Climate model projections are commonly used to assess potential impacts of global warming on a breadth of social, economic, and environmental topics. Modeling centers throughout the world coordinate to apply a consistent suite of radiative forcing experiments so that all model outputs can be collectively analyzed and compared. Three generations of model outputs have been produced and made available to the scientific community through the Coupled Model Intercomparison Project (CMIP): CMIP3 disseminated during the mid-2000s, CMIP5 during the early-2010s, and CMIP6 during the late-2010s. Twenty-first century sea-ice projections from CMIP3 and CMIP5 models have been used in Bayesian network assessments of how climate change could impact the future persistence of polar bears (Ursus maritimus) throughout their range. In this report, we compare sea-ice projections by CMIP6 models to those of CMIP5 models in each of four polar bear ecoregions over the 21st century. We evaluate differences between the two CMIP generations with respect to other sources of variability that affect uncertainties of the model projections: (1) variability from different models; (2) variability from different greenhouse gas emissions scenarios; and (3) natural (internal) variability in the earth’s climate system. We found that natural variability as well as that attributable to models dominated uncertainties in sea-ice projections in all months and ecoregions during the first half of the 21st century, while emissions scenarios dominated uncertainties during the late 21st century. By comparison, we found only slight differences between the CMIP6 and CMIP5 model projections of sea ice. Applying CMIP6 instead of CMIP5 sea-ice projections to the polar bear Bayesian network model developed in 2016, therefore, would not qualitatively change the population status outcomes published therein.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221062","collaboration":"Prepared in cooperation with the U.S Fish and Wildlife Service","usgsCitation":"Douglas, D.C., and Atwood, T.C., 2022, Comparisons of Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) sea-ice projections in polar bear (Ursus maritimus) ecoregions during the 21st century: U.S. Geological Survey Open-File Report 2022–1062, 27 p., https://doi.org/10.3133/ofr20221062.","productDescription":"vii, 27 p.","onlineOnly":"Y","ipdsId":"IP-139269","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":403336,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221062/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1062"},{"id":403335,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1062/ofr20221062.XML"},{"id":403334,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1062/images"},{"id":403333,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1062/ofr20221062.pdf","text":"Report","size":"16.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1062"},{"id":403332,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1062/coverthb.jpg"}],"contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The 2012 Little Bear Fire resulted in substantial loss of vegetation in the Eagle Creek Basin, south-central New Mexico, which has been expected to cause a variety of hydrologic responses that could influence geomorphic change to North Fork Eagle Creek. To monitor geomorphic change, surveys of a downstream study reach of North Fork Eagle Creek were conducted in 2017, 2018, and 2019 by the U.S. Geological Survey in cooperation with the Village of Ruidoso, N. Mex. The study included surveys of select cross sections, woody debris accumulations, and pools found in the channel of the study reach. During 2017–19, high-flow events resulting from both monsoonal rainfall and snowmelt runoff occurred in the study reach, and the events appeared to have caused some minor localized geomorphic changes in the study reach, which were evaluated through comparison of the 2017, 2018, and 2019 survey results.
Comparisons of the cross-section survey results indicated that minor geomorphic changes had occurred in 4 of the 14 cross sections surveyed from 2017 to 2019. These geomorphic changes included aggradation or degradation of surface materials by about 1–2 feet in some parts of the affected cross sections. During the 2019 survey, 164 distinct accumulations of woody debris and 228 pools were identified in the study reach. Of the woody debris accumulations identified during the 2019 survey, 67 were certain to have also been present during the 2018 survey, and 21 were certain to have also been present during all three surveys (2017–19), indicating that most of the woody debris accumulations surveyed in 2017 were likely transported during the high-flow events between the 2017 and 2018 surveys. Most woody debris accumulations identified in 2019 did not appear to have substantially influenced geomorphic change in the locations where they were found but may have driven local geomorphic changes.
Because the study began 5 years after the 2012 Little Bear Fire and the geomorphic scope of the study has so far been limited, it cannot be said that the changes observed between the 2017 and 2019 surveys are representative of a pattern of geomorphic change following the Little Bear Fire. Once geomorphic changes identified during the 2017 through 2019 surveys can be compared with results from the remaining planned geomorphic surveys, it may be possible to develop an understanding of the patterns in geomorphic change following the 2012 Little Bear Fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221041","collaboration":"Prepared in cooperation with Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., and Chavarria, S.B., 2022, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2019: U.S. Geological Survey Open-File Report 2022–1041, 36 p., https://doi.org/10.3133/ofr20221041.","productDescription":"Report: v, 36 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-123645","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":403220,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97ALYNZ","text":"USGS data release","linkHelpText":"Data supporting the 2019 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":501773,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113257.htm","linkFileType":{"id":5,"text":"html"}},{"id":403221,"rank":6,"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":403219,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1041/images"},{"id":403218,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1041/ofr20221041.XML"},{"id":403215,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1041/coverthb.jpg"},{"id":403216,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1041/ofr20221041.pdf","text":"Report","size":"2.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1041"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5,\n 33.0\n ],\n [\n -105.1,\n 33.0\n ],\n [\n -105.1,\n 33.4\n ],\n [\n -105.5,\n 33.4\n ],\n [\n -105.5,\n 33.0\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Anthropogenic activities, including groundwater withdrawals, return flow from irrigated agriculture, and treated wastewater-effluent disposal have the potential to affect groundwater quality in the Lucerne Valley groundwater basin, located in the southwest Mojave Desert. Questions regarding the current state and potential future of groundwater quality in this basin were addressed by (1) considering groundwater data from and findings of historical water-quality studies, (2) evaluating recent (1990–2021) U.S. Geological Survey water-quality and geochemical-tracer data, and (3) assessing groundwater-quality results from samples collected in 2021 to better understand the transport of applied treated wastewater effluent in the subsurface and associated effects of this practice on water quality. As observed by previous studies, differences in groundwater quality existed among the upper, middle, and lower aquifers of the Lucerne Valley groundwater basin, with the lower aquifer characterized by high dissolved-solid content relative to the middle and upper aquifers. Stable and radioisotope tracers indicate that most of the groundwater sampled in the basin was recharged during cooler, wetter climate conditions than those of the present day (2022). Analyses of the 2021 samples collected to examine the subsurface transport of applied treated wastewater effluent were not conclusive but indicate that water from applied treated wastewater effluent is currently (2022) limited to the upper aquifer and likely to remain so given the extensive confining unit below the upper aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221063","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Fackrell, J.K., 2022, Groundwater quality of the Lucerne Valley groundwater basin, California: U.S. Geological Survey Open-File Report 2022-1063, 19 p., https://doi.org/10.3133/ofr20221063.","productDescription":"viii, 19 p.","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-137528","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501818,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113259.htm","linkFileType":{"id":5,"text":"html"}},{"id":403158,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1063/ofr20221063.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022–1063"},{"id":403163,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225048","text":"Scientific Investigations Report 2022-5048","description":"Stamos, C.L., Larsen, J.D., Powell, R.E., Matti, J.C., and Martin, P., 2022, Hydrogeology and simulation of groundwater flow in the Lucerne Valley groundwater basin, California: U.S. Geological Survey Scientific Investigations Report 2022-5048, 120 p., https://doi.org/10.3133/sir20225048.","linkHelpText":"- Hydrogeology and Simulation of Groundwater Flow in the Lucerne Valley Groundwater Basin, California"},{"id":403159,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1063/ofr20221063.xml"},{"id":403160,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1063/images"},{"id":403157,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1063/covrthb.jpg"},{"id":403185,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221063/full","text":"Report","description":"Open-File Report 2022-1063"}],"country":"United States","state":"California","otherGeospatial":"Lucerne Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.666667,\n 34.266667\n ],\n [\n -117.083333,\n 34.266667\n ],\n [\n -117.083333,\n 34.666667\n ],\n [\n -116.666667,\n 34.666667\n ],\n [\n -116.666667,\n 34.266667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
