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Biological soil crusts (biocrusts) are a central component of dryland ecosystems. However, they are highly vulnerable to disturbance and natural recovery may be slow. Therefore, finding ways to enhance the reestablishment of biocrusts after disturbance has been of great interest to researchers. This article provides a review of the laboratory cultivation and field inoculations of biocrust materials in China (mostly published in Chinese). Larger filamentous cyanobacteria (e.g. Microcoleus) are relatively easy, although slow, to grow in culture compared to other biocrust components. Thus, most researchers have focused their efforts on the cyanobacteria and a few species of mosses that are also easily grown but at smaller scale. For all the studies, a small amount of biocrust material was collected and its biomass enhanced under controlled conditions. However, the enhancement was done using various methods and techniques in different regions. These materials were then applied to disturbed field sites, again with various methods. Results show that keeping the inoculated soil surface wet for some time period after inoculation was crucial for restoration success. Cyanobacterial establishment was improved by installing automatic sprinkling using micro‐irrigation techniques and/or physical structures that reduced sediment moving onto the inoculated area. Experimental applications in China showed that cyanobacteria can be successfully inoculated at a large scale (hundreds of ha). Moss inoculation, on the other hand, was only accomplished at a small scale (several m2). To assess whether biocrust restoration can enhance the establishment of a self‐supporting ecosystem, further research is needed on how inoculation affects vegetation diversity and structure and ecological processes.

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Xiaobing","contributorId":181757,"corporation":false,"usgs":false,"family":"Zhou","given":"Xiaobing","email":"","affiliations":[],"preferred":false,"id":787903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Yunge","contributorId":224390,"corporation":false,"usgs":false,"family":"Zhao","given":"Yunge","email":"","affiliations":[],"preferred":false,"id":787904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":787905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Bingchang","contributorId":224388,"corporation":false,"usgs":false,"family":"Zhang","given":"Bingchang","email":"","affiliations":[],"preferred":false,"id":787906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bu, Chongfeng","contributorId":224389,"corporation":false,"usgs":false,"family":"Bu","given":"Chongfeng","email":"","affiliations":[],"preferred":false,"id":787907,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Yuanming","contributorId":173232,"corporation":false,"usgs":false,"family":"Zhang","given":"Yuanming","email":"","affiliations":[{"id":27200,"text":"Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China","active":true,"usgs":false}],"preferred":false,"id":787908,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218295,"text":"70218295 - 2020 - A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support","interactions":[],"lastModifiedDate":"2021-02-23T13:39:06.774641","indexId":"70218295","displayToPublicDate":"2020-02-25T07:36:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support","docAbstract":"

Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the “stochastic impulse-response emulator” (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104657","usgsCitation":"White, J., Knowling, M., Fienen, M., Feinstein, D.T., McDonald, G.W., and Moore, C.R., 2020, A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support: Environmental Modelling and Software, v. 126, 104657, 11 p., https://doi.org/10.1016/j.envsoft.2020.104657.","productDescription":"104657, 11 p.","ipdsId":"IP-106798","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":383595,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":214251,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knowling, Matthew 0000-0002-7273-3495","orcid":"https://orcid.org/0000-0002-7273-3495","contributorId":251904,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDonald, Garry W. 0000-0003-3746-4346","orcid":"https://orcid.org/0000-0003-3746-4346","contributorId":251906,"corporation":false,"usgs":false,"family":"McDonald","given":"Garry","email":"","middleInitial":"W.","affiliations":[{"id":50421,"text":"Market Economics","active":true,"usgs":false}],"preferred":false,"id":810894,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moore, Catherine R.","contributorId":251908,"corporation":false,"usgs":false,"family":"Moore","given":"Catherine","email":"","middleInitial":"R.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810895,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218283,"text":"70218283 - 2020 - A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support","interactions":[],"lastModifiedDate":"2021-02-24T13:11:27.323218","indexId":"70218283","displayToPublicDate":"2020-02-25T06:51:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7599,"text":"Environmental Modeling and Software","active":true,"publicationSubtype":{"id":10}},"title":"A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support","docAbstract":"

Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context, the linear relation between nitrate loading and surface-water/groundwater nitrate concentrations presents an opportunity for employing an efficient model emulator through the application of impulse-response matrices. When paired with first-order second-moment techniques, the emulation strategy gives rise to the “stochastic impulse-response emulator” (SIRE). SIRE is shown to facilitate non-intrusive, near-real time, and risk-based evaluation of nitrate-loading change scenarios, as well as nitrate-loading OUU subject to surface-water/groundwater concentration constraints in high decision variable and parameter dimensions. Two case studies are used to demonstrate SIRE in the nitrate-loading context.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104657","usgsCitation":"White, J., Knowling, M.J., Fienen, M., Feinstein, D.T., McDonald, G.W., and Catherine R. Moore, 2020, A non-intrusive approach for efficient stochastic emulation and optimization of model-based nitrate-loading management decision support: Environmental Modeling and Software, v. 126, 104657, 11 p., https://doi.org/10.1016/j.envsoft.2020.104657.","productDescription":"104657, 11 p.","ipdsId":"IP-114822","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":383585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 175.31982421875,\n -37.65120864327175\n ],\n [\n 175.70159912109375,\n -37.65120864327175\n ],\n [\n 175.70159912109375,\n -37.208456662000174\n ],\n [\n 175.31982421875,\n -37.208456662000174\n ],\n [\n 175.31982421875,\n -37.65120864327175\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":214251,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knowling, Matthew J.","contributorId":251909,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":810819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530 dtfeinst@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":1907,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"dtfeinst@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDonald, Garry W. 0000-0003-3746-4346","orcid":"https://orcid.org/0000-0003-3746-4346","contributorId":251906,"corporation":false,"usgs":false,"family":"McDonald","given":"Garry","email":"","middleInitial":"W.","affiliations":[{"id":50421,"text":"Market Economics","active":true,"usgs":false}],"preferred":false,"id":810822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Catherine R. Moore","contributorId":251911,"corporation":false,"usgs":false,"family":"Catherine R. Moore","affiliations":[{"id":36909,"text":"CSIRO","active":true,"usgs":false}],"preferred":false,"id":810823,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208691,"text":"70208691 - 2020 - Regional ocean models indicate changing limits to biological invasions in the Bering Sea","interactions":[],"lastModifiedDate":"2020-02-24T19:04:31","indexId":"70208691","displayToPublicDate":"2020-02-24T19:01:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1936,"text":"ICES Journal of Marine Science","active":true,"publicationSubtype":{"id":10}},"title":"Regional ocean models indicate changing limits to biological invasions in the Bering Sea","docAbstract":"Minimal vessel traffic and cold water temperatures are believed to limit non-indigenous species (NIS) in high-latitude ecosystems. We evaluated whether suitable conditions exist in the Bering Sea for the introduction, survival, and reproduction of NIS. We compiled temperature and salinity thresholds of known NIS and compared these to ocean conditions projected during two study periods: current (2003-2012) and mid-century (2030-2039). We also explored patterns of vessel traffic and connectivity for U.S. Bering Sea ports. We found the southeastern Bering Sea had suitable conditions for the year-round survival of 80% of NIS assessed (n=42). However, only 52% of NIS had conditions suitable for reproduction or development (n=25). Conditions north of 58° N that include sub-zero winter water temperatures were unsuitable for the survival and reproduction of most NIS. While mid-century models predicted a northward expansion of suitable conditions, conditions for reproduction remained marginal. Within the highly suitable southeastern Bering Sea is the port of Dutch Harbor, which received the most vessel arrivals and ballast water discharge in the U.S. Bering Sea. Our findings illustrate the potential vulnerability of a commercially important subarctic ecosystem and highlight the need to consider NIS reproductive and developmental life phases when evaluating limits to their establishment.","language":"English","publisher":"Oxford Academic","doi":"10.1093/icesjms/fsaa014","usgsCitation":"Droghini, A., Fischbach, A., Watson, J., and Reimer, J., 2020, Regional ocean models indicate changing limits to biological invasions in the Bering Sea: ICES Journal of Marine Science, fsaa014, 11 p., https://doi.org/10.1093/icesjms/fsaa014.","productDescription":"fsaa014, 11 p.","ipdsId":"IP-106911","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":457617,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsaa014","text":"Publisher Index Page"},{"id":372591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Bering Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175.95703125,\n 52.53627304145948\n ],\n [\n -165.7177734375,\n 54.23955053156177\n ],\n [\n -158.81835937499997,\n 57.633639928856965\n ],\n [\n -162.24609375,\n 58.37867853932655\n ],\n [\n -162.4658203125,\n 60.04290359809164\n ],\n [\n -166.06933593749997,\n 60.780618803458935\n ],\n [\n -165.498046875,\n 62.71446210149774\n ],\n [\n -162.24609375,\n 63.95667333648766\n ],\n [\n -165.234375,\n 64.51064316846676\n ],\n [\n -169.27734375,\n 65.53117097417717\n ],\n [\n -171.5625,\n 63.82128765261384\n ],\n [\n -174.0234375,\n 61.543641475549954\n ],\n [\n -175.95703125,\n 52.53627304145948\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 185.88867187499997,\n 64.11060221954631\n ],\n [\n 183.8671875,\n 64.99793920061401\n ],\n [\n 181.142578125,\n 65.6582745198266\n ],\n [\n 180.791015625,\n 65.23830662451157\n ],\n [\n 179.12109374999997,\n 64.35893097894458\n ],\n [\n 178.8134765625,\n 63.68524808030715\n ],\n [\n 180.3955078125,\n 62.512317938386914\n ],\n [\n 178.505859375,\n 62.14497603754045\n ],\n [\n 176.0888671875,\n 62.04213770379758\n ],\n [\n 172.6171875,\n 61.079544234557304\n ],\n [\n 170.3759765625,\n 59.77852198502987\n ],\n [\n 169.6728515625,\n 59.84481485969105\n ],\n [\n 168.6181640625,\n 60.28340847828243\n ],\n [\n 167.255859375,\n 60.19615576604439\n ],\n [\n 165.05859375,\n 59.17592824927136\n ],\n [\n 163.828125,\n 57.938183012205315\n ],\n [\n 164.70703125,\n 56.218923189166624\n ],\n [\n 168.310546875,\n 54.7246201949245\n ],\n [\n 174.111328125,\n 53.04121304075649\n ],\n [\n 179.0771484375,\n 51.944264879028765\n ],\n [\n 185.625,\n 52.429222277955134\n ],\n [\n 185.88867187499997,\n 64.11060221954631\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Droghini, Amanda 0000-0001-6692-2348","orcid":"https://orcid.org/0000-0001-6692-2348","contributorId":222312,"corporation":false,"usgs":false,"family":"Droghini","given":"Amanda","email":"","affiliations":[{"id":40516,"text":"Alaska Center for Conservation Science University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":783024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":783023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watson, Jordan 0000-0002-1686-0377","orcid":"https://orcid.org/0000-0002-1686-0377","contributorId":222313,"corporation":false,"usgs":false,"family":"Watson","given":"Jordan","email":"","affiliations":[{"id":40517,"text":"NOAA Alaska Fisheries Science Center Auke Bay Laboratories","active":true,"usgs":false}],"preferred":false,"id":783026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reimer, Jesika","contributorId":222724,"corporation":false,"usgs":false,"family":"Reimer","given":"Jesika","email":"","affiliations":[{"id":40516,"text":"Alaska Center for Conservation Science University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":783025,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208689,"text":"ofr20201002 - 2020 - Preliminary analyses of volcanic hazards at Kīlauea Volcano, Hawai‘i, 2017–2018","interactions":[],"lastModifiedDate":"2022-04-21T20:29:39.85756","indexId":"ofr20201002","displayToPublicDate":"2020-02-24T15:31:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1002","displayTitle":"Preliminary Analyses of Volcanic Hazards at Kīlauea Volcano, Hawaiʻi, 2017–2018","title":"Preliminary analyses of volcanic hazards at Kīlauea Volcano, Hawai‘i, 2017–2018","docAbstract":"

