Warm water temperatures in the lower Yakima River in central Washington are key limitations to the restoration of Pacific salmon (Onchorhynchus spp.) populations within the Yakima River Basin. Identification of the location and magnitude of cold-water anomalies, which are cooler than ambient river temperatures during summer months, and the processes that create and maintain them is needed to inform salmon restoration efforts within the Yakima River Basin. Longitudinal thermal profiles of nine reaches in the lower Yakima River were surveyed at ambient river velocity during summer 2018 when surface-water temperatures were near their annual maximum and the difference between surface-water and groundwater temperatures was greatest. The profiles were compared to previously published profiles of the same reaches measured in 2001, 2002, 2008, and 2009, and analyzed in the context of hydrologic, geomorphic, and hydrogeologic conditions that may create and maintain cold-water inputs to the river. Cold-water anomalies that departed from expected diurnal increases in water temperature were measured in all nine study reaches and were attributed to diffuse groundwater discharge through the streambed, discrete groundwater discharge at seeps and springs, and cold-water tributaries entering the river. Some cold-water anomalies were measured during repeated surveys in different years, whereas other cold-water anomalies did not persist across surveys. Additionally, some discrete cold-water anomalies were confined to one side of the channel, but others associated with diffuse groundwater discharge were present across the channel for several river miles. Hydrogeologic conditions including the extent and thickness of aquifers connected to the Yakima River, geomorphic conditions including channel gradient, channel geometry, and floodplain extent, and the location of tributaries, irrigation returns, and other surface-water inputs created the large-scale conditions that facilitate the formation and maintenance of cold-water anomalies. Finer-scale geomorphic features such as side channels, gravel-bar alcoves, deep pools, and other locations, where colder water collected and remained relatively unmixed with upstream surface water, were also important factors in the occurrence and distribution of cold-water anomalies. These hydrogeologic and geomorphic conditions, coupled with the alteration of the Yakima River’s hydrologic regime to support irrigation within the Yakima Valley, contributed to the surveyed distribution of cold-water anomalies within the river.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215140","collaboration":"Prepared in cooperation with Benton Conservation District under Washington Department of Ecology Funding Agreement WRYBP-VER1-BentCD-00004 as part of the Yakima Basin Integrated Plan","usgsCitation":"Gendaszek, A.S., and Appel, M., 2021, Thermal heterogeneity and cold-water anomalies within the lower Yakima River, Yakima and Benton Counties, Washington: U.S. Geological Survey Scientific Investigations Report 2021–5140, 45 p., https://doi.org/10.3133/sir20215140.","productDescription":"Report: v, 43 p.; Data Release","numberOfPages":"43","onlineOnly":"Y","ipdsId":"IP-126706","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":394822,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5140/sir20215140.pdf","text":"Report","size":"13 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Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
As part of a long-term cooperative program to monitor water quality within the Scituate Reservoir drainage area, the U.S. Geological Survey in cooperation with the Providence Water Supply Board collected streamflow and water-quality data at the Scituate Reservoir and tributaries. Streamflow and concentrations of chloride and sodium estimated from records of specific conductance were used to calculate loads of chloride and sodium during water year 2018 (October 1, 2017, through September 30, 2018) for tributaries to the Scituate Reservoir, Rhode Island. Streamflow was measured or estimated by the U.S. Geological Survey following standard methods at 23 streamgages; 14 of these streamgages are equipped with instrumentation capable of continuously monitoring water level, specific conductance, and water temperature. Water-quality samples were collected by the Providence Water Supply Board at 36 sampling stations, which also include the 14 continuous-record streamgages maintained by the U.S. Geological Survey, during water year 2018 as part of a long-term sampling program; all stations are in the Scituate Reservoir drainage area. Water-quality data collected by the Providence Water Supply Board are summarized by using values of central tendency and are used, in combination with measured (or estimated) streamflows, to calculate loads and yields (loads per unit area) of selected water-quality constituents for water year 2018.
The largest tributary to the reservoir, the Ponaganset River, which was monitored by the U.S. Geological Survey, contributed a mean streamflow of 33 cubic feet per second to the reservoir during water year 2018. For the same period, annual mean streamflows measured (or estimated) for the other monitoring stations in this study ranged from about 0.34 to about 20 cubic feet per second. Together, tributaries equipped with instrumentation capable of continuously monitoring specific conductance transported about 3,100 metric tons of chloride and 1,900 metric tons of sodium to the Scituate Reservoir during water year 2018; annual chloride yields for the tributaries ranged from 18 to 140 metric tons per square mile, and annual sodium yields ranged from 12 to 80 metric tons per square mile.
At the stations where water-quality samples were collected by the Providence Water Supply Board, the medians of the median concentrations were 25.8 milligrams per liter for chloride, 0.001 milligram per liter as nitrogen for nitrite, 0.11 milligram per liter as nitrogen for nitrate, 0.04 milligram per liter as phosphate for orthophosphate, 1,200 colony forming units per 100 milliliters for total coliform bacteria, and 10 colony forming units per 100 milliliters for Escherichia coli (E. coli). The medians of the median daily loads of chloride, nitrite, nitrate, orthophosphate, total coliform, and E. coli bacteria were 220 kilograms per day, 15 grams per day, less than 890 grams per day, 360 grams per day, 93,000 million colony forming units per day, and less than 700 million colony forming units per day, respectively. The medians of the median yields of chloride, nitrite, nitrate, orthophosphate, total coliform, and E. coli bacteria were 110 kilograms per day per square mile, 5.5 grams per day per square mile, 250 grams per day per square mile, 210 grams per day per square mile, 36,000 million colony forming units per day per square mile, and 410 million colony forming units per day per square mile, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1144","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Smith, K.P., 2022, Streamflow, water quality, and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2018: U.S. Geological Survey Data Report 1144, 36 p., https://doi.org/10.3133/dr1144.","productDescription":"Report: v, 36 p.; Data release; Dataset","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120140","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":501199,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112156.htm","linkFileType":{"id":5,"text":"html"}},{"id":394803,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1144/images/"},{"id":394802,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1144/dr1144.XML"},{"id":394801,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":394800,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WK8N0F","text":"USGS data release","linkHelpText":"Water quality data from the Providence Water Supply Board for tributary streams to the Scituate Reservoir, water year 2018–19"},{"id":394799,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1144/dr1144.pdf","text":"Report","size":"1.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1144"},{"id":394798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1144/coverthb.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir Drainage Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.77642822265625,\n 41.69752591075902\n ],\n [\n -71.54022216796875,\n 41.69752591075902\n ],\n [\n -71.54022216796875,\n 41.92680320648791\n ],\n [\n -71.77642822265625,\n 41.92680320648791\n ],\n [\n -71.77642822265625,\n 41.69752591075902\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Physical processes driving barrier island change during storms are important to understand to mitigate coastal hazards and to evaluate conceptual models for barrier evolution. Spatial variations in barrier island topography, landcover characteristics, and nearshore and back-barrier hydrodynamics can yield complex morphological change that requires models of increasing resolution and physical complexity to predict. Using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system, we investigated two barrier island breaches that occurred on Fire Island, NY during Hurricane Sandy (2012) and at Matanzas, FL during Hurricane Matthew (2016). The model employed a recently implemented infragravity (IG) wave driver to represent the important effects of IG waves on nearshore water levels and sediment transport. The model simulated breaching and other changes with good skill at both locations, resolving differences in the processes and evolution. The breach simulated at Fire Island was 250 m west of the observed breach, whereas the breach simulated at Matanzas was within 100 m of the observed breach. Implementation of the vegetation module of COAWST to allow three-dimensional drag over dune vegetation at Fire Island improved model skill by decreasing flows across the back-barrier, as opposed to varying bottom roughness that did not positively alter model response. Analysis of breach processes at Matanzas indicated that both far-field and local hydrodynamics influenced breach creation and evolution, including remotely generated waves and surge, but also surge propagation through back-barrier waterways. This work underscores the importance of resolving the complexity of nearshore and back-barrier systems when predicting barrier island change during extreme events.