In early October of 2020, the U.S. Geological Survey (USGS) held a virtual workshop to discuss Gulf and Atlantic Coastal Plains site-response models. Earthquake researchers came together to assess (1) research related to proposed Coastal Plains amplification models and (2) USGS plans for implementing these models. Presentations spanned a broad range of topics from Atlantic and Gulf Coastal Plains geophysical properties including seismic velocity and attenuation, to ground motion amplification models and their impacts on seismic hazard. Interspersed with these presentations were discussions regarding the definition and extent of the Atlantic and Gulf Coastal Plains, potential complexities of wave propagation in the Atlantic and Gulf Coastal Plains, and problems that need to be overcome to implement various proposed site-response models. Based on feedback from this workshop, the USGS working group on Coastal Plain Amplification is considering applying published models that depend on sediment thickness. The working group is also exploring potential application of models that depend on the length of path traversed across the Coastal Plain, including the Gulf Coastal Plain ground-motion model adjustments from the Next Generation Attenuation Relationships for the Eastern United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221006","usgsCitation":"Boyd, O.S., Pratt, T.L., Chapman, M.C., Shumway, A., Rezaeian, S., Moschetti, M.P., and Petersen, M.D., 2022, U.S. Geological Survey coastal plain amplification virtual workshop: U.S. Geological Survey Open-File Report 2022–1006, 25 p., https://doi.org/10.3133/ofr20221006.","productDescription":"vi, 25 p.","onlineOnly":"Y","ipdsId":"IP-128818","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":402497,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1006/ofr20221006.pdf","text":"Report","size":"849 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1006"},{"id":402498,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1006/ofr20221006.xml"},{"id":402496,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1006/coverthb.jpg"},{"id":405344,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1006/images"},{"id":405560,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221006/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1006"}],"country":"United States","otherGeospatial":"Gulf and Atlantic Coast Plains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.716796875,\n 25.522614647623293\n ],\n [\n -81.03515625,\n 31.541089879585808\n ],\n [\n -75.5419921875,\n 35.35321610123823\n ],\n [\n -73.95996093749999,\n 40.38002840251183\n ],\n [\n -69.5654296875,\n 41.57436130598913\n ],\n [\n -70.48828125,\n 42.19596877629178\n ],\n [\n -70.224609375,\n 43.51668853502906\n ],\n [\n -66.97265625,\n 44.62175409623324\n ],\n [\n -68.2470703125,\n 45.920587344733654\n ],\n [\n -71.630859375,\n 43.45291889355465\n ],\n [\n -76.025390625,\n 40.613952441166596\n ],\n [\n -78.31054687499999,\n 38.54816542304656\n ],\n [\n -81.4306640625,\n 33.578014746143985\n ],\n [\n -83.056640625,\n 30.86451022625836\n ],\n [\n -92.98828125,\n 30.977609093348686\n ],\n [\n -96.328125,\n 30.29701788337205\n ],\n [\n -98.4375,\n 28.57487404744697\n ],\n [\n -98.0859375,\n 26.470573022375085\n ],\n [\n -97.20703125,\n 25.997549919572112\n ],\n [\n -96.9873046875,\n 27.371767300523047\n ],\n [\n -95.5810546875,\n 28.65203063036226\n ],\n [\n -93.8232421875,\n 29.305561325527698\n ],\n [\n -91.40625,\n 29.19053283229458\n ],\n [\n -89.9560546875,\n 28.8831596093235\n ],\n [\n -88.9892578125,\n 29.84064389983441\n ],\n [\n -86.17675781249999,\n 30.031055426540206\n ],\n [\n -84.111328125,\n 29.49698759653577\n ],\n [\n -83.1005859375,\n 28.76765910569123\n ],\n [\n -82.529296875,\n 26.82407078047018\n ],\n [\n -81.123046875,\n 25.045792240303445\n ],\n [\n -80.5078125,\n 24.726874870506972\n ],\n [\n -79.716796875,\n 25.522614647623293\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 966
Denver, CO 80225
Atmospheric dust deposited to snow cover (dust-on-snow) diminishes snow-surface albedo (SSA) to result in early onset and accelerated rate of melting, effects that challenge management of downstream water resources. During ongoing investigations to identify the light-energy absorbing dust particles most responsible for diminished SSA in the Upper Colorado River Basin of the Colorado Rocky Mountains, we found microplastic particles, which are defined as those less than 5 millimeters in any dimension. In each of the 38 samples that represented the last remaining dust layer during melt seasons of 2013–16, microplastics were identified by size, shape, and color, and their relative amounts were visually estimated using stereomicroscopy. Considering the remote, high-elevation settings of the sample sites, the microplastic particles must have been deposited from the atmosphere. The possible role of microplastics for diminishing SSA of snow cover in the Upper Colorado River Basin may be linked to the solar-energy absorptive properties of polymers and is the subject of ongoing investigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221061","usgsCitation":"Reynolds, R.L., Goldstein, H.L., Kokaly, R.F., and Derry, J., 2022, Microplastic particles in dust-on-snow, Upper Colorado River Basin, Colorado Rocky Mountains, 2013–16: U.S. Geological Survey Open-File Report 2022–1061, 7 p., https://doi.org/10.3133/ofr20221061.","productDescription":"vi, 7 p.","onlineOnly":"Y","ipdsId":"IP-141503","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":501783,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113221.htm","linkFileType":{"id":5,"text":"html"}},{"id":402600,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1061/images"},{"id":402599,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1061/ofr20221061.pdf","text":"Report","size":"2.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1061"},{"id":402598,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1061/coverthb.jpg"},{"id":402601,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1061/ofr20221061.xml"}],"country":"United States","state":"Colorado","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-106.190554,40.997607],[-106.061181,40.996999],[-105.730421,40.996886],[-105.724804,40.99691],[-105.277138,40.998173],[-105.27686,40.998173],[-105.256527,40.998191],[-105.254779,40.99821],[-104.943371,40.998084],[-104.855273,40.998048],[-104.829504,40.99927],[-104.675999,41.000957],[-104.497149,41.001828],[-104.497058,41.001805],[-104.467672,41.001473],[-104.214692,41.001657],[-104.214191,41.001568],[-104.211473,41.001591],[-104.123586,41.001626],[-104.10459,41.001543],[-104.086068,41.001563],[-104.066961,41.001504],[-104.053249,41.001406],[-104.039238,41.001502],[-104.023383,41.001887],[-104.018223,41.001617],[-103.972642,41.001615],[-103.971373,41.001524],[-103.953525,41.001596],[-103.906324,41.001387],[-103.896207,41.00175],[-103.877967,41.001673],[-103.858449,41.001681],[-103.750498,41.002054],[-103.574522,41.001721],[-103.497447,41.001635],[-103.486697,41.001914],[-103.421975,41.002007],[-103.421925,41.001969],[-103.396991,41.002558],[-103.382492,41.002232],[-103.365314,41.001846],[-103.362979,41.001844],[-103.077804,41.002298],[-103.076536,41.002253],[-103.059538,41.002368],[-103.057998,41.002368],[-103.043444,41.002344],[-103.038704,41.002251],[-103.002026,41.002486],[-103.000102,41.0024],[-102.98269,41.002157],[-102.981483,41.002112],[-102.963669,41.002186],[-102.962522,41.002072],[-102.960706,41.002059],[-102.959624,41.002095],[-102.94483,41.002303],[-102.943109,41.002051],[-102.925568,41.00228],[-102.924029,41.002142],[-102.906547,41.002276],[-102.904796,41.002207],[-102.887407,41.002178],[-102.885746,41.002131],[-102.867822,41.002183],[-102.865784,41.001988],[-102.849263,41.002301],[-102.846455,41.002256],[-102.830303,41.002351],[-102.82728,41.002143],[-102.773546,41.002414],[-102.766723,41.002275],[-102.754617,41.002361],[-102.739624,41.00223],[-102.653463,41.002332],[-102.621033,41.002597],[-102.578696,41.002291],[-102.575738,41.002268],[-102.575496,41.0022],[-102.566048,41.0022],[-102.556789,41.002219],[-102.487955,41.002445],[-102.470537,41.002382],[-102.469223,41.002424],[-102.379593,41.002301],[-102.364066,41.002174],[-102.292833,41.002207],[-102.292622,41.00223],[-102.292553,41.002207],[-102.291354,41.002207],[-102.2721,41.002245],[-102.267812,41.002383],[-102.231931,41.002327],[-102.2122,41.002462],[-102.209361,41.002442],[-102.19121,41.002326],[-102.124972,41.002338],[-102.070598,41.002423],[-102.051718,41.002377],[-102.051614,41.002377],[-102.051292,40.749591],[-102.051292,40.749586],[-102.051398,40.697542],[-102.051725,40.537839],[-102.051519,40.520094],[-102.051465,40.440008],[-102.05184,40.396396],[-102.051572,40.39308],[-102.051798,40.360069],[-102.051553,40.349214],[-102.051309,40.338381],[-102.051922,40.235344],[-102.051894,40.229193],[-102.051909,40.162674],[-102.052001,40.148359],[-102.051744,40.003078],[-102.051569,39.849805],[-102.051363,39.843471],[-102.051318,39.833311],[-102.051254,39.818992],[-102.050594,39.675594],[-102.050099,39.653812],[-102.050422,39.646048],[-102.049954,39.592331],[-102.049806,39.574058],[-102.049764,39.56818],[-102.049554,39.538932],[-102.049673,39.536691],[-102.049679,39.506183],[-102.049369,39.423333],[-102.04937,39.41821],[-102.049167,39.403597],[-102.04896,39.373712],[-102.048449,39.303138],[-102.04725,39.13702],[-102.047189,39.133147],[-102.047134,39.129701],[-102.046571,39.047038],[-102.045388,38.813392],[-102.045334,38.799463],[-102.045448,38.783453],[-102.045371,38.770064],[-102.045287,38.755528],[-102.045375,38.754339],[-102.045212,38.697567],[-102.045156,38.688555],[-102.045127,38.686725],[-102.04516,38.675221],[-102.045102,38.674946],[-102.045074,38.669617],[-102.045288,38.615249],[-102.045288,38.615168],[-102.045211,38.581609],[-102.045189,38.558732],[-102.045223,38.543797],[-102.045112,38.523784],[-102.045262,38.505532],[-102.045263,38.505395],[-102.045324,38.453647],[-102.044936,38.41968],[-102.044442,38.415802],[-102.044944,38.384419],[-102.044613,38.312324],[-102.044568,38.268819],[-102.044567,38.268749],[-102.04451,38.262412],[-102.044398,38.250015],[-102.044251,38.141778],[-102.044589,38.125013],[-102.044255,38.113011],[-102.044644,38.045532],[-102.043844,37.928102],[-102.043845,37.926135],[-102.043219,37.867929],[-102.043033,37.824146],[-102.042953,37.803535],[-102.042668,37.788758],[-102.042158,37.760164],[-102.04199,37.738541],[-102.041876,37.723875],[-102.041574,37.680436],[-102.041694,37.665681],[-102.041582,37.654495],[-102.041585,37.644282],[-102.041618,37.607868],[-102.041894,37.557977],[-102.041899,37.541186],[-102.042016,37.535261],[-102.041786,37.506066],[-102.041801,37.469488],[-102.041755,37.434855],[-102.041669,37.43474],[-102.041676,37.409898],[-102.041586,37.38919],[-102.041524,37.375018],[-102.042089,37.352819],[-102.041974,37.352613],[-102.041817,37.30949],[-102.041664,37.29765],[-102.041963,37.258164],[-102.042002,37.141744],[-102.042135,37.125021],[-102.042092,37.125021],[-102.041809,37.111973],[-102.041983,37.106551],[-102.04192,37.035083],[-102.041749,37.034397],[-102.041921,37.032178],[-102.04195,37.030805],[-102.041952,37.024742],[-102.04224,36.993083],[-102.054503,36.993109],[-102.184271,36.993593],[-102.208316,36.99373],[-102.260789,36.994388],[-102.355288,36.994506],[-102.355367,36.994575],[-102.698142,36.995149],[-102.74206,36.997689],[-102.75986,37.000019],[-102.778569,36.999242],[-102.806762,37.000019],[-102.814616,37.000783],[-102.841989,36.999598],[-102.979613,36.998549],[-102.985807,36.998571],[-102.986976,36.998524],[-103.002199,37.000104],[-103.086106,37.000174],[-103.155922,37.000232],[-103.733247,36.998016],[-103.734364,36.998041],[-104.007855,36.996239],[-104.250536,36.994644],[-104.338833,36.993535],[-104.519257,36.993766],[-104.624556,36.994377],[-104.625545,36.993599],[-104.645029,36.993378],[-104.732031,36.993447],[-104.73212,36.993484],[-105.000554,36.993264],[-105.029228,36.992729],[-105.1208,36.995428],[-105.155042,36.995339],[-105.220613,36.995169],[-105.251296,36.995605],[-105.41931,36.995856],[-105.442459,36.995994],[-105.447255,36.996017],[-105.465182,36.995991],[-105.508836,36.995895],[-105.512485,36.995777],[-105.533922,36.995875],[-105.62747,36.995679],[-105.66472,36.995874],[-105.716471,36.995849],[-105.71847,36.995846],[-105.996159,36.995418],[-105.997472,36.995417],[-106.006634,36.995343],[-106.201469,36.994122],[-106.247705,36.994266],[-106.248675,36.994288],[-106.293279,36.99389],[-106.343139,36.99423],[-106.47628,36.993839],[-106.500589,36.993768],[-106.617159,36.992967],[-106.617125,36.993004],[-106.628652,36.993175],[-106.628733,36.993161],[-106.661344,36.993243],[-106.675626,36.993123],[-106.750591,36.992461],[-106.869796,36.992426],[-106.877292,37.000139],[-107.420913,37.000005],[-107.420915,37.000005],[-107.481737,37.000005],[-108.000623,37.000001],[-108.249358,36.999015],[-108.250635,36.999561],[-108.288086,36.999555],[-108.2884,36.99952],[-108.320464,36.999499],[-108.320721,36.99951],[-108.379203,36.999459],[-108.619689,36.999249],[-108.620309,36.999287],[-108.954404,36.998906],[-108.958868,36.998913],[-109.045223,36.999084],[-109.045166,37.072742],[-109.045058,37.074661],[-109