{"pageNumber":"187","pageRowStart":"4650","pageSize":"25","recordCount":46670,"records":[{"id":70223698,"text":"sim3478 - 2021 - Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2020","interactions":[],"lastModifiedDate":"2021-09-13T16:57:52.138458","indexId":"sim3478","displayToPublicDate":"2021-09-13T06:56:23","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3478","displayTitle":"Altitude of the Potentiometric Surface in the Mississippi River Valley Alluvial Aquifer, Spring 2020","title":"Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2020","docAbstract":"

The purpose of this report is to present a potentiometric-surface map for the Mississippi River Valley alluvial aquifer (MRVA). The source data for the map were groundwater-altitude data from wells measured manually or continuously generally in spring 2020 and from the altitude of the top of the water surface measured generally on April 9, 2020, in rivers in the area.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3478","programNote":"Water Availability and Use Science Program","usgsCitation":"McGuire, V.L., Seanor, R.C., Asquith, W.H., Strauch, K.R., Nottmeier, A.M., Thomas, J.C., Tollett, R.W., and Kress, W.H., 2021, Altitude of the potentiometric surface in the Mississippi River Valley alluvial aquifer, spring 2020: U.S. Geological Survey Scientific Investigations Map 3478, 5 sheets, includes 14-p. pamphlet, https://doi.org/10.3133/sim3478.","productDescription":"Pamphlet: vi, 14p.; 5 Sheets: 30.00 x 46.00 inches or smaller; Data Release; Dataset","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119302","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":388770,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388769,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXDIPL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Datasets used to map the potentiometric surface, Mississippi River Valley alluvial aquifer, spring 2020"},{"id":388768,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet5.pdf","text":"Sheet 5—Atchafalaya and Deltaic and Chenier Plain MAP regions","size":"6.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 5"},{"id":388762,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3478/coverthb2.jpg"},{"id":388767,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet4.pdf","text":"Sheet 4—Delta MAP region","size":"4.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 4"},{"id":388763,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_pamphlet.pdf","text":"Pamphlet","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Pamphlet"},{"id":388764,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet1.pdf","text":"Sheet 1—All Mississippi Alluvial Plain (MAP) regions","size":"14.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 1"},{"id":388765,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet2.pdf","text":"Sheet 2—St. Francis and Cache MAP regions","size":"5.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 2"},{"id":388766,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3478/sim3478_sheet3.pdf","text":"Sheet 3—Boeuf and Grand Prairie MAP regions","size":"6.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3478 Sheet 3"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi River Valley Alluvial Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.62597656249999,\n 29.152161283318915\n ],\n [\n -88.76953125,\n 28.8831596093235\n ],\n [\n -88.9453125,\n 29.726222319395504\n ],\n [\n -90.439453125,\n 30.751277776257812\n ],\n [\n -90,\n 33.247875947924385\n ],\n [\n -88.857421875,\n 35.17380831799959\n ],\n [\n -88.59374999999999,\n 36.421282443649496\n ],\n [\n -89.12109375,\n 37.43997405227057\n ],\n [\n -90.1318359375,\n 37.26530995561875\n ],\n [\n -91.318359375,\n 35.10193405724606\n ],\n [\n -91.845703125,\n 32.84267363195431\n ],\n [\n -91.8896484375,\n 31.87755764334002\n ],\n [\n -92.2412109375,\n 29.99300228455108\n ],\n [\n -91.62597656249999,\n 29.152161283318915\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-09-13","noUsgsAuthors":false,"publicationDate":"2021-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"McGuire, Virginia L. 0000-0002-3962-4158 vlmcguir@usgs.gov","orcid":"https://orcid.org/0000-0002-3962-4158","contributorId":404,"corporation":false,"usgs":true,"family":"McGuire","given":"Virginia","email":"vlmcguir@usgs.gov","middleInitial":"L.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seanor, Ronald C. 0000-0001-5735-5580","orcid":"https://orcid.org/0000-0001-5735-5580","contributorId":218443,"corporation":false,"usgs":true,"family":"Seanor","given":"Ronald","email":"","middleInitial":"C.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strauch, Kellan R. 0000-0002-7218-2099","orcid":"https://orcid.org/0000-0002-7218-2099","contributorId":208562,"corporation":false,"usgs":true,"family":"Strauch","given":"Kellan R.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nottmeier, Anna M. 0000-0002-0205-0955 anottmeier@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-0955","contributorId":5283,"corporation":false,"usgs":true,"family":"Nottmeier","given":"Anna","email":"anottmeier@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822373,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomas, Judith C. 0000-0001-7883-1419","orcid":"https://orcid.org/0000-0001-7883-1419","contributorId":202706,"corporation":false,"usgs":true,"family":"Thomas","given":"Judith","email":"","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822374,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tollett, Roland W. 0000-0002-4726-5845 rtollett@usgs.gov","orcid":"https://orcid.org/0000-0002-4726-5845","contributorId":1896,"corporation":false,"usgs":true,"family":"Tollett","given":"Roland","email":"rtollett@usgs.gov","middleInitial":"W.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822375,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kress, Wade H. 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":223007,"corporation":false,"usgs":true,"family":"Kress","given":"Wade H.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822376,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225650,"text":"70225650 - 2021 - Isolating detrital and diagenetic signals in magnetic susceptibility records from methane-bearing marine sediments","interactions":[],"lastModifiedDate":"2021-10-29T13:53:44.169617","indexId":"70225650","displayToPublicDate":"2021-09-12T08:48:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Isolating detrital and diagenetic signals in magnetic susceptibility records from methane-bearing marine sediments","docAbstract":"

Volume-dependent magnetic susceptibility (κ) is commonly used for paleoenvironmental reconstructions in both terrestrial and marine sedimentary environments where it reflects a mixed signal between primary deposition and secondary diagenesis. In the marine environment, κ is strongly influenced by the abundance of ferrimagnetic minerals regulated by sediment transport processes. Post-depositional alteration by H2S, however, can dissolve titanomagnetite, releasing reactive Fe that promotes pyritization and subsequently decreases κ. Here, we provide a new approach for isolating the detrital signal in κ and identifying intervals of diagenetic alteration of κ driven by organoclastic sulfate reduction (OSR) and the anaerobic oxidation of methane (AOM) in methane-bearing marine sediments offshore India. Using the correlation of a heavy mineral proxy from X-ray fluorescence data (Zr/Rb) and κ in unaltered sediments, we predict the primary detrital κ signal and identify intervals of decreased κ, which correspond to increased total sulfur content. Our approach is a rapid, high-resolution method that can identify overprinted κ resulting from pyritization of titanomagnetite due to H2S production in marine sediments. In addition, total organic carbon, total sulfur, and authigenic carbonate δ13C measurements indicate that both OSR and AOM can drive the observed κ loss, but AOM drives the greatest decreases in κ. Overall, our approach can enhance paleoenvironmental reconstructions and provide insight into paleo-positions of the sulfate-methane transition zone, past enhancements of OSR or paleo-methane seepage, and the role of detrital iron oxide minerals on the marine sediment sulfur sink, with consequences influencing the development of chemosynthetic biological communities at methane seeps.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GC009867","usgsCitation":"Johnson, J.P., Phillips, S.C., Clyde, W., Giosan, L., and Torres, M.E., 2021, Isolating detrital and diagenetic signals in magnetic susceptibility records from methane-bearing marine sediments: Geochemistry, Geophysics, Geosystems, v. 22, no. 9, e2021GC009867, 21 p., https://doi.org/10.1029/2021GC009867.","productDescription":"e2021GC009867, 21 p.","ipdsId":"IP-129227","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450835,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gc009867","text":"Publisher Index Page"},{"id":391152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Krishna-Godavari Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 86.8798828125,\n 21.12549763660628\n ],\n [\n 80.6396484375,\n 16.003575733881327\n ],\n [\n 80.37597656249999,\n 14.221788628397572\n ],\n [\n 79.8486328125,\n 10.790140750321738\n ],\n [\n 87.5390625,\n -0.04394530819134536\n ],\n [\n 92.63671875,\n 2.1967272417616712\n ],\n [\n 94.130859375,\n 7.972197714386879\n ],\n [\n 96.416015625,\n 10.617418067950293\n ],\n [\n 95.09765625,\n 15.241789855961722\n ],\n [\n 93.955078125,\n 16.551961721972525\n ],\n [\n 94.21875,\n 18.22935133838668\n ],\n [\n 92.021484375,\n 20.756113874762082\n ],\n [\n 86.8798828125,\n 21.12549763660628\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 73.740234375,\n -1.5818302639606454\n ],\n [\n 72.99316406249999,\n 15.623036831528264\n ],\n [\n 70.751953125,\n 19.518375478601566\n ],\n [\n 65.830078125,\n 19.062117883514652\n ],\n [\n 65.2587890625,\n 9.709057068618208\n ],\n [\n 62.75390625,\n 0.4394488164139768\n ],\n [\n 68.9501953125,\n -2.591888984149953\n ],\n [\n 73.740234375,\n -1.5818302639606454\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"22","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Joel P. L.","contributorId":138502,"corporation":false,"usgs":false,"family":"Johnson","given":"Joel","email":"","middleInitial":"P. L.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":826063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Stephen C. 0000-0003-0858-4701","orcid":"https://orcid.org/0000-0003-0858-4701","contributorId":268177,"corporation":false,"usgs":true,"family":"Phillips","given":"Stephen","email":"","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clyde, William","contributorId":268178,"corporation":false,"usgs":false,"family":"Clyde","given":"William","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":826065,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Giosan, Liviu","contributorId":147870,"corporation":false,"usgs":false,"family":"Giosan","given":"Liviu","email":"","affiliations":[],"preferred":false,"id":826066,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Torres, Marta E.","contributorId":196035,"corporation":false,"usgs":false,"family":"Torres","given":"Marta","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":826067,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243718,"text":"70243718 - 2021 - Modeling watershed carbon dynamics as affected by land cover change and soil erosion","interactions":[],"lastModifiedDate":"2024-05-16T15:35:29.430932","indexId":"70243718","displayToPublicDate":"2021-09-11T08:50:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Modeling watershed carbon dynamics as affected by land cover change and soil erosion","docAbstract":"

