rate that ensures a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought on base flow and groundwater storage. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were about 0.51, 0.48, and 0.48 acre-foot per acre per year, respectively. Given the 155,929-acre modeled aquifer area, these rates correspond to annual yields of about 78,800, 74,900, and 74,700 acre-ft/yr, respectively. For the 20-year EPS scenario, decreasing and increasing recharge by 10 percent resulted in a 6-percent change in the EPS pumping rate in both cases; for the 40- and 50-year EPS scenarios, decreasing and increasing recharge by 10 percent resulted in a 7-percent change in the EPS pumping rate in both cases.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the Salt Fork Red River aquifer and base flows in the Salt Fork Red River. The effects of well withdrawals were evaluated by quantifying differences in groundwater storage and base flow in four 50-year scenarios, which applied (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2015), (3) 2015 pumping rates, and (4) increasing demand pumping rates at simulated wells. The increasing demand pumping rates assumed a cumulative 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwestern Oklahoma. Groundwater storage after 50 years with no pumping was 535,000 acre-ft, or 8,900 acre-ft (1.7 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-table-altitude increase of 0.48 ft. Groundwater storage after 50 years of pumping at the mean rate for the study period (1980–2015) was 519,900 acre-ft, or 6,200 acre-ft (1.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.34 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 513,100 acre-ft, or 13,000 acre-ft (2.5 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.71 ft. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was 509,700 acre-ft, or 16,500 acre-ft (3.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.89 ft.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage. The period January 1983–December 1992 was chosen as the simulated drought period. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought) at the end of the simulated drought period (1992). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1983–92). Upstream inflows from the Salt Fork Red River, Turkey Creek, and Bitter Creek were reduced by 75 percent. Groundwater storage at the end of the drought period (1992) was 479,200 acre-ft, or 53,200 acre-ft (10.0 percent) less than the groundwater storage of the calibrated numerical model at the end of the drought period. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 2.9 ft. At the end of the 10-year hypothetical drought period, simulated base flows at the Mangum gage and USGS streamgage 07301110 Salt Fork Red River near Elmer, Okla., had decreased by about 80 and 70 percent, respectively.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
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Austin, Texas 78754-4501
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","tableOfContents":"Earthquake clustering (grouping in space and time) is a widely observed mode of strain release in the upper crust, although this behavior on individual faults is a departure from classic elastic rebound theory. In this study, we consider factors responsible for a cluster of earthquakes on the Bear River fault zone (BRF), a recently activated, 44-km-long normal fault on the eastern margin of Basin and Range extension in the Rocky Mountains. The entire surface-rupturing history of the BRF, as gleaned from paleoseismic and geomorphic observations, began only 4500 years ago and consists of at least three large events. Rupture of the BRF is spatially complex and is clearly conditioned by preexisting structure. In particular, where the south end of the fault intersects older thrust faults and upturned strata along the south-dipping flank of the Precambrian basement-cored Uinta arch, the main trace ends abruptly in a set of orthogonal splays that accommodate down-dropping of a large hanging-wall graben against the arch. We hypothesize that the geomechanically strong Uinta arch crustal block impeded the development of the BRF and, over time, enabled a significant accumulation of elastic strain energy, eventually giving rise to a pulse of strain release in the mid- to late Holocene. We surmise that variations in fault strength, both in space and time, is a cause of earthquake clustering on the BRF and on other faults that are structurally and tectonically immature. The first two earthquakes on the BRF occurred during the same period of time as a regional cluster of earthquakes in the Middle Rocky Mountains, suggesting that isolated faults in this slowly extending region interact through widespread changes in stress conditions.
