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":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","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
Native steelhead (anadromous form of rainbow trout [Oncorhynchus mykiss]) and bridgelip sucker (Catostomus columbianus) were historically used by the Kah-miltpah (Rock Creek) Band for sustenance, trade, and traditional practices in Rock Creek, a tributary to the Columbia River in southeastern Washington State. Rock Creek flows south to the Columbia River at river kilometer (rkm) 368 and is an intermittent stream of great significance to the Yakama Nation and to the Kah-miltpah Band in particular. Concern over declines in the abundance of these fish in Rock Creek prompted a research and monitoring program to better understand habitat conditions, population status, and limiting factors. In addition to steelhead and bridgelip sucker, coho salmon (Oncorhynchus kisutch) and resident rainbow trout are also present and monitored. Rainbow trout and steelhead will be collectively referred to as O. mykiss. Streamflow is a limiting habitat factor in this system, but steelhead and coho salmon still successfully return to spawn, rear, outmigrate, and survive over summer in many of the isolated pools that provide important refuge for juvenile rearing.
We completed a habitat survey during autumn 2018 to assess the perennial pools during low-flow conditions. In Rock Creek, the overall percentage of habitat recorded as dry was 41, non-pool wet was 42, and pool was 17. The number of pools (n=93) recorded was less than during previous years’ survey efforts (2015–17). The percentage of non-pool wet habitat was generally higher in 2018 than in previous years. This is a likely result of habitat reaches, which in the past, were considered pools but have become shallower and smaller and are now categorized as non-pool wet habitat. However, the fewer habitat reaches categorized as pools in 2018 now have an average length, area, and depth that are generally greater than in past years. In Walaluuks Creek, the percentage of habitat recorded as dry was 53, non-pool wet was 40, and pool was 7. The percentage of pool habitat was the lowest of all years surveyed since 2015.
Fish sampling occurred during autumn after habitat surveys were completed from October 1 to November 9. Fish species distribution, relative abundance, length-frequency distribution, and pool fish density were determined using backpack electrofishing in stratified, systematically selected pools. During fish sampling, 855 O. mykiss were handled and 662 were tagged with a passive integrated transponder (PIT) tag, and 718 coho salmon were handled and 567 were PIT tagged. A total of 536 bridgelip suckers and largescale suckers (Catostomus macrocheilus [n=6]) were handled and 294 were PIT tagged. In Rock Creek, pool abundance estimates were calculated for six pools for both O. mykiss age classes (age 0 and age 1 or older [age 1+]) and one additional pool for age 1+. For pools where age-0 O. mykiss were present, the average pool population abundance was 0.144 (n=6; range: 0.052–0.208) fish per square meter. For age-1+ O. mykiss, the average pool population abundance was 0.045 (n=7; range: 0.002–0.179) fish per square meter. For age-0 O. mykiss in Walaluuks Creek, the average pool abundance was 0.207 fish per square meter (n=7; range: 0.038–0.416), and for age-1+ fish, the average pool abundance was 0.382 fish per square meter (n=6; range: 0.009–0.761). In Rock Creek, coho salmon were more abundant than O. mykiss in pools except for three pools upstream from rkm 20. The average pool abundance for coho salmon was 0.256 fish per square meter (n=8; range: 0.019–0.756) in Rock Creek pools. In Walaluuks Creek, coho salmon were captured in four pools and were not captured in the upstream pools sampled. The average pool abundance for coho salmon in the four lower pools was 0.488 fish per square meter (n=4; range: 0.417–0.548). Bridgelip suckers were captured in all pools in Rock Creek except the pool sampled at rkm 21.8. The average pool abundance for bridgelip suckers was 0.552 fish per square meter (n=7; range: 0.015–1.554) in Rock Creek. Bridgelip suckers were captured in three downstream pools in Walaluuks Creek, and the average abundance was 0.044 fish per square meter (n=3; range: 0.024–0.085).