Process-based ecosystem carbon cycle models typically incorporate vegetation growth, vegetation mortality, and soil respiration as well as the biotic and environmental drivers that influence these variables. However, few spatially explicit process models can efficiently incorporate the influence of land cover change and carbon lateral movement at regional scales or high spatial resolution. This study uses the Land Use and Carbon Scenario Simulator (LUCAS) to demonstrate the development of a fast ecosystem model that not only considers the basic carbon cycle but also incorporates the impact of land cover change, soil erosion, and soil deposition. As input to the LUCAS modeling framework, we used the integrated biosphere simulator (IBIS) to simulate a non-spatial reference carbon cycling scenario without considering land cover change for the Nisqually River watershed in the northwestern United States. We then used the Land Change Monitoring, Assessment, and Projection (LCMAP) remotely sensed 30-m sequential land cover data to generate annual land change history for the Nisqually River area from 1985 to 2017 and used the Unit Stream Powered Erosion and Deposition model (USPED) to estimate annual soil carbon lateral movement. Finally, we combined the annual carbon outputs from IBIS, the land change history from LCMAP, and the soil erosion and deposition from USPED within the LUCAS simulation framework. Results showed that from 1985 to 2017, along with the dynamic land cover changes, total ecosystem biomass carbon increased from 11.4 to 18.6 TgC, mainly due to forest growth. Total ecosystem soil carbon declined from 31.7 to 29.7 TgC, but the overall loss in soil carbon was not uniform across land cover types. Forestland (forest sector) and grassland lost carbon, while wetland, developed land and agricultural land gained carbon. Forest, grassland, and developed land lost 0.0553 TgC during the study period (1.73 Gg of C per year; 1 Gg = 0.001 Tg) from erosion, while wetland gained 0.0071 TgC (0.22 Gg C per year) from deposition. Agricultural land was neutral in terms of soil erosion.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2021.109724","usgsCitation":"Liu, J., Sleeter, B.M., Selmants, P., Diao, J., Zhou, Q., Worstell, B., and Moritsch, M.M., 2021, Modeling watershed carbon dynamics as affected by land cover change and soil erosion: Ecological Modelling, v. 459, 109724, 11 p., https://doi.org/10.1016/j.ecolmodel.2021.109724.","productDescription":"109724, 11 p.","ipdsId":"IP-129044","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450838,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2021.109724","text":"Publisher Index Page"},{"id":436201,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A27GFH","text":"USGS data release","linkHelpText":"Simulated Nisqually River Watershed 30-m resolution 2017 ecosystem carbon variables from the LUCAS model"},{"id":417208,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.16253640390157,\n 47.168168689027254\n ],\n [\n -123.16253640390157,\n 46.28394294633836\n ],\n [\n -121.7181395192194,\n 46.28394294633836\n ],\n [\n -121.7181395192194,\n 47.168168689027254\n ],\n [\n -123.16253640390157,\n 47.168168689027254\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"459","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selmants, Paul C. 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","middleInitial":"C.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diao, Jiaojiao","contributorId":305505,"corporation":false,"usgs":false,"family":"Diao","given":"Jiaojiao","email":"","affiliations":[{"id":33416,"text":"Nanjing Forestry University, China","active":true,"usgs":false}],"preferred":false,"id":873048,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":873049,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Worstell, Bruce 0000-0001-8927-3336","orcid":"https://orcid.org/0000-0001-8927-3336","contributorId":305506,"corporation":false,"usgs":false,"family":"Worstell","given":"Bruce","affiliations":[{"id":66235,"text":"SGT Inc. Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":873050,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moritsch, Monica Mei Jeen 0000-0002-3890-1264","orcid":"https://orcid.org/0000-0002-3890-1264","contributorId":225210,"corporation":false,"usgs":true,"family":"Moritsch","given":"Monica","email":"","middleInitial":"Mei Jeen","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873051,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223695,"text":"sir20215084 - 2021 - Forecasting drought probabilities for streams in the northeastern United States","interactions":[],"lastModifiedDate":"2021-09-13T12:01:34.031419","indexId":"sir20215084","displayToPublicDate":"2021-09-10T14:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5084","displayTitle":"Forecasting Drought Probabilities for Streams in the Northeastern United States","title":"Forecasting drought probabilities for streams in the northeastern United States","docAbstract":"

Maximum likelihood logistic regression (MLLR) models for the northeastern United States forecast drought probability estimates for water flowing in rivers and streams using methods previously identified and developed. Streamflow data from winter months are used to estimate chances of hydrological drought during summer months. Daily streamflow data collected from 1,143 streamgages from April 1, 1877, through October 31, 2018, are used to provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February. This allows estimates of outcomes from 5 to 11 months ahead of their occurrence. Models specific to the northeastern United States were investigated and updated. The MLLR models of drought stream-flow probabilities utilize the explanatory power of temporally linked water flows. Models with strong drought streamflow probability correct-classification rates were produced for streams throughout the northeastern United States. A test of northeastern United States drought streamflow probability predictions found that overall correct-classification rates for drought streamflow probabilities in the northeastern United States exceeded 97 percent when predicting July 2019 drought probability using February 2019 monthly mean streamflow data. Using hydrological drought probability estimates in a water-management context informs understandings of possible future streamflow drought conditions in the northeastern United States, provides warnings of potential future drought conditions, and aids water-management decision making and responses to changing circumstances.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215084","usgsCitation":"Austin, S.H., 2021, Forecasting drought probabilities for streams in the northeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5084, 11 p., https://doi.org/10.3133/sir20215084.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"11","ipdsId":"IP-113685","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":388741,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5084/sir20215084.pdf","text":"Report","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5084"},{"id":388740,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5084/coverthb.jpg"},{"id":388742,"rank":3,"type":{"id":30,"text":"Data 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Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2021-09-02","noUsgsAuthors":false,"publicationDate":"2021-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Austin, Samuel H. 0000-0001-5626-023X saustin@usgs.gov","orcid":"https://orcid.org/0000-0001-5626-023X","contributorId":153,"corporation":false,"usgs":true,"family":"Austin","given":"Samuel","email":"saustin@usgs.gov","middleInitial":"H.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":822358,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70229010,"text":"70229010 - 2021 - Estimating the effects of fish quality and size on the economic value of fishing in Oklahoma streams and rivers: A revealed preference and contingent behavior approach","interactions":[],"lastModifiedDate":"2022-02-25T15:14:48.652424","indexId":"70229010","displayToPublicDate":"2021-09-10T09:11:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the effects of fish quality and size on the economic value of fishing in Oklahoma streams and rivers: A revealed preference and contingent behavior approach","docAbstract":"

Fishing in Oklahoma’s rivers and streams provides a unique experience for anglers in the state. Despite its popularity, information on total demand and economic benefits associated with stream fishing is limited in the state. Research on the role of site quality indicators, such as fish size and quantity, on recreational fishing has shown mixed results. Whether fish size or quantity plays an important role in determining fishing demand and economic value may have important management implications. We estimated the demand and economic value of fishing under varying scenarios by using anglers’ responses to hypothetical behavioral questions related to fishing in Ozark Highland streams and rivers in Oklahoma. We asked how intended number of trips might change in the future given hypothetical increases in catch rates of fish, catch rates of trophy-sized fish, and catch rates of preferred fish species, in combination with anglers’ trip-related data. Under current conditions, we estimated consumer surplus per person per trip to be $55 and aggregate value across all stream anglers in Oklahoma to be $68.51 million. Changes in marginal benefits varied among hypothetical scenarios of fish size and abundance but was maximized with a 25% increase in catch rates of trophy-sized fish. The study findings contribute to the understanding of the economic benefit of fishing in streams and suggest that fish size, rather than fish quantity, is more important to stream anglers in the area.

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Atlanta","active":true,"usgs":true}],"preferred":true,"id":836119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"York, B.","contributorId":280239,"corporation":false,"usgs":false,"family":"York","given":"B.","email":"","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":836120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, A.T.","contributorId":275887,"corporation":false,"usgs":false,"family":"Taylor","given":"A.T.","affiliations":[{"id":54572,"text":"University of Central Oklahoma","active":true,"usgs":false}],"preferred":false,"id":836121,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223914,"text":"70223914 - 2021 - Estimating and forecasting time-varying groundwater recharge in fractured rock: A state-space formulation with preferential and diffuse flow to the water table","interactions":[],"lastModifiedDate":"2021-10-06T16:00:10.10863","indexId":"70223914","displayToPublicDate":"2021-09-09T07:11:15","publicationYear":"2021","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":"Estimating and forecasting time-varying groundwater recharge in fractured rock: A state-space formulation with preferential and diffuse flow to the water table","docAbstract":"

Rapid infiltration following precipitation may result in groundwater contamination from surface contaminants or pathogens. In fractured rock, contaminants can migrate rapidly to points of groundwater withdrawals. In contrast to the temporal availability of groundwater quality chemical indicators, meteorological and groundwater level observations are available in real-time to estimate time-varying recharge, which can act as a surrogate to identify periods of rapid infiltration that may indicate contamination susceptibility. Estimating recharge using methods, such as base-flow recession, unsaturated infiltration models, or Water-Table Fluctuations (WTF), cannot capitalize on currently available technologies and telecommunication infrastructure to conduct real-time recharge estimation at scales relevant to characterizing rapid infiltration. We present a linear, physics-based State-Space (SS) model of one-dimensional infiltration to estimate recharge, which includes preferential and diffuse-flow to the water table. The model can take advantage of real-time data for water-table altitude, precipitation, and evapotranspiration. Model parameters are calibrated over an observation period, and the Kalman Filter (KF) is subsequently applied to continuously update the observed (water-table altitude) and unobserved (groundwater recharge) system states and predict future states as new data become available. The SS/KF algorithm is demonstrated at the Masser Groundwater Recharge Site in Pennsylvania, USA and comparisons are made with recharge estimates from WTF methods. Model results indicate that the frequency of observations (daily versus sub-daily) dictates the allocation between preferential and diffuse flow. Additionally, because infiltration processes encompass many nonlinearities, model parameters estimated from observation periods need to be updated at least seasonally to account for changing recharge conditions.

","language":"English","publisher":"Wiley","doi":"10.1029/2020WR029110","usgsCitation":"Shapiro, A.M., and Day-Lewis, F., 2021, Estimating and forecasting time-varying groundwater recharge in fractured rock: A state-space formulation with preferential and diffuse flow to the water table: Water Resources Research, v. 57, no. 9, e2020WR029110, 30 p., https://doi.org/10.1029/2020WR029110.","productDescription":"e2020WR029110, 30 p.","ipdsId":"IP-122279","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450863,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr029110","text":"Publisher Index Page"},{"id":436205,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VBR9V8","text":"USGS data release","linkHelpText":"Algorithms for model parameter estimation, state estimation, and forecasting applied to a State-Space model coupled with the Kalman Filter for one-dimensional vertical infiltration to fractured rock aquifers"},{"id":436204,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LLXCIC","text":"USGS data release","linkHelpText":"Water Level Altitude in Bedrock Wells and Meteorological Data at the Masser Groundwater Recharge Site between February 1 and December 31, 1999"},{"id":389205,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":823235,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223785,"text":"70223785 - 2021 - Thermal stability of an adaptable, invasive ectotherm: Argentine giant tegus in the Greater Everglades ecosystem, USA","interactions":[],"lastModifiedDate":"2021-09-08T19:02:29.353507","indexId":"70223785","displayToPublicDate":"2021-09-08T11:53:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Thermal stability of an adaptable, invasive ectotherm: Argentine giant tegus in the Greater Everglades ecosystem, USA","docAbstract":"