The Panola Mountain Research Watershed (PMRW) is a 41‐hectare forested catchment within the Piedmont Province of the Southeastern United States. Observations, experimentation, and numerical modelling have been conducted at Panola over the past 35 years. But to date, these studies have not been fully incorporated into a more comprehensive synthesis. Here we describe the evolving perceptual understanding of streamflow generation mechanisms at the PMRW. We show how the long‐term study has enabled insights that were initially unforeseen but are also unachievable in short‐term studies. In particular, we discuss how the accumulation of field evidence, detailed site characterization, and modelling enabled a priori hypotheses to be formed, later rejected, and then further refined through repeated field campaigns. The extensive characterization of the soil and bedrock provided robust process insights not otherwise achievable from hydrometric measurements and numerical modelling alone. We focus on two major aspects of streamflow generation: the role of hillslopes (and their connection to the riparian zone) and the role of catchment storage in controlling fluxes and transit times of water in the catchment. Finally, we present location‐independent hypotheses based on our findings at PMRW and suggest ways to assess the representativeness of PMRW in the broader context of headwater watersheds.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14127","usgsCitation":"Aulenbach, B.T., Hooper, R.P., van Meerveld, H.J., Burns, D., Freer, J.E., Shanley, J.B., Huntington, T., McDonnell, J.J., and Norman E. Peters, 2021, The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed: Hydrological Processes, v. 35, no. 4, e14127, 14 p., https://doi.org/10.1002/hyp.14127.","productDescription":"e14127, 14 p.","ipdsId":"IP-125152","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":385149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.78149414062499,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 33.25706340236547\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooper, Richard P 0000-0002-3329-9622","orcid":"https://orcid.org/0000-0002-3329-9622","contributorId":257488,"corporation":false,"usgs":false,"family":"Hooper","given":"Richard","email":"","middleInitial":"P","affiliations":[{"id":52045,"text":"Tufts University, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":814372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Meerveld, H. J. 0000-0002-7547-3270","orcid":"https://orcid.org/0000-0002-7547-3270","contributorId":257489,"corporation":false,"usgs":false,"family":"van Meerveld","given":"H.","email":"","middleInitial":"J.","affiliations":[{"id":52048,"text":"University of Zurich, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":814373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freer, James E. 0000-0001-6388-7890","orcid":"https://orcid.org/0000-0001-6388-7890","contributorId":188139,"corporation":false,"usgs":false,"family":"Freer","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":814375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McDonnell, Jeffery J. 0000-0002-3880-3162","orcid":"https://orcid.org/0000-0002-3880-3162","contributorId":62723,"corporation":false,"usgs":false,"family":"McDonnell","given":"Jeffery","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":814378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Norman E. Peters 0000-0002-0637-9424","orcid":"https://orcid.org/0000-0002-0637-9424","contributorId":207130,"corporation":false,"usgs":false,"family":"Norman E. Peters","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":814379,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70221418,"text":"70221418 - 2021 - Using bottom trawls to monitor subsurface water clarity in marine ecosystems","interactions":[],"lastModifiedDate":"2021-06-15T11:46:28.05606","indexId":"70221418","displayToPublicDate":"2021-03-15T06:44:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3194,"text":"Progress in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Using bottom trawls to monitor subsurface water clarity in marine ecosystems","docAbstract":"Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, Kd, and optical depth, ζ– apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted Kd from tags to the multi-sensor Kd(490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag Kd was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in Kd occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.
Watershed degradation due to invasion threatens downstream water flows and associated ecosystem services. While this topic has been studied across landscapes that have undergone invasive-driven state changes (e.g., native forest to invaded grassland), it is less well understood in ecosystems experiencing within-system invasion (e.g. native forest to invaded forest). To address this subject, we conducted an integrated ecological and ecohydrological study in tropical forests impacted by invasive plants and animals. We measured soil infiltration capacity in multiple fenced (i.e., ungulate-free)/unfenced and native/invaded forest site pairs along moisture and substrate age gradients across Hawaii to explore the effects of invasion on hydrological processes within tropical forests. We also characterized forest composition, structure and soil characteristics at these sites to assess the direct and vegetation-mediated impacts of invasive species on infiltration capacity. Our models show that invasive ungulates negatively affect soil infiltration capacity consistently across the wide moisture and substrate age gradients considered. Additionally, several soil characteristics known to be affected by invasive ungulates were associated with local infiltration rates, indicating that the long-term secondary effects of high ungulate densities in tropical forests may be stronger than effects observed in this study. The effect of invasive plants on infiltration was complex and likely to depend on their physiognomy within existing forest community structure. These results provide clear evidence for managers that invasive ungulate control efforts can improve ecohydrological function of mesic and wet forest systems critical to protecting downstream and nearshore resources and maintaining groundwater recharge.
Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8–4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7–10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.