From 2017 to 2018, the U.S. Geological Survey (USGS) Hawaiian Volcano Observatory (HVO) responded to ongoing and changing eruptions at Kīlauea Volcano as part of its mission to monitor volcanic processes, issue warnings of dangerous activity, and assess volcanic hazards. To formalize short-term hazards assessments—and, in some cases, issue prognoses for future activity—and make results discoverable to both the public and the authorities, HVO released reports online. These reports were published rapidly, received peer review under the USGS’s Fundamental Science Practice guidelines, and were intended to address a focused question posed by one or more cooperating agencies—for this reason, they were called “cooperator reports.” This Open-File Report concatenates four such products issued in 2017 and 2018 into a single publication. These reports have been reformatted and lightly edited for clarity, but the content has not otherwise been changed from the versions first publicly released.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201002","usgsCitation":"Neal, C.A., and Anderson, K.R., 2020, Preliminary analyses of volcanic hazards at Kīlauea Volcano, Hawai‘i, 2017–2018: U.S. Geological Survey Open-File Report 2020–1002, 34 p., https://doi.org/10.3133/ofr20201002.","productDescription":"iv, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-114660","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":399444,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109722.htm"},{"id":372588,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1002/coverthb.jpg"},{"id":372589,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1002/ofr20201002.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}}],"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 -155.3114,\n 19.1644\n ],\n [\n -154.8036,\n 19.1644\n ],\n [\n -154.8036,\n 19.4433\n ],\n [\n -155.3114,\n 19.4433\n ],\n [\n -155.3114,\n 19.1644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue, Suite A-8
Hilo, HI 96720

","tableOfContents":"

","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-02-24","noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Neal, Christina A. 0000-0002-7697-7825 tneal@usgs.gov","orcid":"https://orcid.org/0000-0002-7697-7825","contributorId":131135,"corporation":false,"usgs":true,"family":"Neal","given":"Christina","email":"tneal@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":783005,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":783004,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208409,"text":"sir20205011 - 2020 - Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio","interactions":[],"lastModifiedDate":"2022-04-25T21:37:51.100623","indexId":"sir20205011","displayToPublicDate":"2020-02-24T12:42:30","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5011","displayTitle":"Hydrologic and Hydraulic Analyses of Selected Streams in Stark County, Ohio","title":"Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio","docAbstract":"

To update and expand a part of the Federal Emergency Management Agency Flood Insurance Study, the U.S. Geological Survey, the Muskingum Watershed Conservancy District, and the Stark County Commissioners began a cooperative study. The study consisted of hydrologic and hydraulic analyses for selected reaches of 14 streams in Stark County, Ohio: Broad-Monter Creek, Chatham Ditch, East Branch Nimishillen Creek, Fairhope Ditch, Firestone Ditch, Hayden Ditch, Middle Branch Nimishillen Creek, Middle Branch Nimishillen Creek Tributary Number 1, Nimishillen Creek, Reemsnyder Ditch, Sherrick Run, unnamed stream, West Branch Nimishillen Creek, and Zimber Ditch. The study totaled nearly 50 miles of stream reaches.

Instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities were estimated using historical streamflow data from the streamgages Nimishillen Creek at North Industry, Ohio (U.S. Geological Survey station number 03118500), and Middle Branch Nimishillen Creek at Canton, Ohio (U.S. Geological Survey station number 03118000), regional flood regression equations, and streamflow urbanization techniques.

The annual exceedance probability streamflows were then used in a Hydrologic Engineering Center-River Analysis System step-backwater model to determine water-surface profiles, flood-inundation boundaries for the 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probability floods, and a regulatory floodway along a selected reach of each stream. Model input included DEM-derived cross sections supplemented with field surveys of open channel cross sections and hydraulic structures, field estimates of roughness values, and annual exceedance probability flood estimates from regional regression equations and historical streamflow data. Flood-inundation boundaries were mapped for the 1- and 0.2-percent annual exceedance probability floods and a regulatory floodway for each stream reach.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205011","collaboration":"Prepared in cooperation with Stark County and the Muskingum Watershed Conservancy District","usgsCitation":"Ostheimer, C.J. and Whitehead, M.T, 2020, Hydrologic and hydraulic analyses of selected streams in Stark County, Ohio: U.S. Geological Survey Scientific Investigations Report 2020–5011, 15 p., https://doi.org/10.3133/sir20205011.","productDescription":"Report: iv, 15 p.; 4 Appendixes; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106471","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":399632,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109726.htm"},{"id":372523,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5011/coverthb.jpg"},{"id":372525,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix1.pdf","text":"Appendix 1","size":"3.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 1","linkHelpText":"– Technical Support Data Notebook"},{"id":372526,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix2.pdf","text":"Appendix 2","size":"780 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 2","linkHelpText":"– Floodway data tables"},{"id":372524,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011.pdf","text":"Report","size":"1.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011"},{"id":372527,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix3.pdf","text":"Appendix 3","size":"1.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 3","linkHelpText":"– Water-surface profiles"},{"id":372528,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5011/sir20205011_appendix4.pdf","text":"Appendix 4","size":"8.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5011 Appendix 4","linkHelpText":"– Flood-inundation maps"},{"id":372529,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YQJ8B7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic models for selected streams in Stark County, Ohio"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"

Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Suite 100
Columbus, OH 43229–1737

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2020-02-24","noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ostheimer, Chad J. 0000-0002-4528-8867","orcid":"https://orcid.org/0000-0002-4528-8867","contributorId":213950,"corporation":false,"usgs":true,"family":"Ostheimer","given":"Chad","email":"","middleInitial":"J.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitehead, Matthew T. 0000-0002-4888-2597 mtwhiteh@usgs.gov","orcid":"https://orcid.org/0000-0002-4888-2597","contributorId":218036,"corporation":false,"usgs":true,"family":"Whitehead","given":"Matthew T.","email":"mtwhiteh@usgs.gov","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208126,"text":"sir20205001 - 2020 - Modeling a 2- and 4-foot drawdown in the Link River to Keno Dam reach of the upper Klamath River, south-central Oregon","interactions":[],"lastModifiedDate":"2022-04-25T20:39:51.337234","indexId":"sir20205001","displayToPublicDate":"2020-02-24T12:23:19","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5001","displayTitle":"Modeling a 2- and 4-Foot Drawdown in the Link River to Keno Dam Reach of the Upper Klamath River, South-Central Oregon","title":"Modeling a 2- and 4-foot drawdown in the Link River to Keno Dam reach of the upper Klamath River, south-central Oregon","docAbstract":"

Executive Summary

The most upstream, pooled reach of the Klamath River in south-central Oregon, from Link River mouth to Keno Dam (Link-Keno), has a water-surface elevation that remains relatively constant throughout the year. Two model scenarios, using an existing two-dimensional hydrodynamic and water-quality model (CE-QUAL-W2), were constructed to examine the effects of lowering the water-surface elevation by 2 and 4 feet (ft) (0.61 and 1.2 meters) throughout an entire calendar year to mimic some of the potential effects of removal or modification of Keno Dam. Model results for these drawdown scenarios were analyzed for changes in velocity, travel time, water temperature, total dissolved solids, inorganic suspended sediment, nutrients, organic matter, chlorophyll a, and dissolved oxygen, compared to the base-case model. The model used in this study had been previously calibrated with the presence of aquatic plants (macrophytes). However, most model analyses were completed for model runs where macrophytes were “turned off” because the species, abundance, and distribution of macrophytes in a lowered-water scenario were all highly uncertain. For comparison, a few model scenario runs were completed with macrophytes enabled within the model. Findings from this study include the following:

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205001","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Sullivan, A.B., and Rounds, S.A., 2020, Modeling a 2- and 4-foot drawdown in the Link River to Keno Dam reach of the upper Klamath River, south-central Oregon: U.S. Geological Survey Scientific Investigations Report 2020–5001, 18 p., https://doi.org/10.3133/sir20205001.","productDescription":"vi, 18 p.","onlineOnly":"Y","ipdsId":"IP-110517","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":372580,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5001/sir20205001.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5001"},{"id":399624,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109725.htm"},{"id":372579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5001/coverthb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.80679321289062,\n 42.24935997537791\n ],\n [\n -121.82121276855469,\n 42.23461834757937\n ],\n [\n -121.81159973144531,\n 42.23055108552288\n ],\n [\n -121.79443359375,\n 42.21478796951033\n ],\n [\n -121.79100036621094,\n 42.20614200929954\n ],\n [\n -121.79786682128905,\n 42.18986405028881\n ],\n [\n -121.80816650390625,\n 42.179179360985074\n ],\n [\n -121.81983947753906,\n 42.17765282939429\n ],\n [\n -121.84249877929688,\n 42.17307301351938\n ],\n [\n -121.85760498046875,\n 42.156786537824686\n ],\n [\n -121.86721801757812,\n 42.146096259707384\n ],\n [\n -121.86653137207031,\n 42.134894984239224\n ],\n [\n -121.85897827148438,\n 42.12114525624141\n ],\n [\n -121.86447143554686,\n 42.10943017110108\n ],\n [\n -121.87271118164062,\n 42.09822241118974\n ],\n [\n -121.88095092773439,\n 42.10739254393655\n ],\n [\n -121.89468383789061,\n 42.12267315117256\n ],\n [\n -121.90979003906249,\n 42.132858175814626\n ],\n [\n -121.93313598632812,\n 42.14151415924112\n ],\n [\n -121.94961547851562,\n 42.14660536151124\n ],\n [\n -121.96197509765625,\n 42.14813264235833\n ],\n [\n -121.96540832519531,\n 42.15933157601718\n ],\n [\n -121.99287414550781,\n 42.16238548673798\n ],\n [\n -122.0203399658203,\n 42.156786537824686\n ],\n [\n -122.03887939453125,\n 42.154241397276955\n ],\n [\n -122.05398559570312,\n 42.14151415924112\n ],\n [\n -122.05261230468751,\n 42.12725661490423\n ],\n [\n -122.03819274902344,\n 42.11808935584944\n ],\n [\n -122.02445983886719,\n 42.13947756370087\n ],\n [\n -122.01896667480467,\n 42.14100501649703\n ],\n [\n -122.00042724609374,\n 42.14253243244731\n ],\n [\n -121.98532104492186,\n 42.13845924136697\n ],\n [\n -121.97776794433594,\n 42.12980284036179\n ],\n [\n -121.95716857910155,\n 42.12369172732473\n ],\n [\n -121.94137573242186,\n 42.121654558645524\n ],\n [\n -121.92764282226564,\n 42.111467732769135\n ],\n [\n -121.90910339355467,\n 42.10637370579324\n ],\n [\n -121.89537048339842,\n 42.097203425683055\n ],\n [\n -121.88919067382812,\n 42.081407056615774\n ],\n [\n -121.8610382080078,\n 42.08599350447723\n ],\n [\n -121.8335723876953,\n 42.105864280581365\n ],\n [\n -121.82052612304688,\n 42.13183974703906\n ],\n [\n -121.7889404296875,\n 42.17001961868589\n ],\n [\n -121.77108764648436,\n 42.180705855725115\n ],\n [\n -121.76353454589844,\n 42.20156425714052\n ],\n [\n -121.77040100097655,\n 42.21580506349499\n ],\n [\n -121.78962707519531,\n 42.233093155022765\n ],\n [\n -121.80404663085939,\n 42.242243757492766\n ],\n [\n -121.80679321289062,\n 42.24935997537791\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2020-02-24","noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Sullivan, Annett B. 0000-0001-7783-3906 annett@usgs.gov","orcid":"https://orcid.org/0000-0001-7783-3906","contributorId":79821,"corporation":false,"usgs":true,"family":"Sullivan","given":"Annett B.","email":"annett@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":780621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounds, Stewart A. 0000-0002-8540-2206 sarounds@usgs.gov","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":905,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"sarounds@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780622,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211176,"text":"70211176 - 2020 - Highly variable rates of survival to metamorphosis in wild boreal toads (Anaxyrus boreas boreas)","interactions":[],"lastModifiedDate":"2020-08-06T19:07:44.092093","indexId":"70211176","displayToPublicDate":"2020-02-24T12:06:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3103,"text":"Population Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Highly variable rates of survival to metamorphosis in wild boreal toads (Anaxyrus boreas boreas)","title":"Highly variable rates of survival to metamorphosis in wild boreal toads (Anaxyrus boreas boreas)","docAbstract":"

Life history theory suggests that long‐lived, pond‐breeding amphibians should have low and highly variable early life‐stage survival rates, but this theoretical expectation is often untested and the causes of variation are usually unknown. We evaluated the impact of hydroperiod, presence of a pathogen (Batrachochytrium dendrobatidis [Bd]), presence of a potential predator (cutthroat trout Oncorhychus clarki stomias) , and whether animals had been reintroduced into a site on survival of early life stages of boreal toads (Anaxyrus boreas boreas ). We used a multistate mark‐recapture framework to estimate survival of boreal toad embryos from egg to metamorphosis at four sites over 5 years. We found substantial spatial and temporal variation in survival to metamorphosis and documented some evidence that monthly tadpole survival was lower in sites with Bd, without trout, and at permanent sites. Our results support theories of amphibian life history, aid in the management of this species of conservation concern, and contribute to our knowledge of the ecology of the species. Additionally, we present methodology that allows practitioners to account for different lengths of time between sampling periods when estimating survival probabilities which is especially applicable to organisms with distinct biological stages.