.044995,37.086429],[-109.045189,37.096271],[-109.045173,37.109464],[-109.045203,37.111958],[-109.045156,37.112064],[-109.045995,37.177279],[-109.045978,37.201831],[-109.045487,37.210844],[-109.045584,37.249351],[-109.046039,37.249993],[-109.04581,37.374993],[-109.043464,37.484711],[-109.043137,37.499992],[-109.041915,37.530653],[-109.041865,37.530726],[-109.041806,37.604171],[-109.042131,37.617662],[-109.042089,37.623795],[-109.042269,37.666067],[-109.041732,37.711214],[-109.04176,37.713182],[-109.041636,37.74021],[-109.042098,37.74999],[-109.041461,37.800105],[-109.041754,37.835826],[-109.041723,37.842051],[-109.041844,37.872788],[-109.041653,37.88117],[-109.041058,37.907236],[-109.043121,37.97426],[-109.042819,37.997068],[-109.04282,37.999301],[-109.041837,38.153022],[-109.041762,38.16469],[-109.054648,38.244921],[-109.060062,38.275489],[-109.059962,38.499987],[-109.060253,38.599328],[-109.059541,38.719888],[-109.057388,38.795456],[-109.054189,38.874984],[-109.053943,38.904414],[-109.053797,38.905284],[-109.053233,38.942467],[-109.053292,38.942878],[-109.052436,38.999985],[-109.051512,39.126095],[-109.050765,39.366677],[-109.051363,39.497674],[-109.05104,39.660472],[-109.050615,39.87497],[-109.050873,40.058915],[-109.050813,40.059579],[-109.050944,40.180712],[-109.050973,40.180849],[-109.050969,40.222662],[-109.050946,40.444368],[-109.050314,40.495092],[-109.050698,40.499963],[-109.049955,40.539901],[-109.050074,40.540358],[-109.048044,40.619231],[-109.048249,40.653601],[-109.048373,40.662602],[-109.049088,40.714562],[-109.048455,40.826081],[-109.050076,41.000659],[-108.884138,41.000094],[-108.631108,41.000156],[-108.526667,40.999608],[-108.500659,41.000112],[-108.250649,41.000114],[-108.181227,41.000455],[-108.046539,41.002064],[-107.918421,41.002036],[-107.625624,41.002124],[-107.367443,41.003073],[-107.317794,41.002967],[-107.241194,41.002804],[-107.000606,41.003444],[-106.857773,41.002663],[-106.453859,41.002057],[-106.439563,41.001978],[-106.437419,41.001795],[-106.43095,41.001752],[-106.391852,41.001176],[-106.386356,41.001144],[-106.321165,40.999123],[-106.217573,40.997734],[-106.190554,40.997607]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 980
Denver, CO 80225
In this study we examine the potential effects of three predicted sea level rise (SLR) scenarios on the nearshore eelgrass (Zostera marina L.) and surf smelt (Hypomesus pretiosus) spawning habitats along a beach on Bainbridge Island, Washington. Baseline bathymetric, geomorphological, and biological surveys were conducted to determine the existing conditions at the study site. The results of these surveys were coupled with a predictive model that estimates SLR-induced changes to coastal ecosystems based upon local topography and land-cover data. This model simulates the changes in nearshore habitat through time. The model inputs for SLR are probable values reported by the Intergovernmental Panel on Climate Change, and by user-defined values. The predicted effects of SLR are presented as (1) habitat type change and (2) the graphic response of developed dry land depicting the influence of shoreline armoring. This report describes the geophysical and biological characteristics at the Bainbridge Island study site, the modeling methods used to produce depictions of habitat changes, and a possible decrease in surf smelt spawning and an increase in eelgrass habitat availability in response to increases in sea level.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221054","usgsCitation":"Smith, C.D., and Liedtke, T.L., 2022, Potential effects of sea level rise on nearshore habitat availability for surf smelt (Hypomesus pretiosus) and eelgrass (Zostera marina), Puget Sound, Washington: U.S. Geological Survey Open-File Report 2022–1054, 17 p., https://doi.org/10.3133/ofr20221054.","productDescription":"Report: v, 17 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-117971","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":402574,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGJ3ZH","text":"USGS data release","description":"USGS Data Release.","linkHelpText":"Data collected in 2010 to evaluate habitat availability for surf smelt and eelgrass in response to sea level rise on Bainbridge Island, Puget Sound, Washington State, USA"},{"id":402572,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1054/coverthb.jpg"},{"id":402573,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1054/ofr20221054.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1054"},{"id":402619,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1054"},{"id":402575,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1054/images"},{"id":402576,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1054/ofr20221054.XML"},{"id":501780,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113219.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.6678466796875,\n 47.487513008956554\n ],\n [\n -122.23937988281251,\n 47.487513008956554\n ],\n [\n -122.23937988281251,\n 47.964180715412276\n ],\n [\n -122.6678466796875,\n 47.964180715412276\n ],\n [\n -122.6678466796875,\n 47.487513008956554\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
In oil spill research, a topic of increasing attention during the last decade has been the environmental impact of the partial oxidation products that result from transformation of the petroleum in freshwater, marine, and terrestrial ecosystems. This report describes the isolation and characterization of the partial oxidation products from crude oil contaminating groundwater at the long-term U.S. Geological Survey Bemidji research site in Minnesota. As the result of a pipeline burst in August 1979, a body of light aliphatic crude oil is present from the land surface to 2 meters below the water table in a shallow sand and gravel aquifer in a remote area outside Bemidji, Minnesota, United States. Biodegradation has resulted in the formation of a plume of dissolved organic carbon (DOC) downgradient from the oil body. Groundwater has also been contaminated in an area known as the spray zone, from vertical infiltration of DOC resulting from biodegradation of oil in the soil column, and possibly from photooxidation of oil at the soil surface. The majority of DOC in the contaminated groundwater is in the form of nonvolatile organic acids (NVOAs) which represent the partial oxidation products of the crude oil constituents. The NVOAs have been classified into three fractions according to their isolation on XAD resins: hydrophobic neutrals (HPON), hydrophobic acids (HPOA), and hydrophilic acids (HPIA). These fractions of NVOAs were isolated from wells downgradient from the oil body (sampling well numbers 533, 532, 530, 515), in the spray zone (603), and from an uncontaminated well upgradient of the oil body (310) between the years 1986 and 1989, and again from wells 530 and 603 in 1998. The samples have been characterized by elemental analysis, radiocarbon dating, carbon-13 nuclear magnetic resonance spectroscopy (13C NMR), and negative-mode (-) electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FTICR-MS), with a particular focus on fractions from wells 310, 530, and 603.
All the characterization data indicate that the NVOAs from contaminated wells are distinguishable from the background DOC. Carbon-14 (14C) ages of NVOAs from contaminated wells ranged from 3,615 to 18,985 years before the present, whereas the background DOC from the aquifer was post-bomb (post 1950). By elemental analysis, NVOAs from contaminated wells had higher sulfur but lower nitrogen contents than the background. By electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry, number average molecular weights determined from assigned molecular formulas ranged from 416 to 486 daltons for the HPOA and HPIA fractions from both background and contaminant wells. NVOAs from contaminated wells had significantly greater numbers of assigned molecular formulas containing sulfur, with elevated concentrations of the S1O4-10 species in particular. Compared to the background, HPOA and HPIA fractions from contaminant wells had a broader range of double bond equivalents (DBEs) within On compound classes (n is number of atoms). Additionally, within On compound classes, contaminant well HPOA fractions had a greater abundance of lower n (less than eight) than the background. Contaminant well double bond equivalents versus carbon number (C#) plots of oxygen compound classes suggest oil-derived aliphatic compounds in the range from C12 to C22 in HPOA and HPIA fractions and oil-derived compounds containing aromatic or saturated rings in the approximate range from C20 to C30 are present in HPOA fractions.
The data suggest the NVOAs originate from biodegradation of several classes of C12 and greater crude oil constituents: sulfur-containing constituents, including possibly the resins and asphaltenes; constituents containing aromatic rings substituted with methyl groups, including alkylaromatic and naph-
thenoaromatic compounds, and C12 to C22 alkyl constituents. The overall similarities of the carbon-13 nuclear magnetic resonance spectra for the well 603 and 530 samples from the two sampling dates suggest that a steady state in the composition of the partial oxidation products in each of the two wells had been reached between 1986–1989 and 1998.
Chief, USGS National Water Quality Laboratory
U.S. Geological Survey
Box 25585, Mail Stop 407
Denver, CO 80225-0585
The Community for Data Integration is a community of practice whose purpose is to advance the data integration capabilities of the U.S. Geological Survey. In fiscal year 2020, the Community for Data Integration held 11 monthly forums, facilitated 13 collaboration areas, and supported 13 projects. The activities supported the broad U.S. Geological Survey priority of producing building blocks for doing integrated predictive science. Specifically, the activities supported tools and methods for findable, accessible, interoperable, and reusable (FAIR) data and wildland fire and water prediction. Through these efforts, community members were informed of new and emerging technologies and data topics that helped them accomplish their professional responsibilities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221034","usgsCitation":"Hsu, L., Liford, A.N., and Donovan, G.C., 2022, Community for data integration 2020 annual report: U.S. Geological\nSurvey Open-File Report 2022–1034, 16 p., https://doi.org/10.3133/ofr20221034.","productDescription":"iii, 16 p.","onlineOnly":"Y","ipdsId":"IP-133268","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":402432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1034/coverthb.jpg"},{"id":402433,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1034/ofr20221034.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1034"},{"id":402434,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1034/images"},{"id":402435,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1034/ofr20221034.xml"}],"contact":"Director, Science Analytics and Synthesis
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225
This report assesses the status and trends of selected ecological health indicators of the Upper Mississippi River System (UMRS) based on the data collected and analyzed by the Long Term Resource Monitoring element of the Upper Mississippi River Restoration program, supplemented with data from other sources. This report has four objectives: providing a brief introduction of the UMRS, including its significance, history, modern-day stressors, and recent research; using ecological indicators to describe the status of the river system and where and how it has changed from circa 1993 to 2019; discussing management and restoration implications of these changes; and highlighting the fundamental role of long-term monitoring in the understanding, management, and restoration of large-floodplain rivers.
