In the Northern Gulf of Mexico, salt marshes are threatened by sea level rise, erosion, and loss of protective barrier islands. These barrier islands provide critical habitat for wildlife, including globally significant populations of marsh and shorebirds. We investigated salt marsh restoration on two Louisiana barrier islands using presence of 8 marsh bird species as an index to evaluate restoration success. Land loss was extensive for both islands prior to restoration, with submerged marsh restored by backfilling sediment into the marsh platform. Restoration methods were similar between the two islands, although Raccoon Island was built to a higher elevation (1.1 m) than Whiskey Island (0.8m). Avian presence was estimated via passive acoustic monitoring and point counts. To evaluate restoration success, we modeled influence of habitat covariates on index species presence in restored and reference (intact) sites over three breeding seasons and modeled occupancy for 6 species. On Whiskey Island, index richness was higher in restored sites. Marsh specialists Seaside Sparrows (Ammospiza maritima ) and Least Bitterns (Ixobrychus exilis ) had higher occupancy in restored areas on Whiskey, while generalist species showed no response to site. These results are likely due to a strong association between habitat and vegetation type, with restored sites dominated by Spartina alterniflora and reference sites by Avicennia germinans . On Raccoon Island, species richness was low across all sites. Our results suggest that restoration efforts were successful in creating salt marsh habitat on Whiskey but not Raccoon as of the time of our study.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13222","usgsCitation":"Byerly, P.A., Waddle, H., Premeaux, A.R., and Leberg, P.L., 2020, Effects of barrier island salt marsh restoration on marsh bird occurrence in the Northern Gulf of Mexico: Restoration Ecology, v. 28, no. 6, p. 1610-1620, https://doi.org/10.1111/rec.13222.","productDescription":"11 p.","startPage":"1610","endPage":"1620","ipdsId":"IP-118357","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":377330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Isles Derniers, Raccoon Island, Whiskey Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.96954345703125,\n 29.014745722129636\n ],\n [\n -90.65711975097656,\n 29.014745722129636\n ],\n [\n -90.65711975097656,\n 29.09517707913941\n ],\n [\n -90.96954345703125,\n 29.09517707913941\n ],\n [\n -90.96954345703125,\n 29.014745722129636\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Byerly, Paige A.","contributorId":237930,"corporation":false,"usgs":false,"family":"Byerly","given":"Paige","email":"","middleInitial":"A.","affiliations":[{"id":36864,"text":"University of Louisiana Lafayette","active":true,"usgs":false}],"preferred":false,"id":795563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waddle, Hardin 0000-0003-1940-2133","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":222187,"corporation":false,"usgs":true,"family":"Waddle","given":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Premeaux, Alexis R.","contributorId":237932,"corporation":false,"usgs":false,"family":"Premeaux","given":"Alexis","email":"","middleInitial":"R.","affiliations":[{"id":36864,"text":"University of Louisiana Lafayette","active":true,"usgs":false}],"preferred":false,"id":795565,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leberg, Paul L.","contributorId":237934,"corporation":false,"usgs":false,"family":"Leberg","given":"Paul","email":"","middleInitial":"L.","affiliations":[{"id":36864,"text":"University of Louisiana Lafayette","active":true,"usgs":false}],"preferred":false,"id":795566,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210797,"text":"70210797 - 2020 - Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain","interactions":[],"lastModifiedDate":"2020-06-25T15:19:50.029027","indexId":"70210797","displayToPublicDate":"2020-06-13T09:25:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain","docAbstract":"Conservation status and management priorities are often informed by population trends. Trend estimates can be derived from population surveys or models, but both methods are associated with sources of uncertainty. Many Arctic-breeding shorebirds are thought to be declining based on migration and/or overwintering population surveys, but data are lacking to estimate the trends of some shorebird species. In addition, for most species, little is known about the stage(s) at which population bottlenecks occur, such as breeding vs. nonbreeding periods. We used previously published and unpublished estimates of vital rates to develop the first large-scale population models for 6 species of Arctic-breeding shorebirds in North America, including separate estimates for 3 subspecies of Dunlin. We used the models to estimate population trends and identify life stages at which population growth may be limited. Our model for the arcticola subspecies of Dunlin agreed with previously published information that the subspecies is severely declining. Our results also linked the decline to the subspecies’ low annual survival rate, thus potentially implicating factors during the nonbreeding period in the East Asian-Australasian Flyway. However, our trend estimates for all species showed high uncertainty, highlighting the need for more accurate and precise estimates of vital rates. Of the vital rates, annual survival had the strongest influence on population trend in all taxa. Improving the accuracy, precision, and spatial and temporal coverage of estimates of vital rates, especially annual survival, would improve demographic model-based estimates of population trends and help direct management to regions or seasons where birds are subject to higher mortality.","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa026","usgsCitation":"Weiser, E.L., Lanctot, R., Brown, S.C., Gates, H., Bety, J., Boldenow, M.L., Brook, R.W., Brown, G.S., English, W.B., Flemming, S.A., Franks, S., Gilchrist, H.G., Giroux, M., Johnson, A.C., Kendall, S., Kennedy, L.V., Koloski, L., Kwon, E., Lamarre, J., Lank, D.B., Latty, C.J., Lecomte, N., Liebezeit, J.R., McGuire, R., McKinnon, L., Nol, E., Payer, D.C., Perz, J., Rausch, J., Robards, M.D., Saalfeld, S.T., Senner, N.R., Smith, P., Soloviev, M., Solovyeva, D.V., Ward, D.H., Wood, P., and Sandercock, B., 2020, Annual adult survival drives trends in Arctic-breeding shorebirds but knowledge gaps in other vital rates remain: The Condor, v. 1222, duaa026, 14 p., https://doi.org/10.1093/condor/duaa026.","productDescription":"duaa026, 14 p.","ipdsId":"IP-114598","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":456413,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duaa026","text":"Publisher Index Page"},{"id":436931,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DZZ1OB","text":"USGS data release","linkHelpText":"Arctic Shorebird Population Model"},{"id":375919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1222","noUsgsAuthors":false,"publicationDate":"2020-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":791464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lanctot, Richard B.","contributorId":77879,"corporation":false,"usgs":false,"family":"Lanctot","given":"Richard B.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":791470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Stephen C.","contributorId":38457,"corporation":false,"usgs":false,"family":"Brown","given":"Stephen","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":791471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gates, H. 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V","contributorId":216257,"corporation":false,"usgs":false,"family":"Solovyeva","given":"Diana","email":"","middleInitial":"V","affiliations":[{"id":39381,"text":"Institute of Biological problems of the North","active":true,"usgs":false}],"preferred":false,"id":791503,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":791504,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Wood, Paul F.","contributorId":203707,"corporation":false,"usgs":false,"family":"Wood","given":"Paul F.","affiliations":[],"preferred":false,"id":791505,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Sandercock, Brett K.","contributorId":223926,"corporation":false,"usgs":false,"family":"Sandercock","given":"Brett K.","affiliations":[],"preferred":false,"id":791506,"contributorType":{"id":1,"text":"Authors"},"rank":38}]}} ,{"id":70227712,"text":"70227712 - 2020 - Model-based clustering reveals patterns in central place use of a marine top predator","interactions":[],"lastModifiedDate":"2022-01-27T16:07:29.994101","indexId":"70227712","displayToPublicDate":"2020-06-12T10:02:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Model-based clustering reveals patterns in central place use of a marine top predator","docAbstract":"Satellite telemetry data are commonly used to quantify habitat selection, examine animal movements, and delineate home ranges. These data also contain valuable information concerning dens, nests, roosts, and other central places that are often associated with important life history events and may exhibit unique characteristics; however, using satellite telemetry data to study central places is complicated by common nuances like locational error and animal movement. We coupled a novel modeling framework that accounts for these nuances with an Argos satellite telemetry dataset to examine the spatiotemporal behavior associated with harbor seal haul-out sites on Kodiak Island, Alaska, USA. The methodology incorporates an observation model that accommodates multiple sources of uncertainty in telemetry data and a flexible Bayesian nonparametric model to uncover latent clustering in the telemetry locations. We also contribute extensions to examine the effect of covariates on site selection and to obtain population-level inference concerning central place