Overwinter and reach survival probabilities were estimated for O. mykiss and coho salmon using a Cormack-Jolly-Seber modeling approach. The best fit survival model for the O. mykiss and coho salmon was a reach only model. The upstream reach includes overwinter survival probability because fish are tagged and released in autumn and primarily migrate the following spring. During 2018, coho salmon (0.568, standard error [SE]=0.027) had a significantly higher probability of overwinter survival than O. mykiss (0.276, SE=0.019). The reach survival probability was higher for O. mykiss than coho salmon in the downstream migratory reaches. Survival was not modeled for bridgelip suckers. For bridgelip suckers, 147 were detected of 294, that were PIT tagged and released in the Rock Creek subbasin (50.0 percent).
Information provided in this report increases our understanding of the status and trends of these populations. It further documents how intermittent streams can support salmonid populations. It also provides insight into potential management and restoration actions that could be beneficial and timing and allocation of resources. Ongoing monitoring work of this population will inform progress towards Rock Creek species recovery goals and contribution to recovery goals for the steelhead Middle Columbia River Distinct Population Segment.
Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The role of subduction zone geometry in the nucleation and propagation of great-sized earthquake ruptures is an important topic for earthquake hazard, since knowing how big an earthquake can be on a given fault is fundamentally important. Past studies have shown subducting bathymetric features (e.g. ridges, fracture zones, seamount chains) may arrest a propagating rupture. Other studies have correlated the occurrence of great-sized earthquakes with flat megathrusts and homogenous stresses over large distances. It remains unclear, however, how subduction zone geometry and the potential for great-sized earthquakes (M 8+) are quantifiably linked—or indeed whether they can be. Here, we examine the potential role of subduction zone geometry in limiting earthquake rupture by mapping the planarity of seismogenic zones in the Slab2 subduction zone geometry database. We build from the observation that historical great-sized earthquakes have preferentially occurred where the surrounding megathrust is broadly planar, and we use this relationship to search for geometrically similar features elsewhere in subduction zones worldwide. Assuming geometry exerts a primary control on earthquake propagation and termination, we estimate the potential size distribution of large (M 7+) earthquakes and the maximum earthquake magnitude along global subduction faults based on geometrical features alone. Our results suggest that most subduction zones are capable of hosting great-sized earthquakes over much of their area. Many bathymetric features previously identified as barriers are indistinguishable from the surrounding megathrust from the perspective of slab curvature, meaning that they either do not play an important role in arresting earthquake rupture or that their influence on slab geometry at depth is not resolvable at the spatial scale of our subduction zone geometry models.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggaa254","usgsCitation":"Plescia, S.M., and Hayes, G., 2020, Geometric controls on megathrust earthquakes: Geophysical Journal International, v. 222, no. 2, p. 1270-1282, https://doi.org/10.1093/gji/ggaa254.","productDescription":"13 p.","startPage":"1270","endPage":"1282","ipdsId":"IP-118723","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"222","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Plescia, Steven M.","contributorId":261740,"corporation":false,"usgs":false,"family":"Plescia","given":"Steven","email":"","middleInitial":"M.","affiliations":[{"id":52978,"text":"Department of Geological Sciences, University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":820518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Gavin P. 0000-0003-3323-0112","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":6157,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820519,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218221,"text":"70218221 - 2020 - Survival estimates for the invasive American bullfrog","interactions":[],"lastModifiedDate":"2021-02-19T19:59:33.384981","indexId":"70218221","displayToPublicDate":"2020-06-05T13:54:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":751,"text":"Amphibia-Reptilia","active":true,"publicationSubtype":{"id":10}},"title":"Survival estimates for the invasive American bullfrog","docAbstract":"American bullfrogs (Lithobates catesbeianus) are significant invaders in many places and can negatively impact native species. Despite their impact and wide distribution, little is known about their demography. We used five years of capture mark-recapture data to estimate annual apparent survival of post-metamorphic bullfrogs in a population on the Buenos Aires National Wildlife Refuge in their invaded range in Arizona, U.S.A. This population is a potential source of colonists into breeding ponds used by the federally threatened Chiricahua leopard frog (L. chiricahuensis). Results from robust-design Cormack-Jolly-Seber models suggested that survival of bullfrogs was influenced by sex and precipitation but not body condition. Survival was higher for females (mean = 0.37; 95% CI=0.15, 0.72) than males (mean = 0.17; 95% CI=0.02, 0.49), and declined with reduced annual precipitation (mean = −0.36, 95% CI = −2.09, 0.84). These survival estimates can be incorporated into models of population dynamics and to help predict spread of bullfrogs.