","language":"English","publisher":"Ecological society of Japan","doi":"10.1002/1438-390X.12044","usgsCitation":"Crockett, J.G., Bailey, L., and Muths, E., 2020, Highly variable rates of survival to metamorphosis in wild boreal toads (Anaxyrus boreas boreas): Population Ecology, v. 62, no. 2, p. 258-268, https://doi.org/10.1002/1438-390X.12044.","productDescription":"11 p.","startPage":"258","endPage":"268","ipdsId":"IP-112574","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437086,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OOB1V","text":"USGS data release","linkHelpText":"Boreal toad metamorph capture, recapture and covariates data, Colorado 2017-2018"},{"id":376436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Crockett, John G.","contributorId":229352,"corporation":false,"usgs":false,"family":"Crockett","given":"John","email":"","middleInitial":"G.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":792953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Larissa L.","contributorId":229353,"corporation":false,"usgs":false,"family":"Bailey","given":"Larissa L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":792954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":229354,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":792955,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210615,"text":"70210615 - 2020 - Lifetime chronicles of selenium exposure linked to deformities in an imperiled migratory fish","interactions":[],"lastModifiedDate":"2020-06-12T17:01:32.421058","indexId":"70210615","displayToPublicDate":"2020-02-24T11:57:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Lifetime chronicles of selenium exposure linked to deformities in an imperiled migratory fish","docAbstract":"

Aquatic ecosystems worldwide face growing threats from elevated levels of contaminants from human activities. Toxic levels of selenium (Se) shown to cause deformities in birds, fish, and mammals can transfer from parents to progeny during embryonic development or accumulate through Se-enriched diets. For migratory species that move across landscapes, tracking exposure to elevated Se is vital to mitigating vulnerabilities. Yet, traditional toxicological investigations resolve only recent Se exposure. Here, we use a novel combination of X-ray fluorescence microscopy and depositional chronology in a biomineral to reveal for the first time provenance, life stage, and duration of toxic Se exposure over the lifetime of an organism. Spinal deformities observed in wild Sacramento Splittail (Pogonichthys macrolepidotus), an imperiled migratory minnow, were attributed to elevated Se acquired through maternal transfer and juvenile feeding on contaminated prey. This novel approach paves the way for diagnosing sources, pathways, and potential for a cumulative exposure of Se relevant for conservation.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b06419","usgsCitation":"Johnson, R.C., Stewart, A.R., Limburg, K., Huang, R., Cocherell, D.E., and Feyrer, F.V., 2020, Lifetime chronicles of selenium exposure linked to deformities in an imperiled migratory fish: Environmental Science & Technology, v. 54, no. 5, p. 2892-2901, https://doi.org/10.1021/acs.est.9b06419.","productDescription":"10 p.","startPage":"2892","endPage":"2901","ipdsId":"IP-109684","costCenters":[{"id":40553,"text":"WMA - Office of the Chief Operating Officer","active":true,"usgs":true}],"links":[{"id":457622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.9b06419","text":"Publisher Index Page"},{"id":375560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Byron","otherGeospatial":"San Joaquin River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.79374694824219,\n 37.90465769755854\n ],\n [\n -121.55685424804688,\n 37.90465769755854\n ],\n [\n -121.55685424804688,\n 38.07836562996712\n ],\n [\n -121.79374694824219,\n 38.07836562996712\n ],\n [\n -121.79374694824219,\n 37.90465769755854\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Rachel C.","contributorId":196877,"corporation":false,"usgs":false,"family":"Johnson","given":"Rachel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":790852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stewart, A. Robin 0000-0003-2918-546X arstewar@usgs.gov","orcid":"https://orcid.org/0000-0003-2918-546X","contributorId":1482,"corporation":false,"usgs":true,"family":"Stewart","given":"A.","email":"arstewar@usgs.gov","middleInitial":"Robin","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":40553,"text":"WMA - Office of the Chief Operating Officer","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":790853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Limburg, Karin 0000-0003-3716-8555","orcid":"https://orcid.org/0000-0003-3716-8555","contributorId":225258,"corporation":false,"usgs":false,"family":"Limburg","given":"Karin","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":790854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huang, Rong","contributorId":225259,"corporation":false,"usgs":false,"family":"Huang","given":"Rong","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":790855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cocherell, Dennis E.","contributorId":174644,"corporation":false,"usgs":false,"family":"Cocherell","given":"Dennis","email":"","middleInitial":"E.","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":790856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790857,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209715,"text":"70209715 - 2020 - Conceptual frameworks","interactions":[],"lastModifiedDate":"2020-04-22T15:25:54.188526","indexId":"70209715","displayToPublicDate":"2020-02-24T10:23:19","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"5","title":"Conceptual frameworks","docAbstract":"

The chapter starts by addressing some of the issues that come from not using a conceptual framework. This point is illustrated using an example with causal factors. The chapter then goes on to explain the mechanics of establishing conceptual frameworks. Lastly, it lays out a step-by-step guide on how to create a framework—generating a set of concepts, specifying the relations between concepts, writing a narrative for the conceptual framework, and rethinking the framework through the entire research project.


","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Basic quantitative research methods for urban planners","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor and Francis","doi":"10.4324/9780429325021-5","collaboration":"","usgsCitation":"Park, K., Grace, J., and Ewing, R., 2020, Conceptual frameworks, chap. 5 of Basic quantitative research methods for urban planners, p. 76-87, https://doi.org/10.4324/9780429325021-5.","productDescription":"12 p.","startPage":"76","endPage":"87","ipdsId":"IP-114830","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":374193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Park, Keunhyun","contributorId":224296,"corporation":false,"usgs":false,"family":"Park","given":"Keunhyun","email":"","affiliations":[{"id":40852,"text":"Utah State University, Department of Landscape Architecture and Environmental Planning","active":true,"usgs":false}],"preferred":false,"id":787650,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grace, James B. 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":221554,"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":787651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ewing, Reid","contributorId":204537,"corporation":false,"usgs":false,"family":"Ewing","given":"Reid","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":787652,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210169,"text":"70210169 - 2020 - Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model","interactions":[],"lastModifiedDate":"2020-05-19T14:20:30.574872","indexId":"70210169","displayToPublicDate":"2020-02-24T09:15:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model","docAbstract":"The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of subgrid or subwatershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) represents the subgrid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-average SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation (CV) values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the subgrid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as increases in the subgrid SWE CV by 1.0 resulted in decreases in runoff ratio by as much as 12 percent in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water limited dryness index were associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modeling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modeling applications.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13735","usgsCitation":"Sexstone, G., Driscoll, J.M., Hay, L., Hammond, J., and Barnhart, T., 2020, Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model: Hydrological Processes, v. 34, no. 11, p. 2365-2380, https://doi.org/10.1002/hyp.13735.","productDescription":"16 p.","startPage":"2365","endPage":"2380","ipdsId":"IP-107421","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":437087,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OEIRJF","text":"USGS data release","linkHelpText":"Data release in support of Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model"},{"id":374919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n 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0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":789389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren 0000-0003-3763-4595","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":205020,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":789390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789391,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":202558,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789392,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222500,"text":"70222500 - 2020 - A machine learning approach to developing ground motion models from simulated ground motions","interactions":[],"lastModifiedDate":"2021-07-30T12:47:26.178465","indexId":"70222500","displayToPublicDate":"2020-02-24T07:44:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"A machine learning approach to developing ground motion models from simulated ground motions","docAbstract":"

We use a machine learning approach to build a ground motion model (GMM) from a synthetic database of ground motions extracted from the Southern California CyberShake study. An artificial neural network is used to find the optimal weights that best fit the target data (without overfitting), with input parameters chosen to match that of state-of-the-art GMMs. We validate our synthetic-based GMM with empirically based GMMs derived from the globally based Next Generation Attenuation West2 data set, finding near-zero median residuals and similar amplitude and trends (with period) of total variability. Additionally, we find that the artificial neural network GMM has similar bias and variability to empirical GMMs from records of the recent \"urn:x-wiley:grl:media:grl60306:grl60306-math-0001\" Ridgecrest event, which neither GMM has included in its formulation. As simulations continue to better model broadband ground motions, machine learning provides a way to utilize the vast amount of synthetically generated data and guide future parameterization of GMMs.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GL086690","usgsCitation":"Withers, K., Moschetti, M.P., and Thompson, E.M., 2020, A machine learning approach to developing ground motion models from simulated ground motions: Geophysical Research Letters, v. 47, no. 6, e2019GL086690, 9 p., https://doi.org/10.1029/2019GL086690.","productDescription":"e2019GL086690, 9 p.","ipdsId":"IP-116117","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.091796875,\n 33.815666308702774\n ],\n [\n -117.31201171875001,\n 32.34284135639302\n ],\n [\n -114.345703125,\n 32.69486597787505\n ],\n [\n -113.7744140625,\n 34.45221847282654\n ],\n [\n -119.091796875,\n 34.45221847282654\n ],\n [\n -119.091796875,\n 33.815666308702774\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Withers, Kyle 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820320,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820322,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260216,"text":"70260216 - 2020 - Spatial and temporal variations in SO2 and PM2.5 levels around Kīlauea volcano, Hawai'i during 2007–2018","interactions":[],"lastModifiedDate":"2024-10-30T11:39:49.568353","indexId":"70260216","displayToPublicDate":"2020-02-24T06:37:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variations in SO2 and PM2.5 levels around Kīlauea volcano, Hawai'i during 2007–2018","docAbstract":"