use. Harbor seal haul-out sites generally occurred in inlets and bays, areas that are isolated from the open water of the Gulf of Alaska. Most individuals selected haul-out sites that were protected from wave exposure. The effects of bathymetry and shoreline complexity on haul-out site selection were variable among individual seals, as were the effects of time of day, time since low tide, and day of year on temporal patterns of haul-out use. As repositories of satellite telemetry data on a wide variety of species accumulate, so do opportunities for using this information to learn about the locations of central places, as well as the temporal patterns in their use. The model-based approach we describe offers a practical and rigorous means for gaining insight concerning these sensitive locations, knowledge of which is important for the effective management and conservation of many species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3123","usgsCitation":"Brost, B.M., Hooten, M., and Small, R., 2020, Model-based clustering reveals patterns in central place use of a marine top predator: Ecosphere, e03123, 15 p., https://doi.org/10.1002/ecs2.3123.","productDescription":"e03123, 15 p.","ipdsId":"IP-079248","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":456420,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3123","text":"Publisher Index Page"},{"id":394975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.171142578125,\n 56.32262930069559\n ],\n [\n -151.885986328125,\n 57.76865857271793\n ],\n [\n -152.479248046875,\n 58.019737000187305\n ],\n [\n -153.74267578125,\n 58.04300405858762\n ],\n [\n -155.115966796875,\n 57.320589769167135\n ],\n [\n -154.171142578125,\n 56.32262930069559\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Brost, Brian M.","contributorId":272252,"corporation":false,"usgs":false,"family":"Brost","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":831864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":831863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Small, Robert J.","contributorId":272253,"corporation":false,"usgs":false,"family":"Small","given":"Robert J.","affiliations":[{"id":56329,"text":"akfg","active":true,"usgs":false}],"preferred":false,"id":831865,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210511,"text":"sir20205030 - 2020 - Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features","interactions":[],"lastModifiedDate":"2020-06-12T16:06:26.425579","indexId":"sir20205030","displayToPublicDate":"2020-06-12T09:45:00","publicationYear":"2020","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":"2020-5030","displayTitle":"Statewide Assessment of Karst Aquifers in New York With an Inventory of Closed-Depression and Focused-Recharge Features","title":"Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features","docAbstract":"Karst is a landscape formed from the dissolution of soluble rock or rock containing minerals that are easily dissolved from within the rock. The landscape is characterized by sinkholes, caves, losing streams, springs, and underground drainage systems, which rapidly move water through the karst. The two forms of karst in New York State include carbonate karst, which forms in carbonate rock (limestone, marble, and dolostone), and evaporite karst, which forms in rock that contains the evaporite minerals gypsum and halite.
Past and recent studies of karst across the State have shown that areas of focused recharge in karstic carbonate rock allow contaminants to enter aquifer systems with little attenuation. Focused areas of recharge need to be identified to help prevent such contamination from sources on or adjacent to the karst. The New York State Departments of Environmental Conservation and Health are collaborating with the agricultural community to make farmers and farm-planning advisors more aware of karst and how to manage daily farming activities to reduce their impact on surface water and groundwater resources, especially in karst areas. There is also a need to make regulators, planners, and the general public aware of New York’s karst resources and to properly protect and manage these resources to protect the quality of groundwater and surface water that can flow into, through, and from karst bedrock.
Using publicly available geospatial data, karst bedrock and closed depressions over or near karst rock were identified across New York. Carbonate, evaporite, and marble geologic units were selected from a statewide 1:250,000-scale bedrock geology dataset. The selected geologic units were intersected with 7.5-minute quadrangle maps to define the study area.
The U.S. Geological Survey has compiled an inventory of closed depressions from statewide digital contour data, scanned 7.5-minute topographic maps known as a digital raster graphics, and light detection and ranging (lidar) digital elevation models. Analysis of the data resulted in the identification of 5,023 closed depressions statewide. The inventory was conducted to eliminate duplication of results from analysis of the three data sources. A series of overlay analyses was conducted using the closed depressions and thematic data known to be key factors in determining the probability of a closed depression contributing to focused groundwater recharge; the thematic data include bedrock geology, soil type, soil infiltration rate, and land cover.
Though the extent of karst development is important in understanding the interaction between surface water and groundwater in karst terrains, some of the worst cases of groundwater contamination in karst can occur where only minor karst features might be present. The presence of karst—be it a short section of a solutioned fracture or an extensive cave system—requires careful consideration, forward-looking environmental planning, and consistent water-quality protection to preserve New York State’s water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205030","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Kappel, W.M., Reddy, J.E., and Root, J.C., 2020, Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features: U.S. Geological Survey Scientific Investigations Report 2020–5030, 74 p., https://doi.org/10.3133/sir20205030.","productDescription":"Report: viii, 74 p.","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090019","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":375401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5030/coverthb.jpg"},{"id":375404,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGN5IJ","text":"USGS data release","linkHelpText":"Data for statewide assessment of New York’s karst aquifers with an inventory of closed-depression and focused-recharge features"},{"id":375534,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030.pdf","text":"Report","size":"19.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5030"},{"id":375482,"rank":2,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030_table1.pdf","text":"Table 1","size":"140 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Stratigraphic column of New York State bedrock indicating those units in which karst features might be present"}],"country":"United States","state":"New 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York\",\"nation\":\"USA \"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Channels change in response to natural or anthropogenic fluctuations in streamflow and/or sediment supply and measurements of channel change are critical to many river management applications. Whereas repeated field surveys are costly and time‐consuming, remote sensing can be used to detect channel change at multiple temporal and spatial scales. Repeat images have been widely used to measure long‐term channel change, but these measurements are only significant if the magnitude of change exceeds the uncertainty. Existing methods for characterizing uncertainty have two important limitations. First, while the use of a spatially variable image co‐registration error avoids the assumption that errors are spatially uniform, this type of error, as originally formulated, can only be applied to linear channel adjustments, which provide less information on channel change than polygons of erosion and deposition. Second, previous methods use a level‐of‐detection (LoD) threshold to remove non‐significant measurements, which is problematic because real changes that occurred but were smaller than the LoD threshold would be removed. In this study, we present a new method of quantifying uncertainty associated with channel change based on probabilistic, spatially varying estimates of co‐registration error and digitization uncertainty that obviates a LoD threshold. The spatially distributed probabilistic (SDP) method can be applied to both linear channel adjustments and polygons of erosion and deposition, making this the first uncertainty method generalizable to all metrics of channel change. Using a case study from the Yampa River, Colorado, we show that the SDP method reduced the magnitude of uncertainty and enabled us to detect smaller channel changes as significant. Additionally, the distributional information provided by the SDP method allowed us to report the magnitude of channel change with an appropriate level of confidence in cases where a simple LoD approach yielded an indeterminate result.