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In 2015, the total amount of water withdrawn in Florida was estimated to be 15,319 million gallons per day (Mgal/d). Saline water accounted for 9,598 Mgal/d (63 percent) and freshwater accounted for 5,721 Mgal/d (37 percent) of the total. Groundwater accounted for 3,604 Mgal/d (63 percent) of freshwater withdrawals and surface water accounted for the remaining 2,117 Mgal/d (37 percent). Surface-water sources accounted for 9,401 Mgal/d (98 percent) of the saline-water withdrawals, and groundwater sources accounted for the remaining 198 Mgal/d (2 percent). The majority of groundwater withdrawals (almost 62 percent) in 2015 were from the Floridan aquifer system, which is used throughout most of the State while the majority of fresh surface-water withdrawals (52 percent) occurred in the Southern Florida Subregion, a hydrologic unit that includes Lake Okeechobee and canals in the Everglades Agricultural Area. Groundwater provided drinking water (public supplied and self-supplied) for 18.324 million people (92 percent of Florida’s population), and fresh surface water provided drinking water for 1.491 million people (8 percent).

Overall, public supply accounted for 39 percent of the total freshwater withdrawals (ground and surface) and 53 percent of groundwater withdrawals, followed by agricultural self-supplied uses, which accounted for 37 percent of the total freshwater withdrawals and 28 percent of groundwater withdrawals. Other self-supplied groundwater withdrawals include commercial-industrial-mining self-supplied (8 percent), recreational-landscape irrigation and domestic self-supplied (5 percent each), and power generation (less than 1 percent). Agricultural self-supplied withdrawals accounted for 51 percent of fresh surface-water withdrawals, followed by power generation (19 percent), public supply (15 percent), recreational-landscape irrigation (10 percent), and commercial-industrial-mining self-supplied (5 percent).

In 1975, agricultural water withdrawals accounted for 43 percent of the total freshwater withdrawals, followed by power generation (24 percent) and public supply (17 percent). By 2000, agricultural withdrawals increased to 48 percent of the total freshwater withdrawals, followed by public supply (30 percent). For 2015, agricultural self-supplied decreased to 37 percent of total freshwater withdrawals, and was surpassed by public supply at 39 percent. Over the 40-year period between 1975 and 2015, increases in freshwater withdrawals caused by large gains in population and the expansion of irrigated acreage were offset by decreases in water used for power generation and commercial-industrial-mining withdrawals. Since 2000, however, irrigated acreage has decreased statewide because of crop disease, storm damage, and urbanization. This decline, coupled with large gains in water conservation measures in the farming industry, has led to agricultural withdrawals in Florida being less than public-supply withdrawals for the first time since water-use data were first reported in 1965.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195147","collaboration":"Prepared in cooperation with the Florida Department of Agricultural and Consumer Services","usgsCitation":"Marella, R.L., 2020, Water withdrawals, uses, and trends in Florida, 2015: U.S. Geological Survey Scientific Investigations Report 2019–5147, 52 p., https://doi.org/10.3133/sir20195147.","productDescription":"Report: vii, 52 p.; Data Release","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-093230","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":372545,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5147/sir20195147.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"

Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-02-26","noUsgsAuthors":false,"publicationDate":"2020-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Marella, Richard L. 0000-0003-4861-9841","orcid":"https://orcid.org/0000-0003-4861-9841","contributorId":221550,"corporation":false,"usgs":true,"family":"Marella","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":778628,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70219560,"text":"70219560 - 2020 - Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America","interactions":[],"lastModifiedDate":"2021-04-13T12:33:13.855719","indexId":"70219560","displayToPublicDate":"2020-02-26T07:31:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America","docAbstract":"

The behavior of wildlife varies seasonally, and that variation can have substantial demographic consequences. This is especially true for long‐distance migrants where the use of landscapes varies by season and, sometimes, age cohort. We tested the hypothesis that distributional patterns of Golden Eagles (Aquila chrysaetos) wintering in eastern North America are age‐structured (i.e., birds of similar ages winter together) through the analysis of 370,307 images collected by motion‐sensitive trail cameras set over bait during the winters of 2012–2013 and 2013–2014. At nine sites with sufficient data for analysis, we documented 145 eagle visits in 2012–2013 and 146 in 2013–2014. We found significant between‐year variation in age structure of wintering eastern Golden Eagles, driven largely by annual differences in the proportion of first‐winter birds. However, although many other species show spatial structure in wintering behavior, our analysis revealed no latitudinal organization among age cohorts of wintering eastern Golden Eagles. The lack of age‐related latitudinal segregation in wintering behavior does not exclude the possibility that these eagles have sex‐based or other types of dominance hierarchies that could result in spatial or temporal segregation. Alternatively, other mechanisms such as food availability or habitat structure may determine the distribution and abundance of Golden Eagles in winter.