","language":"English","publisher":"Brill","doi":"10.1163/15685381-bja10016","usgsCitation":"Howell, P., Muths, E., Sigafus, B.H., and Hossack, B., 2020, Survival estimates for the invasive American bullfrog: Amphibia-Reptilia, v. 41, no. 4, p. 559-564, https://doi.org/10.1163/15685381-bja10016.","productDescription":"6 p.","startPage":"559","endPage":"564","ipdsId":"IP-112892","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":456482,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://figshare.com/articles/journal_contribution/Survival_estimates_for_the_invasive_American_bullfrog/12301367","text":"Publisher Index Page"},{"id":383391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Buenos Aires National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.61148071289062,\n 31.439208864183147\n ],\n [\n -111.3079833984375,\n 31.439208864183147\n ],\n [\n -111.3079833984375,\n 31.85773063158148\n ],\n [\n -111.61148071289062,\n 31.85773063158148\n ],\n [\n -111.61148071289062,\n 31.439208864183147\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Howell, Paige E.","contributorId":173495,"corporation":false,"usgs":false,"family":"Howell","given":"Paige E.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":810470,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":243368,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":810471,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":810472,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810473,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70214644,"text":"70214644 - 2020 - The Moon as a climate-quality radiometric calibration reference","interactions":[],"lastModifiedDate":"2020-10-01T17:45:54.634478","indexId":"70214644","displayToPublicDate":"2020-06-05T12:42:02","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":"The Moon as a climate-quality radiometric calibration reference","docAbstract":"On-orbit calibration requirements for a space-based climate observing system include long-term sensor response stability and reliable inter-calibration of multiple sensors, both contemporaneous and in succession. The difficulties with achieving these for reflected solar wavelength instruments are well known. The Moon can be considered a diffuse reflector of sunlight, and its exceptional photometric stability has enabled development of a lunar radiometric reference, manifest as a model that is queried for the specific conditions of Moon observations. The lunar irradiance model developed by the Robotic Lunar Observatory (ROLO) project has adequate precision for sensor response temporal trending, but a climate-quality lunar reference will require at least an order of magnitude improvement in absolute accuracy. To redevelop the lunar calibration reference with sub-percent uncertainty and SI traceability requires collecting new, high-accuracy Moon characterization measurements. This paper describes specifications for such measurements, along with a conceptual framework for reconstructing the lunar reference using them. Three currently active NASA-sponsored projects have objectives to acquire measurements that can support a climate-quality lunar reference: air-LUSI, dedicated lunar spectral irradiance measurements from the NASA ER-2 high altitude aircraft; ARCSTONE, dedicated lunar spectral reflectance measurements from a small satellite; and Moon viewing opportunities by CLARREO Pathfinder from the International Space Station.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12111837","usgsCitation":"Stone, T.C., Kieffer, H.H., Lukashin, C., and Turpie, K., 2020, The Moon as a climate-quality radiometric calibration reference: Remote Sensing, v. 12, no. 11, p. 1837-1853, https://doi.org/10.3390/rs12111837.","productDescription":"17 p.","startPage":"1837","endPage":"1853","ipdsId":"IP-118345","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":456486,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12111837","text":"Publisher Index Page"},{"id":378965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Moon","volume":"12","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-06-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Stone, Thomas C. 0000-0001-5088-3495 tstone@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-3495","contributorId":242004,"corporation":false,"usgs":true,"family":"Stone","given":"Thomas","email":"tstone@usgs.gov","middleInitial":"C.