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JF006307","usgsCitation":"Hegermiller, C., Warner, J.C., Olabarrieta, M., Sherwood, C.R., and Kalra, T., 2022, Modeling of barrier breaching during Hurricanes Sandy and Matthew: JGR-Earth Surface, v. 127, no. 3, e2021JF006307, 20 p., https://doi.org/10.1029/2021JF006307.","productDescription":"e2021JF006307, 20 p.","ipdsId":"IP-130367","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":449023,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021jf006307","text":"External Repository"},{"id":413242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, New York","city":"Matanzas","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.23320426968054,\n 41.082997080822736\n ],\n [\n -74.23320426968054,\n 40.32905270617809\n ],\n [\n -71.43741334729009,\n 40.32905270617809\n ],\n [\n -71.43741334729009,\n 41.082997080822736\n ],\n [\n -74.23320426968054,\n 41.082997080822736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.335863306947,\n 26.295094198443238\n ],\n [\n -78.26241414026661,\n 27.205116096340547\n ],\n [\n -80.7016669317273,\n 31.630079958177035\n ],\n [\n -82.69413025464559,\n 30.949177652812494\n ],\n [\n -80.2897567468504,\n 26.26830908028633\n ],\n [\n -80.335863306947,\n 26.295094198443238\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Hegermiller, Christie 0000-0002-6383-7508","orcid":"https://orcid.org/0000-0002-6383-7508","contributorId":241895,"corporation":false,"usgs":true,"family":"Hegermiller","given":"Christie","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":true,"id":864757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olabarrieta, Maitane 0000-0002-7619-7992 molabarrieta@usgs.gov","orcid":"https://orcid.org/0000-0002-7619-7992","contributorId":211373,"corporation":false,"usgs":false,"family":"Olabarrieta","given":"Maitane","email":"molabarrieta@usgs.gov","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":864759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":864762,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256727,"text":"70256727 - 2022 - Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA","interactions":[],"lastModifiedDate":"2024-09-03T16:44:50.779458","indexId":"70256727","displayToPublicDate":"2022-01-26T11:39:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA","docAbstract":"Ecosystem structure of wetlands in managed floodplains depends on hydrological processes controlled by geomorphology and water management. Overlapping effects of direct modifications and geomorphic adjustments to management can combine to trigger changes to floodplain ecosystem structure. We examined the case of woody vegetation encroaching into the depressional Catahoula Lake, Louisiana, in the context of regional hydrologic and geomorphic modification in the floodplain of the Mississippi River. Historical aerial photographs indicated woody encroachment into Catahoula Lake for at least 80 years, and the rate of expansion has increased in recent decades. Historical stage analysis revealed that the downstream Red–Atchafalaya–Mississippi River system controls the lower limit of the lake water level when the large rivers are high, but channel enlargement and other hydrological changes there have reduced the frequency of backwater flooding by 42% since 1880. In addition, operation of the water control structure on the lake has altered its hydrological regime to be more regular among years. Historic stage analysis revealed current lake levels are lower in the high-water spring, less variable in the dry period, and lack the extreme high-water events of 100+ years ago, all of which facilitate the expansion of woody vegetation.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5328","usgsCitation":"Keim, R., Dugue, L., Latuso, K., Joshi, S., King, S.L., and Willis, F., 2022, Influences of channel and floodplain modification on expansion of woody vegetation into Catahoula Lake, Louisiana, USA: Earth Surface Processes and Landforms, v. 47, no. 6, p. 1466-1479, https://doi.org/10.1002/esp.5328.","productDescription":"14 p.","startPage":"1466","endPage":"1479","ipdsId":"IP-130218","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":449025,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5328","text":"Publisher Index Page"},{"id":433416,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Catahoula Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.20285714912983,\n 31.44173705449458\n ],\n [\n -92.12418940417722,\n 31.44557223555043\n ],\n [\n -92.0297881102343,\n 31.532780176376406\n ],\n [\n -92.03877870965712,\n 31.576832357365504\n ],\n [\n -92.0803602319895,\n 31.574917477714266\n ],\n [\n -92.13655147838381,\n 31.541400717897048\n ],\n [\n -92.17925682564407,\n 31.505955624529022\n ],\n [\n -92.21521922333639,\n 31.45899413301514\n ],\n [\n -92.20285714912983,\n 31.44173705449458\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Keim, R.F.","contributorId":264646,"corporation":false,"usgs":false,"family":"Keim","given":"R.F.","affiliations":[{"id":54524,"text":"Lousiiana State University","active":true,"usgs":false}],"preferred":false,"id":908787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugue, L.","contributorId":341705,"corporation":false,"usgs":false,"family":"Dugue","given":"L.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Latuso, K.D.","contributorId":341706,"corporation":false,"usgs":false,"family":"Latuso","given":"K.D.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joshi, S.","contributorId":341707,"corporation":false,"usgs":false,"family":"Joshi","given":"S.","email":"","affiliations":[{"id":13314,"text":"Columbia River Inter-Tribal Fish Commission","active":true,"usgs":false}],"preferred":false,"id":908790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908791,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Willis, F.L.","contributorId":341708,"corporation":false,"usgs":false,"family":"Willis","given":"F.L.","email":"","affiliations":[{"id":81776,"text":"Willis Engineering and Scientific","active":true,"usgs":false}],"preferred":false,"id":908792,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227685,"text":"70227685 - 2022 - Testing the potential of streamflow data to predict spring migration of an ungulate herds","interactions":[],"lastModifiedDate":"2022-01-26T16:07:24.226926","indexId":"70227685","displayToPublicDate":"2022-01-26T09:51:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Testing the potential of streamflow data to predict spring migration of an ungulate herds","docAbstract":"In mountainous and high latitude regions, migratory animals exploit green waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the ‘spring pulse.’ Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0262078","usgsCitation":"Alexander, J.S., Murr, M.L., and Eddy-Miller, C.A., 2022, Testing the potential of streamflow data to predict spring migration of an ungulate herds: PLoS ONE, v. 17, no. 1, p. 1-18, https://doi.org/10.1371/journal.pone.0262078.","productDescription":"e0262078, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-125176","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":449034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0262078","text":"Publisher Index Page"},{"id":394871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","otherGeospatial":"Little Snake River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.45703125,\n 40.45321727150385\n ],\n [\n -108.00933837890625,\n 40.70562793820589\n ],\n [\n -107.46826171874999,\n 40.84913799774759\n ],\n [\n -107.0892333984375,\n 40.86991083161536\n ],\n [\n -107.05078125,\n 41.00477542222947\n ],\n [\n -107.490234375,\n 41.539421883822854\n ],\n [\n -108.446044921875,\n 41.54764462357737\n ],\n [\n -108.8031005859375,\n 41.20552261955812\n ],\n [\n -108.45703125,\n 40.45321727150385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Grignolio, Stefano","contributorId":272227,"corporation":false,"usgs":false,"family":"Grignolio","given":"Stefano","email":"","affiliations":[{"id":35987,"text":"Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy","active":true,"usgs":false}],"preferred":false,"id":831783,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Alexander, Jason S. 0000-0002-1602-482X jalexand@usgs.gov","orcid":"https://orcid.org/0000-0002-1602-482X","contributorId":261330,"corporation":false,"usgs":true,"family":"Alexander","given":"Jason","email":"jalexand@usgs.gov","middleInitial":"S.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murr, Marissa L.","contributorId":252938,"corporation":false,"usgs":false,"family":"Murr","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":50476,"text":"Department of Geology and Geophysics, University of Wyoming, Laramie, Wyoming","active":true,"usgs":false}],"preferred":false,"id":831740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eddy-Miller, Cheryl A. 0000-0002-4082-750X","orcid":"https://orcid.org/0000-0002-4082-750X","contributorId":195780,"corporation":false,"usgs":true,"family":"Eddy-Miller","given":"Cheryl","email":"","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":false,"id":831741,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227689,"text":"70227689 - 2022 - Enhanced bioremediation of RDX and co-contaminants perchlorate and nitrate using an anaerobic dehalogenating consortium in a fractured rock aquifer","interactions":[],"lastModifiedDate":"2022-01-26T15:15:10.609363","indexId":"70227689","displayToPublicDate":"2022-01-26T09:00:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1226,"text":"Chemosphere","active":true,"publicationSubtype":{"id":10}},"title":"Enhanced bioremediation of RDX and co-contaminants perchlorate and nitrate using an anaerobic dehalogenating consortium in a fractured rock aquifer","docAbstract":"The potential neurotoxic and carcinogenic effects of the explosives compound RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine) on human health requires groundwater remediation strategies to meet low cleanup goals. Bioremediation of RDX is feasible through biostimulation of native microbes with an organic carbon donor but may be less efficient, or not occur at all, in the presence of the common co-contaminants perchlorate and nitrate. Laboratory tests compared biostimulation with bioaugmentation to achieve anaerobic degradation of RDX, perchlorate, and