","language":"English","publisher":"Wiley","doi":"10.1111/jofo.12325","usgsCitation":"Kenney, M.L., Belthoff, J.R., Carling, M., Miller, T.A., and Katzner, T., 2020, Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America: Journal of Field Ornithology, v. 91, no. 1, p. 92-101, https://doi.org/10.1111/jofo.12325.","productDescription":"10 p.","startPage":"92","endPage":"101","ipdsId":"IP-113723","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457594,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/jofo.12325","text":"External Repository"},{"id":385049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, North Carolina, Pennsylvania, West 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Carolina\",\"nation\":\"USA \"}}]}","volume":"91","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Kenney, Macy L","contributorId":257372,"corporation":false,"usgs":false,"family":"Kenney","given":"Macy","email":"","middleInitial":"L","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carling, Matthew","contributorId":257375,"corporation":false,"usgs":false,"family":"Carling","given":"Matthew","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":814141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Tricia A.","contributorId":190591,"corporation":false,"usgs":false,"family":"Miller","given":"Tricia","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":814142,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":814143,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209900,"text":"70209900 - 2020 - How repeatable is CTmax within individual brook trout over short- and long-time intervals?","interactions":[],"lastModifiedDate":"2020-05-06T12:16:07.524556","indexId":"70209900","displayToPublicDate":"2020-02-26T07:14:53","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":"How repeatable is CTmax within individual brook trout over short- and long-time intervals?","docAbstract":"As stream temperatures increase due to factors such as heated runoff from impervious surfaces, deforestation, and climate change, fish species adapted to cold water streams are forced to move to more suitable habitat, acclimate or adapt to increased thermal regimes, or die. To estimate the potential for adaptation, a (within individual) repeatable metric of thermal tolerance is imperative. Critical thermal maximum (CTmax) is a dynamic test that is widely used to measure thermal tolerance across many taxa and has been used in fishes for decades, but its repeatability in most species is unknown. CTmax tests increase water temperature steadily over time until loss of equilibrium (LOE) is achieved. To determine if CTmax is a consistent metric within individual fish, we measured CTmax on the same lab-held individually-marked adult brook trout Salvelinus fontinalis at three different times (August & September 2016, September 2017). We found that CTmax is a repeatable trait (Repeatability ± S.E.: 0.48 ± 0.14). CTmax of individuals males was consistent over time, but the CTmax of females increased slightly over time. This result indicates that CTmax is a robust, repeatable estimate of thermal tolerance in a cold-water adapted fish.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jtherbio.2020.102559","collaboration":"","usgsCitation":"O’Donnell, M.J., Regish, A.M., McCormick, S.D., and Letcher, B., 2020, How repeatable is CTmax within individual brook trout over short- and long-time intervals?: Journal of Thermal Biology, v. 89, 102559, 6p., https://doi.org/10.1016/j.jtherbio.2020.102559.","productDescription":"102559, 6p.","ipdsId":"IP-108628","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":457596,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jtherbio.2020.102559","text":"Publisher Index Page"},{"id":374484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"O’Donnell, Matthew J. 0000-0002-9089-2377 modonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":2003,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"modonnell@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":788548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regish, Amy M. 0000-0003-4747-4265 aregish@usgs.gov","orcid":"https://orcid.org/0000-0003-4747-4265","contributorId":5415,"corporation":false,"usgs":true,"family":"Regish","given":"Amy","email":"aregish@usgs.gov","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":788549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":788550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":167313,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":788551,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211485,"text":"70211485 - 2020 - Multi‐species occupancy models: Review, roadmap, and recommendations","interactions":[],"lastModifiedDate":"2020-11-13T15:46:27.710581","indexId":"70211485","displayToPublicDate":"2020-02-25T19:40:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Multi‐species occupancy models: Review, roadmap, and recommendations","docAbstract":"