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":800322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kieffer, Hugh H.","contributorId":41137,"corporation":false,"usgs":false,"family":"Kieffer","given":"Hugh","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":800323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lukashin, Constantine","contributorId":242007,"corporation":false,"usgs":false,"family":"Lukashin","given":"Constantine","email":"","affiliations":[{"id":48472,"text":"NASA Langley Reseach Center","active":true,"usgs":false}],"preferred":false,"id":800324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turpie, Kevin","contributorId":242008,"corporation":false,"usgs":false,"family":"Turpie","given":"Kevin","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":800325,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220875,"text":"70220875 - 2020 - Occurrence and geochemistry of lead-210 and polonium-210 radionuclides in public-drinking-water supplies from principal aquifers of the United States","interactions":[],"lastModifiedDate":"2021-05-27T12:38:43.595169","indexId":"70220875","displayToPublicDate":"2020-06-05T07:29:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7760,"text":"Environmental Science Technology","active":true,"publicationSubtype":{"id":10}},"title":"Occurrence and geochemistry of lead-210 and polonium-210 radionuclides in public-drinking-water supplies from principal aquifers of the United States","docAbstract":"On the basis of lifetime cancer risks, lead-210 (210Pb) and polonium-210 (210Po) ≥ 1.0 and 0.7 pCi/L (picocuries per liter), respectively, in drinking-water supplies may pose human-health concerns. 210Pb and 210Po were detected at concentrations greater than these thresholds at 3.7 and 1.5%, respectively, of filtered untreated groundwater samples from 1263 public-supply wells in 19 principal aquifers across the United States. Nationally, 72% of samples with radon-222 (222Rn) concentrations > 4000 pCi/L had 210Pb ≥ 1.0 pCi/L. 210Pb is mobilized by alpha recoil associated with the decay of 222Rn and short-lived progeny. 210Pb concentrations ≥ 1.0 pCi/L occurred most frequently where acidic groundwaters inhibited 210Pb readsorption (felsic-crystalline rocks) and where reducing alkaline conditions favored dissolution of iron–manganese- (Fe–Mn-) oxyhydroxides (which adsorb 210Pb) and formation of lead–carbonate complexes (enhancing lead (Pb) mobility). 210Po concentrations ≥ 0.7 pCi/L occurred almost exclusively in confined Coastal Plain aquifers where old (low percent-modern carbon-14) groundwaters were reducing, with high pH (>7.5) and high sodium/chloride (Na/Cl) ratios resulting from cation exchange. In high-pH environments, aqueous polonium (Po) is poorly sorbed, occurring as dihydrogen polonate (H2PoO3(aq)) or, under strongly reducing conditions, as a hydrogen-polonide anion (HPo–). Fe–Mn- and sulfate-reduction and cation-exchange processes may mobilize polonium from mineral surfaces. Po2+ occurrence in low-to-neutral-pH waters is attenuated by adsorption.
Recreational fisheries have high economic worth, valued at US$190 billion globally. An important, but underappreciated, secondary value of recreational catch is its role as a source of food. This contribution is poorly understood due to difficulty in estimating recreational harvest at spatial scales beyond a single system, as traditionally estimated from individual creel surveys. Here, we address this gap using 28‐year creel surveys of ~300 Wisconsin inland lakes. We develop a statistical model of recreational harvest for individual lakes and then scale‐up to unsurveyed lakes (3,769 lakes; 73% of statewide lake surface area). We generate a statewide estimate of recreational lake harvest of ~4,200 metric tons and an estimated annual angler consumption rate of ~1.1 kg, nearly equal to the total estimated United States per capita freshwater fish consumption. An important ecosystem service, recreational harvest makes significant contributions to human diets and plays an often‐unheralded role in food security.
Delimiting biodiversity units is difficult in organisms in which differentiation is obscured by hybridization, plasticity, and other factors that blur phenotypic boundaries. Such work is more complicated when the focal units are subspecies, the definition of which has not been broadly explored in the era of modern genetic methods. Eastwood manzanita (Arctostaphylos glandulosa Eastw.) is a widely distributed and morphologically complex chaparral shrub species with much subspecific variation, which has proven challenging to categorize. Currently 10 subspecies are recognized, however, many of them are not geographically segregated, and morphological intermediates are common. Subspecies delimitation is of particular importance in this species because two of the subspecies are rare. The goal of this study was to apply an evolutionary definition of “subspecies” to characterize structure within Eastwood manzanita.
We used publicly available geospatial environmental data and reduced‐representation genome sequencing to characterize environmental and genetic differentiation among subspecies. In addition, we tested whether subspecies could be differentiated by environmentally associated genetic variation.