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4926","usgsCitation":"Leonard, C., Legleiter, C.J., Lea, D.M., and Schmidt, J.C., 2020, Measuring channel planform change from image time series: A generalizable, spatially distributed, probabilistic method for quantifying uncertainty: Earth Surface Processes and Landforms, v. 45, no. 11, p. 2727-2744, https://doi.org/10.1002/esp.4926.","productDescription":"18 p.","startPage":"2727","endPage":"2744","ipdsId":"IP-113525","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":436932,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SEBJ3X","text":"USGS data release","linkHelpText":"Aerial photographs from the Yampa and Little Snake Rivers in northwest Colorado used to characterize channel changes occurring between 1954 and 1961"},{"id":378500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Leonard, Christina","contributorId":195596,"corporation":false,"usgs":false,"family":"Leonard","given":"Christina","email":"","affiliations":[],"preferred":true,"id":799076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":799077,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lea, Devin M.","contributorId":240907,"corporation":false,"usgs":false,"family":"Lea","given":"Devin","email":"","middleInitial":"M.","affiliations":[{"id":48159,"text":"Department of Geography, University of Oregon","active":true,"usgs":false}],"preferred":false,"id":799078,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, John C.","contributorId":207751,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37627,"text":"Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":799079,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210999,"text":"70210999 - 2020 - Influence of hydropower outflow characteristics affecting riverbank stability: The lower Osage River case (Missouri, USA)","interactions":[],"lastModifiedDate":"2020-08-26T19:19:54.479689","indexId":"70210999","displayToPublicDate":"2020-06-12T08:27:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"Influence of hydropower outflow characteristics affecting riverbank stability: The lower Osage River case (Missouri, USA)","docAbstract":"This research examined the influences of outflow characteristics affecting riverbank stability. The 130 km stretch of the lower Osage River downstream from Bagnell Dam (Missouri, USA) provided an excellent case study for this purpose. The integrated BSTEM model with the HEC-RAS model was accurately calibrated and validated with data from the U.S. Geological Survey (USGS). Then, the outflow characteristics (peak flow duration, flow drawdown rate, and low flow duration) were investigated individually. The results of this study showed that: 1) Riverbank stability is little affected by the duration time of the peak flow, especially on the reaches far from the dam. 2) Sudden flow drawdown significantly reduces riverbank stability. However, the impact of the drawdown rate decreases with distance from the dam. 3) The duration of the low flow after peak flow influences the riverbank stability value proportional to the distance from the dam. The time of low flow before failure increases as the distance from the dam increases.","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02626667.2020.1772974","usgsCitation":"Mohammed-Ali, W., Mendoza, C., and Holmes, R.R., 2020, Influence of hydropower outflow characteristics affecting riverbank stability: The lower Osage River case (Missouri, USA): Hydrological Sciences Journal, v. 65, no. 10, p. 1784-1793, https://doi.org/10.1080/02626667.2020.1772974.","productDescription":"10 p.","startPage":"1784","endPage":"1793","ipdsId":"IP-110034","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":376250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Lower Osage River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.7081298828125,\n 38.13887716726548\n ],\n [\n -92.1697998046875,\n 38.13887716726548\n ],\n [\n -92.1697998046875,\n 38.302869955150044\n ],\n [\n -92.7081298828125,\n 38.302869955150044\n ],\n [\n -92.7081298828125,\n 38.13887716726548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Mohammed-Ali, Wesam","contributorId":225556,"corporation":false,"usgs":false,"family":"Mohammed-Ali","given":"Wesam","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":792383,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mendoza, Cesar","contributorId":225557,"corporation":false,"usgs":false,"family":"Mendoza","given":"Cesar","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":792384,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmes, Robert R. Jr. 0000-0002-5060-3999 bholmes@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-3999","contributorId":1624,"corporation":false,"usgs":true,"family":"Holmes","given":"Robert","suffix":"Jr.","email":"bholmes@usgs.gov","middleInitial":"R.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":793358,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224298,"text":"70224298 - 2020 - Assessment of fire fuel load dynamics in shrubland ecosystems in the western United States using MODIS products","interactions":[],"lastModifiedDate":"2021-09-21T13:14:01.451306","indexId":"70224298","displayToPublicDate":"2020-06-12T08:11:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of fire fuel load dynamics in shrubland ecosystems in the western United States using MODIS products","docAbstract":"Kodiak Island in southern Alaska is one of the premier examples globally for the study of forearc magmatism. This location contains two Paleocene intrusive belts that formed due to the subduction of a migrating spreading ridge and slab-window: the Kodiak batholith and the trenchward magmatic belt. These magmatic rocks are part of the Sanak-Baranof belt, which extends for greater than 2,100 km along the southern Alaskan margin and vary in age from 61 to 50 Ma west to east.
Trenchward-belt rocks, with an 40Ar/39Ar age of 60.2±0.9 Ma, intrude into the Paleocene Ghost Rocks Formation and are composed of granitoids, basaltic dikes, and small gabbroic plutons that lie along or southward of the Kalsin Bay Fault. Such intrusions were emplaced at shallow levels and have abundant evidence of incomplete intermingling of basaltic and granitic magmas. These textures indicate trenchward-belt intrusions that froze before complete assimilation, leaving behind features such as abundant locally stoped blocks, gabbroic pods within granitic intrusions, and microstructural evidence such as strongly embayed olivine and pyroxene phenocrysts in granitoid bodies.
The Kodiak batholith and satellite intrusions extend for over 110 km along the axis of Kodiak Island and vary in width from 2 to 6 km. These intrude into the Late Cretaceous Kodiak Formation. U-Pb ages on zircon from the intrusions range from 59.2±0.2 Ma in the southwest to 58.4±0.2 Ma near its northwest tip. We interpret these ages as tracking the location of a migrating triple junction and associated slab-window. The batholith is composed of granite and granodiorite, with lesser amounts of tonalite and diorite. The center of the Kodiak batholith contains high-inclusion zones with abundant residual host rock fragments that were carried up from 5–10 km below current exposure levels. These high-inclusion zones contain biotite aggregates, pure quartz clots, and large xenocrysts of sillimanite, kyanite, andalusite, and garnet. This is a higher-pressure mineral assemblage than exists in the batholith metamorphic aureole. Gravity observations and modeling are consistent with the high-inclusion zones extending downward for 5–10 km. The Kodiak batholith results from a migrating triple junction and slab-window that led to high degrees of partial melting within the Kodiak accretionary prism.
Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Invasive predatory lake trout Salvelinus namaycush were discovered in Yellowstone Lake in 1994 and caused a precipitous decrease in abundance of native Yellowstone cutthroat trout Oncorhynchus clarkii bouvieri. Suppression efforts (primarily gillnetting) initiated in 1995 did not curtail lake trout population growth or lakewide expansion. An adaptive management strategy was developed in 2010 that specified desired conditions indicative of ecosystem recovery. Population modeling was used to estimate effects of suppression efforts on the lake trout and establish effort benchmarks to achieve negative population growth (λ < 1). Partnerships enhanced funding support, and a scientific review panel provided guidance to increase suppression gillnetting effort to >46,800 100-m net nights; this effort level was achieved in 2012 and led to a reduction in lake trout biomass. Total lake trout biomass declined from 432,017 kg in 2012 to 196,675 kg in 2019, primarily because of a 79% reduction in adults. Total abundance declined from 925,208 in 2012 to 673,983 in 2019 but was highly variable because of recruitment of age-2 fish. Overall, 3.35 million lake trout were killed by suppression efforts from 1995 to 2019. Cutthroat trout abundance remained below target levels, but relative condition increased, large individuals (> 400 mm) became more abundant, and individual weights doubled, probably because of reduced density. Continued actions to suppress lake trout will facilitate further recovery of the cutthroat trout population and integrity of the Yellowstone Lake ecosystem.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes5020018","usgsCitation":"Koel, T., Arnold, J.L., Bigelow, P., Brenden, T.O., Davis, J.D., Detjens, C.R., Doepke, P., Ertel, B.D., Glassic, H., Gresswell, R.E., Guy, C., MacDonald, D.J., Ruhl, M.E., Stuth, T.J., Sweet, D.P., Syslo, J.M., Thomas, N.A., Tronstad, L., White, P.J., and Zale, A.V., 2020, Yellowstone Lake ecosystem restoration: A case study for invasive fish management: Fishes, v. 5, no. 2, 18, 63 p., https://doi.org/10.3390/fishes5020018.","productDescription":"18, 63 p.","ipdsId":"IP-118729","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":456433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes5020018","text":"Publisher Index Page"},{"id":396739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Lake ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.9619140625,\n 44.01652134387754\n ],\n [\n -109.6875,\n 44.01652134387754\n ],\n [\n -109.6875,\n 44.95702412512118\n ],\n [\n -110.9619140625,\n 44.95702412512118\n ],\n [\n -110.9619140625,\n 44.01652134387754\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Koel, Todd M.","contributorId":272054,"corporation":false,"usgs":false,"family":"Koel","given":"Todd M.