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Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5014","displayTitle":"Extending Seasonal Discharge Records for Streamgage Sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through Water Year 2020","title":"Extending seasonal discharge records for streamgage sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through water year 2020","docAbstract":"Daily mean discharge records are needed for management of selected streams in the Fortymile River Basin. The U.S. Geological Survey, in cooperation with the U.S. Bureau of Land Management, updated a technique for estimating seasonal (partial year) discharge at two short-record streamgage sites in the basin and evaluated the accuracy of the estimates. Daily mean discharge values were estimated for May 15–September 30, 1976–82 and 2006–18, for U.S. Geological Survey streamgage sites 15330000 (North Fork Fortymile River above Middle Fork near Franklin, Alaska) and 15331000 (Middle Fork Fortymile River near mouth near Chicken, Alaska). Relations between discharge for each study streamgage and an index streamgage on the main-stem Fortymile River (15348000, Fortymile River near Steele Creek, Alaska) for concurrent seasonal periods in 2019 and 2020 were developed using the maintenance of variance extension type 3 (MOVE.3) record extension technique. The MOVE.3 regressions were used to estimate daily mean discharges at the study streamgage sites for the selected season for the longer period of record of the index streamgage. Additionally, estimated records were generated from the regressions for the concurrent seasonal periods to evaluate the accuracy of the record extension techniques. The modified Nash-Sutcliffe efficiency coefficients for the estimated records were 0.53 for the North Fork Fortymile River (15330000) and 0.70 for the Middle Fork Fortymile River (15331000) streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215014","collaboration":"Prepared in cooperation with the U.S. Bureau of Land Management","usgsCitation":"Curran, J.H., 2021, Extending seasonal discharge records for streamgage sites on the North Fork Fortymile and Middle Fork Fortymile Rivers, Alaska, through water year 2020: U.S. Geological Survey Scientific Investigations Report 2021–5014, 11 p., https://doi.org/10.3133/sir20215014.","productDescription":"Report: v, 11 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-125266","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":384327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5014/sir20215014.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5014"},{"id":384361,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VCAOEZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Extended seasonal discharge records for selected streamgage sites in the Fortymile River Basin, Alaska, 1976-2020"},{"id":384326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5014/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"North Fork Fortymile River, Middle Fork Fortymile River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -146.46972656249997,\n 62.91523303947614\n ],\n [\n -141.0205078125,\n 62.91523303947614\n ],\n [\n -141.0205078125,\n 65.60387765860433\n ],\n [\n -146.46972656249997,\n 65.60387765860433\n ],\n [\n -146.46972656249997,\n 62.91523303947614\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
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4210 University Drive
Anchorage, Alaska 99508
Freshwater mussels share habitat and are parasites of freshwater fishes during the larval life stage. Therefore, models of fish biogeography may also explain the historical biogeography of freshwater mussels. We tested this assumption using predictions of three biogeographic models constructed for northern Gulf of Mexico drainages on a freshwater mussel species complex. Specifically, we tested (1) if speciation was due to vicariant events of fluctuating sea levels that separated lineages east‐west of the Mobile Basin (Central Gulf Coast speciation hypothesis), (2) if the timing of divergences occurred 8.5–3.5 MYA (Gulf Coast allopatric speciation model) and (3) if diversification in Mississippi River populations was recent and for evidence of population increase consistent with range expansion into northern deglaciated regions (Pleistocene glaciation model).
Eastern North America.
Freshwater mussels (Bivalvia: Unionidae), Lampsilis teres and L. floridensis.
We collected 249 specimens from 73 localities across the group's distribution. We used three molecular markers (COI, NDI & ITSI) to conduct time calibrated Bayesian phylogenetic analyses, phylogeographic analyses (AMOVA & SAMOVA) and demographic analyses including Bayesian skyline plots.
Lampsilis teres and L. floridensis are allopatric species whose distributions meet at the eastern edge of the Mobile Basin. Speciation was estimated to occur in the late Miocene. Populations from isolated river systems surrounding the Gulf of Mexico are almost all monophyletic. Mississippi drainage samples formed a shallow clade with recent diversification and showed evidence of recent population expansion.
The historical biogeography of the L. teres species complex is broadly consistent with tested ichthyofaunal models. The timing of speciation and intraspecific divergences correspond to low sea‐level events suggesting that Gulf Coast sea‐level fluctuations are responsible for dispersal (sea‐level recession) and subsequent cladogenesis (sea‐level inundation). Consistent with numerous other freshwater studies, we found the Mobile Basin to be a suture zone, which may be due to the narrow, offshore continental shelf.