Invasive species globally threaten biodiversity and economies, but the ecophysiological mechanisms underlying their success are often understudied. For those alien species that also exhibit high phenotypic plasticity, such as habitat generalists, adaptations in response to environmental pressures can take place relatively quickly. The Argentine giant tegu (Salvator merianae; tegu) is a large omnivorous lizard from South America that is prolific, long-lived, vagile, and highly adaptable to disturbed environments. They are well suited to the climate of southeastern United States, introduced to several disjunct areas, including the Everglades, where their voracious appetite threatens native wildlife. Tegus undergo winter dormancy (hibernation) to cope with colder temperatures, and while this behavior may facilitate invasion into more temperate regions, it may also present management opportunities. We studied the thermal habits of wild S. merianae within their invaded range in southern Florida, USA. We used radiotelemetry and trail cameras to verify aboveground behaviors, and temperature dataloggers to monitor surface (sun-exposed [Te] and shaded [Ts]), ambient (Ta), subsurface ground (Th), and internal body (Tb) temperatures of a population of free-ranging tegus over several seasons. We evaluated thermal and behavioral data and identified five biologically significant periods: pre-hibernal, hibernal, cold snaps, hibernal-basking, and post-hibernal. We found tegus maintained thermal stability throughout the hibernal period, frequently at temperatures above available thermal microhabitats. Variation in Tb was lowest during hibernation and cold snaps and was less variable than subsurface temperatures despite not leaving their hibernaculum. Hibernal ingress and egress were best predicted by temperature differentials between exposed soil and ambient daily mean temperatures (Te − Ta) and daylength. Though we detected no sex differences, larger animals started hibernation sooner, stayed in hibernation longer, and retained higher fat stores over the study period. One individual did not hibernate, representing only the second record of this behavior. Despite limitations of these descriptive data, this is the first study finely detailing Tb of a population of wild, free-ranging S. merianae over multiple biologically significant time periods and to associate Tb with thermal habitats within its invasive range. Tegus' apparent ability for thermal stability expands the adaptability breadth of this species and underscores the invasion threat.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3579","usgsCitation":"Currylow, A.F., Collier, M., Hanslowe, E.B., Falk, B., Cade, B.S., Moy, S.E., Grajal-Puche, A., Ridgley, F.N., Reed, R., and Yackel Adams, A.A., 2021, Thermal stability of an adaptable, invasive ectotherm: Argentine giant tegus in the Greater Everglades ecosystem, USA: Ecosphere, v. 12, no. 9, p. 1-18, https://doi.org/10.1002/ecs2.3579.","productDescription":"e03579, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-118593","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450869,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3579","text":"Publisher Index Page"},{"id":436206,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QCSKRR","text":"USGS data release","linkHelpText":"Dataset from 2015-2016 thermal and behavior monitoring of Argentine giant tegus in Everglades, Florida"},{"id":388960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades, Southern Glades Wildlife Environmental Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.57441711425781,\n 25.397692428732874\n ],\n [\n -80.57510375976562,\n 25.28536903925994\n ],\n [\n -80.44464111328125,\n 25.28723160236171\n ],\n [\n -80.47039031982422,\n 25.40327484644246\n ],\n [\n -80.55896759033203,\n 25.403584973186703\n ],\n [\n -80.57441711425781,\n 25.397692428732874\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Currylow, Andrea Faye 0000-0003-1631-8964","orcid":"https://orcid.org/0000-0003-1631-8964","contributorId":257055,"corporation":false,"usgs":true,"family":"Currylow","given":"Andrea","email":"","middleInitial":"Faye","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collier, Michelle 0000-0001-5715-448X","orcid":"https://orcid.org/0000-0001-5715-448X","contributorId":265393,"corporation":false,"usgs":false,"family":"Collier","given":"Michelle","email":"","affiliations":[{"id":54672,"text":"National Park Service, Everglades National Park, 40001 SR 9336, Homestead, Florida 33034, USA","active":true,"usgs":false}],"preferred":false,"id":822693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanslowe, Emma B. 0000-0003-4331-6729","orcid":"https://orcid.org/0000-0003-4331-6729","contributorId":265394,"corporation":false,"usgs":false,"family":"Hanslowe","given":"Emma","email":"","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":822694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falk, Bryan G. 0000-0002-9690-5626","orcid":"https://orcid.org/0000-0002-9690-5626","contributorId":265395,"corporation":false,"usgs":false,"family":"Falk","given":"Bryan G.","affiliations":[{"id":54672,"text":"National Park Service, Everglades National Park, 40001 SR 9336, Homestead, Florida 33034, USA","active":true,"usgs":false}],"preferred":false,"id":822695,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cade, Brian S. 0000-0001-9623-9849 cadeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9623-9849","contributorId":1278,"corporation":false,"usgs":true,"family":"Cade","given":"Brian","email":"cadeb@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822696,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moy, Sarah E.","contributorId":265396,"corporation":false,"usgs":false,"family":"Moy","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":822697,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grajal-Puche, Alejandro 0000-0003-1807-4799","orcid":"https://orcid.org/0000-0003-1807-4799","contributorId":265397,"corporation":false,"usgs":false,"family":"Grajal-Puche","given":"Alejandro","affiliations":[{"id":54677,"text":"Department of Biological Sciences, P.O. Box 5640, Northern Arizona University, Flagstaff, Arizona 86011, USA","active":true,"usgs":false}],"preferred":false,"id":822698,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ridgley, Frank N. 0000-0002-6819-2577","orcid":"https://orcid.org/0000-0002-6819-2577","contributorId":265398,"corporation":false,"usgs":false,"family":"Ridgley","given":"Frank","email":"","middleInitial":"N.","affiliations":[{"id":54678,"text":"Zoo Miami, Conservation and Research Department, 12400 SW 152nd St., Miami, Florida 33177, USA","active":true,"usgs":false}],"preferred":false,"id":822699,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822700,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822701,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70224627,"text":"70224627 - 2021 - Hotspot dune erosion on an intermediate beach","interactions":[],"lastModifiedDate":"2021-10-01T13:25:35.688431","indexId":"70224627","displayToPublicDate":"2021-09-08T08:21:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Hotspot dune erosion on an intermediate beach","docAbstract":"

A large, low pressure Nor’easter storm and Hurricane Joaquin contributed to multiple weeks of sustained, elevated wave and water level conditions along the southeastern Atlantic coast of the United States in Fall 2015. Sea level anomalies in excess of 1 m and offshore wave heights of up to 4 m were recorded during these storms, as observed at the U.S. Army Corps of Engineers’ Field Research Facility in Duck, NC, USA. In response to these energetic oceanographic conditions, there were highly variable morphologic changes to the dune over short spatial scales (<km) which included a range of responses from vertical dune scarping to no measureable response. The portion of the study area with the largest dune erosion occurred at a location fronted by an abnormally deep nearshore bathymetric feature, which altered surf-zone waves and hydrodynamics. The pre-storm beach and dune topography also varied throughout the study area, additionally influencing the frequency of dune collision and contributing to the spatially variable erosion patterns. This work uses field datasets and numerical modeling tools to investigate the causation of hotspot dune erosion at the Field Research Facility. Three different numerical models were tested against the available data in order to assess model skill at resolving complex spatial dune erosion patterns. The three models successfully reproduce the general spatial trends in alongshore variable responses, although not necessarily the details of profile response or net erosion magnitude. Analysis of the model outputs, in conjunction with the available field data, suggests that the observed hotspot dune erosion is related to a complex combination of both topographic and bathymetric controls on the processes driving dune erosion. Therefore, the most simplistic model tested, which only accounts for alongshore variations in topographic profile details, can only predict hotspot dune erosion in locations where steep beach and/or dune topography is the primary control on collisional dune impacts. The higher fidelity models, which account for feedback effects from subaqueous morphology, are similarly able to predict the locations of maximum hotspot erosion, but are sensitive to beach over-steepening and/or errors in wave runup calculations that can lead to over-prediction of simulated dune erosion. This work highlights that numerous existing tools are capable of identifying the foredune regions at most risk from hotspot erosion, as well as the need for continued research to improve representation of all relevant intra-storm morphodynamic processes.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2021.103998","usgsCitation":"Cohn, N., Brodie, K., Johnson, B., and Palmsten, M.L., 2021, Hotspot dune erosion on an intermediate beach: Coastal Engineering, v. 170, 103998, 21 p., https://doi.org/10.1016/j.coastaleng.2021.103998.","productDescription":"103998, 21 p.","ipdsId":"IP-124727","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2021.103998","text":"Publisher Index Page"},{"id":390112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"170","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cohn, Nicholas","contributorId":266145,"corporation":false,"usgs":false,"family":"Cohn","given":"Nicholas","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":824404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brodie, Katherine","contributorId":266146,"corporation":false,"usgs":false,"family":"Brodie","given":"Katherine","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":824405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Bradley","contributorId":266147,"corporation":false,"usgs":false,"family":"Johnson","given":"Bradley","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":824406,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":824407,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223821,"text":"70223821 - 2021 - Advancing cave detection using terrain analysis and thermal imagery","interactions":[],"lastModifiedDate":"2021-09-09T12:48:45.771312","indexId":"70223821","displayToPublicDate":"2021-09-08T07:47:23","publicationYear":"2021","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":"Advancing cave detection using terrain analysis and thermal imagery","docAbstract":"
Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) Determine the efficacy of methods designed for terrain analysis and applied to thermal imagery; (2) evaluate the usefulness of predawn and midday imagery for detecting caves; and (3) ascertain which imagery type (predawn, midday, or the difference between those two times) was most informative. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index (TPI), and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. Thereafter, we compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: Predawn— slope, TPI, curvature at 0 m from cave entrance, as well as slope at 1 m from cave entrance; midday— slope, TPI, and unenhanced thermal at 0 m from cave entrance; and difference— TPI and slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13183578","usgsCitation":"Wynne, J.J., Jenness, J., Sonderegger, D., Titus, T.N., Jhabvala, M.D., and Cabrol, N.A., 2021, Advancing cave detection using terrain analysis and thermal imagery: Remote Sensing, v. 13, no. 8, 3578, 25 p., https://doi.org/10.3390/rs13183578.","productDescription":"3578, 25 p.","ipdsId":"IP-098740","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":450878,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13183578","text":"Publisher Index Page"},{"id":436207,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NF0L2I","text":"USGS data release","linkHelpText":"Aircraft-Borne Thermal Imagery and Derived Terrain Analysis Layers, Pisgah Lava Field, California"},{"id":388995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Wynne, J. Judson","contributorId":265476,"corporation":false,"usgs":false,"family":"Wynne","given":"J.","email":"","middleInitial":"Judson","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":822787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenness, Jeff","contributorId":265477,"corporation":false,"usgs":false,"family":"Jenness","given":"Jeff","affiliations":[{"id":54685,"text":"Jenness Enterprises","active":true,"usgs":false}],"preferred":false,"id":822788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sonderegger, Derek","contributorId":265478,"corporation":false,"usgs":false,"family":"Sonderegger","given":"Derek","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":822789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":822790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jhabvala, Murzy D.","contributorId":265479,"corporation":false,"usgs":false,"family":"Jhabvala","given":"Murzy","email":"","middleInitial":"D.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":822791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cabrol, Nathalie A.","contributorId":51382,"corporation":false,"usgs":true,"family":"Cabrol","given":"Nathalie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":822861,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223828,"text":"70223828 - 2021 - Digital elevation models: Terminology and definitions","interactions":[],"lastModifiedDate":"2021-09-09T12:28:36.93368","indexId":"70223828","displayToPublicDate":"2021-09-08T07:27:03","publicationYear":"2021","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":"Digital elevation models: Terminology and definitions","docAbstract":"
Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background. 
","language":"English","publisher":"MDPI","doi":"10.3390/rs13183581","usgsCitation":"Guth, P.L., Van Niekerk, A., Grohmann, C., Muller, J., Hawker, L., Florinsky, I.V., Gesch, D.B., Reuter, H.I., Herrera-Cruz, V., Riazanoff, S., Lopez-Vazquez, C., Carabajal, C.C., Albinet, C., and Strobl, P., 2021, Digital elevation models: Terminology and definitions: Remote Sensing, v. 13, no. 18, 3581, 19 p., https://doi.org/10.3390/rs13183581.","productDescription":"3581, 19 p.","ipdsId":"IP-131782","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":450882,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13183581","text":"Publisher Index Page"},{"id":388992,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"18","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Guth, Peter L.","contributorId":265495,"corporation":false,"usgs":false,"family":"Guth","given":"Peter","email":"","middleInitial":"L.","affiliations":[{"id":54693,"text":"U.S. Naval Academy","active":true,"usgs":false}],"preferred":false,"id":822807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Niekerk, Adriaan","contributorId":265496,"corporation":false,"usgs":false,"family":"Van Niekerk","given":"Adriaan","email":"","affiliations":[{"id":39919,"text":"Stellenbosch University","active":true,"usgs":false}],"preferred":false,"id":822808,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grohmann, Carlos H.","contributorId":265497,"corporation":false,"usgs":false,"family":"Grohmann","given":"Carlos H.","affiliations":[{"id":48623,"text":"University of Sao Paulo","active":true,"usgs":false}],"preferred":false,"id":822809,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muller, Jan-Peter","contributorId":265498,"corporation":false,"usgs":false,"family":"Muller","given":"Jan-Peter","affiliations":[{"id":6957,"text":"University College London","active":true,"usgs":false}],"preferred":false,"id":822810,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hawker, Laurence","contributorId":265499,"corporation":false,"usgs":false,"family":"Hawker","given":"Laurence","email":"","affiliations":[{"id":37322,"text":"University of Bristol","active":true,"usgs":false}],"preferred":false,"id":822811,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Florinsky, Igor V.","contributorId":265500,"corporation":false,"usgs":false,"family":"Florinsky","given":"Igor","email":"","middleInitial":"V.","affiliations":[{"id":49898,"text":"Russian Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":822812,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":822813,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reuter, Hannes I.","contributorId":265501,"corporation":false,"usgs":false,"family":"Reuter","given":"Hannes","email":"","middleInitial":"I.","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":822814,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herrera-Cruz, Virginia","contributorId":265502,"corporation":false,"usgs":false,"family":"Herrera-Cruz","given":"Virginia","email":"","affiliations":[{"id":54696,"text":"Airbus Defence and Space","active":true,"usgs":false}],"preferred":false,"id":822815,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Riazanoff, Serge","contributorId":265503,"corporation":false,"usgs":false,"family":"Riazanoff","given":"Serge","email":"","affiliations":[{"id":54697,"text":"VisioTerra","active":true,"usgs":false}],"preferred":false,"id":822816,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lopez-Vazquez, Carlos","contributorId":265504,"corporation":false,"usgs":false,"family":"Lopez-Vazquez","given":"Carlos","email":"","affiliations":[{"id":54698,"text":"Universidad ORT Uruguay","active":true,"usgs":false}],"preferred":false,"id":822817,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Carabajal, Claudia C.","contributorId":265505,"corporation":false,"usgs":false,"family":"Carabajal","given":"Claudia","email":"","middleInitial":"C.","affiliations":[{"id":54699,"text":"SSAI Inc.","active":true,"usgs":false}],"preferred":false,"id":822818,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Albinet, Clement","contributorId":265506,"corporation":false,"usgs":false,"family":"Albinet","given":"Clement","email":"","affiliations":[{"id":38836,"text":"European Space Agency","active":true,"usgs":false}],"preferred":false,"id":822819,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Strobl, Peter","contributorId":265507,"corporation":false,"usgs":false,"family":"Strobl","given":"Peter","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":822820,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70223775,"text":"sir20215093 - 2021 - A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17","interactions":[],"lastModifiedDate":"2021-09-08T11:52:20.913559","indexId":"sir20215093","displayToPublicDate":"2021-09-07T19:13:38","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5093","displayTitle":"A Machine Learning Approach to Modeling Streamflow with Sparse Data in Ungaged Watersheds on the Wyoming Range, Wyoming, 2012–17","title":"A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17","docAbstract":"

Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse.

To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow.

The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [R2]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period.

The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature.

The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance (R2=0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance (R2=0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent (R2=0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations.

The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215093","usgsCitation":"McShane, R.R., and Eddy-Miller, C.A., 2021, A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17: U.S. Geological Survey Scientific Investigations Report 2021–5093, 29 p., https://doi.org/10.3133/sir20215093.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-117330","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388893,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCP1AE","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input data, model output, and R scripts for a machine learning streamflow model on the Wyoming Range, Wyoming, 2012–17"},{"id":388895,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.xml","text":"Report","size":"219 kB","linkFileType":{"id":8,"text":"xml"}},{"id":388896,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5093/images"},{"id":388894,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5093/coverthb.jpg"},{"id":388892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5093/sir20215093.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5093"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wyoming Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.90972900390625,\n 42.09618442380296\n ],\n [\n -110.01708984374999,\n 42.09618442380296\n ],\n [\n -110.01708984374999,\n 42.68041629144619\n ],\n [\n -110.90972900390625,\n 42.68041629144619\n ],\n [\n -110.90972900390625,\n 42.09618442380296\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-09-07","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"McShane, Ryan R. 0000-0002-3128-0039 rmcshane@usgs.gov","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":195581,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan","email":"rmcshane@usgs.gov","middleInitial":"R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eddy-Miller, Cheryl A. 0000-0002-4082-750X cemiller@usgs.gov","orcid":"https://orcid.org/0000-0002-4082-750X","contributorId":1824,"corporation":false,"usgs":true,"family":"Eddy-Miller","given":"Cheryl A.","email":"cemiller@usgs.gov","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":false,"id":822635,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221175,"text":"ofr20211054 - 2021 - Rigorously valuing the coastal hazard risks reduction provided by potential coral reef restoration in Florida and Puerto Rico","interactions":[],"lastModifiedDate":"2021-09-08T11:34:55.82749","indexId":"ofr20211054","displayToPublicDate":"2021-09-07T16:54:32","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1054","displayTitle":"Rigorously Valuing the Potential Coastal Hazard Risk Reduction Provided by Coral Reef Restoration in Florida and Puerto Rico","title":"Rigorously valuing the coastal hazard risks reduction provided by potential coral reef restoration in Florida and Puerto Rico","docAbstract":"

The restoration of coastal habitats, particularly coral reefs, can reduce risks by decreasing the exposure of coastal communities to flooding hazards. In the United States, the protective services provided by coral reefs were recently assessed in social and economic terms, with the annual protection provided by U.S. coral reefs off the coasts of the State of Florida and the Commonwealth of Puerto Rico estimated to be more than 9,800 people and $859 million (2010 U.S. dollars). Hurricanes Irma and Maria in 2017 caused widespread damage to coral reefs in the State of Florida and the Commonwealth of Puerto Rico. Here we combine engineering, ecologic, geospatial, social, and economic data and tools to provide a rigorous valuation of where potential coral reef restoration could decrease the hazard faced by Florida and Puerto Rico’s reef-fronted coastal communities. The three restoration scenarios considered: (1) Ecological restoration, ‘E25’, which assumes planting 0.25-meter (m)-high corals on a (cross-shore) 25-m-wide reef; (2) Structural plus ecological, ‘S25’, which assumes emplacing a 1.00-m high structure with 0.25-m high corals on top on a 25 m wide reef; and (3) structural plus ecological, ‘S05’, which assumes emplacing a 1.00-m high structure with 0.25-m high corals on top on a 5 m wide reef. Planted corals are assumed to increase hydrodynamic roughness, thereby dissipating incident wave energy and decreasing flooding potential. We used a standardized approach to ‘place’ potential restoration projects throughout the whole (linear) extent of reefs bordering Florida and Puerto Rico to identify where coral reef restoration could be useful for meeting flood reduction benefits. We always sited potential restoration projects within the existing distribution of reefs even though many sites were far (kilometers [km]) offshore and some sites were relatively deep (up to 7 m depth). We followed risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 980 km of Florida and Puerto’s Rico reef-lined shorelines for the three potential coral reef restoration scenarios and compare them to the flood zones without coral reef restoration. We quantified the potential coastal flood risk reduction provided by coral reef restoration using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculate the change in annual expected damages, a measure of the annual protection gained because of coral reef restoration. We found that the benefits of reef restoration off Florida and Puerto Rico are spatially highly variable. In most areas, we found little or no benefit from reef restoration (for example, restoration sites were far offshore or deep). However, there were a number of key areas where reef restoration could have substantial benefits for flood risk reduction. In particular, we estimated the protection gained by Florida and Puerto Rico’s coral reefs from coral reef restoration to result in:

Thus, the annual value of flood risk reduction provided by potential coral reef restoration in Florida and Puerto Rico is more than 3,100 people and $272.9 million (2010 U.S. dollars) in economic activity. These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when potential coral reef restoration in Florida and Puerto Rico can increase critical coastal storm flood reduction benefits. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida and Puerto Rico’s coastal communities.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211054","collaboration":"Prepared in cooperation with the University of California, Santa Cruz","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Cumming, K.A., Cole, A.D., Shope, J.B., Gaido L., C., Viehman, T.S., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the coastal hazard risks reduction provided by potential coral reef restoration in Florida and Puerto Rico: U.S. Geological Survey Open-File Report 2021–1054, 35 p., https://doi.org/10.3133/ofr20211054.","productDescription":"Report: vi, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-125062","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386211,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1054/covrthb.jpg"},{"id":386212,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1054/ofr20211054.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386214,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZQKZR9","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida, the Commonwealth of Puerto Rico, and the Territory of the U.S. Virgin Islands for current and potentially restored coral reefs"},{"id":386215,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211055","text":"Open-File Report 2021-1055","linkHelpText":"- Rigorously Valuing the Impact of Projected Coral Reef Degradation on Coastal Hazard Risk in Florida"},{"id":386216,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211056","text":"Open-File Report 2021-1056","linkHelpText":"- Rigorously Valuing the Impact of Hurricanes Irma and Maria on Coastal Hazard Risk in Florida and Puerto Rico"}],"country":"United States","state":"Florida","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.390380859375,\n 18.594188856740413\n ],\n [\n -66.7529296875,\n 18.70869162255995\n ],\n [\n -67.08251953125,\n 18.729501999072138\n ],\n [\n -67.32421875,\n 18.510865709091377\n ],\n [\n -67.401123046875,\n 18.25021997706561\n ],\n [\n -67.30224609375,\n 17.916022703877665\n ],\n [\n -66.895751953125,\n 17.853290114098012\n ],\n [\n -66.42333984375,\n 17.8742034396575\n ],\n [\n -65.885009765625,\n 17.821915515968854\n ],\n [\n -65.467529296875,\n 17.926475979176438\n ],\n [\n -65.32470703125,\n 18.218916080017465\n ],\n [\n -65.511474609375,\n 18.46918890441719\n ],\n [\n -66.390380859375,\n 18.594188856740413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-09-07","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":816975,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reguero, Borja G. 0000-0001-5526-7157","orcid":"https://orcid.org/0000-0001-5526-7157","contributorId":193831,"corporation":false,"usgs":false,"family":"Reguero","given":"Borja","email":"","middleInitial":"G.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":816976,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cumming, Kristen A. 0000-0003-3647-2678","orcid":"https://orcid.org/0000-0003-3647-2678","contributorId":257561,"corporation":false,"usgs":true,"family":"Cumming","given":"Kristen A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816977,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cole, Aaron","contributorId":214198,"corporation":false,"usgs":false,"family":"Cole","given":"Aaron","email":"","affiliations":[{"id":17620,"text":"UCSC","active":true,"usgs":false}],"preferred":false,"id":816978,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shope, James B.","contributorId":135949,"corporation":false,"usgs":false,"family":"Shope","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":10653,"text":"University of California at Santa Cruz, Earth and Planetary Science Department","active":true,"usgs":false}],"preferred":false,"id":816979,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gaido L., Camila","contributorId":259296,"corporation":false,"usgs":false,"family":"Gaido L.","given":"Camila","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":816981,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Viehman, T. Shay","contributorId":259297,"corporation":false,"usgs":false,"family":"Viehman","given":"T.","email":"","middleInitial":"Shay","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":true,"id":816982,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nickel, Barry A.","contributorId":193833,"corporation":false,"usgs":false,"family":"Nickel","given":"Barry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":816983,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":816984,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70221181,"text":"ofr20211055 - 2021 - Rigorously valuing the impact of projected coral reef degradation on coastal hazard risk in Florida","interactions":[],"lastModifiedDate":"2021-09-08T11:38:32.660407","indexId":"ofr20211055","displayToPublicDate":"2021-09-07T16:53:07","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1055","displayTitle":"Rigorously Valuing the Impact of Projected Coral Reef Degradation on Coastal Hazard Risk in Florida","title":"Rigorously valuing the impact of projected coral reef degradation on coastal hazard risk in Florida","docAbstract":"