Among the hazards posed by volcanoes are the emissions of gases and particles that can affect air quality and damage agriculture and infrastructure. A recent intense episode of volcanic degassing associated with severe impacts on air quality accompanied the 2018 lower East Rift Zone (LERZ) eruption of Kīlauea volcano, Hawai'i. This resulted in a major increase in gas emission rates with respect to usual emission values for this volcano, along with a shift in the source of the dominant plume to a populated area on the lower flank of the volcano. This led to reduced air quality in downwind communities. We analyse open-access data from the permanent air quality monitoring networks operated by the Hawai'i Department of Health (HDOH) and National Park Service (NPS), and report on measurements of atmospheric sulfur dioxide (SO2) between 2007 and 2018 and PM2.5 (aerosol particulate matter with diameter <2.5 μm) between 2010 and 2018. Additional air quality data were collected through a community-operated network of low-cost PM2.5 sensors during the 2018 LERZ eruption. From 2007 to 2018 the two most significant escalations in Kīlauea's volcanic emissions were: the summit eruption that began in 2008 (Kīlauea emissions averaged 5–6 kt/day SO2 from 2008 until summit activity decreased in May 2018) and the LERZ eruption in 2018 when SO2 emission rates reached a monthly average of 200 kt/day during June. In this paper we focus on characterizing the airborne pollutants arising from the 2018 LERZ eruption and the spatial distribution and severity of volcanic air pollution events across the Island of Hawai'i. The LERZ eruption caused the most frequent and severe exceedances of the Environmental Protection Agency (EPA) PM2.5 air quality threshold (35 μg/m3 as a daily average) in Hawai'i in the period 2010–2018. In Kona, for example, the maximum 24-h-mean mass concentration of PM2.5 was recorded as 59 μg/m3 on the twenty-ninth of May 2018, which was one of eight recorded exceedances of the EPA air quality threshold during the 2018 LERZ eruption, where there had been no exceedances in the previous 8 years as measured by the HDOH and NPS networks. SO2 air pollution during the LERZ eruption was most severe in communities in the south and west of the island, as measured by selected HDOH and NPS stations in this study, with a maximum 24-h-mean mass concentration of 728 μg/m3 recorded in Ocean View (100 km west of the LERZ emission source) in May 2018. Data from the low-cost sensor network correlated well with data from the HDOH PM2.5 instruments, confirming that these low-cost sensors provide a robust means to augment reference-grade instrument networks.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2020.00036","usgsCitation":"Whitty, R., Ilyinskaya, E., Mason, E., Wieser, P., Liu, E.J., Schmidt, A., Roberts, T., Pfeffer, M., Brooks, B., Mather, T., Edmonds, M., Elias, T., Schneider, D.J., Oppenheimer, C., Dybwad, A., Nadeau, P.A., and Kern, C., 2020, Spatial and temporal variations in SO2 and PM2.5 levels around Kīlauea volcano, Hawai'i during 2007–2018: Frontiers in Earth Science, v. 8, 36, 17 p., https://doi.org/10.3389/feart.2020.00036.","productDescription":"36, 17 p.","ipdsId":"IP-113251","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467296,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00036","text":"Publisher Index Page"},{"id":463411,"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 \"coordinates\": [\n [\n [\n -155.5280575771671,\n 19.636187244535606\n ],\n [\n -155.5280575771671,\n 19.167002726002252\n ],\n [\n -154.9496591158342,\n 19.167002726002252\n ],\n [\n -154.9496591158342,\n 19.636187244535606\n ],\n [\n -155.5280575771671,\n 19.636187244535606\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Whitty, R.C.W.","contributorId":345714,"corporation":false,"usgs":false,"family":"Whitty","given":"R.C.W.","email":"","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":917429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ilyinskaya, E.","contributorId":149561,"corporation":false,"usgs":false,"family":"Ilyinskaya","given":"E.","affiliations":[],"preferred":false,"id":917430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mason, E.","contributorId":203830,"corporation":false,"usgs":false,"family":"Mason","given":"E.","email":"","affiliations":[{"id":36727,"text":"Engility Corp.","active":true,"usgs":false}],"preferred":false,"id":917431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wieser, P.E.","contributorId":345707,"corporation":false,"usgs":false,"family":"Wieser","given":"P.E.","email":"","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":917432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, E. 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Natural bedrock rivers have various bedforms created by erosion. Flow‐parallel incisional grooves formed longitudinally in bedrock are one common example of such bedforms. Although several studies have been conducted regarding these grooves, their formation processes are not well understood. In this study, we conducted a flume experiment to investigate the relationship between the flow structure and longitudinal grooves. The experimental results strongly suggest that longitudinal grooves are formed by moving sediment concentrated in multiple longitudinal pathways by turbulence‐driven secondary flows. The sediment preferentially abrades the bedrock along these flow‐parallel pathways resulting in longitudinal grooves in the bedrock. Measurements of the flow velocity distribution show that the positions of secondary flow cells producing the initial formation of the grooves are altered by the formation of those grooves. Because displaced secondary flows tend to make the sediment collide with the sidewalls of the longitudinal grooves, the grooves grow wider over time and some grooves partially combine with other adjacent grooves. The initial maximum number of longitudinal grooves Nmax strongly depends on the river width‐depth ratio B/D, which defines the number of secondary flow cells, and can be expressed as Nmax = 0.5B/D. However, because some grooves coalesce with other grooves due to the effects of the displacement of secondary flows, the average number of grooves showed a relationship that can be expressed as N = 0.41B/D. Based on this relationship, we inversely estimated the flow discharge of the Abashiri River using the number of longitudinal grooves observed in the river. The result was consistent with the observed annual maximum flow discharge of the river. This suggests that the number of longitudinal grooves can be used as an indicator for estimation of the formative flow discharge in bedrock rivers.

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In the western portion of its range, where most\tprecipitation arrives during winter as snowfall and summers are dry, snowdrifts that persist into\tthe growing season provide soil moisture recharge that sustain many aspen groves that are\timportant locations of biodiversity within the landscape. There is growing concern that reduced\nsnowpack due to climate change may reduce the long-term persistence and productivity of aspen communities in these regions. In this study, we evaluated the potential for climate change and\tdrought to reduce or eliminate isolated aspen communities in southwestern Idaho. We used a landscape simulation model integrated with inputs from an empirically derived biogeochemical\nmodel of growth, and a species distribution model of regeneration to forecast how changes in\nclimate, declining snowpack, and competition with a conifer species is likely to affect aspen\noccupancy over the next 85-years. We found that simulated reductions in snowpack depth (and\nassociated increases in climatic water deficit) caused a reduction in aspen persistence; aspen\noccupancy was reduced under all high emissions climate scenarios. Douglas-fir (Pseudotsuga\nmenziesii) occupancy also declined under all future climates. Aspen regeneration declined over\nthe course of all simulations, with an ensemble ratio of mortality/establishment increasing over\nthe course of both low and high emissions climate scenarios. Climate-induced mortality of aspen\nclones increased in frequency under all climate scenarios and, under the most severe emissions\nscenarios, contributed to a substantial decline of aspen cover. Our research suggests that\nsnowbanks will be an important determinant of long-term persistence of aspen under changing climate in the region.","language":"English","publisher":"Elseiver","doi":"10.1016/j.foreco.2020.118005","usgsCitation":"Kretchun, A.M., Scheller, R., Shinneman, D.J., Soderquist, B., Maguire, K.C., Link, T., and Strand, E.K., 2020, Long term persistence of aspen in snowdrift-dependent ecosystems: Forest Ecology and Management, v. 462, 118005, https://doi.org/10.1016/j.foreco.2020.118005.","productDescription":"118005","ipdsId":"IP-112095","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2020.118005","text":"Publisher Index Page"},{"id":373273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"462","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kretchun, Alec M","contributorId":223372,"corporation":false,"usgs":false,"family":"Kretchun","given":"Alec","email":"","middleInitial":"M","affiliations":[{"id":40703,"text":"Quantum Spatial","active":true,"usgs":false}],"preferred":false,"id":784879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scheller, Robert M","contributorId":147807,"corporation":false,"usgs":false,"family":"Scheller","given":"Robert M","affiliations":[{"id":16941,"text":"Environmental Science and Management Department, Portland State University","active":true,"usgs":false}],"preferred":false,"id":784880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":784878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soderquist, B","contributorId":223373,"corporation":false,"usgs":false,"family":"Soderquist","given":"B","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":784882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maguire, Kaitlin C. 0000-0001-8193-2384","orcid":"https://orcid.org/0000-0001-8193-2384","contributorId":203419,"corporation":false,"usgs":true,"family":"Maguire","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":784881,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Link, Timothy E","contributorId":223374,"corporation":false,"usgs":false,"family":"Link","given":"Timothy E","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":784883,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Strand, Eva K.","contributorId":149810,"corporation":false,"usgs":false,"family":"Strand","given":"Eva","email":"","middleInitial":"K.","affiliations":[{"id":17832,"text":"University of Idaho Department of Forest, Rangeland, and Fire Sciences","active":true,"usgs":false}],"preferred":false,"id":784884,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229750,"text":"70229750 - 2020 - Evolving infrasound detections from Bogoslof volcano, Alaska: Insights from atmospheric propagation modeling","interactions":[],"lastModifiedDate":"2022-03-16T14:33:25.456937","indexId":"70229750","displayToPublicDate":"2020-02-22T09:29:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Evolving infrasound detections from Bogoslof volcano, Alaska: Insights from atmospheric propagation modeling","docAbstract":"