Recent technological and methodological advances have revolutionized wildlife monitoring. Although most biodiversity monitoring initiatives are geared towards focal species of conservation concern, researchers are increasingly studying entire communities, specifically the spatiotemporal drivers of community size and structure and interactions among species. This has resulted in the emergence of multi‐species occupancy models (MSOMs) as a promising and efficient approach for the study of community ecology. Given the potential of MSOMs for conservation and management action, it is critical to know whether study design and model assumptions are consistent with inference objectives. This is especially true for studies that are designed for a focal species but can give insights about a community. Here, we review the recent literature on MSOMs, identify areas of improvement in the multi‐species study workflow, and provide a reference model for best practices for focal species and community monitoring study design. We reviewed 92 studies published between 2009 and early 2018, spanning 27 countries and a variety of taxa. There is a consistent under‐reporting of details that are central to determining the adequacy of designs for generating data that can be used to make inferences about community‐level patterns of occupancy, including the spatial and temporal extent, types of detectors used, covariates considered, and choice of field methods and statistical tools. This reporting bias could consequently result in skewed estimates, affecting conservation actions and management plans. On the other hand, comprehensive reporting is likely to help researchers working on MSOMs assess the robustness of inferences, in addition to making strides in terms of reproducibility and reusability of data. We use our literature review to inform a roadmap with best practices for MSOM studies, from simulations to design considerations and reporting, for the collection of new data as well as those involving existing datasets.

","language":"English","publisher":"Wiley","doi":"10.1111/ecog.04957","usgsCitation":"Devarajan, K., Tenan, S., and Morelli, T.L., 2020, Multi‐species occupancy models: Review, roadmap, and recommendations: Ecography, v. 43, no. 11, p. 1612-1624, https://doi.org/10.1111/ecog.04957.","productDescription":"14 p.","startPage":"1612","endPage":"1624","ipdsId":"IP-114395","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":457599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.04957","text":"Publisher Index Page"},{"id":376821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Devarajan, Kadambari","contributorId":236828,"corporation":false,"usgs":false,"family":"Devarajan","given":"Kadambari","email":"","affiliations":[],"preferred":false,"id":794271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tenan, Simone","contributorId":177519,"corporation":false,"usgs":false,"family":"Tenan","given":"Simone","email":"","affiliations":[],"preferred":false,"id":794272,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":794273,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208399,"text":"fs20203009 - 2020 - Water-quality comparison of the Gulf Coast aquifer system and Carrizo-Wilcox aquifer in Texas from National Water-Quality Assessment Project Principal Aquifer Surveys, 2013 and 2015","interactions":[],"lastModifiedDate":"2022-04-20T18:25:46.361216","indexId":"fs20203009","displayToPublicDate":"2020-02-25T15:26:57","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3009","displayTitle":"Water-Quality Comparison of the Gulf Coast Aquifer System and Carrizo-Wilcox Aquifer in Texas From National Water-Quality Assessment Project Principal Aquifer Surveys, 2013 and 2015","title":"Water-quality comparison of the Gulf Coast aquifer system and Carrizo-Wilcox aquifer in Texas from National Water-Quality Assessment Project Principal Aquifer Surveys, 2013 and 2015","docAbstract":"