nitrate; a field pilot test was then conducted in a fractured rock aquifer with the selected bioaugmentation approach. Insignificant reduction of RDX, perchlorate, or nitrate was observed by the native microbes in microcosms, with or without biostimulation by addition of lactate. Tests of the RDX-degrading ability of the microbial consortium WBC-2, originally developed for dehalogenation of chlorinated volatile organic compounds, showed first-order biodegradation rate constants ranging from 0.57 to 0.90 per day (half-lives 1.2 to 0.80 days). WBC-2 sustained degradation without daughter product accumulation when repeatedly amended with RDX and lactate for a year. In microcosms with groundwater containing perchlorate and nitrate, RDX degradation began without delay when bioaugmented with 10% WBC-2. Slower RDX degradation occurred with 3% or 5% WBC-2 amendment, indicating a direct relation with cell density. Transient RDX daughter compounds included methylene dinitramine, MNX, and DNX. With WBC-2 amendment, nitrate concentrations immediately decreased to near or below detection, and perchlorate degradation occurred with half-lives of 25 to 34 days. Single-well injection tests with WBC-2 and lactate showed that the onset of RDX degradation coincided with the onset of sulfide production, which was affected by the initial perchlorate concentration. Bioegradation rates in the pilot injection tests agreed well with those measured in the microcosms. These results support bioaugmentation with an anaerobic culture as a remedial strategy for sites contaminated with RDX, nitrate, and perchlorate.","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemosphere.2022.133674","usgsCitation":"Lorah, M.M., Vogler, E., Gebhardt, F.E., Graves, D., and Grabowski, J., 2022, Enhanced bioremediation of RDX and co-contaminants perchlorate and nitrate using an anaerobic dehalogenating consortium in a fractured rock aquifer: Chemosphere, v. 294, p. 1-12, https://doi.org/10.1016/j.chemosphere.2022.133674.","productDescription":"133674, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-133155","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":449043,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1016/j.chemosphere.2022.133674","text":"External Repository"},{"id":394864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Hazardous Test Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.60926818847656,\n 33.55970664841198\n ],\n [\n -106.34422302246094,\n 33.55970664841198\n ],\n [\n -106.34422302246094,\n 33.63234403356961\n ],\n [\n -106.60926818847656,\n 33.63234403356961\n ],\n [\n -106.60926818847656,\n 33.55970664841198\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"294","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Yoon, Y. Yeomin","contributorId":272225,"corporation":false,"usgs":false,"family":"Yoon","given":"Y.","email":"","middleInitial":"Yeomin","affiliations":[],"preferred":false,"id":831779,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Lorah, Michelle M. 0000-0002-9236-587X","orcid":"https://orcid.org/0000-0002-9236-587X","contributorId":224040,"corporation":false,"usgs":true,"family":"Lorah","given":"Michelle","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vogler, Eric","contributorId":272221,"corporation":false,"usgs":false,"family":"Vogler","given":"Eric","email":"","affiliations":[{"id":56372,"text":"Stantec","active":true,"usgs":false}],"preferred":false,"id":831768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gebhardt, Fredrick E.","contributorId":272222,"corporation":false,"usgs":true,"family":"Gebhardt","given":"Fredrick","email":"","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831769,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Duane","contributorId":172428,"corporation":false,"usgs":false,"family":"Graves","given":"Duane","email":"","affiliations":[{"id":27037,"text":"Geosyntec Consultants, Inc., Knoxville, TN","active":true,"usgs":false}],"preferred":false,"id":831770,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grabowski, Jennifer","contributorId":272223,"corporation":false,"usgs":false,"family":"Grabowski","given":"Jennifer","email":"","affiliations":[{"id":56373,"text":"U.S. Pharmacopeia","active":true,"usgs":false}],"preferred":false,"id":831771,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230378,"text":"70230378 - 2022 - A landscape approach for identifying potential reestablishment sites for extirpated stream fishes: an example with Arctic grayling (Thymallus arcticus) in Michigan","interactions":[],"lastModifiedDate":"2022-04-11T13:30:07.813676","indexId":"70230378","displayToPublicDate":"2022-01-26T08:26:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A landscape approach for identifying potential reestablishment sites for extirpated stream fishes: an example with Arctic grayling (Thymallus arcticus) in Michigan","title":"A landscape approach for identifying potential reestablishment sites for extirpated stream fishes: an example with Arctic grayling (Thymallus arcticus) in Michigan","docAbstract":"Habitat degradation combined with climate change increases the threat of extinction for stream fishes. In response to these threats, efforts to reestablish species within formerly occupied streams or translocation to suitable areas may be effective conservation strategies. In the absence of historic species presence data, identifying locations where suitable habitat exists across many fluvial habitats may limit the effectiveness of reestablishments. We present an approach that ranks habitat for stream fish reestablishment over large areas using best available information. Using the locally extirpated Arctic grayling (Thymallus arcticus) in Michigan, USA as an example, we integrate information on species preferences and relationships between species with similar habitat requirements and landscape predictors of habitat to rank stream suitability. We find that unfragmented streams throughout the historical range of Arctic grayling and areas previously unoccupied by the species are potential locations for conservation action. However, we note that projected increases in summer water temperatures may reduce the amount of thermally suitable habitat in some top-ranked locations by up to 30%. Given its inherent flexibility in data requirements, our landscape-level approach may be a valuable tool that supports planning for species reestablishment.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-021-04791-8","usgsCitation":"Tingley, R.W., Infante, D.M., Dean, E., Schemske, D.W., Cooper, A.R., Ross, J., and Daniel, W., 2022, A landscape approach for identifying potential reestablishment sites for extirpated stream fishes: an example with Arctic grayling (Thymallus arcticus) in Michigan: Hydrobiologia, v. 849, p. 1397-1415, https://doi.org/10.1007/s10750-021-04791-8.","productDescription":"19 p.","startPage":"1397","endPage":"1415","ipdsId":"IP-125422","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":398463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5118","displayTitle":"Hydrology of the Yucaipa Groundwater Subbasin: Characterization and Integrated Numerical Model, San Bernardino and Riverside Counties, California","title":"Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California","docAbstract":"Water management in the Santa Ana River watershed in San Bernardino and Riverside Counties in southern California is a complex task with various water purveyors navigating geographic, geologic, hydrologic, and political challenges to provide a reliable water supply to stakeholders. As the population has increased throughout southern California, so has the demand for water. The Yucaipa groundwater subbasin (hereafter referred to as “Yucaipa subbasin”), one of nine groundwater subbasins in what the California Department of Water Resources (DWR) refers to as the Upper Santa Ana Valley groundwater basin (California Department of Water Resources, 2016; the DWR naming convention is used within this report), is no exception; steady population growth since the 1940s and changes in water use has forced local water purveyors to regularly adapt their water infrastructure to meet demand. Groundwater has historically been the dominant source of water in the Yucaipa subbasin although recently, imported water via the California State Water Project has augmented the total water supply. Despite the influx of imported water, overall demand for groundwater continues to rise, and there is concern by local water managers that groundwater levels may adversely impact water supply and (or) decline to a point where it will be uneconomical to produce water, severely limiting the ability of local agencies to meet water-supply demand.
To better understand the hydrogeology and water resources in the Yucaipa subbasin, the U.S. Geological Survey (USGS) and the San Bernardino Valley Municipal Water District initiated a cooperative study to understand the hydrogeologic system of the Yucaipa subbasin and in the encompassing Yucaipa Valley watershed (YVW). A three-dimensional hydrogeologic framework model was constructed to quantify the structure and extent of hydrogeologic units. Historical and present-day groundwater conditions were characterized to evaluate the groundwater-flow system. Lastly, the Yucaipa Integrated Hydrological Model (YIHM) was developed to simulate the integrated surface-water and groundwater systems, including natural and anthropogenic (that is, human influenced) recharge and discharge throughout the study area from 1947 to 2014.
The Yucaipa subbasin is an inland groundwater basin located about 12 miles (mi) southeast of the City of San Bernardino and about 75 mi east of Los Angeles, California. The subbasin encompasses about 39 square miles (mi2), including the City of Yucaipa. The geographic extent of the Yucaipa subbasin was established by the California Department of Water Resources, who defined the boundaries of the subbasin based on hydrogeologic transitions between crystalline rock and basin-fill sediments, active fault strands, surface-water drainage divides, and a portion of an adjudicated groundwater management boundary. Two groundwater subbasins of the Upper Santa Ana Valley groundwater basin are adjacent to the Yucaipa subbasin, the San Bernardino groundwater subbasin to the west and the San Timoteo groundwater subbasin to the south.