Bogoslof volcano, a back-arc volcano in Alaska’s Aleutian arc, began an eruptive sequence in mid-December 2016 that ended in late August 2017, with 70 individual eruptive episodes. Because there were no local seismic or infrasound stations on the island, the Alaska Volcano Observatory (AVO) relied on distant geophysical networks and remote sensing techniques to assess activity during the eruption. AVO maintains six infrasound arrays to monitor activity along the Aleutian arc: Adak, the Island of Four Mountains, Okmok, Akutan, Sand Point, and Dillingham. Eruption detection at infrasound arrays is subject to local as well as mesoscale meteorological conditions that vary greatly over both short and long timescales. Infrasound detections from the array nearest to Bogoslof (Okmok), with a latency of about 3 min, played a crucial role in monitoring activity during the eruption. Despite the relative proximity of the Okmok array to Bogoslof (60 km), infrasound detections were not uniformly observed with only about two-thirds of the events successfully detected. The farthest array at Dillingham (816 km) detected approximately half of the explosive events, with all other arrays detecting less than half of the events. We compare observations with infrasound propagation model predictions, using both normal mode and parabolic equation forward models, to interpret the variation in detections of the 70 explosive events across the AVO infrasound network. The forward models utilize the newly created, publicly available AVO-G2S atmospheric reconstruction using numerical weather predictions data for the lower atmosphere, coupled with upper atmosphere empirical models of wind speeds and temperature. We find that long-range detections (> 100 km) of Bogoslof events are largely aligned with seasonal variability in favorable propagation conditions, while regional detections (< 100 km) are less consistent with propagation modeling. Understanding the output of numerical models in comparison to past observations will facilitate their use in future operational settings for AVO and other observatories.

","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-1360-3","usgsCitation":"Schwaiger, H., Lyons, J.J., Iezzi, A., Fee, D., and Haney, M.M., 2020, Evolving infrasound detections from Bogoslof volcano, Alaska: Insights from atmospheric propagation modeling: Bulletin of Volcanology, v. 82, 27, 14 p., https://doi.org/10.1007/s00445-020-1360-3.","productDescription":"27, 14 p.","ipdsId":"IP-108990","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":397149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -179.5166015625,\n 48.40003249610685\n ],\n [\n -157.5,\n 48.40003249610685\n ],\n [\n -157.5,\n 58.768200159239576\n ],\n [\n -179.5166015625,\n 58.768200159239576\n ],\n [\n -179.5166015625,\n 48.40003249610685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Schwaiger, Hans 0000-0001-7397-8833","orcid":"https://orcid.org/0000-0001-7397-8833","contributorId":214983,"corporation":false,"usgs":true,"family":"Schwaiger","given":"Hans","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":838174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":838175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fee, David 0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":838176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838177,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228770,"text":"70228770 - 2020 - The effects of fire on the thermal environment of sagebrush communities","interactions":[],"lastModifiedDate":"2022-02-18T13:13:50.362045","indexId":"70228770","displayToPublicDate":"2020-02-22T07:09:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2476,"text":"Journal of Thermal Biology","active":true,"publicationSubtype":{"id":10}},"title":"The effects of fire on the thermal environment of sagebrush communities","docAbstract":"