The data were collected in the six Long Term Resource Monitoring element study reaches that spanned much of the UMRS and the various gradients contained therein. These study reaches included Navigation Pools 4, 8, 13, and 26; the part of the Unimpounded Reach of the Upper Mississippi River between Grand Tower and Cairo, Illinois; and the La Grange Pool on the Illinois River. The indicators included in this report describe the status and trends for the hydrology, geomorphology, floodplain vegetation, water quality, vegetation, and fishes of the UMRS. Many of the indicators of river ecosystem health changed significantly over the nearly 30 years of our evaluation. However, there was substantial spatial variability in the magnitude and timing of those changes among study reaches. Few indicators changed everywhere or nowhere; most indicators changed in some reaches but not others. The quantitative assessments of these indicators describe how the conditions of the river differ across hydrogeomorphic and climate gradients and through time and are intended to support the restoration and management of the UMRS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221039","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","programNote":"Species Management Research Program and Land Management Research Program","usgsCitation":"Houser, J.N., ed., 2022, Ecological status and trends of the Upper Mississippi and Illinois Rivers (ver. 1.1, July 2022): U.S. Geological Survey Open-File Report 2022–1039, 199 p., https://doi.org/10.3133/ofr20221039.","productDescription":"xiv, 199 p.","numberOfPages":"220","onlineOnly":"N","ipdsId":"IP-125605","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":402335,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1039/coverthb2.jpg"},{"id":402336,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1039/ofr20221039.pdf","text":"Report","size":"41.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1039"},{"id":403771,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2022/1039/versionHist.txt","size":"3.11 kB","linkFileType":{"id":2,"text":"txt"}},{"id":501772,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113198.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, North Dakota, South Dakota, Wisconsin","otherGeospatial":"Illinois River, upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.329833984375,\n 46.172222978455395\n ],\n [\n -90.340576171875,\n 46.30140615437332\n ],\n [\n -92.384033203125,\n 46.49082901981415\n ],\n [\n -93.087158203125,\n 46.74738913515841\n ],\n [\n -93.262939453125,\n 47.42065432071318\n ],\n [\n -94.3505859375,\n 47.76148371616669\n ],\n [\n -95.712890625,\n 47.30903424774781\n ],\n [\n -96.1083984375,\n 46.912750956378915\n ],\n [\n -96.1083984375,\n 46.18743678432541\n ],\n [\n -96.6357421875,\n 45.89000815866184\n ],\n [\n -97.393798828125,\n 45.867062714815475\n ],\n [\n -97.591552734375,\n 45.72152152227954\n ],\n [\n -96.50390625,\n 44.56699093657141\n ],\n [\n -95.43823242187499,\n 43.51668853502906\n ],\n [\n -95.042724609375,\n 42.70665956351041\n ],\n [\n -94.559326171875,\n 41.82045509614034\n ],\n [\n -94.02099609375,\n 41.20345619205131\n ],\n [\n -93.043212890625,\n 39.66491373749128\n ],\n [\n -92.61474609375,\n 39.172658670429946\n ],\n [\n -91.790771484375,\n 38.84826438869913\n ],\n [\n -90.977783203125,\n 38.762650338334154\n ],\n [\n -90.76904296874999,\n 38.736946065676\n ],\n [\n -91.14257812499999,\n 38.35888785866677\n ],\n [\n -91.58203125,\n 37.43997405227057\n ],\n [\n -90.8349609375,\n 36.86204269508728\n ],\n [\n -89.18701171875,\n 37.046408899699564\n ],\n [\n -88.72558593749999,\n 37.87485339352928\n ],\n [\n -88.87939453125,\n 38.685509760012\n ],\n [\n -87.69287109375,\n 40.863679665481676\n ],\n [\n -86.748046875,\n 41.178653972331674\n ],\n [\n -86.781005859375,\n 41.5579215778042\n ],\n [\n -87.593994140625,\n 41.46742831254425\n ],\n [\n -87.86865234374999,\n 42.374778361114195\n ],\n [\n -88.494873046875,\n 43.31718491566705\n ],\n [\n -89.3408203125,\n 43.810747313446996\n ],\n [\n -89.6484375,\n 43.937461690316646\n ],\n [\n -89.40673828125,\n 44.50434127765394\n ],\n [\n -89.329833984375,\n 46.172222978455395\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: June 22, 2022; Version 1.1: July 14, 2022","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
The National Cooperative Geologic Mapping Program is publishing a strategic plan titled “Renewing the National Cooperative Geologic Mapping Program as the Nation’s Authoritative Source for Modern Geologic Knowledge.” The plan provides a vision, mission, and goals for the program for the years 2020–30:
Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
In 2016, an interdisciplinary, international group of 53 scientists introduced a framework named “the FAIR Principles” for addressing 21st century scientific data challenges. The FAIR Principles are increasingly used as a guide for producing digital scientific products that are findable, accessible, interoperable, and reusable (FAIR), especially to enable use of such products in automated systems. Data aligned with the FAIR Principles can increase the efficiency of science integration capabilities such as those envisioned for the U.S. Geological Survey (USGS) Earth Monitoring, Analyses, and Projections (EarthMAP) initiative.
The FAIR Principles clearly define the characteristics of reusable scientific products, but it is less clear how to facilitate consistency in achieving these characteristics across the Bureau. USGS data are produced by local research projects distributed over more than 100 centers in 7 regions. After data are approved for release, they could be managed in numerous repositories and online data systems. The diversity of USGS data is illustrated by the topical range of the USGS mission areas: Core Science Systems, Ecosystems, Energy and Minerals, Natural Hazards, and Water Resources. In the USGS context, realizing the EarthMAP vision for automated, predictive, integrated science that provides timely and actionable results involves providing knowledge and support services and developing the skills, infrastructure, and culture to enable Bureau-wide implementation of the FAIR Principles.
In 2019, the USGS Community for Data Integration funded a project to convene a broadly representative workshop and produce recommendations to enable consistency with the FAIR Principles across the USGS. The workshop, held in Fort Collins, Colorado, in September 2019, brought together 28 participants for 3 days to engage with the FAIR Principles, analyze USGS use cases, and discuss the roles of data producers and managers, data storage and catalogs, value-added services, and policy makers in implementing the FAIR Principles. Workshop participants agreed that scientific reproducibility requires the extension of the FAIR Principles beyond measured data to include physical samples, research methods, software, and tools at the USGS. Workshop discussions focused on how the USGS can implement the FAIR Principles by supporting research teams in creating data, metadata, and other scientific products and also by supporting enterprise systems that maintain and leverage the products’ consistency with the FAIR Principles.
The resulting FAIR roadmap of recommendations describes nine proposed interdependent strategies that could be achieved by coordinated actions taken by different parts of the USGS. A proposed early action would be the creation of a coordinating council that includes representatives from the groups engaged in activities consistent with better alignment with the FAIR Principles. The nine proposed strategies, which are presented in more detail in this roadmap report, focus on enabling improvements to individual data products, providing infrastructure, and structuring administrative activities to support an organizational culture that values the FAIR Principles.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221043","usgsCitation":"Lightsom, F.L., Hutchison, V.B., Bishop, B., Debrewer, L.M., Govoni, D.L., Latysh, N., and Stall, S., 2022, Opportunities to improve alignment with the FAIR Principles for U.S. Geological Survey data: U.S. Geological Survey Open-File Report 2022–1043, 23 p., https://doi.org/10.3133/ofr20221043.","productDescription":"vi, 23 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125836","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":402133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1043/coverthb.jpg"},{"id":402134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1043/ofr20221043.pdf","text":"Report","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1043"},{"id":402135,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1043/images/"},{"id":402136,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1043/ofr20221043.XML"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 4 (October–December), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221033","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Tuli, F.T.Z., Shaw, J.L., Hasan, M.N., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021: U.S. Geological Survey Open-File Report 2022–1033, 38 p., https://doi.org/10.3133/ofr20221033.","productDescription":"Report: vii, 38 p.; Dataset","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-137795","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":401936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1033/ofr20221033.pdf","text":"Report","size":"3.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1033"},{"id":401934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1033/coverthb.jpg"},{"id":402059,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221033/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":401939,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":401938,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1033/images"},{"id":401937,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1033/ofr20221033.XML"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
As a first step toward understanding the feasibility of using arbuscular mycorrhizal fungi (AMF) in reconstruction practice, we addressed four objectives: (1) compare root-associated AMF communities of plants between high-quality remnant prairies and reconstructed prairies, (2) compare root-associated AMF communities between plant species that declined in reconstructions and species that were thriving, (3) compare AMF communities collected from roots of plants in geographically separate parts of Minnesota and Iowa, and (4) assess the relationship between AMF communities and soil abiotic factors. We collected soil and root samples in 8 prairies reconstructed in 2005 (and monitored through 2015) and 6 remnant prairies, and the samples were separated into 6 geographically determined clusters, each containing 1–2 reconstructions and 1 remnant. Sequencing was completed on 1,188 deoxyribonucleic acid extracts from individual plant root samples, and fungal sequences were clustered to operational taxonomic units at 97-percent identity. Nonmetric multidimensional scaling was used to visualize differences in species composition of AMF communities among plant species and field sites. Permutational analysis of variance was completed to test for differences in AMF community composition between the 2 types of sites (remnants and reconstructions), among plant species, and among the 6 site clusters. AMF communities differed between remnant and reconstructed prairies, with one exception, and AMF associated with individual plant species also tended to differ, depending on whether the plant species’ roots were collected from remnant or reconstructed prairie. On the other hand, we did not determine that, as a group, species in decline in the reconstructions we had monitored were more likely to harbor different AMF communities compared to species not in decline in the reconstructions. Significant interactions between site type and clusters indicate geographic variation in AMF communities. Total carbon and nitrogen, and organic matter, were higher in remnant soils, whereas phosphorus, which at high concentrations reduces the value of AMF to plants, was much higher in soils collected from reconstructions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221055","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, North Dakota State University, University of Minnesota, and University of Groningen","programNote":"Land Management Research Program","usgsCitation":"Vink, S.N., Aldrich-Wolfe, L., Huerd, S.C., Larson, J.L., Vacek, S.C., Drobney, P.M., Barnes, M., Viste-Sparkman, K., Jordan, N.R., and Larson, D.L., 2022, Belowground mutualisms to support prairie reconstruction—Improving prairie habitat using mycorrhizal inoculum: U.S. Geological Survey Open-File Report 2022–1055, 18 p., https://doi.org/10.3133/ofr20221055.","productDescription":"Report: vi, 18 p.; 2 Data Releases","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-138435","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":402010,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221055/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":402009,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95R5UNN","text":"USGS data release","linkHelpText":"Arbuscular mycorrhizal fungi in remnant and reconstructed prairies in Minnesota and Iowa, 2019 (ver. 2.0, April 2022)"},{"id":402007,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1055/images"},{"id":402006,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1055/ofr20221055.XML"},{"id":402005,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1055/ofr20221055.pdf","text":"Report","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1055"},{"id":402004,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1055/coverthb.jpg"},{"id":402008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N8X0ZY","text":"USGS data release","linkHelpText":"Management of remnant tallgrass prairie by grazing or fire in western Minnesota, 2016–2017"}],"country":"United States","state":"Iowa, Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.26220703125,\n 46.37725420510028\n ],\n [\n -94.81201171875,\n 46.37725420510028\n ],\n [\n -94.81201171875,\n 47.931066347509784\n ],\n [\n -96.26220703125,\n 47.931066347509784\n ],\n [\n -96.26220703125,\n 46.37725420510028\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.767578125,\n 44.5278427984555\n ],\n [\n -94.28466796874999,\n 44.5278427984555\n ],\n [\n -94.28466796874999,\n 45.706179285330855\n ],\n [\n -96.767578125,\n 45.706179285330855\n ],\n [\n -96.767578125,\n 44.5278427984555\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.93310546874999,\n 40.9964840143779\n ],\n [\n -91.91162109374997,\n 40.9964840143779\n ],\n [\n -91.91162109374997,\n 41.78769700539063\n ],\n [\n -93.93310546874999,\n 41.78769700539063\n ],\n [\n -93.93310546874999,\n 40.9964840143779\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Ratings are used for a variety of reasons in water-resources investigations. The simplest rating relates discharge to the stage of the river. From a pure hydrodynamics perspective, all rivers and streams have some form of hysteresis in the relation between stage and discharge because of unsteady flow as a flood wave passes. Simple ratings are unable to represent hysteresis in a stage/discharge relation. A dynamic rating method is capable of capturing hysteresis owing to the variable energy slope caused by unsteady momentum and pressure.