The Yucaipa subbasin is encompassed by the YVW, which is in turn comprised of three sub-watersheds that represent surface-water flow across and within the Yucaipa subbasin. Although the Yucaipa subbasin is the specific area of interest for this study, the entire YVW was considered for the purposes of characterizing the hydrogeology of the Yucaipa subbasin and for development of the YIHM.
The purposes of this report are to (1) describe the hydrologic and hydrogeologic settings of the Yucaipa subbasin and aquifer system, (2) describe the construction and calibration of the fully coupled groundwater and surface-water flow model for the Yucaipa subbasin and the encompassing YVW, referred to as the YIHM, and (3) present numerical results, including water budgets and hydraulic heads, and the effect of pumping and climate stresses (precipitation and temperature) on water-budget components.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215118","collaboration":"Prepared in cooperation with San Bernardino Valley Municipal Water District","usgsCitation":"Cromwell, G., and Alzraiee, A., eds., 2022, Hydrology of the Yucaipa groundwater subbasin: Characterization and integrated numerical model, San Bernardino and Riverside Counties, California: U.S. Geological Survey Scientific Investigations Report 2021–5118, 4 p., https://doi.org/10.3133/sir20215118.","productDescription":"Executive Summary: vi, 4 p.; Chapter A: viii, 81 p.; Chapter B: xii, 76 p.; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123424","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":394827,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5118/images"},{"id":394823,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F7OYQR","text":"Data release of hydrogeologic data of the Yucaipa groundwater subbasin, San Bernardino and Riverside Counties, California"},{"id":394776,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5118/covrthb.jpg"},{"id":394777,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118.pdf","text":"Executive Summary","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":502121,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112154.htm","linkFileType":{"id":5,"text":"html"}},{"id":394835,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118b.xml"},{"id":394834,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118a.xml"},{"id":394826,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5118/sir20215118.xml"},{"id":394825,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K540DV","text":"GSFLOW model to evaluate the effect of groundwater pumpage and climate stresses on the integrated hydrologic system of the Yucaipa subbasin, Yucaipa Valley watershed, San Bernardino and Riverside Counties, California"}],"country":"United States","state":"California","county":"Riverside County, San Bernardino County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.257080078125,\n 33.899486813913285\n ],\n [\n -116.87736511230469,\n 33.899486813913285\n ],\n [\n -116.87736511230469,\n 34.098159345215535\n ],\n [\n -117.257080078125,\n 34.098159345215535\n ],\n [\n -117.257080078125,\n 33.899486813913285\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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The U.S. Geological Survey, in cooperation with the Savannah Valley Utility District, evaluated the groundwater hydrology of the Valley and Ridge carbonate rock aquifer in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, from 2007 through 2009. The evaluation included, and built on, the results of test drilling conducted in the area in 1974 to determine the potential for groundwater as a source of public supply for the utility and the results of an investigation conducted to define recharge areas for wells used by groundwater-source public-supply water systems throughout Hamilton County in the early 1990s.
Groundwater-level data collected from wells open to the aquifer in the study area were used to prepare potentiometric-surface maps for fall 1992, spring and fall 1993, summer 2008, and spring 2009 conditions. Two primary groundwater basins were delineated from the maps—the larger of which coincides with the watershed of Savannah Creek in the southern part of the study area and the smaller of which coincides with the watershed of Gunstocker Creek in the northern part of the study area. Both basins are characterized by potentiometric surfaces that contain a central area of low-altitude groundwater levels and low gradients relative to the basin margins that reflect the orientation of enhanced permeability along dissolution-enlarged features that have developed parallel to strike in the aquifer. The recharge area of the Savannah Creek groundwater basin is estimated to be about 31 square miles, and the recharge area of the Gunstocker Creek groundwater basin is estimated to be about 17 square miles.
Recharge to the aquifer in the Savannah Creek and Gunstocker Creek groundwater basins primarily occurs in the uplands area along White Oak Mountain in the eastern part of the study area and along the western boundaries of the basins. Groundwater flows toward the potentiometric lows in each basin, discharging as base flow to the streams and to springs locally. Groundwater withdrawals for public supply by the utility influence the potentiometric low in the north-central part of the Savannah Creek groundwater basin and disrupt flow in the creek and nearby Anderson Spring, particularly during the summer and fall seasons. No large groundwater withdrawals currently occur in the Gunstocker Creek basin, but there is potential for groundwater supply development in the basin.
A conceptual model of the groundwater hydrology of the area developed from the evaluation indicates that Chickamauga Lake is the base-level control on groundwater discharge from the Savannah Creek and Gunstocker Creek basins and that lake stage affects the potentiometric surfaces and groundwater discharge in the most downgradient parts of the basins as a result of inferred hydraulic connection between the aquifer and the lake. The model also infers that captured surface water from sections of Savannah Creek and the Hiwassee River that are embayed by the lake could recharge the aquifer and serve as a source of water withdrawn by wells in each basin if the potentiometric surfaces were lowered to altitudes less than the stage of the lake, particularly under potential future groundwater-development scenarios in the Gunstocker Creek basin.
Geochemical analysis of samples collected from six wells for the study indicate that groundwater in the Valley and Ridge aquifer in the area generally is a calcium-magnesium-bicarbonate type, and although the water generally is hard, it is suitable for most uses. Trace-element concentrations were less than primary drinking-water criteria in all the samples.
Results of the investigation indicate that options are available for additional groundwater withdrawal in the study area. Water-level data collected since 1975 at the Savannah Valley Utility District Smith Road well site indicate that some additional amount of groundwater is available for withdrawal from the aquifer in the Savannah Creek groundwater basin. The potentiometric low within the Gunstocker Creek groundwater basin indicates that an area with enhanced permeability is present as a northeastern counterpart to the potentiometric low within the Savannah Creek basin. Because the Gunstocker Creek basin is about one-half the total area of the Savannah Creek basin, a commensurate decrease in available groundwater storage is likely. Furthermore, groundwater withdrawal locations in the Gunstocker Creek basin would be closer to—and possibly connected hydraulically to—the Hiwassee River, thus increasing the potential for induced surface-water recharge in the basin if sustained drawdown from pumping lowered groundwater levels to altitudes less than the stage of the river.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215135","isbn":"978-1-4113-4435-8","collaboration":"Prepared in cooperation with the Savannah Valley Utility District","programNote":"Water Availability and Use Science Program","usgsCitation":"Carmichael, J.K., 2022, Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09: U.S. Geological Survey Scientific Investigations Report 2021–5135, 31 p., 5 pls., https://doi.org/10.3133/sir20215135.","productDescription":"Report: vii, 31 p.; Data Release; 5 Plates: 20.00 x 30.00 inches or smaller","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-104265","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Fluvial systems provide a variety of habitats that support thousands of species including many that are threatened or endangered. Moreover, these habitats, which range from aquatic and riparian to floodplain, are important for the variety of ecosystem services they provide. In addition to water temperature and streamflow change, geomorphic change is important and warrants consideration as one of the several potential threats to these habitats posed by climate change. The geomorphic response of fluvial systems to global warming in temperate environments, for example, caused by an increase in the frequency and magnitude of floods, is important because geomorphology is a primary determinant of habitat availability and quality. Possible geomorphic responses include increased erosion and (or) deposition in the river channel, riparian zone, and floodplain with associated habitat implications. Geomorphic changes caused by global warming can be beneficial (e.g., increased habitat complexity) or detrimental (e.g., mortality caused by scour or burial) to biota. The ability of a species to respond to and survive disturbances, including geomorphic changes, will depend on the nature of the disturbances and the sensitivity and adaptive capabilities of the species. Post-flood recovery often is rapid; however, for certain species (e.g., periphyton, macroinvertebrates), changes in community composition may persist. Increased flood frequency, sediment mobility, and associated geomorphic changes potentially will result in more frequent and persistent changes in habitat and community composition in the affected fluvial systems.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3938","usgsCitation":"Juracek, K.E., and Fitzpatrick, F., 2022, Geomorphic responses of fluvial systems to climate change: A habitat perspective: River Research and Applications, v. 38, no. 4, p. 757-775, https://doi.org/10.1002/rra.3938.","productDescription":"19 p.","startPage":"757","endPage":"775","ipdsId":"IP-127640","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Juracek, Kyle E. 0000-0002-2102-8980 kjuracek@usgs.gov","orcid":"https://orcid.org/0000-0002-2102-8980","contributorId":2022,"corporation":false,"usgs":true,"family":"Juracek","given":"Kyle","email":"kjuracek@usgs.gov","middleInitial":"E.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":835745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209612,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835746,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228233,"text":"70228233 - 2022 - Terrestrial ecosystem modeling with IBIS: Progress and future vision","interactions":[],"lastModifiedDate":"2022-03-30T15:19:39.880176","indexId":"70228233","displayToPublicDate":"2022-01-24T09:21:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5535,"text":"Journal of Resources and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Terrestrial ecosystem modeling with IBIS: Progress and future vision","docAbstract":"Dynamic Global Vegetation Models (DGVM) are powerful tools for studying complicated ecosystem processes and global changes. This review article synthesizes the developments and applications of the Integrated Biosphere Simulator (IBIS), a DGVM, over the past two decades. IBIS has been used to evaluate carbon, nitrogen, and water cycling in terrestrial ecosystems, vegetation changes, land-atmosphere interactions, land-aquatic system integration, and climate change impacts. Here we summarize model development work since IBIS v2.5, covering hydrology (evapotranspiration, groundwater, lateral routing), vegetation dynamics (plant functional type, land cover change), plant physiology (phenology, photosynthesis, carbon allocation, growth), biogeochemistry (soil carbon and nitrogen processes, greenhouse gas emissions), impacts of natural disturbances (drought, insect damage, fire) and human induced land use changes, and computational improvements. We also summarize IBIS model applications around the world in evaluating ecosystem productivity, carbon and water budgets, water use efficiency, natural disturbance effects, and impacts of climate change and land use change on the carbon cycle. Based on this review, visions of future cross-scale, cross-landscape and cross-system model development and applications are discussed.