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arnold, Jeffrey L.","contributorId":278609,"corporation":false,"usgs":false,"family":"Arnold","given":"Jeffrey","email":"","middleInitial":"L.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bigelow, Patricia E.","contributorId":276048,"corporation":false,"usgs":false,"family":"Bigelow","given":"Patricia E.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brenden, Travis O.","contributorId":126759,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","email":"","middleInitial":"O.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":837116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Jeffery D.","contributorId":287835,"corporation":false,"usgs":false,"family":"Davis","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":837117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Detjens, Colleen R.","contributorId":270712,"corporation":false,"usgs":false,"family":"Detjens","given":"Colleen","email":"","middleInitial":"R.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837118,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Doepke, Philip D.","contributorId":278610,"corporation":false,"usgs":false,"family":"Doepke","given":"Philip D.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837121,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ertel, Brian D.","contributorId":181863,"corporation":false,"usgs":false,"family":"Ertel","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":837182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Glassic, Hayley C.","contributorId":278613,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":837183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gresswell, Robert E. 0000-0003-0063-855X bgresswell@usgs.gov","orcid":"https://orcid.org/0000-0003-0063-855X","contributorId":152031,"corporation":false,"usgs":true,"family":"Gresswell","given":"Robert","email":"bgresswell@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":837184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Guy, Christopher S","contributorId":120991,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher S","affiliations":[],"preferred":false,"id":837185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"MacDonald, Drew J.","contributorId":270660,"corporation":false,"usgs":false,"family":"MacDonald","given":"Drew","email":"","middleInitial":"J.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":837186,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ruhl, Michael E.","contributorId":287915,"corporation":false,"usgs":false,"family":"Ruhl","given":"Michael","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":837187,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stuth, Todd J.","contributorId":287916,"corporation":false,"usgs":false,"family":"Stuth","given":"Todd","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":837188,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Sweet, David P.","contributorId":287917,"corporation":false,"usgs":false,"family":"Sweet","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":837189,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Syslo, John M.","contributorId":276045,"corporation":false,"usgs":false,"family":"Syslo","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":837190,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Thomas, Nathan A.","contributorId":270658,"corporation":false,"usgs":false,"family":"Thomas","given":"Nathan","email":"","middleInitial":"A.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":837191,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Tronstad, Lusha M.","contributorId":224819,"corporation":false,"usgs":false,"family":"Tronstad","given":"Lusha M.","affiliations":[{"id":40947,"text":"Wyoming Natural Diversity Database, University of Wyoming, Laramie, WY, USA","active":true,"usgs":false}],"preferred":false,"id":837192,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"White, Patrick J.","contributorId":169530,"corporation":false,"usgs":false,"family":"White","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":837193,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Zale, Alexander V. 0000-0003-1703-885X zale@usgs.gov","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":3010,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"zale@usgs.gov","middleInitial":"V.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837194,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70262599,"text":"70262599 - 2020 - The ocean's impact on slow slip events","interactions":[],"lastModifiedDate":"2025-01-21T17:37:28.332157","indexId":"70262599","displayToPublicDate":"2020-06-11T11:34:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The ocean's impact on slow slip events","docAbstract":"We test the hypothesis that ocean seafloor pressures impart stresses that alter the initiation or termination of transient slow slip events (SSEs) on shallow submarine and near-coastal faults, using simulated seafloor pressures and a new catalog of SSEs in the Hikurangi subduction zone. We show that seafloor pressures may be represented by an average time history over the ~100-km dimensions of the study area. We account for SSE uncertainties and the multiplicity of processes that affect SSEs statistically by estimating the probabilities of rejecting the null hypothesis that SSE initiation or termination pressures are those to be expected by chance sampling of known (modeled) seafloor pressures, with low probabilities indicating some causal connection. No impact of ocean pressure changes on SSE initiation is detectable, but a correlation with their terminations is suggested. SSE slip that weakens the fault and makes it more sensitive to small stress changes may explain results.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL087273","usgsCitation":"Gomberg, J.S., Baxter, P.J., Smith, E.G., Ariyoshi, K., and Chiswell, S., 2020, The ocean's impact on slow slip events: Geophysical Research Letters, v. 47, no. 14, e2020GL087273, 15 p., https://doi.org/10.1029/2020GL087273.","productDescription":"e2020GL087273, 15 p.","ipdsId":"IP-115199","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":499853,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/ffe5bbfe0ddb4ed785934362b7777064","text":"External Repository"},{"id":480845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 177.28639581617597,\n -38.17149200548941\n ],\n [\n 177.28639581617597,\n -40.523586910552204\n ],\n [\n 180.53561304652231,\n -40.523586910552204\n ],\n [\n 180.53561304652231,\n -38.17149200548941\n ],\n [\n 177.28639581617597,\n -38.17149200548941\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baxter, Peter J.","contributorId":201839,"corporation":false,"usgs":false,"family":"Baxter","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":924648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Euan G. C.","contributorId":194943,"corporation":false,"usgs":false,"family":"Smith","given":"Euan","email":"","middleInitial":"G. C.","affiliations":[],"preferred":false,"id":924649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ariyoshi, Keisuke","contributorId":349718,"corporation":false,"usgs":false,"family":"Ariyoshi","given":"Keisuke","affiliations":[{"id":40272,"text":"Japan Agency for Marine-Earth Science and Technology","active":true,"usgs":false}],"preferred":false,"id":924650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chiswell, Steve","contributorId":242932,"corporation":false,"usgs":false,"family":"Chiswell","given":"Steve","email":"","affiliations":[{"id":48587,"text":"National Institute of Water & Atmospheric Research Ltd","active":true,"usgs":false}],"preferred":false,"id":924651,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216750,"text":"70216750 - 2020 - Asymmetric benefits of a heterospecific breeding association vary with habitat, conspecific abundance and breeding stage","interactions":[],"lastModifiedDate":"2021-10-26T16:06:27.581353","indexId":"70216750","displayToPublicDate":"2020-06-11T09:27:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Asymmetric benefits of a heterospecific breeding association vary with habitat, conspecific abundance and breeding stage","docAbstract":"Heterospecific breeding associations may benefit individuals by mitigating predation risk but may also create costs if they increase competition for resources or are more easily detectable by predators. Our understanding of the interactions among hetero‐ and conspecifics is often lacking in mixed species colonies. Here, we test how the presence of hetero‐ and conspecifics influence nest and chick survival for two listed (under the U.S. Endangered Species Act) migratory species breeding on the Missouri River, USA. We monitored 2507 piping plover Charadrius melodus nests and 3245 chicks as well as 1060 least tern Sternula antillarum nests and 1374 chicks on Lake Sakakawea, the Garrison River Reach and the Gavins Point Reach for varying years between 2007 and 2016. Piping plover nest and chick survival improved with the presence and abundance of least terns, but least terns only benefited from piping plover presence for certain study areas and breeding stages. Piping plover nest survival was also improved by the presence and abundance of conspecifics on the Garrison River Reach and was negatively influenced by conspecific presence on Lake Sakakawea. Least tern chick survival improved with the presence of other least terns only on the Gavins Point Reach. Ultimately, the heterospecific breeding association between plovers and terns is mutualistic but asymmetric and is moderated by habitat, abundance of conspecifics and breeding stage. Our results highlight that spatiotemporal variation in the interactions among individuals breeding in groups precludes simple generalizations and suggests that management focused on one species may restrict benefits to that focal species if nest site requirements for heterospecifics are not also included.