A compilation is made of the largest and second-largest magnetic-storm-maximum intensities, −Dst1 and −Dst2, for solar cycles 14–24 (1902–2016) by sampling Oulu Dcx for cycles 19–24, using published −Dstm values for 4 intense storms in cycles 14, 15, and 18 (1903, 1909, 1921, 1946), and calculating 15 new storm-maximum −Dstm values (reported here) for cycles 14–18. Three different models are fitted to the cycle-ranked −Dst1 and −Dst2 values using a maximum-likelihood algorithm: A Gumbel model, an unconstrained Generalized-Extreme-Value model, and a Weibull model constrained to have a physically justified maximum storm intensity of −Dstm = 2500 nT. All three models are good descriptions of the data. Since the best model is not clearly revealed with standard statistical tests, inference is precluded of the source process giving rise to storm-maximum −Dstm values. Of the three candidate models, the constrained Weibull gives the lowest superstorm occurrence probabilities. Using the compiled data and the constrained Weibull model, a once-per-century storm intensity is estimated to be −Dst1 = 663 nT, with a bootstrap 68% confidence interval of [497, 694] nT. Similarly, the probability that a future storm will have an intensity exceeding that of the March 1989 superstorm, −Dstm > 565 nT, is 0.246 per cycle with a 68% confidence interval of [0.140, 0.311] per cycle. Noting (possibly slight) ambiguity in the rankings of storm intensities, using the same methods, but storms more intense than those identified for cycles 14–16, would yield a higher once-per-century intensity and a higher probability for a −Dstm > 565 nT storm.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020SW002579","usgsCitation":"Love, J.J., 2021, Extreme-event magnetic storm probabilities derived from rank statistics of historical Dst intensities for solar cycles 14-24: Space Weather, v. 19, no. 4, e2020SW002579, 25 p., https://doi.org/10.1029/2020SW002579.","productDescription":"e2020SW002579, 25 p.","ipdsId":"IP-124185","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":490073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020sw002579","text":"Publisher Index Page"},{"id":387701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820608,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70231209,"text":"70231209 - 2021 - A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells","interactions":[],"lastModifiedDate":"2022-05-03T13:37:01.222443","indexId":"70231209","displayToPublicDate":"2021-03-11T08:10:39","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":10741,"text":"Gulf Watch Alaska Long-Term Monitoring Program Synthesis Report","active":true,"publicationSubtype":{"id":3}},"chapter":"1","title":"A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells","docAbstract":"We use over 100 in situ and remotely sensed temperature datasets to investigate thermal variability within and across the intertidal nearshore, coastal and offshore waters of the northern Gulf of Alaska. For the years 1970 through 2019 we document a warming trend of 0.24±0.10 °C per decade for the coastal northern shelf (0-250 m depth average) and a Gulf-wide sea surface temperature (SST) trend of 0.25±0.11 °C per decade. The Gulf-wide SST trend in the last halfcentury is more than twice that of the 0.11±0.003 °C warming rate computed for 1900-2019. Decorrelation length scales vary regionally and correlation of synoptic scale fluctuations (less than one month) between two stations rapidly degrades with increasing station distance, accounting for less than 10% of the covariance for separations exceeding 100 km. In contrast, stations separated by as much as 500 km retain 50% of their covariance in common for seasonal and sub-seasonal fluctuations. While satellite-based measures often capture most of the daily SST anomaly in coastal and offshore waters, a significant portion of the variance (30-40%) can remain unresolved, even exceeding 75% in the nearshore realm. Similarly, the North Pacific and Gulf of Alaska leading modes of SST variability leave large fractions (25-50%) of the subseasonal thermal variance unresolved. These evaluations show the importance of in situ temperature records for studies that seek to understand mechanistic responses of marine organisms to habitat variability at biologically important time and space scales. We find that near-bottom temperature anomalies on the outer shelf vary inversely with surface temperatures and with near-bottom salinity, suggesting that thermal anomalies are also linked with nutrient flux anomalies. A case study of the recent Pacific marine heatwave and transition out of preceding cool years shows that the northern Gulf of Alaska surface temperatures (0-50 m) were elevated from 2014 to 2019 relative to the long-term record. Coastal temperatures warmed contemporaneously with offshore waters through the 2013 calendar year. In contrast, deep inner shelf waters (200-250 m) exhibited delayed warming relative to the surface and relative to deep waters offshore at the same depth. While offshore surface waters cooled from early 2014 into 1-2 Science Synthesis Final Report Gulf Watch Alaska, 2021 early 2016, the shelf continued to warm over this time as the effects of local air-sea and advective heat fluxes continued to permeate across the northern Gulf. These results highlight the importance of different heating mechanisms for surface and near-bottom waters across the northern Gulf of Alaska.