","language":"English","publisher":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences","doi":"10.5814/j.issn.1674-764x.2022.01.001","usgsCitation":"Liu, J., Lu, X., Zhu, Q., Yuan, W., Yuan, Q., Zhang, Z., Guo, Q., and Deering, C., 2022, Terrestrial ecosystem modeling with IBIS: Progress and future vision: Journal of Resources and Ecology, v. 13, no. 1, p. 2-16, https://doi.org/10.5814/j.issn.1674-764x.2022.01.001.","productDescription":"15 p.","startPage":"2","endPage":"16","ipdsId":"IP-132711","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":395617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":833489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Xuehe","contributorId":175216,"corporation":false,"usgs":false,"family":"Lu","given":"Xuehe","email":"","affiliations":[{"id":27538,"text":"International Institute for Earth System Science, Nanjing University, Xianlin Avenue 163, Nanjing 210093","active":true,"usgs":false}],"preferred":false,"id":833490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false},{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false}],"preferred":false,"id":833491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yuan, Wenping","contributorId":274900,"corporation":false,"usgs":false,"family":"Yuan","given":"Wenping","affiliations":[{"id":56683,"text":"Sun Yat-sen University, China","active":true,"usgs":false}],"preferred":false,"id":833492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yuan, Quanzhi","contributorId":274901,"corporation":false,"usgs":false,"family":"Yuan","given":"Quanzhi","email":"","affiliations":[{"id":56684,"text":"Sichuan Normal University, China","active":true,"usgs":false}],"preferred":false,"id":833493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Zhen 0000-0003-0899-1139","orcid":"https://orcid.org/0000-0003-0899-1139","contributorId":149173,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":833494,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Guo, Qingxi","contributorId":274902,"corporation":false,"usgs":false,"family":"Guo","given":"Qingxi","email":"","affiliations":[{"id":56685,"text":"Northeast Forestry University, China","active":true,"usgs":false}],"preferred":false,"id":833495,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Deering, Carol 0000-0003-3565-6264 cdeering@usgs.gov","orcid":"https://orcid.org/0000-0003-3565-6264","contributorId":3001,"corporation":false,"usgs":true,"family":"Deering","given":"Carol","email":"cdeering@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":833496,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70233564,"text":"70233564 - 2022 - A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty","interactions":[],"lastModifiedDate":"2022-07-26T11:55:11.529587","indexId":"70233564","displayToPublicDate":"2022-01-24T06:49:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty","docAbstract":"An open-source tool has been developed to facilitate constrained single- and multi-objective optimization under uncertainty (CMOU) analyses. The tool uses the well-known PEST interface protocols to communicate with the underlying forward simulation, making it non-intrusive. The tool contains a built-in parallel run manager to make use of heterogeneous and distributed computing resources. Several popular and well-known evolutionary algorithms are implemented and can be combined with a range of approaches to represent uncertainty in model-derived constraint/objective values. These attributes serve to address the current barrier to adopt advanced CMOU analyses for a wide range of decision-support problems across the environmental modeling spectrum. We demonstrate the capabilities of the CMOU tool on a well-known analytical benchmark problem that we augmented to include uncertainty, as well as on a synthetic density-dependent coastal groundwater management benchmark problem. Both demonstrations highlight the importance of explicitly accounting for uncertainty to convey risk and reliability in pareto-optimal design.
Over the past two decades, extensive monitoring has been conducted in the St. Clair – Detroit River System to describe spatial and temporal patterns of lake sturgeon (Acipenser fulvescens). To characterize spatial patterns in juvenile lake sturgeon (<1000 mm TL) based on survey collections, ‘hot spots’ were identified through optimized hot spot analysis (HSA). This HSA was then interpolated by inverse distance weighted analysis to determine extent of identified ‘hot spots’ and ‘cold spots’. Additionally, habitat variables (i.e., water depth, water velocity, and dominant substrate type) were investigated using a single season occupancy model to determine their influence on juvenile lake sturgeon occupancy probability. In total, 1203 juvenile lake sturgeon were captured across 4197 surveys. Three unique ‘hot spots’ were identified; western Lake Erie, Fighting Island in the Detroit River, and the North Channel in the St. Clair River. Interpolated ‘hot spots’ encompassed 73.1 km² in western Lake Erie, 4.7 km² near Fighting Island, and 6.6 km² in the North Channel. Detection probabilities within ‘hot spots’ ranged from 8.8%–43.4%. No habitat variables significantly predicted juvenile lake sturgeon occupancy. Juvenile lake sturgeon were captured in western Lake Erie where the water depth was >5.1 m and odds of occupancy increased with increased water velocity. Juvenile lake sturgeon in the Detroit and St. Clair River ‘hot spots’ were captured at sites with mean benthic water velocities ranging from 0.20–0.60 m/s and where water depth was >7.3 m. Irrespective of waterbody, 69% of all juveniles were detected over dominant sand and gravel substrates. These results provide valuable insight about juvenile habitat use that can help managers formulate effective conservation and restoration strategies supporting the continued recovery of Great Lakes lake sturgeon.