The consequences of environmental disturbance and management are difficult to quantify for spatially structured populations because changes in one location carry through to other areas as a result of species movement. We develop a metric, G, for measuring the contribution of a habitat or pathway to network-wide population growth rate in the face of environmental change. This metric is different from other contribution metrics, as it quantifies effects of modifying vital rates for habitats and pathways in perturbation experiments. Perturbation treatments may range from small degradation or enhancement to complete habitat or pathway removal. We demonstrate the metric using a simple metapopulation example and a case study of eastern monarch butterflies. For the monarch case study, the magnitude of environmental change influences the ordering of node contribution. We find that habitats within which all individuals reside during one season are the most important to short-term network growth under complete removal scenarios, whereas the central breeding region contributes most to population growth over all but the strongest disturbances. The metric G provides for more efficient management interventions that proactively mitigate impacts of expected disturbances to spatially structured populations.
use changes in one location carry through to other areas due to species movement. We develop a metric, G, for measuring the contribution of a habitat or pathway to network-wide population growth rate in the face of environmental change. This metric is different than other contribution metrics as it quantifies effects of modifying vital rates for habitats and pathways in perturbation experiments. Perturbation treatments may range from small degradation or enhancement to complete habitat or pathway removal. We demonstrate the metric using a simple metapopulation example and a case study of eastern monarch butterflies. For the monarch case study, the magnitude of environmental change influences ordering of node contribution. We find that habitats through which all migrants flow are the most important to short-term network growth under complete-removal scenarios. The metric G provides for more efficient management interventions that proactively mitigate impacts of expected disturbances to spatially structured populations.
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/709009","usgsCitation":"Sample, C., Bieri, J., Allen, B.L., Dementieva, Y., Carson, A., Higgins, C., Piatt, S., Qiu, S., Stafford, S., Mattsson, B., Semmens, D., Diffendorfer, J., and Thogmartin, W.E., 2020, Quantifying the contribution of habitats and pathways to a spatially structured population facing environmental change: American Naturalist, v. 196, no. 2, p. 157-168, https://doi.org/10.1086/709009.","productDescription":"12 p.","startPage":"157","endPage":"168","ipdsId":"IP-110963","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":456440,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/709009","text":"Publisher Index Page"},{"id":377324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"196","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sample, Christine","contributorId":201597,"corporation":false,"usgs":false,"family":"Sample","given":"Christine","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":795676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bieri, Joanna A.","contributorId":201599,"corporation":false,"usgs":false,"family":"Bieri","given":"Joanna A.","affiliations":[{"id":36213,"text":"University of Redlands","active":true,"usgs":false}],"preferred":false,"id":795677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Benjamin L.","contributorId":193210,"corporation":false,"usgs":false,"family":"Allen","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":795678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dementieva, Yulia","contributorId":219841,"corporation":false,"usgs":false,"family":"Dementieva","given":"Yulia","email":"","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":795679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carson, Alyssa","contributorId":219842,"corporation":false,"usgs":false,"family":"Carson","given":"Alyssa","email":"","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":795680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Higgins, Connor","contributorId":237967,"corporation":false,"usgs":false,"family":"Higgins","given":"Connor","email":"","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":795681,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Piatt, Sadie","contributorId":219844,"corporation":false,"usgs":false,"family":"Piatt","given":"Sadie","email":"","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":795682,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Qiu, Shirley","contributorId":219845,"corporation":false,"usgs":false,"family":"Qiu","given":"Shirley","email":"","affiliations":[{"id":35881,"text":"Emmanuel College","active":true,"usgs":false}],"preferred":false,"id":795683,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stafford, Summer","contributorId":219846,"corporation":false,"usgs":false,"family":"Stafford","given":"Summer","email":"","affiliations":[{"id":36213,"text":"University of Redlands","active":true,"usgs":false}],"preferred":false,"id":795684,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mattsson, Brady J.","contributorId":171612,"corporation":false,"usgs":false,"family":"Mattsson","given":"Brady J.","affiliations":[{"id":26928,"text":"Univ. of Vienna","active":true,"usgs":false}],"preferred":false,"id":795685,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":795686,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":795687,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":795688,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70210940,"text":"70210940 - 2020 - Geochronologic and Hf-isotope framework of Proterozoic rocks from central New Mexico, USA: Formation of the Mazatzal crustal province in an extended continental margin arc","interactions":[],"lastModifiedDate":"2020-07-08T15:58:01.581242","indexId":"70210940","displayToPublicDate":"2020-06-11T08:52:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Geochronologic and Hf-isotope framework of Proterozoic rocks from central New Mexico, USA: Formation of the Mazatzal crustal province in an extended continental margin arc","docAbstract":"The growth of southern Laurentia has been attributed to the accretion of juvenile arc terranes during the successive 1.74-1.68 Ga Yavapai and 1.65-1.60 Ga Mazatzal orogenies. However, in light of the increasing importance of the ca. 1.49-1.40 Ga Mesoproterozoic Picuris orogeny, the tectonic setting in which the Mazatzal crustal province and its distinctive quartzite-rhyolite successions were generated needs additional examination. The Sandia-Manzano-Los Pinos uplift in central New Mexico is an ideal place to characterize the tectonic history of the Mazatzal crustal province. A comprehensive geochronologic and Hf-isotopic dataset for Proterozoic rocks of the Sandia-Manzano-Los Pinos uplift is presented. Plutonic and metavolcanic rocks in the Sandia-Manzano-Los Pinos uplift were emplaced in three pulses at 1668-1655 Ma, 1587 Ma, and 1459-1453 Ma. Hf-isotope data from the Paleoproterozoic plutonic rocks are juvenile, with both leucogranite and arc-related granodiorite yielding εHf(t) values ranging from +6 to +12, compared to the coeval depleted mantle value of +10 at ca. 1.65 Ga. Inherited zircon in Paleoproterozoic rocks suggest that crust older than 1.7 Ga was involved in their genesis. Hf-isotope data from Mesoproterozoic plutonic rocks in the Sandia-Manzano-Los Pinos uplift are consistent with derivation from 1.7-1.6 Ga lithosphere. Detrital zircon indicate that metasedimentary rocks of the lower Manzano Group were derived primarily from local sources that have U-Pb-Hf isotope compositions similar to the plutonic rocks which intrude and volcanic rocks that underlie the Manzano Group. The detrital zircon provenance of the Manzano Group broadens up-section from unimodal populations with age peaks at ca. 1.65 Ga to include 1.7-3.0 Ga detrital zircon derived from older Laurentian sources like the Yavapai and Mojave provinces. We offer a new model for the formation of the Mazatzal crustal province of New Mexico as a continental margin arc built on top of the previously assembled Yavapai province. The Manzano Group quartzite-rhyolite succession was formed by lithospheric extension above a north-dipping, southward retreating subduction zone. The Manzano Group was then subjected to ca. 1.65 Ga syn-magmatic tectonism and later intracratonic contractional tectonism, likely during the 1.46-1.40 Ga Picuris orogeny.","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2020.105820","usgsCitation":"Holland, M.E., Grambling, T.A., Karlstrom, K.E., Jones, J.V., Nagotko, K.N., and Daniel, C.G., 2020, Geochronologic and Hf-isotope framework of Proterozoic rocks from central New Mexico, USA: Formation of the Mazatzal crustal province in an extended continental margin arc: Precambrian Research, v. 347, 105820, 19 p., https://doi.org/10.1016/j.precamres.2020.105820.","productDescription":"105820, 19 p.","ipdsId":"IP-119090","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":436934,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U0P2ZY","text":"USGS data release","linkHelpText":"U-Pb Isotopic Data and Ages of Zircon from the Manzano Mountains, New Mexico"},{"id":376146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.45703125,\n 32.52828936482526\n ],\n [\n -103.46923828124999,\n 32.52828936482526\n ],\n [\n -103.46923828124999,\n 36.38591277287651\n ],\n [\n -108.45703125,\n 36.38591277287651\n ],\n [\n -108.45703125,\n 32.52828936482526\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"347","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holland, Mark E.","contributorId":228842,"corporation":false,"usgs":false,"family":"Holland","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":792239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grambling, Tyler A.","contributorId":228843,"corporation":false,"usgs":false,"family":"Grambling","given":"Tyler","email":"","middleInitial":"A.