Our analyses do not show genetic differentiation among subspecies of Eastwood manzanita, with the exception of one of the two rare subspecies. In addition, our environmental analyses did not show ecological differentiation, though limitations of the analysis prevent strong conclusions.
Genetic structure within Eastwood manzanita does not correspond to current subspecies circumscriptions, but rather reflects geographic distribution. Our study suggests that subspecies concepts need to be reconsidered in long‐lived plant species, especially in the age of next‐generation sequencing.
","language":"English","publisher":"Botanical Society of America","doi":"10.1002/ajb2.1496","usgsCitation":"Huang, Y., Morrison, G.R., Brelsford, A., Franklin, J., Jolles, D.D., Keeley, J., Parker, V., Saavedra, N., Sanders, A.C., Stoughton, T., Wahlert, G.A., and Litt, A., 2020, Subspecies differentiation in an enigmatic chaparral shrub species: American Journal of Botany, v. 107, no. 6, p. 923-940, https://doi.org/10.1002/ajb2.1496.","productDescription":"18 p.","startPage":"923","endPage":"940","ipdsId":"IP-115824","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":456496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ajb2.1496","text":"Publisher Index Page"},{"id":375519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Huang, Yi","contributorId":225188,"corporation":false,"usgs":false,"family":"Huang","given":"Yi","email":"","affiliations":[{"id":41068,"text":"University of California, Riverside, Riverside, CA 92521","active":true,"usgs":false}],"preferred":false,"id":790719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morrison, Glen R.","contributorId":225189,"corporation":false,"usgs":false,"family":"Morrison","given":"Glen","email":"","middleInitial":"R.","affiliations":[{"id":41068,"text":"University of California, Riverside, Riverside, CA 92521","active":true,"usgs":false}],"preferred":false,"id":790720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brelsford, Alan","contributorId":225190,"corporation":false,"usgs":false,"family":"Brelsford","given":"Alan","email":"","affiliations":[{"id":41068,"text":"University of California, Riverside, Riverside, CA 92521","active":true,"usgs":false}],"preferred":false,"id":790721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Franklin, Janet","contributorId":192373,"corporation":false,"usgs":false,"family":"Franklin","given":"Janet","affiliations":[],"preferred":false,"id":790722,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jolles, Diana D","contributorId":225191,"corporation":false,"usgs":false,"family":"Jolles","given":"Diana","email":"","middleInitial":"D","affiliations":[{"id":41069,"text":"Plymouth State University, Plymouth, NH 03264","active":true,"usgs":false}],"preferred":false,"id":790723,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":790724,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parker, V Thomas","contributorId":225192,"corporation":false,"usgs":false,"family":"Parker","given":"V Thomas","affiliations":[{"id":41070,"text":"San Francisco State University, San Francisco, CA 94132","active":true,"usgs":false}],"preferred":false,"id":790725,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Saavedra, Natalie","contributorId":225193,"corporation":false,"usgs":false,"family":"Saavedra","given":"Natalie","email":"","affiliations":[{"id":41068,"text":"University of California, Riverside, Riverside, CA 92521","active":true,"usgs":false}],"preferred":false,"id":790726,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sanders, Andrew C","contributorId":225194,"corporation":false,"usgs":false,"family":"Sanders","given":"Andrew","email":"","middleInitial":"C","affiliations":[{"id":41068,"text":"University of California, Riverside, Riverside, CA 92521","active":true,"usgs":false}],"preferred":false,"id":790727,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stoughton, Thomas","contributorId":225195,"corporation":false,"usgs":false,"family":"Stoughton","given":"Thomas","email":"","affiliations":[{"id":41069,"text":"Plymouth State University, Plymouth, NH 03264","active":true,"usgs":false}],"preferred":false,"id":790728,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wahlert, Gregory A.","contributorId":225196,"corporation":false,"usgs":false,"family":"Wahlert","given":"Gregory","email":"","middleInitial":"A.","affiliations":[{"id":41071,"text":"University of California, Santa Barbara, Santa Barbara, CA 93106","active":true,"usgs":false}],"preferred":false,"id":790729,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Litt, Amy","contributorId":225197,"corporation":false,"usgs":false,"family":"Litt","given":"Amy","email":"","affiliations":[{"id":41068,"text":"University of California, Riverside, Riverside, CA 92521","active":true,"usgs":false}],"preferred":false,"id":790730,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70210188,"text":"70210188 - 2020 - Drought early warning and forecasting","interactions":[],"lastModifiedDate":"2022-04-14T19:24:53.748626","indexId":"70210188","displayToPublicDate":"2020-06-03T14:16:14","publicationYear":"2020","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"Drought