Understanding the thermal tolerances of stream fishes, including sport fishes, is important for assessing thermal stressors that are common across the landscape. Our study objectives were to determine the thermal tolerances of 17 stream fishes (15 species and 2 genetically distinct populations of juvenile Smallmouth Bass Micropterus dolomieu: the Neosho subspecies M. dolomieu velox and the Ouachita strain M. sp. cf. dolomieu velox). Fish were collected from the field and acclimated to laboratory conditions at 20°C or 25°C, with dissolved oxygen maintained above 6 mg/L. We determined the critical thermal maximum (CTM) using an incomplete block design with 9–11 replications for each species. During trials, we increased the water temperature at a rate of 2°C per hour until fish experienced loss of equilibrium. The estimated CTM ranged from 32.43°C to 38.26°C among species. The CTM values differed significantly between taxonomic groups and species, including the genetically distinct populations of Smallmouth Bass. The Neosho subspecies of Smallmouth Bass had a significantly lower thermal tolerance than the Ouachita strain at both acclimation temperatures; however, the magnitude of the difference was about 0.5°C greater at the higher acclimation temperature. Closely related species, including the Bigeye Shiner Notropis boops and Kiamichi Shiner N. ortenburgeri, had significantly different thermal tolerances despite occupying similar riverine locations. Our results suggest that our perceptions of a species’ thermal tolerance based on that of closely related species or that of species using similar habitat may be incorrect. Moreover, the differences in thermal tolerances among populations may be an important consideration for conservation and management actions, such as stocking decisions. Laboratory data such as those provided in this study can be integrated with field data to better assess thermal responses of fishes in a changing environment.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10749","usgsCitation":"Brewer, S.K., Mollenhauer, R., Alexander, J., and Moore, D., 2022, Critical thermal maximum of stream fishes including distinct populations of Smallmouth Bass: North American Journal of Fisheries Management, v. 42, no. 2, p. 352-360, https://doi.org/10.1002/nafm.10749.","productDescription":"9 p.","startPage":"352","endPage":"360","ipdsId":"IP-128880","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Ouachita Mountain ecoregion, Ozark Highlands ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.8664012156679,\n 36.99681119417036\n ],\n [\n -95.5111277927191,\n 36.20967738751284\n ],\n [\n -95.222344013415,\n 35.57857852939986\n ],\n [\n -94.48359481054393,\n 35.687747319317126\n ],\n [\n -94.62462874927394,\n 36.45854467258876\n ],\n [\n -94.62462874927394,\n 37.01290083943225\n ],\n [\n -94.8664012156679,\n 36.99681119417036\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.43940916621646,\n 34.76329149945565\n ],\n [\n -96.02893897983913,\n 34.719164102780056\n ],\n [\n -96.6840830246433,\n 34.27659649424358\n ],\n [\n -96.35114096908727,\n 33.96540469177903\n ],\n [\n -94.48236943144916,\n 33.992123122916226\n ],\n [\n -94.43940916621646,\n 34.76329149945565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":908839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mollenhauer, R.","contributorId":276144,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"R.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, J.","contributorId":305320,"corporation":false,"usgs":false,"family":"Alexander","given":"J.","email":"","affiliations":[],"preferred":false,"id":908841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, D.E.","contributorId":205713,"corporation":false,"usgs":false,"family":"Moore","given":"D.E.","email":"","affiliations":[],"preferred":false,"id":908842,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227620,"text":"70227620 - 2022 - Using surrogate taxa to inform response methods for invasive Grass Carp in the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2022-02-15T16:27:30.782829","indexId":"70227620","displayToPublicDate":"2022-01-21T09:10:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Using surrogate taxa to inform response methods for invasive Grass Carp in the Laurentian Great Lakes","docAbstract":"Sampling method decisions are critical for the effective monitoring and management of fisheries. Deploying the most effective sampling methodologies is particularly important when responding to new invasive species, where early response efforts have the best chances for eradication. In the Laurentian Great Lakes, the invasive Grass Carp Ctenopharyngodon idella is sampled using boat electrofishing and the combination method of boat electrofishing within and around a trammel net enclosure. We conducted a field study to compare the effectiveness of the two methods. We used capture data for surrogate taxa (i.e., Common Carp Cyprinus carpio and buffalo Ictiobus spp.) to compare the two methods because few Grass Carp were collected during the study. The sampling methods were compared within an occupancy modeling framework using an information-criteria model selection approach to evaluate seven alternative models. The base model included sampling method, year, water temperature, and sampling effort as covariates in the detection submodel and assumed that occupancy probability was constant across sites. The other six models built on the base model by including site, water body type (i.e., lentic vs. lotic), and interaction covariates in the detection submodel. The top-performing model, built on the base model, accounted for the influence of water body type and assumed the exchangeability of site effects in the detection submodel. The results indicated that the detection probabilities for both taxa were higher for the combination method than for boat electrofishing, with a median estimated difference in detection probability between the two methods of 0.11 (95% CI: 0.04–0.22) for Common Carp and 0.18 (95% CI: 0.08–0.28) for buffalo. Given that the combination method was more effective for detecting the surrogate taxa, we expect the combination method may be preferable to only boat electrofishing for Grass Carp removal.
Long-term (>20 y) suspended sediment (SS) and particulate organic carbon (POC) records are relatively rare and yet are necessary for understanding linkages between climate, erosion and carbon export. We estimated long-term (>23 y) SS and POC yields from four nested catchments that ranged from <1 to 54 km2 in area across the Reynolds Creek Experimental Watershed and Critical Zone Observatory (RCEW-CZO) in southwestern Idaho, USA. We found strong relationships between log10SS and log10POC (R2 = 0.38–0.86) that varied across catchments but remained robust across years, one dry and one of the wettest water years on record. Mean annual SS yields varied from 18 to 89 g SS m−2 y−1 and POC from 0.6 to 11.0 g C m−2 y−1 across the four catchments. Water yield explained much of the temporal variation (72%–85%) in SS and POC yields except in a small, snow-dominated headwater catchment where it explained 15%–51%. The largest five water years accounted for 69%–84% of the total SS and POC yields in catchments with 24 y records. All catchments had positive slopes (>0) for SS and POC concentration-discharge (C-Q) relationships, with large catchments exhibiting greater slopes (0.66–0.97) than smaller ones (0.14–0.16). In addition, most catchments were dominated (80%) by clockwise hysteretic curves. Lack of seasonal exhaustion in the SS-POC relationships, positive C-Q and clockwise relations indicated that these systems were transport-rather than supply limited, and that sediment and POC appeared to be sourced from channel/bank erosion and remobilization. POC yields represent 1%–10% of mean water year net ecosystem exchange depending on elevation; lower elevation catchments may shift from being carbon sinks to sources after accounting for fluvial POC export associated with changes in climate.
No abstract available.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.2c00052","usgsCitation":"Manceau, A., Brossier, R., Janssen, S., and Poulin, B., 2022, Response to comment on “Mercury isotope fractionation by internal demethylation and biomineralization reactions in seabirds: Implications for environmental mercury science”: Principles and limitations of source tracing and process tracing with stable isotope signatures: Environmental Science and Technology, v. 56, no. 3, p. 2065-2068, https://doi.org/10.1021/acs.est.2c00052.","productDescription":"4 p.","startPage":"2065","endPage":"2068","ipdsId":"IP-136180","costCenters":[{"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":449086,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-03688547","text":"External Repository"},{"id":394752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Manceau, Alain 0000-0003-0845-611X","orcid":"https://orcid.org/0000-0003-0845-611X","contributorId":194255,"corporation":false,"usgs":false,"family":"Manceau","given":"Alain","email":"","affiliations":[],"preferred":false,"id":831520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brossier, Romain 0000-0002-7195-8123","orcid":"https://orcid.org/0000-0002-7195-8123","contributorId":267387,"corporation":false,"usgs":false,"family":"Brossier","given":"Romain","email":"","affiliations":[{"id":55486,"text":"University of Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":831521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poulin, Brett 0000-0002-5555-7733","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":260893,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett","affiliations":[{"id":52706,"text":"Department of Environmental Toxicology, University of California Davis, Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":831523,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227501,"text":"70227501 - 2022 - How many Ciscoes are needed for stocking in the Laurentian Great Lakes?","interactions":[],"lastModifiedDate":"2022-07-07T16:31:43.081561","indexId":"70227501","displayToPublicDate":"2022-01-20T08:21:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"How many Ciscoes are needed for stocking in the Laurentian Great Lakes?","docAbstract":"Historically, Cisco Coregonus artedi and deepwater ciscoes Coregonus spp. were the most abundant and ecologically important fish species in the Laurentian Great Lakes, but anthropogenic influences caused nearly all populations to collapse by the 1970s. Fishery managers have begun exploring the feasibility of restoring populations throughout the basin, but questions regarding hatchery propagation and stocking remain. We used historical and contemporary stock-recruit parameters previously estimated for Ciscoes in Wisconsin waters of Lake Superior, with estimates of age-1 Cisco rearing habitat (broadly defined as total ha ≤ 80 m depth) and natural mortality, to estimate how many fry (5.5 months post-hatch), fall fingerling (7.5 months post-hatch), and age-1 (at least 12 months post-hatch) hatchery-reared Ciscoes are needed for stocking in the Great Lakes to mimic recruitment rates in Lake Superior, a lake that has undergone some recovery. Estimated stocking densities suggested that basin-wide stocking would require at least 0.641-billion fry, 0.469-billion fall fingerlings, or 0.343-billion age-1 fish for a simultaneous restoration effort targeting historically important Cisco spawning and rearing areas in Lakes Huron, Michigan, Erie, Ontario, and Saint Clair. Numbers required for basin-wide stocking were considerably greater than current or planned coregonine production capacity, thus simultaneous stocking in the Great Lakes is likely not feasible. Provided current habitat conditions do not preclude Cisco restoration, managers could maximize the effectiveness of available production capacity by concentrating stocking efforts in historically important spawning and rearing areas, similar to the current stocking effort in Saginaw Bay, Lake Huron. Other historically important Cisco spawning and rearing areas within each lake (listed in no particular order) include: (1) Thunder Bay in Lake Huron, (2) Green Bay in Lake Michigan, (3) the islands near Sandusky, Ohio, in western Lake Erie, and (4) the area near Hamilton, Ontario, and Bay of Quinte in Lake Ontario. Our study focused entirely on Ciscoes but may provide a framework for describing future stocking needs for deepwater ciscoes.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-21-025","usgsCitation":"Rook, B.J., Hansen, M.J., and Bronte, C.R., 2022, How many Ciscoes are needed for stocking in the Laurentian Great Lakes?: Journal of Fish and Wildlife Management, v. 13, no. 1, p. 28-49, https://doi.org/10.3996/JFWM-21-025.","productDescription":"22 p.","startPage":"28","endPage":"49","ipdsId":"IP-131557","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":449095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-025","text":"Publisher Index Page"},{"id":394577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n 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and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":831192,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227645,"text":"70227645 - 2022 - A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms","interactions":[],"lastModifiedDate":"2022-01-24T13:07:15.729427","indexId":"70227645","displayToPublicDate":"2022-01-20T07:02:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms","docAbstract":"The Biological Condition Gradient (BCG) is a conceptual model used to describe incremental changes in biological condition along a gradient of increasing anthropogenic stress. As coral reefs collapse globally, scientists and managers are focused on how to sustain the crucial structure and functions, and the benefits that healthy coral reef ecosystems provide for many economies and societies. We developed a numeric (quantitative) BGC model for the coral reefs of Puerto Rico and the US Virgin Islands to transparently facilitate ecologically meaningful management decisions regarding these fragile resources. Here, reef conditions range from natural, undisturbed conditions to severely altered or degraded conditions. Numeric decision rules were developed by an expert panel for scleractinian corals and other benthic assemblages using multiple attributes to apply in shallow-water tropical fore reefs with depths <30 m. The numeric model employed decision rules based on metrics (e.g., % live coral cover, coral species richness, pollution-sensitive coral species, unproductive and sediment substrates, % cover by Orbicella spp.) used to assess coral reef condition. Model confirmation showed the numeric BCG model predicted the panel’s median site ratings for 84% of the sites used to calibrate the model and 89% of independent validation sites. The numeric BCG model is suitable for adaptive management applications and supports bioassessment and criteria development. It is a robust assessment tool that could be used to establish ecosystem condition that would aid resource managers in evaluating and communicating current or changing conditions, protect water and habitat quality in areas of high biological integrity, or develop restoration goals with stakeholders and other public beneficiaries.