The U.S. Geological Survey’s National Water-Quality Assessment (NAWQA) Project assessed the quality of groundwater in aquifers that are important sources of drinking water in the United States. One major aquifer in Texas that was assessed by NAWQA in 2013 is the coastal lowlands aquifer system, which is often referred to in Texas as the “Gulf Coast aquifer system.” The coastal lowlands aquifer system supplies water for millions of people; self-supplied (private) well withdrawals in 2005 from this aquifer system were the sixth largest among all major aquifer systems in the Nation. A major aquifer in Texas that was assessed by NAWQA in 2015 is the Texas coastal uplands aquifer system; the Carrizo-Wilcox aquifer is one of several aquifers that compose this aquifer system in Texas. The rocks composing the Texas coastal uplands aquifer system extend east from Texas as part of the Mississippi embayment aquifer system and underlie areas of several States. The Texas coastal uplands aquifer system and Mississippi embayment aquifer system are often collectively referred to as the “Mississippi embayment-Texas coastal uplands aquifer system.” Self-supplied withdrawals from the Mississippi embayment-Texas coastal uplands aquifer system in 2005 were the eighth largest among all major aquifer systems in the Nation. The coastal lowlands aquifer system and Mississippi embayment-Texas coastal uplands aquifer system were assessed as part of the NAWQA Principal Aquifer Surveys (PAS), which were designed to evaluate constituent concentrations in water samples obtained from domestic and public-supply wells prior to any treatment. PAS assessments like these allow for the comparison of water-quality concentrations in untreated groundwater using preestablished benchmarks for the protection of human health and for aesthetic qualities such as taste, color, and odor. The use of preestablished benchmarks can provide a basis for comparison of groundwater quality among principal aquifers.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203009","collaboration":"U.S. Geological Survey National Water-Quality Assessment","usgsCitation":"Ging, P.B., 2020, Water-quality comparison of the Gulf Coast aquifer system and Carrizo-Wilcox aquifer in Texas from National Water-Quality Assessment Project Principal Aquifer Surveys, 2013 and 2015: U.S. Geological Survey Fact Sheet 2020–3009, 4 p., https://doi.org/10.3133/fs20203009.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-111986","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":399199,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109727.htm"},{"id":372560,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3009/fs20203009.pdf","text":"Report","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 20220–3009"},{"id":372559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3009/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Gulf Coast aquifer system, Carrizo-Wilcox aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.5,\n 25.8378\n ],\n [\n -93.5069,\n 25.8378\n ],\n [\n -93.5069,\n 33.5433\n ],\n [\n -100.5,\n 33.5433\n ],\n [\n -100.5,\n 25.8378\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-02-25","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ging, Patricia B. 0000-0001-5491-8448","orcid":"https://orcid.org/0000-0001-5491-8448","contributorId":222263,"corporation":false,"usgs":true,"family":"Ging","given":"Patricia","email":"","middleInitial":"B.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781732,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70221837,"text":"70221837 - 2020 - Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre","interactions":[],"lastModifiedDate":"2021-07-09T19:30:10.52931","indexId":"70221837","displayToPublicDate":"2020-02-25T14:24:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre","docAbstract":"

Phytoplankton play key roles in the oceans by regulating global biogeochemical cycles and production in marine food webs. Global warming is thought to affect phytoplankton production both directly, by impacting their photosynthetic metabolism, and indirectly by modifying the physical environment in which they grow. In this respect, the Bermuda Atlantic Time-series Study (BATS) in the Sargasso Sea (North Atlantic gyre) provides a unique opportunity to explore effects of warming on phytoplankton production across the vast oligotrophic ocean regions because it is one of the few multidecadal records of measured net primary productivity (NPP). We analysed the time series of phytoplankton primary productivity at BATS site using machine learning techniques (ML) to show that increased water temperature over a 27-year period (1990–2016), and the consequent weakening of vertical mixing in the upper ocean, induced a negative feedback on phytoplankton productivity by reducing the availability of essential resources, nitrogen and light. The unbalanced availability of these resources with warming, coupled with ecological changes at the community level, is expected to intensify the oligotrophic state of open-ocean regions that are far from land-based nutrient sources.