early warning and forecasting","docAbstract":"Drought risk management involves three pillars: drought early warning, drought vulnerability and risk assessment, and drought preparedness, mitigation, and response. This book collects in one place a description of all the key components of the first pillar, and describes strategies for fitting these pieces together. The best modern drought early warning systems incorporate and integrate a broad array of environmental information sources: weather station observations, satellite imagery, land surface and crop model simulations, and weather and climate model forecasts, and analyze this information in context-relevant ways that take into account exposure and vulnerability. Drought Early Warning and Forecasting: Theory and Practice assembles a comprehensive overview of these components, providing examples drawn from the Famine Early Warning Systems Network and the United States Drought Monitor. This book simultaneously addresses the physical, social, and information management aspects of drought early warning, and informs readers about the tools, techniques, and conceptual models required to effectively identify, predict, and communicate potential drought-related disasters.
This book is a key text for postgraduate scientists and graduate and advanced undergraduate students in hydrology, geography, earth sciences, meteorology, climatology, and environmental sciences programs. Professionals dealing with disaster management and drought forecasting will also find this book beneficial to their work.
","language":"English","publisher":"Elsevier","isbn":"9780128140116","usgsCitation":"Funk, C., and Shukla, S., 2020, Drought early warning and forecasting, 238 p.","productDescription":"238 p.","ipdsId":"IP-115627","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":377959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377958,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.elsevier.com/books/drought-early-warning-and-forecasting/funk/978-0-12-814011-6"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":789477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shukla, Shraddhanand","contributorId":145802,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":789478,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211202,"text":"70211202 - 2020 - Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing","interactions":[],"lastModifiedDate":"2020-07-17T17:25:42.112539","indexId":"70211202","displayToPublicDate":"2020-06-03T12:18:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing","docAbstract":"Landslides modify the natural landscape and cause fatalities and property damage worldwide. Quantifying landslide dynamics is challenging due to the stochastic nature of the environment. With its large area of ~1 km2 and perennial motions at ~10–20 mm per day, the Slumgullion landslide in Colorado, USA, represents an ideal natural laboratory to better understand landslide behavior. Here, we use hybrid remote sensing data and methods to recover the four-dimensional surface motions during 2011–2018. We refine the boundaries of an area of ~0.35 km2 below the crest of the prehistoric landslide. We construct a mechanical framework to quantify the rheology, subsurface channel geometry, mass flow rate, and spatiotemporally dependent pore-water pressure feedback through a joint analysis of displacement and hydrometeorological measurements from ground, air and space. Our study demonstrates the importance of remotely characterizing often inaccessible, dangerous slopes to better understand landslides and other quasi-static mass fluxes in natural and industrial environments, which will ultimately help reduce associated hazards.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-020-16617-7","usgsCitation":"Hu, X., Bürgmann, R., Schulz, W.H., and Fielding, E.J., 2020, Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing: Nature Communications, v. 11, 2792, 9 p., https://doi.org/10.1038/s41467-020-16617-7.","productDescription":"2792, 9 p.","ipdsId":"IP-117085","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":456500,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-16617-7","text":"Publisher Index Page"},{"id":436941,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TCQDD5","text":"USGS data release","linkHelpText":"Data from in-situ displacement monitoring, Slumgullion landslide, Hinsdale County, Colorado"},{"id":376466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Slumgullion landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.30132102966309,\n 37.97600347500009\n ],\n [\n -107.22647666931152,\n 37.97600347500009\n ],\n [\n -107.22647666931152,\n 38.01212375706868\n ],\n [\n -107.30132102966309,\n 38.01212375706868\n ],\n [\n -107.30132102966309,\n 37.97600347500009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Hu, Xie","contributorId":177306,"corporation":false,"usgs":false,"family":"Hu","given":"Xie","email":"","affiliations":[],"preferred":false,"id":793138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bürgmann, Roland","contributorId":195087,"corporation":false,"usgs":false,"family":"Bürgmann","given":"Roland","affiliations":[],"preferred":false,"id":793139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":793140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fielding, Eric