Thermal heterogeneity provides options for organisms during extreme temperatures that can contribute to their fitness. Sagebrush (Artemisia spp.) communities exhibit vegetation heterogeneity that creates thermal variation at fine spatial scales. However, fire can change vegetation and thereby variation within the thermal environment of sagebrush communities. To describe spatial and temporal thermal variation of sagebrush communities following wildfire, we measured black bulb temperature (Tbb) at 144 random points dispersed within unburned and burned communities, for 24-h at each random point. We observed a wide thermal gradient in unburned (−7.3° to 63.3 °C) and burned (−4.6° to 64.8 °C) sagebrush communities. Moreover, unburned and burned sagebrush communities displayed high thermal heterogeneity relative to ambient temperature (Tair). Notably, Tbb varied by 47 °C in both unburned and burned communities when Tair was 20 °C. However, fire greatly reduced the buffering capacity and thermal refuge of Wyoming big sagebrush (A. tridentata wyomingensis) communities during low and high Tair. Furthermore, fire increased Tbb in Wyoming big sagebrush and mountain big sagebrush (A. t. vaseyana) during the mid-day hours. These results demonstrate how fire changes the thermal environment of big sagebrush communities and the importance of shrub structure which can provide thermal refuge for organisms in burned communities during extreme low and high Tair.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jtherbio.2019.102488","usgsCitation":"Anthony, C., Hagen, C., Dugger, K., and Elmore, R.D., 2020, The effects of fire on the thermal environment of sagebrush communities: Journal of Thermal Biology, v. 89, 102488, 9 p., https://doi.org/10.1016/j.jtherbio.2019.102488.","productDescription":"102488, 9 p.","ipdsId":"IP-113899","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anthony, Christopher R.","contributorId":279695,"corporation":false,"usgs":false,"family":"Anthony","given":"Christopher R.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":835376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagen, Christian A.","contributorId":279696,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":835377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":835375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elmore, R. Dwayne","contributorId":279697,"corporation":false,"usgs":false,"family":"Elmore","given":"R.","email":"","middleInitial":"Dwayne","affiliations":[{"id":57346,"text":"oksu","active":true,"usgs":false}],"preferred":false,"id":835378,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208644,"text":"70208644 - 2020 - Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony","interactions":[],"lastModifiedDate":"2022-03-31T18:52:42.924432","indexId":"70208644","displayToPublicDate":"2020-02-22T06:42:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony","docAbstract":"Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040725","usgsCitation":"Pastick, N., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., and Wu, Z., 2020, Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony: Remote Sensing, v. 12, no. 4, 725, 17 p.; Data release, https://doi.org/10.3390/rs12040725.","productDescription":"725, 17 p.; Data release","ipdsId":"IP-114798","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457631,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040725","text":"Publisher Index Page"},{"id":437093,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91NJ2PD","text":"USGS data release","linkHelpText":"Near real time estimation of annual exotic herbaceous fractional cover in the sagebrush ecosystem 30m, USA, July 2020"},{"id":437092,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKPT07","text":"USGS data release","linkHelpText":"Weekly cloud free Harmonized Landsat Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) data for western United States (2016 &amp;amp;ndash; 2019)."},{"id":437091,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XT1BV2","text":"USGS data release","linkHelpText":"Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States (2016 - 2019)"},{"id":372534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397944,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZZSX5Q","text":"USGS data release","description":"USGS data release","linkHelpText":"Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.728515625,\n 40.97989806962013\n ],\n [\n -114.7412109375,\n 40.97989806962013\n ],\n [\n -114.7412109375,\n 44.18220395771566\n ],\n [\n -121.728515625,\n 44.18220395771566\n ],\n [\n -121.728515625,\n 40.97989806962013\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Pastick, Neal 0000-0002-4321-6739","orcid":"https://orcid.org/0000-0002-4321-6739","contributorId":222683,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":782880,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782883,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":782881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parajuli, Sujan 0000-0002-1652-3063","orcid":"https://orcid.org/0000-0002-1652-3063","contributorId":222684,"corporation":false,"usgs":true,"family":"Parajuli","given":"Sujan","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":782882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782884,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":782885,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209323,"text":"70209323 - 2020 - The NASA hydrological forecast system for food and water security applications","interactions":[],"lastModifiedDate":"2020-08-05T13:51:35.378688","indexId":"70209323","displayToPublicDate":"2020-02-21T16:42:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"The NASA hydrological forecast system for food and water security applications","docAbstract":"Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning System Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multi-model, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage); and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multi-model ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its one-to-five month forecasts successfully capture known historic drought events. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-18-0264.1","usgsCitation":"Arsenault, K., Shukla, S., Hazra, A., Getirana, A., McNally, A., Kumar, S., Koster, R., Peters-Lidard, C., Zaitchik, B., Badr, H., Jung, H.C., Narapusetty, B., , N., Wang, S., Mocko, D.M., Funk, C., Harrison, L., Husak, G.J., Adoum, A., Galu, G., Magadzire, T., Roningen, J., Shaw, M.J., Eylander, J., Bergaoui, K., McDonnell, R.A., and Verdin, J., 2020, The NASA hydrological forecast system for food and water security applications: Bulletin of the American Meteorological Society, v. 101, no. 7, p. 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Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal subsurface thaw dynamics of an aufeis feature inferred from geophysical methods","docAbstract":"Aufeis are sheets of ice unique to cold regions that originate from repeated flooding and freezing events during the winter. They have hydrological importance associated with summer flows and possibly winter insulation, but little is known about the seasonal dynamics of the unfrozen sediment layer beneath them. This layer may support perennial groundwater flow in regions with otherwise continuous permafrost. For this study, ground penetrating radar (GPR) were collected in September 2016 (maximum thaw) and April 2017 (maximum frozen) at the Kuparuk aufeis field on the North Slope of Alaska. Supporting surface nuclear magnetic resonance (NMR) data were collected during the maximum frozen campaign. These point-in-time geophysical data sets were augmented by continuous subsurface temperature data and periodic Structure-from-Motion digital elevation models (DEM) collected seasonally. GPR and difference DEM data showed maximum ice thicknesses of up to 6 m over the sediment surface. Below the ice, GPR and NMR identified regions of permafrost and regions of seasonally frozen sediment (i.e., the active layer) underlain by a substantial perennially unfrozen zone or “lateral talik” that ranged from 0 m to over 13 m thick. The seasonally frozen cobble layer above the talik was typically 3 to 5 m thick, with freezing apparently enabled by relatively high thermal diffusivity of the overlying ice and rock cobbles. The large talik beneath the aufeis and active layer suggests that year-round groundwater flow and coupled heat transport occurs beneath much of the feature. Highly permeable alluvial material and discrete zones of apparent groundwater upwelling indicated by geophysical and ground temperature data allows direct connection between the aufeis and the talik below.","language":"English","publisher":"Wiley","doi":"10.1029/2019JF005345","usgsCitation":"Terry, N., Grunewald, E., Briggs, M.A., Gooseff, M., Huryn, A.D., Kass, M.A., Tape, K., Hendrickson, P., and Lane, J., 2020, Seasonal subsurface thaw dynamics of an aufeis feature inferred from geophysical methods: Journal of Geophysical Research: Earth Surface, v. 125, no. 3, e2019JF005345, 18 p., https://doi.org/10.1029/2019JF005345.","productDescription":"e2019JF005345, 18 p.","ipdsId":"IP-106506","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":372655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","county":"Kuparuk River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.37011718749997,\n 69.79413569863112\n ],\n [\n -148.55712890625,\n 70.44415495538642\n ],\n [\n -149.30419921875,\n 70.49557354093136\n ],\n [\n -149.85351562499997,\n 69.80172356231073\n ],\n [\n -149.37011718749997,\n 69.79413569863112\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":783169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grunewald, Elliot","contributorId":193963,"corporation":false,"usgs":false,"family":"Grunewald","given":"Elliot","email":"","affiliations":[],"preferred":false,"id":783170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":783171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gooseff, Michael","contributorId":181942,"corporation":false,"usgs":false,"family":"Gooseff","given":"Michael","affiliations":[],"preferred":false,"id":783172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huryn, Alexander D. 0000-0002-1365-2361","orcid":"https://orcid.org/0000-0002-1365-2361","contributorId":20164,"corporation":false,"usgs":false,"family":"Huryn","given":"Alexander","email":"","middleInitial":"D.","affiliations":[{"id":28219,"text":"The University of Alabama, Department of Biological Sciences, Tuscaloosa, AL 35487","active":true,"usgs":false}],"preferred":false,"id":783173,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kass, M. 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Bats make up one-fifth of all mammalian species worldwide and are found on every continent except Antarctica. They contribute to overall ecosystem health by suppressing pest insects and pollinating plants and spreading seeds. Eight North American bat species are listed as federally endangered or threatened, and more than one-half are of current conservation concern in the United States, Canada, or Mexico.

The U.S. Geological Survey (USGS) leads, manages, and coordinates the multinational North American Bat Monitoring Program (NABat) as well as conducts scientific research on bats. USGS and NABat partners help resource managers and policymakers make informed decisions regarding the conservation of bats across North America. USGS science also helps inform decision making with respect to WNS surveillance and bat vulnerability; mitigation of potential impacts of energy development on bats; prelisting conservation efforts for regulatory agencies; and land management practices.

Partners are essential to the success of NABat. The data contributed by NABat partners provide baseline knowledge on the distribution and abundance of bats, which is used to evaluate the impacts of the threats to bats across North America. These data are also the foundation for the scientific information used to set conservation priorities and evaluate the effectiveness of management actions.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203008","usgsCitation":"Reichert, B.E., and Soileau, S.C., 2020, U.S. Geological Survey science in support of the North American Bat Monitoring Program (NABat): U.S. Geological Survey Fact Sheet 2020–3008, 2 p., https://doi.org/10.3133/fs20203008.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-113139","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":372513,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://www.nabatmonitoring.org/","text":"North American Bat Monitoring Program (NABat)"},{"id":372512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3008/fs20203008.pdf","text":"Report","size":"3.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3008"},{"id":372461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3008/coverthb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.28515625,\n 14.944784875088372\n ],\n [\n -90.263671875,\n 16.46769474828897\n ],\n [\n -91.318359375,\n 17.308687886770034\n ],\n [\n -87.890625,\n 18.22935133838668\n ],\n [\n -87.099609375,\n 21.207458730482642\n ],\n [\n -90.087890625,\n 21.12549763660628\n ],\n [\n -92.724609375,\n 18.979025953255267\n ],\n [\n -95.09765625,\n 18.646245142670608\n ],\n [\n -97.91015624999999,\n 24.206889622398023\n ],\n [\n -96.240234375,\n 28.76765910569123\n ],\n [\n -90.615234375,\n 29.611670115197377\n ],\n [\n -88.76953125,\n 28.844673680771795\n ],\n [\n -84.814453125,\n 29.6880527498568\n ],\n [\n -81.73828125,\n 25.403584973186703\n ],\n [\n -80.15625,\n 24.926294766395593\n ],\n [\n -79.716796875,\n 26.745610382199022\n ],\n [\n -80.947265625,\n 30.751277776257812\n ],\n [\n -75.234375,\n 35.38904996691167\n ],\n [\n -73.740234375,\n 39.36827914916014\n ],\n [\n -67.587890625,\n 44.08758502824516\n ],\n [\n -65.478515625,\n 42.87596410238256\n ],\n [\n -59.58984374999999,\n 45.706179285330855\n ],\n [\n -52.55859375,\n 46.558860303117164\n ],\n [\n -55.72265625,\n 53.69670647530323\n ],\n [\n -64.423828125,\n 60.58696734225869\n ],\n [\n -73.65234375,\n 62.59334083012024\n ],\n [\n -77.95898437499999,\n 62.71446210149774\n ],\n [\n -97.998046875,\n 68.78414378041504\n ],\n [\n -126.91406249999999,\n 70.31873847853124\n ],\n [\n -143.173828125,\n 70.19999407534661\n ],\n [\n -157.32421875,\n 71.44117085172385\n ],\n [\n -166.9921875,\n 68.8159271333607\n ],\n [\n -168.22265625,\n 65.62202261510642\n ],\n [\n -164.267578125,\n 59.7563950493563\n ],\n [\n -170.5078125,\n 52.96187505907603\n ],\n [\n -151.34765625,\n 57.27904276497778\n ],\n [\n -145.8984375,\n 60.19615576604439\n ],\n [\n -138.515625,\n 58.722598828043374\n ],\n [\n -134.296875,\n 54.97761367069628\n ],\n [\n -132.71484375,\n 52.3755991766591\n ],\n [\n -125.68359374999999,\n 48.40003249610685\n ],\n [\n -124.541015625,\n 39.90973623453719\n ],\n [\n -121.28906250000001,\n 33.797408767572485\n ],\n [\n -118.91601562499999,\n 32.84267363195431\n ],\n [\n -116.103515625,\n 30.06909396443887\n ],\n [\n -115.6640625,\n 27.059125784374068\n ],\n [\n -110.0390625,\n 22.67484735118852\n ],\n [\n -107.22656249999999,\n 22.755920681486405\n ],\n [\n -105.8203125,\n 19.31114335506464\n ],\n [\n -102.74414062499999,\n 17.5602465032949\n ],\n [\n -96.6796875,\n 15.029685756555674\n ],\n [\n -94.658203125,\n 15.707662769583518\n ],\n [\n -92.28515625,\n 14.944784875088372\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