The Yucaipa groundwater subbasin (referred to in this report as the Yucaipa subbasin) is located about 75 miles (mi) east of of Los Angeles and about 12 mi southeast of the City of San Bernardino. In the Yucaipa subbasin, as in much of southern California, limited annual rainfall and large water demands can strain existing water supplies; therefore, understanding local surface water and groundwater conditions is essential for managing these resources. To better understand the hydrogeology and water resources in the Yucaipa subbasin, especially groundwater, the San Bernardino Valley Municipal Water District and the U.S. Geological Survey initiated a cooperative study to evaluate the hydrogeologic system of the Yucaipa subbasin and the encompassing Yucaipa Valley watershed. Previous studies of the area provided information on general geologic and hydrologic conditions, but this study provides the first comprehensive definition of the hydrogeology of the subsurface throughout the entire subbasin.
The Yucaipa subbasin is located between the northwest trending San Andreas fault zone and San Jacinto fault. Several northeast-trending dip-slip faults dissect the Yucaipa subbasin, providing the mechanism for structural relief within the sediment-filled subbasin and between the subbasin and surrounding mountains and highlands. Several of these dip-slip faults have been previously identified as potential barriers to groundwater flow. This report provides a synthesis of previous studies and a discussion of the geologic interpretations that were used as the foundation for hydrogeologic classification of the Yucaipa subbasin. Notably, this report (1) adopts the recently named and classified sedimentary deposits of Live Oak Canyon geologic formation and extends the mapped distribution of the formation into the Yucaipa subbasin, and (2) adopts the interpretation that activity along the Banning fault predates the deposition of most basin-fill sedimentary materials in the Yucaipa subbasin.
Four hydrogeologic units were classified in the Yucaipa subbasin: (1) crystalline basement, (2) consolidated sedimentary materials, (3) unconsolidated sediment, and (4) surficial materials. The crystalline basement unit forms the bottom boundary of the aquifer system, and the three other units comprise the basin-fill aquifer system. The four hydrogeologic units vary in extent, thickness, and structural relief across the subbasin, with the unconsolidated sediment unit serving as the primary aquifer unit. A three-dimensional hydrogeologic framework model was developed for the Yucaipa subbasin and surrounding area to characterize the thickness, extent, and hydrogeologic variability of the aquifer system. Geologic maps, borehole geophysical logs, drillers’ lithology logs, and depth-to-basement gravity data were used to map and interpolate the subsurface extent and structure of the hydrogeologic units within the subbasin. Faults and structures of geologic and (or) hydrogeologic importance were included in the model for future evaluation of their potential effects on groundwater flow. The resulting hydrogeologic framework is consistent with existing geologic concepts and the tectonic and structural history of the Yucaipa subbasin and surrounding area. The framework is also suitable for use in basin-scale hydrogeologic investigations.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change is rapidly driving global biodiversity declines. How wetland macroinvertebrate assemblages are responding is unclear, a concern given their vital function in these ecosystems. Using a data set from 769 minimally impacted depressional wetlands across the globe (467 temporary and 302 permanent), we evaluated how temperature and precipitation (average, range, variability) affects the richness and beta diversity of 144 macroinvertebrate families. To test the effects of climatic predictors on macroinvertebrate diversity, we fitted generalized additive mixed-effects models (GAMM) for family richness and generalized dissimilarity models (GDMs) for total beta diversity. We found non-linear relationships between family richness, beta diversity, and climate. Maximum temperature was the main climatic driver of wetland macroinvertebrate richness and beta diversity, but precipitation seasonality was also important. Assemblage responses to climatic variables also depended on wetland water permanency. Permanent wetlands from warmer regions had higher family richness than temporary wetlands. Interestingly, wetlands in cooler and dry-warm regions had the lowest taxonomic richness, but both kinds of wetlands supported unique assemblages. Our study suggests that climate change will have multiple effects on wetlands and their macroinvertebrate diversity, mostly via increases in maximum temperature, but also through changes in patterns of precipitation. The most vulnerable wetlands to climate change are likely those located in warm-dry regions, where entire macroinvertebrate assemblages would be extirpated. Montane and high-latitude wetlands (i.e., cooler regions) are also vulnerable to climate change, but we do not expect entire extirpations at the family level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.153052","usgsCitation":"Epele, L., Grech, M.G., Williams-Subiza, E.A., Stenert, C., McLean, K., Greig, H., Maltchik, L., Pires, M.M., Bird, M.S., Boissezon, A., Boix, D., Demierre, E., García, P., Gascón, S., Jeffries, M., Kneitel, J.M., Loskutov, O., Manzo, L.M., Mataloni, G., Mlambo, M.C., Oertli, B., Sala, J., Scheibler, E.E., Wu, H., Wissinger, S., and Batzer, D., 2022, Perils of life on the edge: Climatic threats to global diversity patterns of wetland macroinvertebrates: Science of the Total Environment, v. 820, 153052, 10 p., https://doi.org/10.1016/j.scitotenv.2022.153052.","productDescription":"153052, 10 p.","ipdsId":"IP-127993","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":449106,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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To investigate sources of PAHs and metals to streambed sediment, we sampled pavement dust, soil, and streambed sediment in 10 urban watersheds in three regions of the United States and applied a fallout-radionuclide-based sediment-source analysis to quantify the pavement dust contribution to stream sediment (%dust). We also mapped the area of sealcoated pavement in each watershed (%sealed) to investigate the role of coal-tar pavement sealant (CTS) as a PAH source. Median total and carbon-normalized total PAH concentrations were significantly higher in streambed sediment in the Northeast (54.3 mg/kg and 2.71 mg/gOC) and Southeast (5.37 mg/kg and 1.36 mg/gOC), where CTS is commonly used, than in the Northwest (2.11 mg/kg and 0.071 mg/gOC), where CTS is rarely used. Generalized additive models indicated that %sealed and in some cases %dust significantly affected total PAH concentrations in streambed sediments. The %dust was a significant variable for common urban metals: Cu, Pb, and Zn. These findings advance our quantitative understanding of the role of pavement dust as a source and a vector of contaminants to urban streams.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.1c00414","usgsCitation":"Van Metre, P.C., Mahler, B., Qi, S.L., Gellis, A.C., Fuller, C.C., and Schmidt, T., 2022, Sediment sources and sealed-pavement area drive polycyclic aromatic hydrocarbon and metal occurrence in urban streams: Environmental Science and Technology, v. 56, no. 3, p. 1615-1626, https://doi.org/10.1021/acs.est.1c00414.","productDescription":"12 p.","startPage":"1615","endPage":"1626","ipdsId":"IP-121967","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":435995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9729ZGL","text":"USGS data release","linkHelpText":"Mapped 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moisture response to seasonal drought conditions and post-thinning forest structure","interactions":[],"lastModifiedDate":"2022-08-01T16:55:02.409081","indexId":"70230062","displayToPublicDate":"2022-01-19T06:17:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Soil moisture response to seasonal drought conditions and post-thinning forest structure","docAbstract":"Prolonged drought conditions in semi-arid forests can lead to widespread vegetation stress and mortality. However, the distribution of these effects is not spatially uniform. We measured soil water potential at high spatial and temporal resolution using 112 sensors distributed across a ponderosa pine forest in northern Arizona, USA, during two abnormally dry years with below-average total precipitation. We used the data to assess the effects of fore-summer drought period on the timing, magnitude, and extent of drying throughout the top 100 cm of the soil profile. Additionally, we use high spatial resolution terrestrial lidar measurements of forest structure to develop relationships between soil drying and fine-scale forest structure. We find that increasing drought from 2019 to 2020 caused significantly earlier onset of soil dying at all depths (25, 50 and 100 cm) and more days below a critical drying threshold for ponderosa pine. Additionally, our results show that significantly drier soils are found in areas with higher stand-level basal area, canopy cover and tree density, and shorter trees. Our results from the unprecedented spatial and temporal resolution data suggest that tailored restoration thinning with specific tree density and size parameters can be used to increase and prolong the availability of deep soil water to trees during drought.