J.","contributorId":218096,"corporation":false,"usgs":false,"family":"Fielding","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":793141,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211689,"text":"70211689 - 2020 - Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers","interactions":[],"lastModifiedDate":"2020-08-07T14:15:21.151798","indexId":"70211689","displayToPublicDate":"2020-06-03T09:13:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers","docAbstract":"Recursive movement patterns have been used to detect behavioral structure within individual movement trajectories in the context of foraging ecology, home-ranging behavior, and predator avoidance. Some animals exhibit movement recursions to locations that are tied to reproductive functions, including nests and dens; while existing literature recognizes that, no method is currently available to explicitly target different types of revisited locations. Moreover, the temporal persistence of recursive movements to a breeding location can carry information regarding the fate of breeding attempts, but it has never been used as a metric to quantify recursive movement patterns. Here, we introduce a method to locate breeding attempts and estimate their fate from GPStracking data of central place foragers. We tested the performance of our method in three bird species differing in breeding ecology (wood stork (Mycteria americana), lesser kestrel (Falco naumanni), Mediterranean gull (Ichthyaetus melanocephalus)) and implemented it in the R package ‘nestR’. Methods: We identified breeding sites based on the analysis of recursive movements within individual tracks. Using trajectories with known breeding attempts, we estimated a set of species-specific criteria for the identification of nest sites, which we further validated using non-reproductive individuals as controls. We then estimated individual nest survival as a binary measure of reproductive fate (success, corresponding to fledging of at least one chick, or failure) from nest-site revisitation histories during breeding attempts, using a Bayesian hierarchical modeling approach that accounted for temporally variable revisitation patterns, probability of visit detection, and missing data. Results: Across the three species, positive predictive value of the nest-site detection algorithm varied between 87 and 100% and sensitivity between 88 and 92%, and we correctly estimated the fate of 86–100% breeding attempts.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-020-00201-1","usgsCitation":"Picardi, S., Smith, B., Boone, M.E., Frederick, P.C., Cecere, J.G., Rubolini, D., Serra, L., Pirrello, S., Borkhataria, R.R., and Basille, M., 2020, Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers: Movement Ecology, v. 8, 24, 14 p., https://doi.org/10.1186/s40462-020-00201-1.","productDescription":"24, 14 p.","ipdsId":"IP-105411","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456508,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-020-00201-1","text":"Publisher Index Page"},{"id":377175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2020-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Picardi, Simona 0000-0002-2623-6623","orcid":"https://orcid.org/0000-0002-2623-6623","contributorId":237045,"corporation":false,"usgs":false,"family":"Picardi","given":"Simona","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":795078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Brian 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":218457,"corporation":false,"usgs":true,"family":"Smith","given":"Brian","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":795079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boone, Matthew E. 0000-0002-8070-4715","orcid":"https://orcid.org/0000-0002-8070-4715","contributorId":237046,"corporation":false,"usgs":false,"family":"Boone","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":795080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frederick, Peter C.","contributorId":215042,"corporation":false,"usgs":false,"family":"Frederick","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":39161,"text":"Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America","active":true,"usgs":false}],"preferred":false,"id":795081,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cecere, Jacopo G. 0000-0002-4925-2730","orcid":"https://orcid.org/0000-0002-4925-2730","contributorId":237048,"corporation":false,"usgs":false,"family":"Cecere","given":"Jacopo","email":"","middleInitial":"G.","affiliations":[{"id":47591,"text":"Istituto Superiore per la Protezione e la Ricerca Ambientale","active":true,"usgs":false}],"preferred":false,"id":795082,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rubolini, Diego 0000-0003-2703-5783","orcid":"https://orcid.org/0000-0003-2703-5783","contributorId":237050,"corporation":false,"usgs":false,"family":"Rubolini","given":"Diego","email":"","affiliations":[{"id":47592,"text":"Università degli Studi di