A dynamic rating method developed to compute discharge from stage for compact channel geometry, referred to as DYNMOD, previously has been developed through a simplification of the one-dimensional Saint-Venant equations. A dynamic rating method, which accommodates compound and compact channel geometry, referred to as DYNPOUND, has been developed through a similar simplification as a part of this study. The DYNMOD and DYNPOUND methods were implemented in the Python programming language. Discharge time series computed with the dynamic rating method implementations were then compared to simulated discharge time series and discrete discharge measurements made at U.S. Geological Survey streamgage sites.
Four sets of stage and discharge time series were created using one-dimensional unsteady simulation software with compound channel geometry to compare the results of both dynamic rating methods to results from the full one-dimensional shallow water equations. Discharge time series were computed from stage time series using DYNMOD and DYNPOUND. DYNPOUND outperformed DYNMOD in all four scenarios. The minimum and maximum mean squared logarithmic error (MSLE) for the DYNMOD results were 2.75×10−2 and 3.40×10−2, respectively. The minimum and maximum MSLE for the DYNPOUND results were 2.51×10−7 and 1.91×10−4, respectively.
The dynamic rating methods were calibrated for six U.S. Geological Survey streamgage sites using observed discharge data collected at the sites. The calibration objective for each site was to minimize the MSLE of the discharge computed with the rating method with respect to observed discharge. For each site, the calibration included all field measurements within a selected water year. The DYNMOD method failed to compute discharge for the full calibration time series for three sites. A method fails to compute when the implementation returns a nonfinite value at a time step. Because the values computed for following time steps are dependent on the previous time step, a nonfinite value results in nonfinite values that follow. For the three sites for which DYNMOD computed the complete discharge time series, the minimum MSLE for calibration was 2.19×10−3 and the maximum was 9.77×10−3. The MSLE of the DYNPOUND computed discharge calibration time series for the six sites ranged from 3.70×10−3 to 1.25. For each site, an event-based time period was selected to compare the discharge time series computed with the dynamic rating methods to discrete discharge field measurements made at the streamgage sites. The DYNMOD-computed discharge time series for the three sites had an MSLE range of 2.76×10−3 to 3.14×10−2. The range of MSLE for the six DYNPOUND sites was 3.64×10−3 to 7.23×10−2. Although the DYNMOD method outperforms the DYNPOUND method when calibrated streamgage sites are under consideration, the DYNMOD method failed to compute a discharge time series at three of the six sites. The DYNPOUND method, therefore, was more robust than the DYNMOD method. Improvements to the implementation of the DYNPOUND method may improve the accuracy of the method.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221031","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"Domanski, M., Holmes, R.R., Jr., and Heal, E.N., 2022, Dynamic rating method for computing discharge from time-series stage data: U.S. Geological Survey Open-File Report 2022–1031, 48 p., https://doi.org/10.3133/ofr20221031.","productDescription":"Report: vii, 48 p.; 2 Data Releases; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-128037","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501770,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113161.htm","linkFileType":{"id":5,"text":"html"}},{"id":400457,"rank":5,"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":400459,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YUV9DG","text":"USGS data release","linkHelpText":"Dynamic stage to discharge rating model archive"},{"id":400458,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P955QRPQ","text":"USGS data release","linkHelpText":"Dynamic rating method for computing discharge from time series stage data—Site datasets"},{"id":400454,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1031/ofr20221031.XML"},{"id":400455,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1031/images"},{"id":400453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1031/ofr20221031.pdf","text":"Report","size":"2.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1031"},{"id":400452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1031/coverthb.jpg"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Mercury is a global contaminant that can be detrimental to wildlife and human health. Anthropogenic emissions and point sources are primarily responsible for elevated mercury concentrations in sediments and waters. Mercury can physically move and chemically transform in the environment, resulting in biomagnification of mercury, in the form of methylmercury, in the food web and causing elevated mercury concentrations in upper trophic levels. The ability to measure total mercury concentrations in the environment has existed for several decades and makes it possible to detect hotspots that might exist because of ongoing or previous anthropogenic activity. However, recent (within the past 15 years) developments in mass spectrometry have made it possible to complete low level stable isotope analysis allowing for the determination of mercury sources—natural and anthropogenic—in the environment through “fingerprinting.” Grubers Grove Bay in Lake Wisconsin, the focus area of this study, was determined to have elevated mercury levels even after multiple remediation efforts, resulting in its listing on the Federal list of impaired waters pursuant to the Clean Water Act. Adjacent to the bay is the former Badger Army Ammunition Plant, which manufactured ammunition for the U.S. Army during the early and middle 20th century, after which it was put on standby before being fully decommissioned. This study assesses mercury concentrations in the sediments and suspended particulate matter of Grubers Grove Bay, Wiegands Bay, and upstream sites, and in adjacent soils on the former Badger Army Ammunition Plant site. This study confirmed that mercury contamination exists in the sediments of Grubers Grove Bay even after dredging attempts by the U.S. Army. Additionally, using isotope ratios and a two-endmember mixing model, it was determined that soil from within Badger Army Ammunition Plant’s former site contributed a substantial amount of mercury to the bay. This result was supported by an observed gradient of high to low mercury concentrations from the innermost (nearest Badger Army Ammunition Plant) to the outermost (farthest from Badger Army Ammunition Plant) part of the bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221051","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Routhier, E.J., Janssen, S.E., Tate, M.T., Ogorek, J.M., DeWild, J.F., and Krabbenhoft, D.P., 2022, Assessment of mercury in sediments and waters of Grubers Grove Bay, Wisconsin: U.S. Geological Survey Open-File Report 2022–1051, 20 p., https://doi.org/10.3133/ofr20221051.","productDescription":"Report: vii, 20 p.; Data release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-133343","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":501779,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113162.htm","linkFileType":{"id":5,"text":"html"}},{"id":401822,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990MFHU","text":"USGS data release","linkHelpText":"Gruber's Grove Bay mercury site assessment"},{"id":401821,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1051/images"},{"id":401819,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1051/ofr20221051.pdf","text":"Report","size":"2.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1051"},{"id":401818,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1051/coverthb.jpg"},{"id":401820,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1051/ofr20221051.XML"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Grubers Grove Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.74800109863281,\n 43.32467816302811\n ],\n [\n -89.64569091796874,\n 43.32467816302811\n ],\n [\n -89.64569091796874,\n 43.393572674883146\n ],\n [\n -89.74800109863281,\n 43.393572674883146\n ],\n [\n -89.74800109863281,\n 43.32467816302811\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Mechanistic river models capable of simulating hydrodynamics and stream temperature are valuable tools for investigating thermal conditions and their relation to streamflow in river basins where upstream water storage and management decisions have an important influence on river reaches with threatened fish populations. In the Willamette River Basin in northwestern Oregon, a two-dimensional, hydrodynamic water-quality model (CE‑QUAL‑W2) has been used to investigate the downstream effects of dam operations and other anthropogenic influences on stream temperature. By simulating the managed releases of water and various temperatures from the large Willamette Valley Project dams upstream of the modeling domain, these models can be used to investigate riverine temperature conditions and their relation to streamflow to determine where and when conditions are most challenging for threatened fish populations and how dam operations and flow management can affect and optimize thermal conditions in the river.
The original models were initially developed to simulate conditions in spring–autumn of 2001 and 2002. This report documents (1) the upgrade of the river models to CE‑QUAL‑W2 version 4.2 and (2) the update of those models to simulate conditions that occurred from March through October of 2011, 2015, and 2016. These years were selected to represent a range of climatic and hydrologic conditions in the Willamette River Basin, including a “cool, wet” year (2011), a “hot, dry” year (2015), and a “normal” year (2016). Six submodels comprise the modeling system updated in this report; each submodel can be run independently or run with the others as a system. These models include the Coast Fork and Middle Fork Willamette River submodel, which includes the Coast Fork and Middle Fork Willamette Rivers, the Row River, and Fall Creek; the McKenzie River submodel, which includes the South Fork McKenzie River downstream of Cougar Dam and the McKenzie River from its confluence with the South Fork McKenzie River to its mouth; the South Santiam River submodel, which comprises the South Santiam River from Foster Dam to the Santiam River; the North Santiam and Santiam River submodel, which includes the Santiam River and the North Santiam River downstream of Big Cliff Dam; the Upper Willamette River submodel, which includes the Willamette River from Eugene to Salem; and the Middle Willamette River submodel, which includes the Willamette River from Salem to Willamette Falls near Oregon City.