The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. In the United States, the physical protective services provided by coral reefs were recently assessed, in social and economic terms, with the annual protection provided by U.S. coral reefs off the coast of the State of Florida estimated to be more than 5,600 people and $675 million (2010 U.S. dollars). Degradation of coral reef ecosystems over the past several decades and during tropical storm events has caused regional-scale erosion of the shallow seafloor that serves as a protective barrier against coastal hazards along Southeast Florida, increasing risks to coastal populations. Here we combine engineering, ecologic, geospatial, social, and economic data and tools to provide a rigorous valuation of the increased hazard faced by Florida’s reef-fronted coastal communities because of the projected degradation of its adjacent coral reefs. We followed risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 430 kilometers of Florida’s reef-lined shorelines for both the current and projected future coral reef conditions. We quantified the coastal flood risk increase caused by coral reef degradation using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculated the change in annual expected damages, a measure of the annual protection lost because of projected coral reef degradation. We found that degradation of the coral reefs off Florida increases future risks significantly. In particular, we estimated the protection lost by Florida’s coral reefs from projected coral reef degradation will result in:

Thus, the annual value of increased flood risk caused by the projected degradation of Florida’s coral reefs is more than 7,300 people and $823.6 million (2010 U.S. dollars). These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when degradation of Florida’s coral reefs will decrease critical coastal storm flood reduction benefits. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida’s coastal communities.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211055","collaboration":"Prepared in cooperation with the University of California, Santa Cruz","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Yates, K.K., Cumming, K.A., Cole, A.D., Shope, J.B., Gaido L., C., Zawada, D.G., Arsenault, S.R., Fehr, Z.W., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the impact of projected coral reef degradation on coastal hazard risk in Florida: U.S. Geological Survey Open-File Report 2021–1055, 27 p., https://doi.org/10.3133/ofr20211055.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-125063","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386221,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211054","text":"Open-File Report 2021-1054","linkHelpText":"- Rigorously Valuing the Potential Coastal Hazard Risk Reduction Provided by Coral Reef Restoration in Florida and Puerto Rico"},{"id":386222,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211056","text":"Open-File Report 2021-1056","linkHelpText":"- Rigorously Valuing the Impact of Hurricanes Irma and Maria on Coastal Hazard Risk in Florida and Puerto Rico"},{"id":386220,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D9LDEP","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida with and without projected coral reef degradation"},{"id":386218,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1055/covrthb.jpg"},{"id":386219,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1055/ofr20211055.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-09-07","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reguero, Borja G. 0000-0001-5526-7157","orcid":"https://orcid.org/0000-0001-5526-7157","contributorId":193831,"corporation":false,"usgs":false,"family":"Reguero","given":"Borja","email":"","middleInitial":"G.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":816991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yates, Kimberly K. 0000-0001-8764-0358 kyates@usgs.gov","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":420,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"kyates@usgs.gov","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cumming, Kristen A. 0000-0003-3647-2678","orcid":"https://orcid.org/0000-0003-3647-2678","contributorId":257561,"corporation":false,"usgs":true,"family":"Cumming","given":"Kristen A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole, Aaron","contributorId":214198,"corporation":false,"usgs":false,"family":"Cole","given":"Aaron","email":"","affiliations":[{"id":17620,"text":"UCSC","active":true,"usgs":false}],"preferred":false,"id":816994,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shope, James B.","contributorId":135949,"corporation":false,"usgs":false,"family":"Shope","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":10653,"text":"University of California at Santa Cruz, Earth and Planetary Science Department","active":true,"usgs":false}],"preferred":false,"id":816995,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gaido L., Camila","contributorId":259296,"corporation":false,"usgs":false,"family":"Gaido L.","given":"Camila","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":816996,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zawada, David G. 0000-0003-4547-4878 dzawada@usgs.gov","orcid":"https://orcid.org/0000-0003-4547-4878","contributorId":1898,"corporation":false,"usgs":true,"family":"Zawada","given":"David G.","email":"dzawada@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":816997,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Arsenault, Stephanie R.","contributorId":213439,"corporation":false,"usgs":false,"family":"Arsenault","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":38758,"text":"CNTS Contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":816998,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fehr, Zachery W. 0000-0001-7885-2885","orcid":"https://orcid.org/0000-0001-7885-2885","contributorId":215764,"corporation":false,"usgs":true,"family":"Fehr","given":"Zachery","email":"","middleInitial":"W.","affiliations":[{"id":25340,"text":"Cherokee Nation Technologies","active":true,"usgs":false}],"preferred":true,"id":817002,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nickel, Barry A.","contributorId":193833,"corporation":false,"usgs":false,"family":"Nickel","given":"Barry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":816999,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":817000,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70221184,"text":"ofr20211056 - 2021 - Rigorously valuing the impact of Hurricanes Irma and Maria on coastal hazard risks in Florida and Puerto Rico","interactions":[],"lastModifiedDate":"2021-09-08T11:42:32.832366","indexId":"ofr20211056","displayToPublicDate":"2021-09-07T16:52:22","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1056","displayTitle":"Rigorously Valuing the Impact of Hurricanes Irma and Maria on Coastal Hazard Risk in Florida and Puerto Rico","title":"Rigorously valuing the impact of Hurricanes Irma and Maria on coastal hazard risks in Florida and Puerto Rico","docAbstract":"

The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. In the United States, the physical protective services provided by coral reefs were recently assessed in social and economic terms, with the annual protection provided by U.S. coral reefs off the coasts of the State of Florida and the Commonwealth of Puerto Rico estimated to be more than 9,800 people and $859 million (2010 U.S. dollars). Hurricanes Irma and Maria in 2017 caused widespread damage to coral reefs in the State of Florida and the Commonwealth of Puerto Rico. These damages were measured in post-storm surveys of reefs and assessed in terms of their impact on reef condition and height, which are critical parameters for evaluating the coastal defense benefits of reefs. We combined engineering, ecologic, geospatial, social, and economic data and tools to value the increased risks in Florida and Puerto Rico from hurricane-induced damages to their adjacent coral reefs. We followed risk-based valuation approaches to map flooding at 10-square-meter resolution along all 980 kilometers of Florida and Puerto Rico’s reef-lined shorelines considering reef condition before (undamaged) and after (damaged) the 2017 hurricanes. We quantified the coastal flood risk increase caused by the hurricane-induced damage to the coral reefs using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis for return-interval storm events. Using the damages associated with each storm probability, we also calculated the change in annual expected damages, a measure of the annual protection lost because of the reef damage caused by the 2017 hurricanes. We found that the damages to the coral reefs off Florida and Puerto Rico from Hurricanes Irma and Maria increased future risks significantly. In particular, we estimated the protection lost by Florida and Puerto Rico’s coral reefs from the 2017 hurricanes to result in:

Thus, the annual value of increased flood risk caused by the damage to Florida and Puerto Rico’s coral reefs from hurricanes in 2017 is more than 4,300 people and $181.5 mil-lion (2010 U.S. dollars) in economic impacts. These data provide stakeholders and decision makers with a spatially explicit, rigorous valuation of how, where, and when the damage from the 2017 hurricanes decreased critical coastal storm flood reduction benefits to Florida and Puerto Rico’s coral reefs. These results help identify areas where reef management, recovery, and restoration could potentially help reduce the risk to, and increase the resiliency of, Florida and Puerto Rico’s coastal communities.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211056","collaboration":"Prepared in cooperation with the University of California, Santa Cruz and the National Oceanic and Atmospheric Administration","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Viehman, T.S., Cumming, K.A., Cole, A.D., Shope, J.B., Groves, S.H., Gaido L., C., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the impact of Hurricanes Irma and Maria on coastal hazard risks in Florida and Puerto Rico: U.S. Geological Survey Open-File Report 2021–1056, 29 p., https://doi.org/10.3133/ofr20211056.","productDescription":"Report: v, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-125064","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386227,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EHOBKO","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida and the Commonwealth of Puerto Rico before and after Hurricanes Irma and Maria due to the storms' damage to the coral reefs"},{"id":386229,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211055","text":"Open-File Report 2021-1055","linkHelpText":"- Rigorously Valuing the Impact of Projected Coral Reef Degradation on Coastal Hazard Risk in Florida"},{"id":386225,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1056/covrthb.jpg"},{"id":386226,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1056/ofr20211056.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386228,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211054","text":"Open-File Report 2021-1054","linkHelpText":"- Rigorously Valuing the Potential Coastal Hazard Risk Reduction Provided by Coral Reef Restoration in Florida and Puerto Rico"}],"country":"United States","state":"Florida","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.79345703125,\n 27.401032392938866\n ],\n [\n -80.716552734375,\n 26.82407078047018\n ],\n [\n -80.68359375,\n 26.352497858154024\n ],\n [\n -80.74951171875,\n 25.671235828577043\n ],\n [\n -80.650634765625,\n 25.3241665257384\n ],\n [\n -80.88134765625,\n 24.886436490787712\n ],\n [\n -81.2548828125,\n 24.73685348477069\n ],\n [\n -81.27685546875,\n 24.607069137709683\n ],\n [\n -80.771484375,\n 24.726874870506972\n ],\n [\n -80.22216796875,\n 25.16517336866393\n ],\n [\n -80.101318359375,\n 25.671235828577043\n ],\n [\n -79.94750976562499,\n 26.322960198925365\n ],\n [\n -80.00244140625,\n 26.941659545381516\n ],\n [\n -80.277099609375,\n 27.44004046509707\n ],\n [\n -80.79345703125,\n 27.401032392938866\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.368408203125,\n 18.542116654448996\n ],\n [\n -66.961669921875,\n 18.646245142670608\n ],\n [\n -67.269287109375,\n 18.552532366385577\n ],\n [\n -67.357177734375,\n 18.198043686762652\n ],\n [\n -67.236328125,\n 17.916022703877665\n ],\n [\n -66.59912109375,\n 17.832374329567518\n ],\n [\n -65.643310546875,\n 17.95783210227242\n ],\n [\n -65.291748046875,\n 18.22935133838668\n ],\n [\n -65.54443359375,\n 18.500447458475094\n ],\n [\n -66.368408203125,\n 18.542116654448996\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Coastal and Marine Science Center
2885 Mission St.
Santa Cruz, CA 95060