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"The Pacific marine heatwave: Monotoring during a major perturbation in the Gulf of Alaska","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Exxon Valdez Oil Spill Trustee Council","usgsCitation":"Danielson, S.L., Hennon, T.D., Monson, D., Suryan, R.M., Campbell, R.W., Baird, S.J., Holderied, K., and Weingartner, T., 2021, A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells: Gulf Watch Alaska Long-Term Monitoring Program Synthesis Report, 56 p.","productDescription":"56 p.","startPage":"1-1","endPage":"1-56","ipdsId":"IP-119985","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":400044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":400013,"type":{"id":15,"text":"Index Page"},"url":"https://gulfwatchalaska.org/resources/reports/science-synthesis-reports/"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.95703125,\n 54.87660665410869\n ],\n [\n -134.208984375,\n 58.401711667608\n ],\n [\n -140.712890625,\n 60.50052541051131\n ],\n [\n -147.568359375,\n 62.186013857194226\n ],\n [\n -153.017578125,\n 61.3546135846894\n ],\n [\n -161.89453125,\n 55.57834467218206\n ],\n [\n -164.443359375,\n 54.316523240258256\n ],\n [\n -162.24609375,\n 52.74959372674114\n ],\n [\n -143.7890625,\n 51.01375465718821\n ],\n [\n -132.626953125,\n 52.908902047770255\n ],\n [\n -130.95703125,\n 54.87660665410869\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Danielson, Seth L.","contributorId":256682,"corporation":false,"usgs":false,"family":"Danielson","given":"Seth","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":842031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hennon, Tyler D.","contributorId":291317,"corporation":false,"usgs":false,"family":"Hennon","given":"Tyler","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":842032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":842033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suryan, Rob M.","contributorId":291318,"corporation":false,"usgs":false,"family":"Suryan","given":"Rob","email":"","middleInitial":"M.","affiliations":[{"id":62685,"text":"Alaska Fisheries Science Center, NOAA","active":true,"usgs":false}],"preferred":false,"id":842034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell, Rob W.","contributorId":251805,"corporation":false,"usgs":false,"family":"Campbell","given":"Rob","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":842035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baird, Steven J.","contributorId":12375,"corporation":false,"usgs":false,"family":"Baird","given":"Steven","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":842036,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holderied, Kristine","contributorId":291319,"corporation":false,"usgs":false,"family":"Holderied","given":"Kristine","affiliations":[{"id":62686,"text":"Kasitsna Bay Laboratory, NOAA","active":true,"usgs":false}],"preferred":false,"id":842037,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weingartner, Thomas","contributorId":291321,"corporation":false,"usgs":false,"family":"Weingartner","given":"Thomas","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":842038,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219168,"text":"70219168 - 2021 - Submarine lava deltas of the 2018 eruption of Kilauea volcano","interactions":[],"lastModifiedDate":"2021-04-08T15:27:07.976909","indexId":"70219168","displayToPublicDate":"2021-03-11T07:54:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Submarine lava deltas of the 2018 eruption of Kīlauea volcano","title":"Submarine lava deltas of the 2018 eruption of Kilauea volcano","docAbstract":"Hawaiian and other ocean island lava flows that reach the coastline can deposit significant volumes of lava in submarine deltas. The catastrophic collapse of these deltas represents one of the most significant, but least predictable, volcanic hazards at ocean islands. The volume of lava deposited below sea level in delta-forming eruptions and the mechanisms of delta construction and destruction are rarely documented. Here, we report on bathymetric surveys and ROV observations following the Kīlauea 2018 eruption that, along with a comparison to the deltas formed at Pu‘u ‘Ō‘ō over the past decade, provide new insight into delta formation. Bathymetric differencing reveals that the 2018 deltas contain more than half of the total volume of lava erupted. In addition, we find that the 2018 deltas are comprised largely of coarse-grained volcanic breccias and intact lava flows, which contrast with those at Pu‘u ‘Ō‘ō that contain a large fraction of fine-grained hyaloclastite. We attribute this difference to less efficient fragmentation of the 2018 ‘a‘ā flows leading to fragmentation by collapse rather than hydrovolcanic explosion. We suggest a mechanistic model where the characteristic grain size influences the form and stability of the delta with fine grain size deltas (Pu‘u ‘Ō‘ō) experiencing larger landslides with greater run-out supported by increased pore pressure and with coarse grain size deltas (Kīlauea 2018) experiencing smaller landslides that quickly stop as the pore pressure rapidly dissipates. This difference, if validated for other lava deltas, would provide a means to assess potential delta stability in future eruptions.