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":792240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karlstrom, Karl E.","contributorId":228844,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Karl","email":"","middleInitial":"E.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":792241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":792242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nagotko, Kimberly N.","contributorId":228845,"corporation":false,"usgs":false,"family":"Nagotko","given":"Kimberly","email":"","middleInitial":"N.","affiliations":[{"id":16651,"text":"Bucknell University","active":true,"usgs":false}],"preferred":false,"id":792243,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Daniel, Christopher G.","contributorId":195246,"corporation":false,"usgs":false,"family":"Daniel","given":"Christopher","email":"","middleInitial":"G.","affiliations":[{"id":25242,"text":"Department of Biology, Bucknell University, Lewisburg, Pennsylvania 17837, USA","active":true,"usgs":false}],"preferred":false,"id":792244,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211023,"text":"70211023 - 2020 - Land use effects on sediment nutrient processes in a heavily modified watershed using structural equation models","interactions":[],"lastModifiedDate":"2020-07-10T13:10:51.180487","indexId":"70211023","displayToPublicDate":"2020-06-11T08:07:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Land use effects on sediment nutrient processes in a heavily modified watershed using structural equation models","docAbstract":"Contemporary land use can affect sediment nutrient processes in rivers draining heavily modified watersheds; however, studies linking land use to sediment nutrient processes in large river networks are limited. In this study, we developed and evaluated structural equation models (SE models) for denitrification and phosphorus retention capacity to determine direct and indirect linkages between current land use and sediment nutrient processes during baseflow in the Fox River watershed, Wisconsin USA. A large spatial-scale dataset used for this study included sediment nitrogen and phosphorus retention measurements and land use information for 106 sites. The SE models for the Fox River watershed identified direct links between current land use and in-stream sediment nutrient processes. Sub-watersheds with agricultural land consisting of more natural land cover had lower surface water nitrate concentrations and higher denitrification enzyme activity than sub-watersheds with less alternative cover. This suggests that best management practices implemented in the Fox River watershed that restore natural land cover can improve water quality through nitrogen removal on the agricultural landscape and in the river network. Best management practices are not having the same measurable affect on phosphorus in the river network, most likely due to legacy phosphorus stored in the sediment.","language":"English","publisher":"Wiley","doi":"10.1029/2019WR026655","usgsCitation":"Kreiling, R.M., Thoms, M.C., Bartsch, L., Larson, J.H., and Christensen, V., 2020, Land use effects on sediment nutrient processes in a heavily modified watershed using structural equation models: Water Resources Research, v. 56, no. 7, e2019WR026655, 17 p., https://doi.org/10.1029/2019WR026655.","productDescription":"e2019WR026655, 17 p.","ipdsId":"IP-108469","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fox River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.95654296875,\n 44.61393394730626\n ],\n [\n -88.5498046875,\n 44.692088041727786\n ],\n [\n -88.26416015625001,\n 45.166547157856016\n ],\n [\n -88.714599609375,\n 45.62940492064498\n ],\n [\n -89.241943359375,\n 45.96642454131025\n ],\n [\n -89.8681640625,\n 45.706179285330855\n ],\n [\n -89.95605468750001,\n 44.95702412512118\n ],\n [\n -89.5166015625,\n 44.41808794374846\n ],\n [\n -89.439697265625,\n 43.929549935614574\n ],\n [\n -89.52758789062501,\n 43.57243174740972\n ],\n [\n -89.351806640625,\n 43.37311218381999\n ],\n [\n -88.93432617187501,\n 43.31718491566708\n ],\n [\n -88.53881835937499,\n 43.36512572875844\n ],\n [\n -88.11035156249999,\n 43.45291889355465\n ],\n [\n -87.82470703125,\n 44.134913443750726\n ],\n [\n -87.53906250000001,\n 44.574817404670306\n ],\n [\n -87.659912109375,\n 44.62957319195102\n ],\n [\n -87.879638671875,\n 44.527842798455474\n ],\n [\n -87.95654296875,\n 44.61393394730626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":202193,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thoms, Martin C. 0000-0002-8074-0476","orcid":"https://orcid.org/0000-0002-8074-0476","contributorId":145710,"corporation":false,"usgs":false,"family":"Thoms","given":"Martin","email":"","middleInitial":"C.","affiliations":[{"id":16205,"text":"Riverine Landscapes Research Laboratory, University of New England, NSW, Australia","active":true,"usgs":false}],"preferred":false,"id":792461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792464,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223126,"text":"70223126 - 2020 - Modified GIC estimation using 3-D Earth conductivity","interactions":[],"lastModifiedDate":"2021-08-11T12:19:07.036472","indexId":"70223126","displayToPublicDate":"2020-06-10T07:16:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"Modified GIC estimation using 3-D Earth conductivity","docAbstract":"Geomagnetically induced currents (GICs) are quasi-direct current (DC) electric currents that flow in technological conductors during geomagnetic storms. Extreme GICs are hazardous to man-made infrastructure. GICs enter and exit the technological systems, such as the electric power grid, at grounding points, and their magnitudes depend on the currents that flow underground. They are, therefore, a function of the Earth's electrical conductivity, represented at ground level as Earth impedances, as well as the resistance parameters of the power network. Traditional GIC estimation practices are based on Earth impedances obtained from laterally homogeneous or piecewise layered-Earth models. We refer to these methods, collectively, as the 1-D approximation. However, GIC hazard mitigation can be improved with more accurate GIC modeling that takes the spatially heterogeneous Earth's conductivity into account. Here, we propose a modified approximation for GIC estimation that is very similar to the 1-D approximation but is instead derived from empirical 3-D Earth impedances. Our formulation sets up the computation of static, frequency-dependent power line telluric response functions, which, once computed, may be considered part of the power grid system model. These response functions may then be used for historical scenario analysis of GIC hazards and for simplified real-time, albeit approximate, GIC estimation in a power grid. This modest modification to the simpler local field formulation approach avoids real-time integration of geoelectric fields along power lines while taking the realistic 3-D Earth into account in a rigorous manner. Once implemented, the method provides a power grid operator with the benefits of convenience and computational speed for a first look real-time operational GIC hazard assessment. We estimate that the proposed modified 3-D GIC modeling approach produces GIC values that are well within 50% of those obtained with the full-scale power line integration of spatially variable geoelectric fields, for storms comparable in scale to the 2003 Halloween storm, all geological structures, and power lines located in the contiguous United States and other low- to middle-latitude regions.
Time-averaged shear wave velocity over the upper 30 m of the earth’s surface (VS30) is a key parameter for estimating ground motion amplification as both a predictive and a diagnostic tool for earthquake hazards. The first-order approximation of VS30 is commonly obtained through a topographic slope–based or terrain proxy due to the widely available nature of digital elevation models. However, better-constrained VS30 maps have been developed in many regions. Such maps preferentially employ various combinations of VS30 measurements, higher-resolution elevation models, lithologic, geologic, geomorphic, and other proxies and often utilize refined interpolation schemes. We develop a new hybrid global VS30 map database that defaults to the global slope-based VS30 map, but smoothly inserts regional VS30 maps where available. In addition, we present comparisons of the default slope-based proxy maps against the new hybrid version in terms of VS30 and amplification ratio maps, and uncertainties in assigned VS30 values.