NABat Program Coordinator
USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526–8118

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-02-20","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Reichert, Brian E. 0000-0002-9640-0695","orcid":"https://orcid.org/0000-0002-9640-0695","contributorId":204260,"corporation":false,"usgs":true,"family":"Reichert","given":"Brian","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":782056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soileau, Suzanna C. 0000-0002-4331-0098","orcid":"https://orcid.org/0000-0002-4331-0098","contributorId":222392,"corporation":false,"usgs":true,"family":"Soileau","given":"Suzanna","email":"","middleInitial":"C.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":782057,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208590,"text":"sir20195142 - 2020 - Assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument, New Mexico","interactions":[],"lastModifiedDate":"2022-04-25T20:20:35.352401","indexId":"sir20195142","displayToPublicDate":"2020-02-21T13:52:10","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5142","displayTitle":"Assessment of Soil and Water Resources in the Organ Mountains-Desert Peaks National Monument, New Mexico","title":"Assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument, New Mexico","docAbstract":"

The Organ Mountains-Desert Peaks National Monument (Monument) in southern New Mexico was established in 2014. Given anticipated future demands in the Monument for recreation, livestock grazing, and maintenance of rights-of-way (for example, pipelines and powerlines), the Bureau of Land Management (BLM) needs a better understanding of the current soil and water resources and how infrastructure improvements could affect these resources and the watershed. Specifically, the BLM is concerned with infiltration and erosion and their relations to existing or planned infrastructure, such as roads, campgrounds, location of livestock grazing, and rights-of-way. Alternatives to the current land-use conditions, land-management practices, and infrastructure will be assessed by BLM to best protect Monument resources. The U.S. Geological Survey, in cooperation with the BLM, conducted a study to assess the soil and water resources within the Monument to provide an inventory and compilation of natural-resource information needed by resource managers for the BLM’s land-use planning process for this new national monument. The overall objectives of this study were to (1) compile and interpret existing soil- and water-resource data for the Monument and (2) provide a basic assessment of the surface hydrological effects of selected alternatives to current land use and infrastructure. Data were compiled by using geographic information system software and evaluated for hydrologic and landscape properties that influence infiltration, runoff, and erosion. The effects of changing vegetation were simulated by using different scenarios in the Rangeland Hydrology and Erosion Model. Results of this model indicate areas where soil loss or runoff may occur.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195142","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Blake, J.M., Mitchell, A.C., Shephard, Z., Ball, G., Chavarria, S., and Douglas-Mankin, K.R., 2020, Assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5142, 64 p., https://doi.org/10.3133/sir20195142.","productDescription":"Report: x, 64 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","ipdsId":"IP-098054","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":372464,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5142/sir20195142.pdf","text":"Report","size":"87.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5142"},{"id":399617,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109724.htm"},{"id":372465,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JVHA4Z","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Database associated with the assessment of soil and water resources in the Organ Mountains-Desert Peaks National Monument"},{"id":372463,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5142/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Organ Mountains-Desert Peaks National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.3,\n 31.8511\n ],\n [\n -106.4639,\n 31.8511\n ],\n [\n -106.4639,\n 32.6628\n ],\n [\n -107.3,\n 32.6628\n ],\n [\n -107.3,\n 31.8511\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-02-21","noUsgsAuthors":false,"publicationDate":"2020-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Blake, Johanna M. 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":211907,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchell, Aurelia C. 0000-0003-3302-4546","orcid":"https://orcid.org/0000-0003-3302-4546","contributorId":222580,"corporation":false,"usgs":true,"family":"Mitchell","given":"Aurelia C.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shephard, Zachary M. 0000-0003-2994-3355","orcid":"https://orcid.org/0000-0003-2994-3355","contributorId":222581,"corporation":false,"usgs":true,"family":"Shephard","given":"Zachary","email":"","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ball, Grady 0000-0003-3030-055X","orcid":"https://orcid.org/0000-0003-3030-055X","contributorId":222582,"corporation":false,"usgs":true,"family":"Ball","given":"Grady","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chavarria, Shaleene 0000-0001-8792-1010","orcid":"https://orcid.org/0000-0001-8792-1010","contributorId":222578,"corporation":false,"usgs":true,"family":"Chavarria","given":"Shaleene","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782633,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":222579,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[{"id":40563,"text":"Former NMWSC","active":true,"usgs":false}],"preferred":false,"id":782634,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210507,"text":"70210507 - 2020 - Final project memorandum: Southeast Climate Adaptation Science Center Project","interactions":[],"lastModifiedDate":"2020-07-22T18:45:45.080259","indexId":"70210507","displayToPublicDate":"2020-02-21T11:25:03","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5883,"text":"Cooperator Report","active":true,"publicationSubtype":{"id":1}},"title":"Final project memorandum: Southeast Climate Adaptation Science Center Project","docAbstract":"

Low-lying public lands along the northern Gulf of Mexico coast are vulnerable to sea-level rise. Coastal planners and resource managers in the region have requested customized information that can be used to concisely communicate local sea-level rise scenarios and identify potential impacts to the missions of management agencies.

In this project, researchers worked with the Northern Gulf of Mexico Sentinel Site Cooperative to develop information sheets outlining potential sea-level rise scenarios for the region through 2100 including their potential impact on the missions of federally-managed lands. Researchers drew from existing information on regional and global sea-level rise scenarios to develop customized information sheets for 54 federally-managed lands in the region (e.g., National Wildlife Refuges, National Park Service lands, National Estuarine Research Reserves) (Chivoiu et al. 2020).

This project was developed in response to a request for customized sea-level rise scenario products from partners at the Northern Gulf of Mexico Sentinel Site Cooperative and the U.S. Fish and Wildlife Service’s Gulf Restoration Program. The results of this project support the conservation stewardship missions of the U.S. Fish and Wildlife Service and National Park Service by providing science to inform management of their lands along the Gulf of Mexico.

","language":"English","publisher":"Southeast Climate Adaptation Science Center","usgsCitation":"Osland, M., and Collini, R.C., 2020, Final project memorandum: Southeast Climate Adaptation Science Center Project: Cooperator Report, 5 p.","productDescription":"5 p.","ipdsId":"IP-115952","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":375415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375396,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f8c6557e4b0546c0c397b4c/5ba005b1e4b08583a5c277eb"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.85937499999999,\n 25.64152637306577\n ],\n [\n -82.880859375,\n 30.29701788337205\n ],\n [\n -86.30859375,\n 30.751277776257812\n ],\n [\n -90.17578124999999,\n 30.675715404167743\n ],\n [\n -94.921875,\n 30.06909396443887\n ],\n [\n -98.173828125,\n 28.459033019728043\n ],\n [\n -97.470703125,\n 25.403584973186703\n ],\n [\n -96.767578125,\n 27.371767300523047\n ],\n [\n -93.8671875,\n 28.844673680771795\n ],\n [\n -91.845703125,\n 28.998531814051795\n ],\n [\n -89.56054687499999,\n 28.38173504322308\n ],\n [\n -88.505859375,\n 29.38217507514529\n ],\n [\n -86.044921875,\n 29.611670115197377\n ],\n [\n -85.25390625,\n 28.998531814051795\n ],\n [\n -84.0234375,\n 29.305561325527698\n ],\n [\n -83.3203125,\n 28.22697003891834\n ],\n [\n -82.96875,\n 27.137368359795584\n ],\n [\n -81.82617187499999,\n 25.16517336866393\n ],\n [\n -80.85937499999999,\n 25.64152637306577\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":205379,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":790457,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collini, Renee C.","contributorId":195567,"corporation":false,"usgs":false,"family":"Collini","given":"Renee","email":"","middleInitial":"C.","affiliations":[{"id":34311,"text":"Northern Gulf of Mexico Sentinel Site Cooperative, Dauphin Island, AL, USA","active":true,"usgs":false}],"preferred":false,"id":790458,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}