The models included in this report were originally developed, calibrated, and documented by other researchers. As part of the model updates described here, some model parameters were adjusted to improve stability and decrease runtime. Boundary conditions including meteorological, hydrologic, and thermal parameters were developed and updated for model years 2011, 2015, and 2016. In many cases, the data sources used to drive the 2001 and 2002 models were no longer available, which required the use of new data sources, the determination of a proxy record, or the development of appropriate estimation techniques. Goodness-of-fit statistics for the updated models show a good model fit, with the models simulating subdaily water temperatures at most comparable locations with a mean absolute error of generally less than 1 °C and often nearing 0.5 °C, depending on the individual submodel, and a reasonably low bias. The subdaily mean error for the South Santiam River submodel produced the highest bias of any of the submodels. Goodness-of-fit statistics indicate that the results may be biased cool (ranging from -0.43 °C in 2016 to -0.80 °C in 2011 for subdaily results), but the only water temperature data available for comparison on the South Santiam River is itself estimated, and those estimates are known to be too high in summer. Depending on future modeling needs, that submodel may warrant further refinement, along with additional data collection to properly define and minimize any model bias.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221017","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Stratton Garvin, L.E., Rounds, S.A., and Buccola, N.L., 2022, Updates to models of streamflow and water temperature for 2011, 2015, and 2016 in rivers of the Willamette River Basin, Oregon: U.S. Geological Survey Open-File Report 2022–1017, 73 p., https://doi.org/10.3133/ofr20221017.","productDescription":"Report: x, 73 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119723","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":401872,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1017/ofr20221017.XML"},{"id":401871,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1017/images"},{"id":401815,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225035","text":"SIR 2022-5035 —","linkHelpText":"The thermal landscape of the Willamette River—Patterns and controls on stream temperature and implications for flow management and cold-water salmonids"},{"id":401814,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225034","text":"SIR 2022-5034 —","linkHelpText":"Assessment of habitat availability for juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) in the Willamette River, Oregon"},{"id":501762,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113157.htm","linkFileType":{"id":5,"text":"html"}},{"id":401754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1017/coverthb.jpg"},{"id":401755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1017/ofr20221017.pdf","text":"Report","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1017"},{"id":401756,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P908DXKH","text":"USGS data release","description":"USGS data release","linkHelpText":"CE-QUAL-W2 models for the Willamette River and major tributaries below U.S. Army Corps of Engineers dams—2011, 2015, and 2016"},{"id":401813,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225006","text":"SIR 2022-5006 —","linkHelpText":"Tracking heat in the Willamette River system, Oregon"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.134765625,\n 42.779275360241904\n ],\n [\n -120.673828125,\n 42.779275360241904\n ],\n [\n -120.673828125,\n 45.9511496866914\n ],\n [\n -123.134765625,\n 45.9511496866914\n ],\n [\n -123.134765625,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Low-streamflow statistics, such as the annual minimum 7-day streamflow (which is the 7-day streamflow likely to be exceeded in 9 out of 10 years on average [7Q10]), that are computed by using the full historical streamflow record may not accurately represent current conditions at sites with statistically significant trends in low streamflow over time. Recent research suggests that using a contemporary subset of the historical streamflow record (specifically, the most recent 30 years) to compute an estimate of 7Q10 more accurately represents current streamflow conditions when a statistically significant trend in the streamflow record is present. This report presents the results of a Monte Carlo simulation experiment on artificial low-streamflow records, derived from the characteristics of streamflows at 174 U.S. Geological Survey streamgages, to test whether a similar approach is appropriate for the computation of the annual minimum 7-day streamflow exceeded in 1 out of 2 years on average (7Q2) and the annual minimum 7-day streamflow exceeded in 19 out of 20 years on average (7Q20). The results indicate that using the most recent 30-year subset of the low-streamflow record also may be the best approach when computing 7Q2 and 7Q20 at sites where a statistically significant trend in low streamflows is detected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211111","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Schalk, L., Dudley, R.W., and Blum, A.G., 2022, Methods for computing 7Q2 and 7Q20 low-streamflow statistics to account for possible trends: U.S. Geological Survey Open-File Report 2021–1111, 15 p., https://doi.org/10.3133/ofr20211111.","productDescription":"iv, 15 p.","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119807","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":401572,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1111/images/"},{"id":401570,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1111/ofr20211111.pdf","text":"Report","size":"996 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1111"},{"id":401569,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1111/coverthb.jpg"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Regional groundwater recharge commonly is estimated using a threshold-type water-budget approach in which groundwater recharge is assumed to occur when water in the plant-root zone exceeds the soil’s moisture storage capacity. A water budget of the plant-soil system accounts for water inputs (rainfall, fog interception, irrigation, septic-system leachate, and other inputs), water outputs (runoff, evaporation, transpiration, and recharge), and changes in stored water during a specified time interval. Water budgets can be computed on any desired interval, including annual, monthly, daily, and subdaily intervals. In general, uncertainty in recharge estimates is expected to be lower using daily or subdaily intervals relative to monthly and annual intervals. Average recharge rates computed over a period of a year or multiple years are commonly determined from water budgets computed using a daily computation interval capable of capturing rainfall and land-cover changes during the period.
This report documents the Water-budget Accounting for Tropical Regions Model, or WATRMod, code that can be used to estimate spatially variable, daily water-budget components in tropical-island and other appropriate settings. The purpose of this report is to provide descriptions of WATRMod’s (1) approach to computing a daily water budget, (2) represented processes, (3) limitations, and (4) execution procedure, input requirements, output files, and example files. The model computes a daily water budget for each hydrologically independent subarea within the overall study area. A subarea is defined by its climatic, soil, land-cover, and human-related (for example, adding irrigation or other water) characteristics. The water-budget model can represent processes including rainfall, fog interception, irrigation, septic-system leachate, direct recharge that bypasses the plant-soil system, runoff, canopy evaporation in forested areas, evapotranspiration, and groundwater recharge. The water-budget model can represent either one of the following different accounting orders: (1) accounting for loss of water by evapotranspiration before accounting for recharge, and (2) accounting for recharge before accounting for evapotranspiration. WATRMod’s limitations include: (1) uncharacterized, subdaily transient changes in water inputs and outputs from the plant-soil system, (2) unrepresented precipitation in the form of snow and sublimation, and (3) routing runoff from one subarea to an adjacent subarea that is not directly represented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221013","usgsCitation":"Oki, D.S., 2022, Water-budget accounting for tropical regions model (WATRMod) documentation: U.S. Geological Survey Open-File Report 2022-1013, 77 p., https://doi.org/10.3133/ofr20221013.","productDescription":"Report: viii, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"Y","ipdsId":"IP-126805","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":501758,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113077.htm","linkFileType":{"id":5,"text":"html"}},{"id":401381,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VPAY41","text":"WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files","description":"Oki, D.S., 2022, WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files: U.S. Geological Survey data release, https://doi.org/10.5066/P9VPAY41."},{"id":401379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1013/covrthb.jpg"},{"id":401380,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1013/ofr20221013.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1013"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.3848876953125,\n 20.555652403773365\n ],\n [\n -156.0003662109375,\n 20.6379249854131\n ],\n [\n -155.9454345703125,\n 20.776659051878816\n ],\n [\n -156.26678466796875,\n 20.964004409178308\n ],\n [\n -156.47003173828125,\n 20.925527866647226\n ],\n [\n -156.610107421875,\n 21.056307701901847\n ],\n [\n -156.72271728515625,\n 20.94604992010052\n ],\n [\n -156.67327880859375,\n 20.822875478868443\n ],\n [\n -156.55792236328122,\n 20.761250430919652\n ],\n [\n -156.48651123046875,\n 20.771523019513364\n ],\n [\n -156.4617919921875,\n 20.622502259344817\n ],\n [\n -156.3848876953125,\n 20.555652403773365\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
On January 18, 2022, groundwater levels were measured in selected wells in the Hālawa area, O‘ahu, Hawai‘i, constituting a synoptic groundwater-level survey (shortened herein to “synoptic survey”) of the area. Groundwater levels were measured mainly from 9:00 a.m. to 12:00 p.m. (times listed in Hawai‘i standard time) and provide a snapshot of groundwater levels during the survey period. Following a reported fuel release that affected groundwater quality in the Red Hill area, several production wells were shut down in the weeks prior to the synoptic survey. These wells include the Red Hill Shaft (shut down on November 28, 2021) and the Hālawa Shaft (shut down on December 3, 2021, except for weekly, short-duration operations for water-quality sampling). Groundwater levels measured in wells during the synoptic survey ranged from 16.81 to 20.19 feet above mean sea level. The groundwater levels measured on January 18, 2022, were about 0.3 to 0.6 feet higher than those measured at common sites during a synoptic groundwater-level survey on December 23, 2021.
The groundwater levels collected during the multiagency synoptic survey contain uncertainty because of several potential sources of error associated with (1) the accuracy of the measuring tapes used, (2) the accuracy of the measuring-point altitude at the top of each well, (3) well plumbness and alignment, (4) human error, and (5) changing conditions during the survey period. Because of these potential sources of error, comparability of groundwater-level measurements may be affected. Some of the sources of uncertainty can be addressed and lead to improved accuracy and comparability of the groundwater levels. For example, uncertainty associated with the measuring-point altitudes can be addressed by resurveying measuring-point altitudes to a common vertical datum using consistent surveying methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221048","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, January 18, 2022, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1048, 11 p., https://doi.org/10.3133/ofr20221048.","productDescription":"Report: v, 11 p.; Data Release","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-138445","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":401209,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1048/covrthb.jpg"},{"id":401210,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1048/ofr20221048.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":401211,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the nation","description":"U.S. Geological Survey, 2022, USGS water data for the nation: U.S. Geological Survey National Water Information System database, https://doi.org/10.5066/F7P55KJN."},{"id":401230,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221018","text":"Open-File Report 2022–1018","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, December 23, 2021, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1018, 10 p., Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, December 23, 2021, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1018, 10 p., https://doi.org/10.3133/ofr20221018..","linkHelpText":"- December 23, 2021, Red Hill Synoptic Groundwater-Level Survey, Hālawa Area, O‘ahu, Hawai‘i"},{"id":404437,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221069","text":"Open-File Report 2022-1069","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, Groundwater-level monitoring from January 17 to March 3, 2022, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1069, 29 p., https://doi.org/10.3133/ofr20221069.","linkHelpText":"- Groundwater-Level Monitoring from January 17 to March 3, 2022, Hālawa Area, O‘ahu, Hawai‘i"},{"id":501778,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113078.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Hālawa Area, O‘ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.97103881835938,\n 21.317522325157526\n ],\n [\n -157.84194946289062,\n 21.317522325157526\n ],\n [\n -157.84194946289062,\n 21.410883719938866\n ],\n [\n -157.97103881835938,\n 21.410883719938866\n ],\n [\n -157.97103881835938,\n 21.317522325157526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Anthropogenic barriers to main-stem and tributary passage are one of the primary threats associated with declining populations of Pacific Lamprey (Entosphenus tridentatus) in the Columbia River Basin. Juvenile lamprey are of special interest because their downstream migration to the ocean may be affected by barriers such as dams or water diversions. Telemetry studies that describe the movement and passage of juvenile lamprey have not been possible until the recent development of a micro-transmitter specifically for use in juvenile lamprey and eels. Through a collaborative research approach, we used these prototype transmitters and acoustic monitoring arrays installed for a juvenile salmon (Oncorhynchus spp.) migration study to evaluate juvenile lamprey movements in the Yakima River (river kilometer 179 to the river mouth) in 2019 and 2020. We tagged and released 152 juvenile lamprey from April 30 to June 5, 2019, and on June 9, 2020. Lamprey were released 6.9 kilometers (km) upstream from Wapato Dam, 1.2 km upstream from Prosser Dam, and into the canal and tailrace at Prosser Dam. Most tagged lamprey did not initiate downstream movements within the 18 days of tag life, as evidenced by our detections of lamprey in the highest numbers at the first monitoring site downstream from their release site, with limited or no detections at sites farther downstream. There was no evidence of missed detections (lamprey detected at a downstream site without corresponding detections upstream). Overall detections of tagged lamprey were low: 27.0 percent in 2019 and 48.0 percent in 2020. River flows were less than the 10-year average during the monitoring period and water temperatures were variable. Lamprey arrived at detections sites predominantly during periods of darkness (85.3–96.6 percent) following daytime releases. Travel rates through the study area ranged from 0.2 to 45.3 kilometers per day, and lamprey generally remained at each detection station for less than about 20 minutes. Groups of lamprey released together generally had similar travel rates with a small number of fish that moved more quickly or slowly than the remainder of the group. In addition to monitoring the migration and behavior of juvenile lamprey, we also assessed some assumptions of survival models (determining downstream drift of purposely killed fish and empirically measuring transmitter operating life) to benefit future evaluations focused on migration survival of juvenile lamprey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221052","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Yakama Nation Fisheries, McNary Fisheries Compensation Committee, Bonneville Power Administration, and the Pacific Northwest National Laboratory","usgsCitation":"Liedtke, T.L., Lampman, R.T., Monk, P., Hansen, A.C., Kock, T.J., Beals, T.E., Deng, D.Z., and Porter, M.S., 2022, Monitoring the movements of juvenile Pacific Lamprey (Entosphenus tridentatus) in the Yakima River, Washington, using acoustic telemetry, 2019–20: U.S. Geological Survey Open-File Report 2022–1052, 28 p., https://doi.org/10.3133/ofr20221052.","productDescription":"Report: viii, 28 p.; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-133893","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":401158,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1052/images"},{"id":401157,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://app.streamnet.org/files/822/","text":"Pacific States Marine Fisheries Commission, StreamNet—Fish Data for the Northwest data files"},{"id":401156,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1052/ofr20221052.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1052"},{"id":401155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1052/covrthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.684814453125,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.01985337287631\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The delta smelt (Hypomesus transpacificus) is a small, euryhaline fish species endemic to the Sacramento–San Joaquin Delta; it is protected under the U.S. and California Endangered Species Acts, and because of declines in population abundance, the delta smelt may be vulnerable to extinction. The California Department of Water Resources (DWR) is conducting studies to test the viability of using domesticated fish to supplement the wild population of delta smelt. These studies have focused on examining the health and survival of domesticated delta smelt placed inside enclosures (circular cages that are approximately 1.5 meters tall by 1 meter in diameter) into the wild. We completed two parts within this study using underwater cameras inside the enclosures to observe fish behavior and their responses to certain stimuli. In both parts of the study, delta smelt behaviors were broadly categorized into two basic categories: (1) normal and (2) alarm. Normal behavior was characterized as calm, non-polarized, and docile swimming behavior. Alarm behavior was characterized by sudden and rapid darting, polarized frantic swimming activity, and tighter schooling polarization of individuals.