","language":"English","publisher":"Sage","doi":"10.1177/8755293020911137","usgsCitation":"Heath, D.C., Wald, D.J., Worden, C., Thompson, E.M., and Smoczyk, G.M., 2020, A global hybrid VS30 map with a topographic slope–based default and regional map insets: Earthquake Spectra, v. 36, no. 3, p. 1570-1584, https://doi.org/10.1177/8755293020911137.","productDescription":"15 p.","startPage":"1570","endPage":"1584","ipdsId":"IP-117706","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.35351562499999,\n 21.37124437061831\n ],\n [\n -90.52734374999999,\n 21.37124437061831\n ],\n [\n -90.52734374999999,\n 48.8936153614802\n ],\n [\n -127.35351562499999,\n 48.8936153614802\n ],\n [\n -127.35351562499999,\n 21.37124437061831\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Heath, David C. 0000-0002-6645-422X","orcid":"https://orcid.org/0000-0002-6645-422X","contributorId":238114,"corporation":false,"usgs":false,"family":"Heath","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":796075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Worden, C. Bruce 0000-0003-1181-685X","orcid":"https://orcid.org/0000-0003-1181-685X","contributorId":189051,"corporation":false,"usgs":false,"family":"Worden","given":"C. Bruce","affiliations":[],"preferred":false,"id":796077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smoczyk, Gregory M. 0000-0002-6591-4060 gsmoczyk@usgs.gov","orcid":"https://orcid.org/0000-0002-6591-4060","contributorId":5239,"corporation":false,"usgs":true,"family":"Smoczyk","given":"Gregory","email":"gsmoczyk@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796079,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211905,"text":"70211905 - 2020 - Comparison of SELDM simulated total-phosphorus concentrations with ecological impervious-area criteria","interactions":[],"lastModifiedDate":"2020-08-11T19:04:49.848316","indexId":"70211905","displayToPublicDate":"2020-06-09T14:02:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2255,"text":"Journal of Environmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of SELDM simulated total-phosphorus concentrations with ecological impervious-area criteria","docAbstract":"Ecological studies indicate that impervious cover (IC) greater than approximately 5%–20% may have adverse effects on receiving-stream ecology. It is difficult to separate the effects of runoff quality from other effects of urbanization on receiving streams. This study presents the results of a numerical experiment to assess the effects of increasing IC on water quality using the Stochastic Empirical Loading and Dilution Model (SELDM). Hydrologic and physiographic variables representative of southern New England were used to simulate receiving water quality in a basin with IC ranging from 0.1% to 30%. Simulation results mirror the results of ecological studies; event mean concentrations (EMCs) of total phosphorus (TP) increase proportionally to the logarithms of imperviousness for a given risk percentile. Simulation results indicated that commonly used stormwater treatment methods may be insufficient for mitigating the effects of imperviousness. Therefore, disconnection, rather than treatment, may be needed to protect water quality, and efforts to preserve undeveloped stream basins may be more effective than efforts to remediate conditions in highly developed basins. Results also indicate that commonly used water-quality criteria may be too restrictive for stormwater because TP EMCs frequently exceed these criteria, even in minimally developed basins.
The hydrogeology below large surface water features such as rivers and estuaries is universally under-informed at the long reach to basin scales (tens of km+). This challenge inhibits the accurate modeling of fresh/saline groundwater interfaces and groundwater/surface water exchange patterns at management-relevant spatial extents. Here we introduce a towed, floating transient electromagnetic (TEM) system (i.e. FloaTEM) for rapid (up to 15 km/h) high resolution electrical mapping of the subsurface below large water bodies to depths often a factor of 10 greater than other towed instruments. The novel FloaTEM system is demonstrated at a range of diverse 4th through 6th-order riverine settings across the United States including 1) the Farmington River, near Hartford, Connecticut; 2) the Upper Delaware River near Barryville, New York; 3) the Tallahatchie River near Shellmound, Mississippi; and, 4) the Eel River estuary, on Cape Cod, near Falmouth, Massachusetts. Airborne frequency-domain electromagnetic and land-based towed TEM data are also compared at the Tallahatchie River site, and streambed geologic scenarios are explored with forward modeling. A range of geologic structures and pore water salinity interfaces were identified. Process-based interpretation of the case study data indicated FloaTEM can resolve varied sediment-water interface materials, such as the accumulation of fines at the bottom of a reservoir and permeable sand/gravel riverbed sediments that focus groundwater discharge. Bedrock layers were mapped at several sites, and aquifer confining units were defined at comparable resolution to airborne methods. Terrestrial fresh groundwater discharge with flowpaths extending hundreds of meters from shore was also imaged below the Eel River estuary, improving on previous hydrogeological characterizations of that nutrient-rich coastal exchange zone. In summary, the novel FloaTEM system fills a critical gap in our ability to characterize the hydrogeology below surface water features and will support more accurate prediction of groundwater/surface water exchange dynamics and fresh-saline groundwater interfaces.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.140074","usgsCitation":"Lane, J.W., Briggs, M., Maurya, P., White, E.A., Pedersen, J., Auken, E., Terry, N., Minsley, B.J., Kress, W., LeBlanc, D.R., Adams, R.F., and Johnson, C., 2020, Characterizing the diverse hydrogeology underlying rivers and estuaries using new floating transient electromagnetic methodology: Science of the Total Environment, v. 740, 140074, 14 p., https://doi.org/10.1016/j.scitotenv.2020.140074.","productDescription":"140074, 14 p.","ipdsId":"IP-119384","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456460,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.140074","text":"Publisher Index Page"},{"id":436936,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E5JBAF","text":"USGS data release","linkHelpText":"Floating and Towed Transient Electromagnetic Surveys used to Characterize Hydrogeology underlying Rivers and Estuaries: March - December 2018"},{"id":385330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"740","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lane, John W. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":219742,"corporation":false,"usgs":true,"family":"Lane","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":814802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":814803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maurya, PK","contributorId":257644,"corporation":false,"usgs":false,"family":"Maurya","given":"PK","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":814804,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Eric A. 0000-0002-7782-146X eawhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7782-146X","contributorId":1737,"corporation":false,"usgs":false,"family":"White","given":"Eric","email":"eawhite@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":814805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pedersen, JB","contributorId":257645,"corporation":false,"usgs":false,"family":"Pedersen","given":"JB","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":814806,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Auken, Esben","contributorId":193991,"corporation":false,"usgs":false,"family":"Auken","given":"Esben","email":"","affiliations":[],"preferred":false,"id":814807,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":814808,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":814809,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814810,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"LeBlanc, Denis R. 0000-0002-4646-2628","orcid":"https://orcid.org/0000-0002-4646-2628","contributorId":219907,"corporation":false,"usgs":true,"family":"LeBlanc","given":"Denis","email":"","middleInitial":"R.","affiliations":[{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814811,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Adams, Ryan F. 0000-0001-7299-329X rfadams@usgs.gov","orcid":"https://orcid.org/0000-0001-7299-329X","contributorId":5499,"corporation":false,"usgs":true,"family":"Adams","given":"Ryan","email":"rfadams@usgs.gov","middleInitial":"F.