","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-020-59989-y","usgsCitation":"D’Alelio, D., Rampone, S., Cusano, L.M., Morfino, V., Russo, L., Sanseverino, N., Cloern, J.E., and Lomas, M.W., 2020, Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre: Scientific Reports, v. 10, 3287, 12 p., https://doi.org/10.1038/s41598-020-59989-y.","productDescription":"3287, 12 p.","ipdsId":"IP-111898","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":457603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-59989-y","text":"Publisher Index Page"},{"id":387061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"North Atlantic Gyre","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.3359375,\n 23.885837699862005\n ],\n [\n -38.84765625,\n 28.613459424004414\n ],\n [\n -19.51171875,\n 34.016241889667015\n ],\n [\n -17.75390625,\n 41.11246878918088\n ],\n [\n -26.3671875,\n 47.754097979680026\n ],\n [\n -41.66015625,\n 46.6795944656402\n ],\n [\n -61.17187499999999,\n 39.639537564366684\n ],\n [\n -69.78515625,\n 35.31736632923788\n ],\n [\n -76.9921875,\n 31.203404950917395\n ],\n [\n -75.41015624999999,\n 26.902476886279832\n ],\n [\n -71.54296874999999,\n 23.563987128451217\n ],\n [\n -64.3359375,\n 23.885837699862005\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Alelio, Domenico","contributorId":260813,"corporation":false,"usgs":false,"family":"D’Alelio","given":"Domenico","email":"","affiliations":[{"id":27945,"text":"Stazione Zoologica Anton Dohrn","active":true,"usgs":false}],"preferred":false,"id":818883,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rampone, Salvatore","contributorId":260814,"corporation":false,"usgs":false,"family":"Rampone","given":"Salvatore","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cusano, Luigi Maria","contributorId":260815,"corporation":false,"usgs":false,"family":"Cusano","given":"Luigi","email":"","middleInitial":"Maria","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morfino, Valerio","contributorId":260816,"corporation":false,"usgs":false,"family":"Morfino","given":"Valerio","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818886,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russo, Luca","contributorId":260817,"corporation":false,"usgs":false,"family":"Russo","given":"Luca","email":"","affiliations":[{"id":27945,"text":"Stazione Zoologica Anton Dohrn","active":true,"usgs":false}],"preferred":false,"id":818887,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sanseverino, Nadia","contributorId":260818,"corporation":false,"usgs":false,"family":"Sanseverino","given":"Nadia","email":"","affiliations":[{"id":52676,"text":"Università degli Studi del Sannio","active":true,"usgs":false}],"preferred":false,"id":818888,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cloern, James E. 0000-0002-5880-6862 jecloern@usgs.gov","orcid":"https://orcid.org/0000-0002-5880-6862","contributorId":1488,"corporation":false,"usgs":true,"family":"Cloern","given":"James","email":"jecloern@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":818889,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lomas, Michael W.","contributorId":260819,"corporation":false,"usgs":false,"family":"Lomas","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":13692,"text":"Bigelow Laboratory for Ocean Sciences","active":true,"usgs":false}],"preferred":false,"id":818890,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209079,"text":"70209079 - 2020 - Prioritizing water security in the management of vector borne diseases: Lessons from Oaxaca, Mexico","interactions":[],"lastModifiedDate":"2020-03-16T06:18:12","indexId":"70209079","displayToPublicDate":"2020-02-25T14:22:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5050,"text":"Geohealth News","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing water security in the management of vector borne diseases: Lessons from Oaxaca, Mexico","docAbstract":"Changes in human water use, along with temperature and rainfall patterns, are facilitating habitat spread and distribution of Aedes aegypti and Aedes albopictus mosquitoes, the primary vectors for the transmission of Dengue, Chikungunya, and Zika viruses in the Americas. Artificial containers and wetspots provide major sources of mosquito larval habitat in residential areas. Mosquito abatement and control strategies remain the most effective public health interventions for minimizing the impact of these vector borne diseases. Understanding how water insecurity is conducive to the establishment and elimination of endemic