Lake St. Clair Metropark Beach (LSCMB) in Michigan is a public beach near the mouth of the Clinton River that has a history of beach closures for public health concerns. The Clinton River is designated as a Great Lakes Area of Concern, and the park has a Beneficial Use Impairment for beach closings because of elevated Escherichia coli (E. coli) concentrations. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and in collaboration with the Michigan Department of the Environment, Great Lakes, and Energy, Macomb County Health Department, and Huron-Clinton Metroparks, completed a 2-year study to determine sources of E. coli in LSCMB. Samples were collected during dry and wet weather periods to observe the sampling sites under different conditions. Nearshore surface water samples were collected biweekly July through October in 2018 and May through September in 2019. There were 20 sampling sites along the shoreline of the park and in the channel north of the park. In addition to collecting nearshore surface-water samples, samples were collected from shallow groundwater, lake-bottom material, standing water on the beach and surrounding the recreational beach area, solids (beach sands and detritus), and offshore surface-water sites. In 2019, additional samples for microbial source tracking (MST) were collected on three dates in midsummer and were analyzed for human (HF183) and bird/waterfowl (GFD) MST markers. The concentrations of E. coli at LSCMB (in order of highest to lowest E. coli concentrations) were as follows: shallow groundwater nearest to the water’s edge, surface sands and organic matter (detritus), standing water on the beach, nearshore surface water in and surrounding the recreational beach area, lake-bottom material, and offshore surface water. The combination of low E. coli concentrations offshore and higher concentrations nearshore indicate nearshore sources, possibly from beach sands or groundwater, rather than sources coming from offshore Lake St. Clair waters. The subset of samples for MST analysis did not have enough positive results to illustrate MST trends, but this study demonstrated that both human and waterfowl sources can affect the water quality at LCSMB.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215089","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Fogarty, L.R., Maurer, J.A., Hyslop, I.M., Totten, A.R., Kephart, C.M., and Brennan, A.K., 2021, Understanding sources and distribution of Escherichia coli at Lake St. Clair Metropark Beach, Macomb County, Michigan: U.S. Geological Survey Scientific Investigations Report 2021–5089, 34 p., https://doi.org/10.3133/sir20215089.","productDescription":"Report: ix, 34 p.; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-125120","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":502112,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112127.htm","linkFileType":{"id":5,"text":"html"}},{"id":394369,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5089/images"},{"id":394366,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5089/coverthb.jpg"},{"id":394367,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5089/sir20215089.pdf","text":"Report","size":"8.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5089"},{"id":394368,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Michigan","county":"Macomb County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-83.1025,42.8884],[-82.9839,42.8939],[-82.8674,42.8958],[-82.7384,42.8967],[-82.7276,42.6807],[-82.7366,42.6755],[-82.7474,42.6731],[-82.7578,42.6656],[-82.7593,42.6611],[-82.7593,42.6598],[-82.7594,42.6589],[-82.7765,42.6544],[-82.7901,42.6552],[-82.8002,42.6541],[-82.8093,42.6466],[-82.8168,42.635],[-82.8176,42.6323],[-82.82,42.6215],[-82.8178,42.616],[-82.8119,42.6103],[-82.7989,42.6081],[-82.7941,42.6053],[-82.7906,42.5997],[-82.7901,42.5983],[-82.7765,42.5957],[-82.7741,42.5933],[-82.7774,42.5912],[-82.7837,42.5891],[-82.7853,42.5823],[-82.7822,42.5708],[-82.7843,42.5672],[-82.785,42.5654],[-82.7874,42.5664],[-82.7904,42.5692],[-82.7984,42.5717],[-82.8139,42.5717],[-82.8252,42.5702],[-82.8348,42.5659],[-82.8458,42.559],[-82.849,42.5563],[-82.848,42.5518],[-82.8525,42.5487],[-82.8551,42.547],[-82.8623,42.5408],[-82.871,42.5288],[-82.873,42.5261],[-82.8771,42.5194],[-82.8829,42.5027],[-82.8824,42.4886],[-82.8831,42.4873],[-82.8832,42.485],[-82.884,42.4823],[-82.8836,42.4786],[-82.8831,42.4759],[-82.8827,42.4713],[-82.8727,42.4611],[-82.8687,42.4546],[-82.8687,42.4537],[-82.9691,42.4492],[-83.0843,42.4463],[-83.0867,42.5355],[-83.0905,42.6238],[-83.0986,42.801],[-83.1025,42.8884]]]},\"properties\":{\"name\":\"Macomb\",\"state\":\"MI\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911
Advances in global lightning detection have provided novel ways to characterize explosive volcanism. However, researchers are still at the early stages of understanding how volcanic plumes become electrified on different spatial and temporal scales. We deconstructed the phreatomagmatic eruption of Taal volcano (Philippines) on 12 January 2020 to investigate the origin of its powerful volcanic thunderstorm. Satellite analysis indicated that the water-rich plume rose >10 km high before creating lightning detected by Vaisala's global lightning data set (GLD360). Flash rates increased with plume heights and cloud expansion over time, producing >70 flashes min–1. Photographs revealed a highly electrified region at the base of the umbrella cloud, where we infer strong convective updrafts and icy collisions enhanced the electrical activity. These findings inform a conceptual model with overlapping regimes of charge generation in wet eruptions—initially due to ash particle collisions near the vent, followed by thunderstorm-like electrification in icy regions of the upper plume. Despite the wide reach of Taal's ash cloud, most of the lightning occurred within 20–30 km of the volcano, producing thousands of hazardous cloud-to-ground flashes over a densely populated area. The eruption demonstrates that volcanic lightning can pose a hazard in its own right, embedded within the broader hazards of explosive volcanism in an urban setting.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G49490.1","usgsCitation":"Van Eaton, A.R., Smith, C.M., Pavolonis, M.J., and Said, R., 2022, Eruption dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines: Geology, v. 50, no. 4, p. 491-495, https://doi.org/10.1130/G49490.1.","productDescription":"5 p.","startPage":"491","endPage":"495","ipdsId":"IP-128543","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467203,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/geol.s.17265287","text":"External Repository"},{"id":466649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","otherGeospatial":"Taal volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 120.90268362290232,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 14.102133334852596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra M","contributorId":257012,"corporation":false,"usgs":false,"family":"Smith","given":"Cassandra","email":"","middleInitial":"M","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":924037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavolonis, Michael J.","contributorId":199297,"corporation":false,"usgs":false,"family":"Pavolonis","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":924038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Said, Ryan 0000-0002-8095-4204","orcid":"https://orcid.org/0000-0002-8095-4204","contributorId":257003,"corporation":false,"usgs":false,"family":"Said","given":"Ryan","email":"","affiliations":[{"id":51953,"text":"Vaisala, Inc.","active":true,"usgs":false}],"preferred":false,"id":924039,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}