The first part of the study took place in a semi-controlled agricultural pond on the campus of the University of California, Davis. At this agricultural pond, we developed methods of observation and documented how fish behaved in response to enclosure disturbances associated with routine cleaning and service that is required during extended field deployments of the enclosures. We observed that delta smelt behavior changed from normal to alarm at the onset of an enclosure service and from alarm to normal within about 2 minutes after the service ended.
The second part of the study was completed in cooperation with the DWR. In October 2019, DWR deployed three enclosures in the Sacramento River near Rio Vista, California. To monitor survival rate of delta smelt, DWR permitted us to deploy cameras in one enclosure to document the frequency and duration of alarm behaviors exhibited by delta smelt and the frequency, duration, and intensity of three types of disturbances: (1) noise generated from passing boats, (2) noise generated from the enclosure moving in response to wave energy, and (3) vertical movements of the enclosure generated from wave energy. Alarm behaviors averaged about 2 minutes in duration and occurred most frequently during the evening compared to midday or morning. Each disturbance variable exhibited substantial variability in duration and intensity and occurred least frequently during the morning and evening compared to midday. Alarm behaviors appeared to be most associated with high intensity enclosure noises and vertical movements; however, limited replicate samples prohibited developing a statistical relation. Alarm behaviors did not directly contribute to injury or mortality of individual delta smelt; however, indirect or sublethal effects of alarm behaviors were not examined.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221028","collaboration":"Prepared in cooperation with California Department of Water Resources","programNote":"Water Availability and Use Science Program","usgsCitation":"Enos, E., Patton, O., and Feyrer, F., 2022, Underwater videographic observations of domesticated Delta smelt in field enclosures: U.S. Geological Survey Open-File Report 2022–1028, 17 p., https://doi.org/10.3133/ofr20221028.","productDescription":"Report: vii, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-120423","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":401000,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221028/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Open-File Report 2022-1028"},{"id":400874,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CY39ZG","text":"Underwater videographic observations of cultured Delta Smelt in field enclosures—Video clips and summary data","description":"Enos, E.R., Patton, O.J., and Feyrer, F.V., 2020, Underwater videographic observations of cultured Delta Smelt in field enclosures—Video clips and summary data: U.S. Geological Survey data release, https://doi.org/10.5066/P9CY39ZG."},{"id":400873,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1028/images"},{"id":400872,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1028/ofr20221028.xml"},{"id":400870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1028/covrthb.jpg"},{"id":400871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1028/ofr20221028.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1028"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.1514892578125,\n 37.896530447543\n ],\n [\n -120.311279296875,\n 37.896530447543\n ],\n [\n -120.311279296875,\n 39.01064750994083\n ],\n [\n -122.1514892578125,\n 39.01064750994083\n ],\n [\n -122.1514892578125,\n 37.896530447543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Yuma Ridgway’s rail (Rallus obsoletus yumanensis, hereafter, rail) is an endangered species for which patches of emergent marsh within the Salton Sea watershed comprise a substantial part of habitat for the species’ disjointed range in the southwestern United States. These areas of emergent marsh include (1) marshes managed by federal (particularly the U.S. Fish and Wildlife Service’s Sonny Bono Salton Sea National Wildlife Refuge), state (California Department of Fish and Wildlife), and local (Imperial Irrigation District) resource agencies that are sustained by direct deliveries of Colorado River water and (2) unmanaged marshes sustained by agricultural drainage water. Management of rail habitat in this arid environment is complicated by increasingly limited availability of unimpaired freshwater owing to water management decisions associated with the Quantification Settlement Agreement and risks posed by potentially harmful concentrations of selenium found in agricultural drainage water that can readily bioaccumulate in aquatic food webs.
To provide timely science for managers, herein we report summary statistics for managed and unmanaged emergent marshes sampled at the Salton Sea during the rail breeding season of 2016 pertaining to (1) selenium concentrations in food webs representing dietary pathways of selenium exposure and (2) patterns of rail occupancy and inter-marsh movements, estimated abundance, and regional population size of rail. For selenium-specific objectives, we sampled unfiltered surface water, midge larvae (Chironomidae), water boatmen (Corixidae), mosquitofish (Gambusia spp.), and crayfish (Astacidae). Selenium samples were collected from 15 fixed sampling points, each in managed and unmanaged marshes, during late February, April, and June 2016, which corresponded to rail pre-nesting, nesting, and fledgling reproductive life-stages, respectively. Two areas within the two treatment types (managed versus unmanaged marsh) were of particular interest to help assess risks associated with changing sea dynamics and different water-management strategies: (1) a large unmanaged marsh (Morton Bay) unintentionally created in approximately 2008 when it became separated from the Salton Sea as water inflows began to drop and a berm formed from accumulated sediment and (2) a restored marsh (HZ9A) managed by the Sonny Bono Salton Sea National Wildlife Refuge, which is currently supplied with Colorado River water but may be sustained in the future by a blend of clean (that is, low selenium) Colorado River and agricultural drainage water with higher selenium from the Alamo River. Hence, baseline data for these marshes are important for future management decisions. We also report selenium concentrations in rail blood, head feathers, and breast feathers from rails captured as part of the movement study. Results indicated relatively higher risks from dietary selenium exposure for rails occupying unmanaged marshes compared to managed marshes and similar risks among unmanaged marshes. However, risks also were potentially elevated for rails occupying some managed marshes (that is, the Hazard Marshes), where relatively high proportions of Chironomidae and mosquitofish exceeded dietary thresholds for selenium effects on avian reproduction.
For rail-specific objectives, we quantified occupancy and spatial distribution using call count data analyzed with imperfect detection models. Imperfect detection models allowed us to jointly estimate detection probability and abundance of detected rails in association with habitats. We then used estimates of detection probability and abundance at the habitat level to extrapolate rail population abundance for the Salton Sea region. Inter- and intra-marsh movements were described from over 5,000 locations obtained from 15 radio-marked rails. Resultant space use patterns indicated that, in general, selenium risk to individuals is not equally shared because of high levels of territoriality and very limited movement throughout the landscape. Moreover, the largest contiguous blocks of habitat are associated with unmanaged marshlands located on the former southeastern shoreline and outside traditional management areas and authorities. Thus, a substantial proportion of the rail population that is using unmanaged marsh on the southeastern shoreline may have disproportionate risk of elevated selenium exposure, yet how that risk translates to population-level effects remains unknown.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221045","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ricca, M.A., Overton, C.T., Anderson, T.W., Merritt, A., Harrity, E., Matchett, E., and Casazza, M.L., 2022, Yuma Ridgway’s rail selenium exposure and occupancy within managed and unmanaged emergent marshes at the Salton Sea: U.S. Geological Survey Open-File Report 2022–1045, 49 p., https://doi.org/10.3133/ofr20221045.","productDescription":"Report: x, 49 p.; 2 Data Releases","numberOfPages":"49","onlineOnly":"Y","ipdsId":"IP-115651","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":400780,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1045/ofr20221045.xml"},{"id":501775,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113059.htm","linkFileType":{"id":5,"text":"html"}},{"id":400770,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R39F33","text":"Selenium concentrations in Yuma Ridgway's Rails occupying managed and unmanaged emergent marshes at the Salton Sea","description":"Ricca, M.A, Overton, C.T., Anderson, T.W., Merritt, A., Harrity, E. Matchett, E., and Casazza, M.L., 2022, Selenium concentrations in Yuma Ridgway’s Rails occupying managed and unmanaged emergent marshes at the Salton Sea: U.S. Geological Survey data release, https://doi.org/10.5066/P9R39F33."},{"id":400769,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JRP0L6","text":"Yuma Ridgway’s Rail (Rallus obsoletus yumanensis) Population Surveys, Rail Movement, and Potential Habitat at the Salton Sea of California","description":"Overton, C.T., Ricca, M.A., Anderson, T.W., Merritt, A.M., Harrity, E., Matchett, E.L., Casazza, M.L., 2022, Yuma Ridgway’s rail (Rallus obsoletus yumanensis) population surveys, rail movement, and potential habitat at the Salton Sea of California: U.S. Geological Survey data release, https://doi.org/10.5066/P9JRP0L6."},{"id":400768,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1045/images"},{"id":400767,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221045/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400766,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1045/ofr20221045.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":400765,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1045/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Salton Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.6365966796875,\n 33.128351191631566\n ],\n [\n -115.51849365234374,\n 33.128351191631566\n ],\n [\n -115.51849365234374,\n 33.30298618122413\n ],\n [\n -115.6365966796875,\n 33.30298618122413\n ],\n [\n -115.6365966796875,\n 33.128351191631566\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the fifth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. The report contains information pertinent to data collection during the 2020 water year, from October 2019 to September 2020. The addition of water-quality data collection at St. Johns River at Buffalo Bluff near Satsuma was the only modification to the previously installed network.