ABSTRACT: The ‘bet hedging’ life history strategy of long-lived iteroparous species reduces short-term reproductive output to minimize the risk of reproductive failure over a lifetime. For desert-dwelling ectotherms living in variable and unpredictable environments, reproductive output is further influenced by precipitation and temperature via effects on food availability and limits on activity. We assembled multiple (n = 12) data sets on egg production for the threatened Agassiz’s desert tortoise Gopherus agassizii across its range and used these data to build a range-wide predictive model of annual reproductive output as a function of annual weather variation and individual-level attributes (body size and prior-year reproductive status). Climate variables were more robust predictors of reproductive output than individual-level attributes, with overall reproductive output positively related to prior-year precipitation and an earlier start to the spring activity season, and negatively related to spring temperature extremes (monthly temperature range in March-April). Reproductive output was highest for individuals with larger body sizes that reproduced in the previous year. Expected annual reproductive output from 1990-2018 varied from 2-5 to 6-12 eggs female-1 yr-1 , with a weak decline in expected reproductive output over this time (p = 0.02). Climate-driven environmental variation in expected reproductive output was highly correlated across all 5 Recovery Units for this species (Pearson’s r > 0.9). Overall, our model suggests that climate change could strongly impact the reproductive output of Agassiz’s desert tortoise, and could have a negative population-level effect if precipitation is significantly reduced across the species’ range as predicted under some climate models.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01103","usgsCitation":"Mitchell, C.I., Friend, D., Phillips, L.T., Hunter, E., Lovich, J.E., Agha, M., Puffer, S., Cummings, K.L., Medica, P.A., Esque, T., Nussear, K.E., and Shoemaker, K.T., 2021, ‘Unscrambling’ the drivers of egg production in Agassiz’s desert tortoise: Climate and individual attributes predict reproductive output: Endangered Species Research, v. 44, p. 217-230, https://doi.org/10.3354/esr01103.","productDescription":"14 p.","startPage":"217","endPage":"230","ipdsId":"IP-121127","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453130,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01103","text":"Publisher Index Page"},{"id":436463,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97WD6AH","text":"USGS data release","linkHelpText":"Mojave Desert Tortoise (Gopherus agassizii) Morphometrics and Egg Data from Seven Sites across the Mojave, (1997-2002)"},{"id":436462,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97XT7HF","text":"USGS data release","linkHelpText":"Agassiz's desert tortoise and egg data from the Sonoran Desert of California (1997-2000, 2015-2018)"},{"id":384375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Arizona, Nevada, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 33.211116472416855\n ],\n [\n -112.763671875,\n 33.211116472416855\n ],\n [\n -112.763671875,\n 37.16031654673677\n ],\n [\n 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USA","active":true,"usgs":false}],"preferred":true,"id":812083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phillips, Lauren T. 0000-0003-3110-1755","orcid":"https://orcid.org/0000-0003-3110-1755","contributorId":255289,"corporation":false,"usgs":false,"family":"Phillips","given":"Lauren","email":"","middleInitial":"T.","affiliations":[{"id":51512,"text":"Department of Geography, University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":false,"id":812084,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunter, Elizabeth A.","contributorId":149399,"corporation":false,"usgs":false,"family":"Hunter","given":"Elizabeth A.","affiliations":[],"preferred":false,"id":812085,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 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0000-0002-3789-3856","orcid":"https://orcid.org/0000-0002-3789-3856","contributorId":255290,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin","email":"","middleInitial":"T.","affiliations":[{"id":51513,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno. 1664 N Virginia St, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":false,"id":812093,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ]}