Milano","active":true,"usgs":false}],"preferred":false,"id":795083,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Serra, Lorenzo 0000-0002-8911-8050","orcid":"https://orcid.org/0000-0002-8911-8050","contributorId":237052,"corporation":false,"usgs":false,"family":"Serra","given":"Lorenzo","email":"","affiliations":[{"id":47591,"text":"Istituto Superiore per la Protezione e la Ricerca Ambientale","active":true,"usgs":false}],"preferred":false,"id":795084,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pirrello, Simone 0000-0002-9471-106X","orcid":"https://orcid.org/0000-0002-9471-106X","contributorId":237054,"corporation":false,"usgs":false,"family":"Pirrello","given":"Simone","email":"","affiliations":[{"id":47591,"text":"Istituto Superiore per la Protezione e la Ricerca Ambientale","active":true,"usgs":false}],"preferred":false,"id":795085,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Borkhataria, Rena R.","contributorId":197425,"corporation":false,"usgs":false,"family":"Borkhataria","given":"Rena","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":795086,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Basille, Mathieu","contributorId":175274,"corporation":false,"usgs":false,"family":"Basille","given":"Mathieu","email":"","affiliations":[],"preferred":false,"id":795087,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70229337,"text":"70229337 - 2020 - Remarkable response of native fishes to invasive trout suppression varies with trout density, temperature, and annual hydrology","interactions":[],"lastModifiedDate":"2022-03-04T13:15:54.648199","indexId":"70229337","displayToPublicDate":"2020-06-03T07:12:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Remarkable response of native fishes to invasive trout suppression varies with trout density, temperature, and annual hydrology","docAbstract":"This report provides an overview of model-based climate science in a risk management context. In addition, it summarizes how the U.S. Geological Survey (USGS) will continue to follow best scientific practices and when and how the results of this research will be delivered to the U.S. Department of the Interior (DOI) and other stakeholders to inform policymaking. Climate change is a risk management challenge for society because of the uncertain consequences for natural and human systems across decades to centuries. Climate-related science activities within the USGS emphasize research on adaptation to climate change. This research helps inform adaptive management processes and planning activities within other DOI bureaus and by DOI stakeholders.
Global climate models are sophisticated numerical representations of the Earth’s climate system. Research groups from around the world regularly participate in a coordinated effort to produce a suite of climate models. This global effort provides a test bed to assess model performance and analyze projections of future change under various prescribed climate scenarios. These climate scenarios describe a plausible future outcome associated with a specific set of societal actions. Because scenarios are developed in a risk-based framework with a high degree of uncertainty about future societal developments, they are usually not assigned a formal likelihood of occurrence. Examining a range of projected climate outcomes based on multiple scenarios is a recommended best practice because it allows decision makers to better consider both short- and long-term risks and opportunities.
As part of its routine science practices, the USGS regularly reviews the state of knowledge of climate science, develops and maintains best practices in using global climate models to project climate change impacts, and provides data and interpretations of potential impacts to the DOI and other stakeholders. Management and policy decisions within the DOI will reflect different tolerances for risk, which has implications for what type of information should be considered and how that information should be used. It is suggested that a followup document be produced that would describe in more detail how these management decisions with differing risk tolerances can be made effectively and consistently in light of an uncertain future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201058","usgsCitation":"Terando, A., Reidmiller, D., Hostetler, S.W., Littell, J.S., Beard, T.D., Jr., Weiskopf, S.R., Belnap, J., and Plumlee, G.S., 2020, Using information from global climate models to inform policymaking—The role of the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2020–1058, 25 p., https://doi.org/10.3133/ofr20201058.","productDescription":"v, 25 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118715","costCenters":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"links":[{"id":375144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1058/coverthb.jpg"},{"id":375198,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1058/ofr20201058.pdf","text":"Report","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1058"}],"contact":"National Climate Adaptation Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 516
Reston, VA 20192
https://www.usgs.gov/land-resources/
climate-adaptation-science-centers