Discharge and salinity varied widely during the data collection period, which included above-average rainfall for 3 of the 5 counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 5 years considered for this study. Annual mean discharge at Clapboard Creek was highest among the tributaries, followed by Ortega River, Durbin Creek, Pottsburg Creek at U.S. 90, Cedar River, Trout River, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, and Broward River, whose annual mean was lowest. Annual mean discharge at 8 of the 10 tributary monitoring sites was higher for the 2020 water year than for the 2019 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 5 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2020 water year than for the 2019 water year except for Buffalo Bluff, which remained the same.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2019 water year, annual mean salinity at all monitoring locations was higher for the 2020 water year except at the tributary sites of Trout River, Dunn Creek, and Clapboard Creek, which were lower, and Durbin Creek, which remained the same. The 2020 annual mean salinity on the main-stem of the St. Johns River was the highest since the beginning of the study in 2016 at Dancy Point, Racy Point, Shands Bridge, below Shands Bridge, above Buckman Bridge, and Jacksonville (Acosta Bridge). Among the tributary sites, annual mean salinity rankings for 2020 were highest for Julington Creek and Ortega River, which were the second-highest on record for those sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221024","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2022, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2020: U.S. Geological Survey Open-File Report 2022–1024, 48 p., https://doi.org/10.3133/ofr20221024.","productDescription":"Report: ix, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-133884","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":400657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1024/coverthb.jpg"},{"id":400658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1024"},{"id":400659,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.XML"},{"id":400660,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1024/images"},{"id":400661,"rank":5,"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":401171,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":501767,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113057.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.27935791015625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 29.14736383122664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
A cooperative study between the National Park Service (NPS) and the U.S. Geological Survey (USGS) characterized groundwater quality and hydrogeology in parts of Pinnacles National Park. The water-quality investigation assessed the geochemistry of springs, wells, surface water, and precipitation and analyzed geochemistry of rock formations that affect the water chemistry through water-rock interaction. The hydrogeology investigation used geophysical and groundwater level data to characterize groundwater-flow processes in the alluvial deposits of Bear Valley and the Chalone Creek watershed.
Analysis of aqueous geochemical parameters in water samples from perennial springs, water-supply wells, and surface waters was conducted for samples collected after the dry season (autumnal) and after the wet season (vernal) to assess changes in geochemistry due to changes in groundwater levels or flow resulting from precipitation. The chemistry of bulk precipitation collected during the wet season was also analyzed. Bedrock samples were analyzed for geochemical parameters to help constrain groundwater sources, flow paths, and weathering. The geochemical investigations show a correspondence between the source rock and the spring-water chemistry that can be attributed to the mineralogy of the source rock. The narrow range of strontium isotopes in water samples, sourced in geochemically and mineralogically disparate rocks, indicates that the bedrock groundwater is relatively old and has reached quasi-steady state with respect to weathering of susceptible minerals.
Groundwater-level monitoring indicated that the water table is shallow—from 0 to 10 meters (m) below land surface. In southern Bear Valley and in the Chalone Creek alluvium, water levels rose and declined by several meters over each annual cycle of this study. In northern Bear Valley, water levels rose modestly over two wet seasons but declined during a third wet season. In Bear Valley, groundwater/surface-water interaction occurs along the perennial reach of Sandy Creek. Groundwater discharges to the upstream part of the reach, becomes surface water and is partly consumed by evapotranspiration, and infiltrates farther downstream. In the Chalone Creek alluvium, runoff-generated surface-water flow in intermittent stream reaches is a major component of groundwater recharge. After the onset of significant streamflow, creek water rapidly recharges groundwater until water levels rise to nearly the creek level. Groundwater levels generally remain high throughout the wet season, then gradually decline after the creek becomes dry.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221026","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Scheiderich, K., Tiedeman, C.R., Hsieh, P.A., 2022, Aqueous geochemistry of waters and hydrogeology of alluvial deposits, Pinnacles National Park, California: U.S. Geological Survey Open-File Report 2022-1026, 39 p., https://doi.org/10.3133/ofr20221026.","productDescription":"Report: viii, 39 p.; 3 Data Releases","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-129434","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":400733,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZXRC0","text":"Streamflow data collected by the wading method, Pinnacles National Park, California, 2018","description":"Tiedeman, C.R., Ingebritsen, S.E., and Hsieh, P.A., 2021, Streamflow data collected by the wading method, Pinnacles National Park, California, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9IZXRC0."},{"id":400732,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AMDH71","text":"Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020","description":"Tiedeman, C.R., and Hsieh, P.A., 2021, Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9AMDH71."},{"id":400435,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1026/covrthb.jpg"},{"id":400731,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BMM0XG","text":"Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 2016-2017","description":"Scheiderich, K.D., 2021, Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 2016-2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9BMM0XG."},{"id":400436,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1026/ofr20221026.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Pinnacles National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.20666503906249,\n 36.4729263733008\n ],\n [\n -121.2070083618164,\n 36.458430880233415\n ],\n [\n -121.20237350463867,\n 36.45856894533104\n ],\n [\n -121.20254516601562,\n 36.444761218880885\n ],\n [\n -121.2070083618164,\n 36.44448503928196\n ],\n [\n -121.20769500732422,\n 36.429431764426866\n ],\n [\n -121.16134643554686,\n 36.43012234551576\n ],\n [\n -121.16151809692383,\n 36.44448503928196\n ],\n [\n -121.1543083190918,\n 36.443932677134846\n ],\n [\n -121.15190505981445,\n 36.44641827582959\n ],\n [\n -121.15293502807619,\n 36.449732283551036\n ],\n [\n -121.15550994873045,\n 36.45235577259431\n ],\n [\n -121.15568161010742,\n 36.45912120326335\n ],\n [\n -121.15224838256837,\n 36.45359844696098\n ],\n [\n -121.1491584777832,\n 36.454703029683984\n ],\n [\n -121.14830017089845,\n 36.45746441766761\n ],\n [\n -121.14469528198242,\n 36.46519578093882\n ],\n [\n -121.1543083190918,\n 36.46353912513365\n ],\n [\n -121.15516662597656,\n 36.46837093895339\n ],\n [\n -121.15516662597656,\n 36.47610121467339\n ],\n [\n -121.15636825561523,\n 36.47817169332646\n ],\n [\n -121.16117477416992,\n 36.47817169332646\n ],\n [\n -121.1656379699707,\n 36.48341665833054\n ],\n [\n -121.15585327148438,\n 36.48383071939411\n ],\n [\n -121.15224838256837,\n 36.4856249584318\n ],\n [\n -121.14624023437499,\n 36.489903360753495\n ],\n [\n -121.14366531372069,\n 36.49335351308354\n ],\n [\n -121.14761352539061,\n 36.493905523194286\n ],\n [\n -121.1553382873535,\n 36.48879927955005\n ],\n [\n -121.15859985351561,\n 36.48948933214638\n ],\n [\n -121.15842819213867,\n 36.50204721530844\n ],\n [\n -121.14933013916016,\n 36.502461176783044\n ],\n [\n -121.14898681640626,\n 36.51418916543914\n ],\n [\n -121.15156173706053,\n 36.523846174745536\n ],\n [\n -121.15568161010742,\n 36.5267430425095\n ],\n [\n -121.15568161010742,\n 36.5323985193006\n ],\n [\n -121.1659812927246,\n 36.532812318439724\n ],\n [\n -121.16804122924806,\n 36.53405370257286\n ],\n [\n -121.171817779541,\n 36.53267438563933\n ],\n [\n -121.1733627319336,\n 36.535570922786015\n ],\n [\n -121.1759376525879,\n 36.53777773541221\n ],\n [\n -121.17834091186522,\n 36.542880748389024\n ],\n [\n -121.18520736694336,\n 36.542742833547834\n ],\n [\n -121.18555068969725,\n 36.53212265197782\n ],\n [\n -121.19361877441405,\n 36.531984717947445\n ],\n [\n -121.19379043579102,\n 36.535570922786015\n ],\n [\n -121.19791030883789,\n 36.53543299490577\n ],\n [\n -121.19791030883789,\n 36.542191171722756\n ],\n [\n -121.19327545166016,\n 36.54246700312735\n ],\n [\n -121.1934471130371,\n 36.548535045042556\n ],\n [\n -121.20014190673827,\n 36.5474318001238\n ],\n [\n -121.20288848876955,\n 36.54798342455154\n ],\n [\n -121.20409011840819,\n 36.55074148763941\n ],\n [\n -121.21576309204102,\n 36.555016290922055\n ],\n [\n -121.21971130371094,\n 36.555154183865234\n ],\n [\n -121.22228622436523,\n 36.553223660269474\n ],\n [\n -121.22709274291991,\n 36.55418892809582\n ],\n [\n -121.2312126159668,\n 36.55046568575947\n ],\n [\n -121.23258590698242,\n 36.54715598643371\n ],\n [\n -121.23138427734375,\n 36.54177742277067\n ],\n [\n -121.23104095458983,\n 36.51750027583155\n ],\n [\n -121.23979568481445,\n 36.51763823569006\n ],\n [\n -121.24082565307616,\n 36.51060196931051\n ],\n [\n -121.24528884887695,\n 36.51046399690832\n ],\n [\n -121.24563217163087,\n 36.50273714986985\n ],\n [\n -121.23258590698242,\n 36.502323189870765\n ],\n [\n -121.22777938842772,\n 36.49473353098341\n ],\n [\n -121.22571945190428,\n 36.494871531420934\n ],\n [\n -121.22434616088866,\n 36.48962734192797\n ],\n [\n -121.21885299682617,\n 36.48879927955005\n ],\n [\n -121.21267318725585,\n 36.49017937859546\n ],\n [\n -121.21129989624022,\n 36.494319528195426\n ],\n [\n -121.20872497558592,\n 36.49100742621996\n ],\n [\n -121.2118148803711,\n 36.486177023622\n ],\n [\n -121.21730804443358,\n 36.48507288930754\n ],\n [\n -121.21644973754881,\n 36.47306441258654\n ],\n [\n -121.20666503906249,\n 36.4729263733008\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
WMA- Laboratory & Analytical Services Division
U.S. Geological Survey
USGS Headquarters
12201 Sunrise Valley Drive
Reston, VA 20192