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-09-07","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":817003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reguero, Borja G. 0000-0001-5526-7157","orcid":"https://orcid.org/0000-0001-5526-7157","contributorId":193831,"corporation":false,"usgs":false,"family":"Reguero","given":"Borja","email":"","middleInitial":"G.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":817004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Viehman, T. Shay","contributorId":259297,"corporation":false,"usgs":false,"family":"Viehman","given":"T.","email":"","middleInitial":"Shay","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":true,"id":817005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cumming, Kristen A. 0000-0003-3647-2678","orcid":"https://orcid.org/0000-0003-3647-2678","contributorId":257561,"corporation":false,"usgs":true,"family":"Cumming","given":"Kristen A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":817006,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole, Aaron","contributorId":214198,"corporation":false,"usgs":false,"family":"Cole","given":"Aaron","email":"","affiliations":[{"id":17620,"text":"UCSC","active":true,"usgs":false}],"preferred":false,"id":817007,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shope, James B.","contributorId":135949,"corporation":false,"usgs":false,"family":"Shope","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":10653,"text":"University of California at Santa Cruz, Earth and Planetary Science Department","active":true,"usgs":false}],"preferred":false,"id":817008,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Groves, Sarah H.","contributorId":259300,"corporation":false,"usgs":false,"family":"Groves","given":"Sarah","email":"","middleInitial":"H.","affiliations":[{"id":16685,"text":"National Oceanic and Atmopheric Administration","active":true,"usgs":false}],"preferred":true,"id":817009,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gaido L., Camila","contributorId":259296,"corporation":false,"usgs":false,"family":"Gaido L.","given":"Camila","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":817010,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nickel, Barry A.","contributorId":193833,"corporation":false,"usgs":false,"family":"Nickel","given":"Barry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":817011,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":817012,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70223441,"text":"70223441 - 2021 - Geologic framework of Mount Diablo, California","interactions":[],"lastModifiedDate":"2021-08-27T15:53:43.154746","indexId":"70223441","displayToPublicDate":"2021-09-07T10:48:59","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geologic framework of Mount Diablo, California","docAbstract":"

The basic stratigraphic and structural framework of Mount Diablo is described using a revised geologic map, gravity data, and aeromagnetic data. The mountain is made up of two distinct stratigraphic assemblages representing different depocenters that were juxtaposed by ~20 km of late Pliocene and Quaternary right-lateral offset on the Greenville-Diablo-Concord fault. Both assemblages are composed of Cretaceous and Cenozoic strata overlying a compound basement made up of the Franciscan and Great Valley complexes. The rocks are folded and faulted by late Neogene and Quaternary compressional structures related to both regional plate-boundary–normal compression and a restraining step in the strike-slip fault system. The core of the mountain is made up of uplifted basement rocks. Late Neogene and Quaternary deformation is overprinted on Paleogene extensional deformation that is evidenced at Mount Diablo by significant attenuation in the basement rocks and by an uptilted stepped graben structure on the northeast flank. Retrodeformation of the northeast flank suggests that late Early to early Late Cretaceous strata may have been deposited against and across a steeply west-dipping basement escarpment. The location of the mountain today was a depocenter through the Late Cretaceous and Paleogene and received shallow-marine deposits periodically into the late Miocene. Uplift of the mountain itself happened mostly in the Quaternary.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Regional geology of Mount Diablo, California: Its tectonic evolution on the North America plate boundary","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.1217(01)","usgsCitation":"Graymer, R., and Langenheim, V., 2021, Geologic framework of Mount Diablo, California, chap. of Regional geology of Mount Diablo, California: Its tectonic evolution on the North America plate boundary, v. 217, p. 1-34, https://doi.org/10.1130/2021.1217(01).","productDescription":"34 p.","startPage":"1","endPage":"34","ipdsId":"IP-113894","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":450894,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/figure/Supplemental_Material_Geologic_framework_of_Mount_Diablo_California/15148989","text":"External Repository"},{"id":388591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mount Diablo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.025146484375,\n 37.7897092979573\n ],\n [\n -121.80198669433592,\n 37.7897092979573\n ],\n [\n -121.80198669433592,\n 37.92632597629602\n ],\n [\n -122.025146484375,\n 37.92632597629602\n ],\n [\n -122.025146484375,\n 37.7897092979573\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"217","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Graymer, Russell 0000-0003-4910-5682","orcid":"https://orcid.org/0000-0003-4910-5682","contributorId":207816,"corporation":false,"usgs":true,"family":"Graymer","given":"Russell","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":822042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langenheim, Victoria 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":206990,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","affiliations":[],"preferred":true,"id":822043,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229777,"text":"70229777 - 2021 - Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","interactions":[],"lastModifiedDate":"2022-03-17T15:32:45.548565","indexId":"70229777","displayToPublicDate":"2021-09-07T10:17:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","docAbstract":"
  1. Translocations are essential for re-establishing wildlife populations. As they sometimes fail, it is critical to assess factors that influence their success pre-translocation.
  2. Socioecological suitability models (SESMs) integrate social acceptance and ecological suitability to enable identification of areas where wildlife populations will expand, which makes it likely that SESMs will also be useful for predicting translocation success.
  3. To inform site selection for potential elk Cervus canadensis reintroduction to north-eastern Minnesota, United States, we developed broadscale maps of social acceptance from surveys of local residents and landowners, animal use equivalence (AUE) from forage measured in the field and empirical conflict risk from geospatial data. Resulting SESMs integrated social acceptance favourability scores, AUE and conflict risk, and weighted SESMs showed the relative influences of acceptance and conflict.
  4. Social acceptance was positive for local residents and landowners (mean ≥ 5.4; scale of 1–7). AUE (scaled to an elk home range) ranged between 1 and 9 elk/16 km2 during winter, and from 14 to 83 elk/16 km2 during summer. Human–elk conflict risk was low (mean ≤ 0.10; scaled 0–1), increasing from north to south. Geographical distributions differed for social acceptance, AUE and conflict risk, and weighted SESMs revealed unsuitable areas that were otherwise obscured.
  5. Synthesis and applications. Integrating human–wildlife conflict risk into SESMs shows where social acceptance of translocated species is likely to erode, even where viewed favourably pre-translocation, to inform translocation planning by highlighting interactions between key factors. Such integrated models supplement existing reintroduction biology frameworks by supporting decision-making and knowledge development. In north-eastern Minnesota, natural resource managers who are considering elk reintroductions are using SESMs reported here to identify where human–elk conflict is unlikely to result in an isolated elk population and where addressing concerns for area residents about conflict risk is essential.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14021","usgsCitation":"McCann, N.P., Walberg, E.M., Forester, J., Schrage, M.W., Fulton, D.C., and Ditmer, M., 2021, Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate: Journal of Applied Ecology, v. 58, no. 12, p. 2810-2820, https://doi.org/10.1111/1365-2664.14021.","productDescription":"11 p.","startPage":"2810","endPage":"2820","ipdsId":"IP-127289","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502433,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":397248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Cloquet Valley Study Area, Fond du Lac Study Area, Nemadji Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.33984375,\n 46.195042108660154\n ],\n [\n -92.10937499999999,\n 46.195042108660154\n ],\n [\n -92.10937499999999,\n 47.338822694822\n ],\n [\n -93.33984375,\n 47.338822694822\n ],\n [\n -93.33984375,\n 46.195042108660154\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"McCann, Nicholas P.","contributorId":288723,"corporation":false,"usgs":false,"family":"McCann","given":"Nicholas","email":"","middleInitial":"P.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walberg, Eric M.","contributorId":288724,"corporation":false,"usgs":false,"family":"Walberg","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":838247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forester, James D.","contributorId":288725,"corporation":false,"usgs":false,"family":"Forester","given":"James D.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schrage, Michael W.","contributorId":288729,"corporation":false,"usgs":false,"family":"Schrage","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":61835,"text":"Fond du Lac Band of Lake Superior Chippewa","active":true,"usgs":false}],"preferred":false,"id":838249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838245,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ditmer, Mark A.","contributorId":288732,"corporation":false,"usgs":false,"family":"Ditmer","given":"Mark A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838250,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263928,"text":"70263928 - 2021 - Improved scaling relationships for seismic moment and average slip of strike-slip earthquakes incorporating fault slip rate, fault width and stress drop","interactions":[],"lastModifiedDate":"2025-02-28T16:10:09.918844","indexId":"70263928","displayToPublicDate":"2021-09-07T10:05:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Improved scaling relationships for seismic moment and average slip of strike-slip earthquakes incorporating fault slip rate, fault width and stress drop","docAbstract":"

We develop a self‐consistent scaling model relating magnitude Mw to surface rupture length (⁠LE⁠), surface displacement DE⁠, and rupture width WE⁠, for strike‐slip faults. Knowledge of the long‐term fault‐slip rate SF improves magnitude estimates. Data are collected for 55 ground‐rupturing strike‐slip earthquakes that have geological estimates of LE⁠, DE⁠, and SF⁠, and geophysical estimates of WE⁠. We begin with the model of Anderson et al. (2017), which uses a closed form equation for the seismic moment of a surface‐rupturing strike‐slip fault of arbitrary aspect ratio and given stress drop, ΔτC⁠. Using WE estimates does not improve Mw estimates. However, measurements of DE plus the relationship between ΔτC and surface slip provide an alternate approach to study WE⁠. A grid of plausible stress drop and width pairs were used to predict displacement and earthquake magnitude. A likelihood function was computed from within the uncertainty ranges of the corresponding observed Mw and DE values. After maximizing likelihoods over earthquakes in length bins, we found the most likely values of WE for constant stress drop; these depend on the rupture length. The best‐fitting model has the surprising form WE∝logLE—a gentle increase in width with rupture length. Residuals from this model are convincingly correlated to the fault‐slip rate and also show a weak correlation with the crustal thickness. The resulting model thus supports a constant stress drop for ruptures of all lengths, consistent with teleseismic observation. The approach can be extended to test other observable factors that might improve the predictability of magnitude from a mapped fault for seismic hazard analyses.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210113","usgsCitation":"Anderson, J.G., Biasi, G., Angster, S.J., and Wesnousky, S., 2021, Improved scaling relationships for seismic moment and average slip of strike-slip earthquakes incorporating fault slip rate, fault width and stress drop: Bulletin of the Seismological Society of America, v. 111, no. 5, p. 2379-2392, https://doi.org/10.1785/0120210113.","productDescription":"14 p.","startPage":"2379","endPage":"2392","ipdsId":"IP-117223","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, John G.","contributorId":140379,"corporation":false,"usgs":false,"family":"Anderson","given":"John","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":929142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angster, Stephen J. 0000-0001-9250-8415","orcid":"https://orcid.org/0000-0001-9250-8415","contributorId":225610,"corporation":false,"usgs":true,"family":"Angster","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wesnousky, Stephen G.","contributorId":351624,"corporation":false,"usgs":false,"family":"Wesnousky","given":"Stephen G.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":929145,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223483,"text":"sim3470 - 2021 - Geologic map of Olympus Mons caldera, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:13:31.216229","indexId":"sim3470","displayToPublicDate":"2021-09-07T10:02:40","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3470","displayTitle":"Geologic Map of Olympus Mons Caldera, Mars","title":"Geologic map of Olympus Mons caldera, Mars","docAbstract":"