mosquito populations, particularly in arid or semi-arid regions, is a vital component in shaping these intervention strategies.","language":"English","publisher":"AGU","doi":"10.1029/2019gh000201","usgsCitation":"Akanda, A.S., Johnson, K.D., Ginsberg, H., and Couret, J., 2020, Prioritizing water security in the management of vector borne diseases: Lessons from Oaxaca, Mexico: Geohealth News, v. 4, no. 3, e2019GH000201, 5 p., https://doi.org/10.1029/2019gh000201.","productDescription":"e2019GH000201, 5 p.","ipdsId":"IP-112940","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gh000201","text":"Publisher Index Page"},{"id":373277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","city":"Oaxaca","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.18505859374999,\n 16.74142754700361\n ],\n [\n -95.877685546875,\n 16.74142754700361\n ],\n [\n -95.877685546875,\n 17.403062993328923\n ],\n [\n -97.18505859374999,\n 17.403062993328923\n ],\n [\n -97.18505859374999,\n 16.74142754700361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Akanda, Ali S","contributorId":223365,"corporation":false,"usgs":false,"family":"Akanda","given":"Ali","email":"","middleInitial":"S","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":784850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Kristine D.","contributorId":168716,"corporation":false,"usgs":false,"family":"Johnson","given":"Kristine","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":784851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":784849,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Couret, Janelle","contributorId":194159,"corporation":false,"usgs":false,"family":"Couret","given":"Janelle","affiliations":[],"preferred":false,"id":784852,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208576,"text":"ofr20201017 - 2020 - Have humans influenced volcanic activity on the lower East Rift Zone of Kīlauea Volcano? A publication review","interactions":[],"lastModifiedDate":"2022-04-21T20:42:02.667167","indexId":"ofr20201017","displayToPublicDate":"2020-02-25T09:56:47","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":"2020-1017","displayTitle":"Have Humans Influenced Volcanic Activity on the Lower East Rift Zone of Kīlauea Volcano? A Publication Review","title":"Have humans influenced volcanic activity on the lower East Rift Zone of Kīlauea Volcano? A publication review","docAbstract":"

Since the 2018 eruption of Kīlauea Volcano, the topic of whether commercial developments not only caused the eruption to occur in the lower East Rift Zone (LERZ), but also caused its high eruption rate has been a subject of public discussion. We review Kīlauea Volcano publications from the past several decades and show that the eruptive behavior of the volcano has varied and that the 2018 eruption was similar to past eruptions in many ways. We find no evidence to support any human influence on Kīlauea Volcano.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201017","usgsCitation":"Kauahikaua, J. and Trusdell, F., 2020, Have humans influenced volcanic activity on the lower East Rift Zone of Kīlauea Volcano? A publication review: U.S. Geological Survey Open-File Report 2020–1017, 17 p., https://doi.org/10.3133/ofr20201017.","productDescription":"iv, 17","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-111187","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":372511,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1017/ofr20201017.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1017"},{"id":372510,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1017/coverthb.jpg"},{"id":399456,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109729.htm"}],"country":"United States","state":"Hawaii","otherGeospatial":"Lower East Rift Zone of Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3741455078125,\n 19.19835036116298\n ],\n [\n -155.26016235351562,\n 19.281332062593734\n ],\n [\n -155.20523071289062,\n 19.26059057084779\n ],\n [\n -155.14892578125,\n 19.264479800497103\n ],\n [\n -155.0665283203125,\n 19.30466310133747\n ],\n [\n -154.9456787109375,\n 19.37334071336406\n ],\n [\n -154.82070922851562,\n 19.474360774988355\n ],\n [\n -154.80422973632812,\n 19.530024424775405\n ],\n [\n -154.92233276367188,\n 19.596019240312494\n ],\n [\n -154.9456787109375,\n 19.621892180319374\n ],\n [\n -154.97177124023438,\n 19.641294152538578\n ],\n [\n -154.97177124023438,\n 19.676211792974332\n ],\n [\n -155.050048828125,\n 19.590844152960923\n ],\n [\n -155.12832641601562,\n 19.520964205879825\n ],\n [\n -155.18875122070312,\n 19.449759112405612\n ],\n [\n -155.26565551757812,\n 19.42256346067618\n ],\n [\n -155.35629272460938,\n 19.359089245934307\n ],\n [\n -155.38787841796872,\n 19.299478713495898\n ],\n [\n -155.4071044921875,\n 19.23206673568465\n ],\n [\n -155.4071044921875,\n 19.19186565046399\n ],\n [\n -155.3741455078125,\n 19.19835036116298\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Contacts, 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":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":782575,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trusdell, Frank A. 0000-0002-0681-0528 trusdell@usgs.gov","orcid":"https://orcid.org/0000-0002-0681-0528","contributorId":754,"corporation":false,"usgs":true,"family":"Trusdell","given":"Frank A.","email":"trusdell@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":782576,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70234239,"text":"70234239 - 2020 - Climate- and disturbance-driven changes in subsistence berries in coastal Alaska: Indigenous knowledge to inform ecological inference","interactions":[],"lastModifiedDate":"2022-08-04T13:21:28.230199","indexId":"70234239","displayToPublicDate":"2020-02-25T08:15:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1911,"text":"Human Ecology Review","active":true,"publicationSubtype":{"id":10}},"title":"Climate- and disturbance-driven changes in subsistence berries in coastal Alaska: Indigenous knowledge to inform ecological inference","docAbstract":"Berry-producing plants are a key subsistence resource in Indigenous Alaskan\ncommunities. High-latitude coastal regions are particularly impacted by global climate\nchange due to their location at the land-sea ecotone subjecting them to terrestrial\nstressors as well as shifts in ocean dynamics. While vegetation changes have been\ndocumented for the subarctic coastal region of Alaska, we know little about permafrost underlain\nportions of the landscape that support berry communities. To fill this gap\nsurveys were administered in four communities within the Yukon-Kuskokwim Delta, to\nidentify observations of changes in berry resources and integrate Indigenous\nKnowledge of drivers of changes with climate projections. We found that while most\nparticipants agreed on several observed changes and drivers, many important\ndifferences were identified at the community level. Regional consensus can be used in\ncombination with climate projections to forecast potential future impacts to berry\nresources while distinctions at the community level can help us to untangle local scale\ndisturbances.","language":"English","publisher":"Springer","doi":"10.1007/s10745-020-00138-4","usgsCitation":"Herman-Mercer, N.M., Loehman, R.A., Toohey, R.C., and Paniyak, C., 2020, Climate- and disturbance-driven changes in subsistence berries in coastal Alaska: Indigenous knowledge to inform ecological inference: Human Ecology Review, v. 48, no. 1, p. 85-99, https://doi.org/10.1007/s10745-020-00138-4.","productDescription":"15 p.","startPage":"85","endPage":"99","ipdsId":"IP-111953","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":404808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-Kuskokwim Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -167.27783203125,\n 59.60109549032134\n ],\n [\n -161.89453125,\n 59.60109549032134\n ],\n [\n -161.89453125,\n 63.361982464431236\n ],\n [\n -167.27783203125,\n 63.361982464431236\n ],\n [\n -167.27783203125,\n 59.60109549032134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Herman-Mercer, Nicole M. 0000-0001-5933-4978 nhmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-5933-4978","contributorId":3927,"corporation":false,"usgs":true,"family":"Herman-Mercer","given":"Nicole","email":"nhmercer@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":848297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":848298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Toohey, Ryan C. 0000-0001-8248-5045 rtoohey@usgs.gov","orcid":"https://orcid.org/0000-0001-8248-5045","contributorId":5674,"corporation":false,"usgs":true,"family":"Toohey","given":"Ryan","email":"rtoohey@usgs.gov","middleInitial":"C.