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814812,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Johnson, Carole D. 0000-0001-6941-1578","orcid":"https://orcid.org/0000-0001-6941-1578","contributorId":245365,"corporation":false,"usgs":true,"family":"Johnson","given":"Carole D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":814813,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70263557,"text":"70263557 - 2020 - Optimizing earthquake early warning alert distance strategies using the July 2019 Mw6.4 and Mw7.1 Ridgecrest, California, earthquakes","interactions":[],"lastModifiedDate":"2025-02-13T16:51:37.501189","indexId":"70263557","displayToPublicDate":"2020-06-09T10:48:49","publicationYear":"2020","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}},"title":"Optimizing earthquake early warning alert distance strategies using the July 2019 Mw6.4 and Mw7.1 Ridgecrest, California, earthquakes","docAbstract":"The ShakeAlert earthquake early warning system aims to alert people who experience modified Mercalli intensity (MMI) IV+ shaking during an earthquake using source estimates (magnitude and location) to estimate median‐expected peak ground motions with distance, then using these ground motions to determine median‐expected MMI and thus the extent of MMI IV shaking. Because median ground motions are used, even if magnitude and location are correct, there will be people outside the alert region who experience MMI IV shaking but do not receive an alert (missed alerts). We use 91,000 “Did You Feel It?” survey responses to the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes to determine which ground‐motion to intensity conversion equation (GMICE) best fits median MMI with distance. We then explore how incorporating uncertainty from the ground‐motion prediction equation and the GMICE in the alert distance calculation can produce more accurate MMI IV alert regions for a desired alerting strategy (e.g., aiming to alert 95% of people who experience MMI IV+ shaking), assuming accurate source characterization. Without incorporating ground‐motion uncertainties, we find MMI IV alert regions using median‐expected ground motions alert fewer than 20% of the population that experiences MMI IV+ shaking. In contrast, we find >94% of the people who experience MMI IV+ shaking can be included in the MMI IV alert region when two standard deviations of ground‐motion uncertainty are included in the alert distance computation. The optimal alerting strategy depends on the false alert tolerance of the community due to the trade‐off between minimizing missed and false alerts. This is especially the case for situations like the Mw 6.4 earthquake when alerting 95% of the 5 million people who experience MMI IV+ also results in alerting 14 million people who experience shaking below this level and do not need to take protective action.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200022","usgsCitation":"Saunders, J.K., Aagaard, B.T., Baltay Sundstrom, A.S., and Minson, S.E., 2020, Optimizing earthquake early warning alert distance strategies using the July 2019 Mw6.4 and Mw7.1 Ridgecrest, California, earthquakes: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1872-1886, https://doi.org/10.1785/0120200022.","productDescription":"15 p.","startPage":"1872","endPage":"1886","ipdsId":"IP-115105","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482040,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.78214580574435,\n 37.13529952586548\n ],\n [\n -118.8143775521145,\n 37.13529952586548\n ],\n [\n -118.8143775521145,\n 34.29388629344095\n ],\n [\n -115.78214580574435,\n 34.29388629344095\n ],\n [\n -115.78214580574435,\n 37.13529952586548\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Saunders, Jessie Kate 0000-0001-5340-6715","orcid":"https://orcid.org/0000-0001-5340-6715","contributorId":290634,"corporation":false,"usgs":true,"family":"Saunders","given":"Jessie","email":"","middleInitial":"Kate","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":927336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927338,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211027,"text":"70211027 - 2020 - Changes in climate and land cover affect seasonal streamflow forecasts in the Rio Grande headwaters","interactions":[],"lastModifiedDate":"2023-03-27T17:19:48.95791","indexId":"70211027","displayToPublicDate":"2020-06-09T09:51:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Changes in climate and land cover affect seasonal streamflow forecasts in the Rio Grande headwaters","docAbstract":"Seasonal streamflow forecast bias, changes in climate, snowpack, and land cover, and the effects of these changes on relations between basin‐wide snowpack, SNOw TELemetry (SNOTEL) station snowpack, and seasonal streamflow were evaluated in the headwaters of the Rio Grande, Colorado. Results indicate that shifts in the seasonality of precipitation and changing climatology are consistent with periods of overprediction and underprediction in streamflow forecasts. Multiple linear regression of SNOTEL data, postcedent precipitation, and land‐cover changes explained 2%–18% more variability in streamflow prediction than using SNOTEL station data alone. Simulated basin‐wide snowpack from a physically based model had significant negative trends in snow water equivalent (−4.33 mm/yr) and snow‐covered area (−0.05%/yr) during the melt period April–June. Simulated streamflow from a precipitation‐runoff model increased an average 5% when the effects of bark beetle‐induced tree mortality were compared to a baseline simulation with static vegetation. The effects of a 2013 wildfire increased simulated seasonal streamflow an average 35% for 1–4 years postfire. The combined effects of climate and land‐cover changes on snowpack‐streamflow relations highlight the difficulty in seasonal streamflow forecasting, which has important implications for water‐resource management.
","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.12863","usgsCitation":"Penn, C.A., Clow, D.W., Sexstone, G.A., and Murphy, S.F., 2020, Changes in climate and land cover affect seasonal streamflow forecasts in the Rio Grande headwaters: Journal of the American Water Resources Association, v. 56, no. 5, p. 882-902, https://doi.org/10.1111/1752-1688.12863.","productDescription":"21 p.","startPage":"882","endPage":"902","ipdsId":"IP-109042","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":436937,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B08S5N","text":"USGS data release","linkHelpText":"Model input and output for hydrologic simulations in the Rio Grande Headwaters, Colorado, using the Precipitation-Runoff Modeling System (PRMS)"},{"id":376258,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108,\n 37.25\n ],\n [\n -106,\n 37.25\n ],\n [\n -106,\n 38.25\n ],\n [\n -108,\n 38.25\n ],\n [\n -108,\n 37.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Penn, Colin A. 0000-0002-5195-2744 cpenn@usgs.gov","orcid":"https://orcid.org/0000-0002-5195-2744","contributorId":5336,"corporation":false,"usgs":true,"family":"Penn","given":"Colin","email":"cpenn@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sexstone, Graham A. 0000-0001-8913-0546 sexstone@usgs.gov","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":5159,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham","email":"sexstone@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":792478,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212498,"text":"70212498 - 2020 - The impact of sediment supply on the initiation and magnitude of runoff-generated debris flows","interactions":[],"lastModifiedDate":"2020-08-18T14:37:14.303197","indexId":"70212498","displayToPublicDate":"2020-06-09T09:37:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The impact of sediment supply on the initiation and magnitude of runoff-generated debris flows","docAbstract":"Rainfall intensity‐duration (ID) thresholds are commonly used to assess the potential for runoff‐generated debris flows, but the sensitivity of these thresholds to sediment supply, which can change rapidly with time, is relatively unexplored. Furthermore, debris flows often self‐organize into distinct surges, but the factors controlling the magnitude and frequency of these surges, including sediment supply and grain size, are poorly constrained. We use a combination of numerical modeling and debris flow monitoring data from Chalk Cliffs, Colorado, USA, to explore how sediment supply influences rainfall ID thresholds for debris flows and surge properties. Results suggest that rainfall ID thresholds only become sensitive to sediment supply below a sediment thickness threshold. Surge magnitude is a nonmonotonic function of sediment supply (i.e., channel bed sediment thickness and grain size) with the largest surges tending to form at intermediate values of sediment availability with intermediate grain sizes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL087643","usgsCitation":"Tang, H., McGuire, L.A., Kean, J.W., and Smith, J.B., 2020, The impact of sediment supply on the initiation and magnitude of runoff-generated debris flows: Geophysical Research Letters, v. 47, no. 14, e2020GL087643, 13 p., https://doi.org/10.1029/2020GL087643.","productDescription":"e2020GL087643, 13 p.","ipdsId":"IP-118444","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":456464,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl087643","text":"Publisher Index Page"},{"id":377602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Chalk Cliffs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.31280899047852,\n 38.7080176975273\n ],\n [\n -106.17565155029297,\n 38.7080176975273\n ],\n [\n -106.17565155029297,\n 38.76613041372937\n ],\n [\n -106.31280899047852,\n 38.76613041372937\n ],\n [\n -106.31280899047852,\n 38.7080176975273\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Tang, Hui","contributorId":215352,"corporation":false,"usgs":false,"family":"Tang","given":"Hui","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":796587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":796588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796590,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211707,"text":"70211707 - 2020 - Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence","interactions":[],"lastModifiedDate":"2020-08-07T13:28:29.468565","indexId":"70211707","displayToPublicDate":"2020-06-09T08:24:46","publicationYear":"2020","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}},"title":"Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence","docAbstract":"We use a large instrumental dataset from the 2019 Ridgecrest earthquake sequence (Rekoske et al., 2019, 2020) to examine repeatable source‐, path‐, and site‐specific ground motions. A mixed‐effects analysis is used to partition total residuals relative to the Boore et al. (2014; hereafter, BSSA14) ground‐motion model. We calculate the Arias intensity stress drop for the earthquakes and find strong correlation with our event terms, indicating that they are consistent with source processes. We look for physically meaningful trends in the partitioned residuals and test the ability of BSSA14 to capture the behavior we observe in the data.
We find that BSSA14 is a good match to the median observations for