The Mars volcano, Olympus Mons, is probably the best known extraterrestrial volcano. The summit forms a nested caldera with six overlapping collapse pits that collectively measure ~65 x ~80 kilometers (km). Numerous wrinkle ridges and graben occur on the caldera floor, and topographic data indicate >1.2 km of elevation change since the formation of the floor as a series of lava lakes. The paths of lava flows on the south and southeast flanks do not conform to present-day topography. Mapping at a scale of 1:200,000 shows that the summit area displays a complex volcanic history that has  numerous similarities to terrestrial shield volcanoes. Pangboche crater is a large (~10-km-diameter) crater of impact origin that lies on the south flank of the caldera and, because of the elevation and lack of volatiles, it displays numerous features more similar to fresh lunar craters than to impact craters on Mars.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3470","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Mouginis-Mark, P.J., 2021, Geologic map of Olympus Mons caldera, Mars: U.S. Geological Survey Scientific Investigations Map 3470, 6 p., 1 sheet, scale 1:200,000, https://doi.org/10.3133/sim3470.","productDescription":"Report: iv, 6 p.; Metadata; Read Me; 1 Sheet: 38.06 x 40.11 inches","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-107079","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436209,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95C2UHD","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3470 Geologic Map of Olympus Mons Caldera, Mars"},{"id":388840,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_gis_files.zip","text":"SIM 3470 GIS Files","size":"260 MB","linkFileType":{"id":6,"text":"zip"}},{"id":388595,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_sheet.pdf","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388596,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_metadata.txt","size":"20 KB","linkFileType":{"id":2,"text":"txt"}},{"id":388592,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3470/covrthb.jpg"},{"id":388593,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_pamphlet.pdf","text":"Pamphlet to Accompany Map Sheet","size":"1 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388594,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_readme.docx","text":"Read Me","size":"25 KB docx"},{"id":405427,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P95C2UHD","text":"Interactive map","description":"Mouginis-Mark, P.J., 2021, Geologic map of Olympus Mons caldera, Mars: U.S. Geological Survey Scientific Investigations Map 3470, 6 p., 1 sheet, scale 1:200,000, https://doi.org/10.3133/sim3470.","linkHelpText":"- Geologic Map of Olympus Mons Caldera, Mars, 1:200K. Mouginis-Mark (2021)"},{"id":388598,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3470/sim3470_metadata.xml","size":"20 KB","linkFileType":{"id":8,"text":"xml"}}],"contact":"

Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-09-08","noUsgsAuthors":false,"publicationDate":"2021-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Mouginis-Mark, Peter J. 0000-0002-7173-6141","orcid":"https://orcid.org/0000-0002-7173-6141","contributorId":36793,"corporation":false,"usgs":false,"family":"Mouginis-Mark","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":822129,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70223753,"text":"70223753 - 2021 - A protocol for modelling generalised biological responses using latent variables in structural equation models","interactions":[],"lastModifiedDate":"2021-09-08T11:58:05.588979","indexId":"70223753","displayToPublicDate":"2021-09-07T09:21:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5943,"text":"One Ecosystem","active":true,"publicationSubtype":{"id":10}},"title":"A protocol for modelling generalised biological responses using latent variables in structural equation models","docAbstract":"In this paper we consider the problem of how to quantitatively characterize the degree to which a study object exhibits a generalized response. By generalized response, we mean a multivariate response where numerous individual properties change in concerted fashion due to some internal integration. In latent variable structural equation modeling (LVSEM), we would typically approach this situation using a latent variable to represent a general property of interest (e.g., performance) and multiple observed indicator variables that reflect the specific features associated with that general property. While ecologists have used LVSEM in a number of cases, there is substantial potential for its wider application. One obstacle is that LV models can be complex and easily over-specified, degrading their value as a means of generalization. It can also be challenging to diagnose causes of misspecification and understand which model modifications are sensible. In this paper we present a protocol, consisting of a series of questions, designed to guide the researchers through the evaluation process. These questions address (1) theoretical development, (2) data requirements, (3) whether responses to perturbation are general, (4) unique reactions by individual measures, and (5) how far generality can be extended. For this illustration, we reference a recent study considering the potential consequences of maintaining biodiversity as part of agricultural management on the overall quality of grapes used for wine making. We extend our presentation to include the complexities that occur when there are multiple species with unique reactions.","language":"English","publisher":"Pensoft Publishers","doi":"10.3897/oneeco.6.e67320","usgsCitation":"Grace, J., and Steiner, M., 2021, A protocol for modelling generalised biological responses using latent variables in structural equation models: One Ecosystem, v. 6, e67320, 20 p., https://doi.org/10.3897/oneeco.6.e67320.","productDescription":"e67320, 20 p.","ipdsId":"IP-128690","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450898,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/oneeco.6.e67320","text":"Publisher Index Page"},{"id":388870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2021-07-08","publicationStatus":"PW","contributors":{"editors":[{"text":"Akomolafe, Gbenga","contributorId":265354,"corporation":false,"usgs":false,"family":"Akomolafe","given":"Gbenga","email":"","affiliations":[],"preferred":false,"id":822627,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Grace, James B. 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":220095,"corporation":false,"usgs":true,"family":"Grace","given":"James B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":822551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steiner, Magdalena","contributorId":265327,"corporation":false,"usgs":false,"family":"Steiner","given":"Magdalena","email":"","affiliations":[{"id":54645,"text":"University of Fribourg, Ecology and Evolution, Department of Biology","active":true,"usgs":false}],"preferred":false,"id":822552,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223756,"text":"70223756 - 2021 - Instrumental variable methods in structural equation models","interactions":[],"lastModifiedDate":"2021-09-07T14:17:22.994951","indexId":"70223756","displayToPublicDate":"2021-09-07T09:12:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Instrumental variable methods in structural equation models","docAbstract":"
  1. Instrumental variable regression (RegIV) provides a means for detecting and correcting parameter bias in causal models. Widely used in economics, recently several papers have highlighted its potential utility for ecological applications. Little attention has thus far been paid to the fact that IV methods can also be implemented within structural equation models (SEMIV). In this paper I present the motivations, requirements and basic procedures for using SEMIV.
  2. I first consider causal inference and IVs from the perspective of a randomized experiment with partial control of the cause of interest. I consider common sources of bias, the role of randomization and limits to its capacity to exclude bias. Sources of bias include omitted confounders, reciprocal causation, reverse causation and measurement error, all of which can all be seen as a single problem—endogeneity. The approach to estimating IV models most commonly used in econometric practice, two-stage least squares regression (2SLS), is explained, followed by a brief exposition of the covariance modelling approach used in SEM. Using data from an ecological field experiment, I illustrate the use of the treatment variable as an IV and then illustrate procedures for evaluating candidate variables that might serve as additional IVs.
  3. IV methods are shown to be useful for both detecting endogeneity and removing its influences. I illustrate some of the ways that bias can be generated, as well as diagnostic capabilities and means for remedy embedded within SEM. Procedures for screening and evaluating additional IVs reveal valuable lessons regarding the theoretical requirements and empirical standards for IVs.
  4. SEMIV provides a useful way to detect and control for bias. I suggest that the use of IVs within the SEM framework can support the simultaneous pursuit of causal inference and explanatory modelling, a common pair of aspirations for ecologists. Moving forward, there is a need for a better understanding of the capabilities of SEMIV and requirements for successful application.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1111/2041-210X.13600","usgsCitation":"Grace, J., 2021, Instrumental variable methods in structural equation models: Methods in Ecology and Evolution, v. 12, no. 7, p. 1148-1157, https://doi.org/10.1111/2041-210X.13600.","productDescription":"10 p.","startPage":"1148","endPage":"1157","ipdsId":"IP-123523","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13600","text":"Publisher Index Page"},{"id":388864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-04-10","publicationStatus":"PW","contributors":{"editors":[{"text":"Morrissey, Michael","contributorId":202680,"corporation":false,"usgs":false,"family":"Morrissey","given":"Michael","email":"","affiliations":[],"preferred":false,"id":822559,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Grace, James 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":206247,"corporation":false,"usgs":true,"family":"Grace","given":"James","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":822553,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70224585,"text":"70224585 - 2021 - Conservation of northwestern and southwestern pond turtles: Threats, population size estimates, and population viability analysis","interactions":[],"lastModifiedDate":"2021-12-10T17:00:00.435086","indexId":"70224585","displayToPublicDate":"2021-09-07T07:57:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Conservation of northwestern and southwestern pond turtles: Threats, population size estimates, and population viability analysis","docAbstract":"

Accurate status assessments of long-lived, widely distributed taxa depend on the availability of long-term monitoring data from multiple populations. However, monitoring populations across large temporal and spatial scales is often beyond the scope of any one researcher or research group. Consequently, wildlife managers may be tasked with utilizing limited information from different sources to detect range-wide evidence of population declines and their causes. When assessments need to be made under such constraints, the research and management communities must determine how to extrapolate from variable population data to species-level inferences. Here, using three different approaches, we integrate and analyze data from the peer-reviewed literature and government agency reports to inform conservation for northwestern pond turtles (NPT) Actinemys marmorata and southwestern pond turtles (SPT) Actinemys pallida. Both NPT and SPT are long-lived freshwater turtles distributed along the west coast of the United States and Mexico. Conservation concerns exist for both species; however, SPT may face more severe threats and are thought to exist at lower densities throughout their range than NPT. For each species, we ranked the impacts of 13 potential threats, estimated population sizes, and modeled population viability with and without long-term droughts. Our results suggest that predation of hatchlings by invasive predators, such as American bullfrogs Lithobates catesbeianus and Largemouth Bass Micropterus salmoides, is a high-ranking threat for NPT and SPT. Southwestern pond turtles may also face more severe impacts associated with natural disasters (droughts, wildfires, and floods) than do NPT. Population size estimates from trapping surveys indicate that SPT have smaller population sizes on average than do NPT (P = 0.0003), suggesting they may be at greater risk of local extirpation. Population viability analysis models revealed that long-term droughts are a key environmental parameter; as the frequency of severe droughts increases with climate change, the likelihood of population recovery decreases, especially when census sizes are low. Given current population trends and vulnerability to natural disasters throughout their range, we suggest that conservation and recovery actions first focus on SPT to prevent further population declines.