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":848299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paniyak, Cynthia","contributorId":294543,"corporation":false,"usgs":false,"family":"Paniyak","given":"Cynthia","email":"","affiliations":[{"id":63599,"text":"Chevak Traditional Council","active":true,"usgs":false}],"preferred":false,"id":848300,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208834,"text":"70208834 - 2020 - Amphibian responses in the aftermath of extreme climate events","interactions":[],"lastModifiedDate":"2020-03-03T08:11:04","indexId":"70208834","displayToPublicDate":"2020-02-25T08:08:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Amphibian responses in the aftermath of extreme climate events","docAbstract":"Climate change-induced extinctions are estimated to eliminate one in six known species by the end of the century. One major factor that will contribute to these extinctions is extreme climatic events. Here, we show the ecological impacts of recent record warm air temperatures and simultaneous peak drought conditions in California. From 2008–2016, the southern populations of a wide-ranging endemic amphibian (the California newt, Taricha torosa) showed a 20% reduction to mean body condition and significant losses to variation in body condition linked with extreme climate deviations. However, body condition in northern populations remained relatively unaffected during this period. Range-wide population estimates of change to body condition under future climate change scenarios within the next 50 years suggest that northern populations will mirror the loss of body condition recently observed in southern populations. This change is predicated on latter 21st century climate deviations that resemble recent conditions in Southern California. Thus, the ecological consequences of climate change have already occurred across the warmer, drier regions of Southern California, and our results suggest that predicted climate vulnerable regions in the more mesic northern range likely will not provide climate refuge for numerous amphibian communities.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-020-60122-2","usgsCitation":"Bucciarelli, G.M., Clark, M., Delaney, K.S., Riley, S.P., Shaffer, H.B., Fisher, R.N., Honeycutt, R., and Kats, L.B., 2020, Amphibian responses in the aftermath of extreme climate events: Scientific Reports, v. 10, 3409, 7 p., https://doi.org/10.1038/s41598-020-60122-2.","productDescription":"3409, 7 p.","ipdsId":"IP-115754","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457609,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-60122-2","text":"Publisher Index 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Bradley","contributorId":222930,"corporation":false,"usgs":false,"family":"Shaffer","given":"H.","email":"","middleInitial":"Bradley","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":783562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Honeycutt, Rodney L","contributorId":222931,"corporation":false,"usgs":false,"family":"Honeycutt","given":"Rodney L","affiliations":[{"id":37949,"text":"Pepperdine University","active":true,"usgs":false}],"preferred":false,"id":783564,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kats, Lee B.","contributorId":208330,"corporation":false,"usgs":false,"family":"Kats","given":"Lee","email":"","middleInitial":"B.","affiliations":[{"id":37783,"text":"Seaver College, Pepperdine University","active":true,"usgs":false}],"preferred":false,"id":783565,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209759,"text":"70209759 - 2020 - Practices of biological soil crust rehabilitation in China: Experiences and challenges","interactions":[],"lastModifiedDate":"2020-08-26T18:51:29.848551","indexId":"70209759","displayToPublicDate":"2020-02-25T07:58:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Practices of biological soil crust rehabilitation in China: Experiences and challenges","docAbstract":"

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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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. 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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":40553,"text":"WMA - Office of the Chief Operating Officer","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western 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 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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":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","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 B. 0000-0001-7863-3930","orcid":"https://orcid.org/0000-0001-7863-3930","contributorId":203492,"corporation":false,"usgs":true,"family":"Withers","given":"Kyle","middleInitial":"B.","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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