","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-20-094","usgsCitation":"Manzo, S., Nicholson, E.G., Devereux. Zachary, Fisher, R., Brown, C., Scott, P., and Shaffer, H.B., 2021, Conservation of northwestern and southwestern pond turtles: Threats, population size estimates, and population viability analysis: Journal of Fish and Wildlife Management, v. 12, no. 2, p. 485-501, https://doi.org/10.3996/JFWM-20-094.","productDescription":"17 p.","startPage":"485","endPage":"501","ipdsId":"IP-130146","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":450903,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-094","text":"Publisher Index Page"},{"id":389944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Manzo, Stephanie","contributorId":240852,"corporation":false,"usgs":false,"family":"Manzo","given":"Stephanie","email":"","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":824191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicholson, E. Griffin","contributorId":240850,"corporation":false,"usgs":false,"family":"Nicholson","given":"E.","email":"","middleInitial":"Griffin","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":824192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Devereux. Zachary","contributorId":266038,"corporation":false,"usgs":false,"family":"Devereux. Zachary","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":824193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Christopher W. 0000-0002-2545-9171","orcid":"https://orcid.org/0000-0002-2545-9171","contributorId":240860,"corporation":false,"usgs":true,"family":"Brown","given":"Christopher W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824195,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Peter A","contributorId":240864,"corporation":false,"usgs":false,"family":"Scott","given":"Peter A","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":824196,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaffer, H. Bradley","contributorId":202769,"corporation":false,"usgs":false,"family":"Shaffer","given":"H.","email":"","middleInitial":"Bradley","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":824197,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225492,"text":"70225492 - 2021 - The evolution of geospatial reasoning, analytics, and modeling","interactions":[],"lastModifiedDate":"2021-10-18T11:55:39.769997","indexId":"70225492","displayToPublicDate":"2021-09-07T06:54:45","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The evolution of geospatial reasoning, analytics, and modeling","docAbstract":"

The field of geospatial analytics and modeling has a long history coinciding with the physical and cultural evolution of humans. This history is analyzed relative to the four scientific paradigms: (1) empirical analysis through description, (2) theoretical explorations using models and generalizations, (3) simulating complex phenomena and (4) data exploration. Correlations among developments in general science and those of the geospatial sciences are explored. Trends identify areas ripe for growth and improvement in the fourth and current paradigm that has been spawned by the big data explosion, such as exposing the ‘black box’ of GeoAI training and generating big geospatial training datasets. Future research should focus on integrating both theory- and data-driven knowledge discovery.


","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The Geographic Information Science & Technology Body of Knowledge","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University Consortium for Geographic Information Science","doi":"10.22224/gistbok/2021.3.4","usgsCitation":"Arundel, S., and Li, W., 2021, The evolution of geospatial reasoning, analytics, and modeling, chap. of The Geographic Information Science & Technology Body of Knowledge, https://doi.org/10.22224/gistbok/2021.3.4.","ipdsId":"IP-127804","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":450910,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.22224/gistbok/2021.3.4","text":"Publisher Index Page"},{"id":390602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":825264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Wenwen 0000-0003-2237-9499","orcid":"https://orcid.org/0000-0003-2237-9499","contributorId":219356,"corporation":false,"usgs":false,"family":"Li","given":"Wenwen","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":825265,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223723,"text":"fs20213050 - 2021 - Virginia and Landsat","interactions":[],"lastModifiedDate":"2023-02-22T17:53:58.502511","indexId":"fs20213050","displayToPublicDate":"2021-09-07T06:37:53","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3050","displayTitle":"Virginia and Landsat","title":"Virginia and Landsat","docAbstract":"

From the shores of Jamestown and spreading north, south, and west, the lands that became the State of Virginia were some of the first in North America top experience rapid landscape change from European settlement. Imagery and data from the USGS Landsat series of satellites offer an unparalleled resource for the study, understanding, and preservation of Virginia’s land and water resources. From monitoring the health of water bodies to managing invasive species to planning for a range of climate change effects, the USGS National Land Imaging Program’s stewardship and public delivery of Landsat data have benefitted Virginians in myriad ways.

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\"}}]}","edition":"Version 1.0: September 7, 2021; Version 1.1: February 22, 2023","contact":"

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U.S. Geological Survey
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","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-09-07","revisedDate":"2023-02-22","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":822489,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70227361,"text":"70227361 - 2021 - Post audit of simulated groundwater flow to a short-lived (2019-2020) crater lake at Kīlauea Volcano","interactions":[],"lastModifiedDate":"2022-01-11T12:52:24.354298","indexId":"70227361","displayToPublicDate":"2021-09-06T06:48:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Post audit of simulated groundwater flow to a short-lived (2019-2020) crater lake at Kīlauea Volcano","docAbstract":"

About 14.5 months after the 2018 eruption and summit collapse of Kīlauea Volcano, Hawaiʻi, liquid water started accumulating in the deepened summit crater, forming a lake that attained 51 m depth before rapidly boiling off on December 20, 2020, when an eruption from the crater wall poured lava into the lake. Modeling the growth of the crater lake at Kīlauea summit is important for assessing the potential for explosive volcanism. Our current understanding of the past 2500 years of eruptive activity at Kīlauea suggests a slight dominance of explosive behavior over effusive. The deepened summit crater and presence of the crater lake in 2019 raised renewed concerns about explosive activity. Groundwater models using hydraulic-property data from a nearby drillhole successfully forecast the timing and rate of lake filling. Here we compare the groundwater-model predictions with observational data through the demise of the crater lake, examine the implications for local water-table configuration, consider the potential role of evaporation and recharge (neglected in previous models), and briefly discuss the energetics of the rapid boil-off. This post audit of groundwater-flow models of Kīlauea summit shows that simple models can sometimes be used effectively to simulate complex settings such as volcanoes.

","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwat.13133","usgsCitation":"Flinders, A.F., Kauahikaua, J.P., Hsieh, P.A., and Ingebritsen, S.E., 2021, Post audit of simulated groundwater flow to a short-lived (2019-2020) crater lake at Kīlauea Volcano: Groundwater, v. 60, no. 1, p. 64-70, https://doi.org/10.1111/gwat.13133.","productDescription":"7 p.","startPage":"64","endPage":"70","ipdsId":"IP-128614","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":394173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.41671752929688,\n 19.15165474470855\n ],\n [\n -155.03082275390622,\n 19.15165474470855\n ],\n [\n -155.03082275390622,\n 19.530024424775405\n ],\n [\n -155.41671752929688,\n 19.530024424775405\n ],\n [\n -155.41671752929688,\n 19.15165474470855\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"60","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Flinders, Ashton F. 0000-0003-2483-4635 aflinders@usgs.gov","orcid":"https://orcid.org/0000-0003-2483-4635","contributorId":196960,"corporation":false,"usgs":true,"family":"Flinders","given":"Ashton","email":"aflinders@usgs.gov","middleInitial":"F.","affiliations":[{"id":153,"text":"California Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":false,"id":830587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":830588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true}],"preferred":true,"id":830589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":830590,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232157,"text":"70232157 - 2021 - Towards building a sustainable future: Positioning ecological modelling for impact in ecosystems management","interactions":[],"lastModifiedDate":"2022-06-09T13:46:21.236984","indexId":"70232157","displayToPublicDate":"2021-09-04T08:42:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1107,"text":"Bulletin of Mathematical Biology","active":true,"publicationSubtype":{"id":10}},"title":"Towards building a sustainable future: Positioning ecological modelling for impact in ecosystems management","docAbstract":"As many ecosystems worldwide are in peril, efforts to manage them sustainably require scientific advice. While numerous researchers around the world use a great variety of models to understand ecological dynamics and their responses to disturbances, only a small fraction of these models are ever used to inform ecosystem management. There seems to be a perception that ecological models are not useful for management, even though mathematical models are indispensable in many other fields. We were curious about this mismatch, its roots, and potential ways to overcome it. We searched the literature on recommendations and best practices for how to make ecological models useful to the management of ecosystems and we searched for ‘success stories’ from the past. We selected and examined several cases where models were instrumental in ecosystem management. We documented their success and asked whether and to what extent they followed recommended best practices. We found that there is not a unique way to conduct a research project that is useful in management decisions. While research is more likely to have impact when conducted with many stakeholders involved and specific to a situation for which data are available, there are great examples of small groups or individuals conducting highly influential research even in the absence of detailed data. We put the question of modelling for ecosystem management into a socio-economic and national context and give our perspectives on how the discipline could move forward.","language":"English","publisher":"Springer Nature","doi":"10.1007/s11538-021-00927-y","usgsCitation":"DeAngelis, D., Franco, D., Hastings, A., Hilker, F.M., Lenhart, S., Lutscher, F., Petrovskaya, N., Petrovskii, S., and Tyson, R.C., 2021, Towards building a sustainable future: Positioning ecological modelling for impact in ecosystems management: Bulletin of Mathematical Biology, v. 83, no. 10, 107, 28 p., https://doi.org/10.1007/s11538-021-00927-y.","productDescription":"107, 28 p.","ipdsId":"IP-126721","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450928,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11538-021-00927-y","text":"Publisher Index Page"},{"id":401981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"83","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":844376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Franco, Daniel","contributorId":292355,"corporation":false,"usgs":false,"family":"Franco","given":"Daniel","email":"","affiliations":[{"id":62878,"text":"Universidad Nacional de Educacion a Distancia (UNED)","active":true,"usgs":false}],"preferred":false,"id":844377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hastings, Alan","contributorId":175365,"corporation":false,"usgs":false,"family":"Hastings","given":"Alan","email":"","affiliations":[],"preferred":false,"id":844378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hilker, Frank M.","contributorId":292356,"corporation":false,"usgs":false,"family":"Hilker","given":"Frank","email":"","middleInitial":"M.","affiliations":[{"id":62879,"text":"Osnabrueck University","active":true,"usgs":false}],"preferred":false,"id":844379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lenhart, Suzanne","contributorId":292357,"corporation":false,"usgs":false,"family":"Lenhart","given":"Suzanne","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":844380,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lutscher, Frithjof","contributorId":195716,"corporation":false,"usgs":false,"family":"Lutscher","given":"Frithjof","email":"","affiliations":[],"preferred":false,"id":844381,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Petrovskaya, Natalia","contributorId":292358,"corporation":false,"usgs":false,"family":"Petrovskaya","given":"Natalia","email":"","affiliations":[{"id":7157,"text":"University of Birmingham","active":true,"usgs":false}],"preferred":false,"id":844382,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Petrovskii, Sergei","contributorId":292359,"corporation":false,"usgs":false,"family":"Petrovskii","given":"Sergei","email":"","affiliations":[{"id":27194,"text":"University of Leicester","active":true,"usgs":false}],"preferred":false,"id":844383,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tyson, Rebecca C.","contributorId":292360,"corporation":false,"usgs":false,"family":"Tyson","given":"Rebecca","email":"","middleInitial":"C.","affiliations":[{"id":62881,"text":"University of British Columbia-Okanagan","active":true,"usgs":false}],"preferred":false,"id":844384,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ]}