Landscape-level geomorphic processes influence the spatial and temporal arrangement of fish habitats in freshwater ecosystems and fishes move across riverscapes, selecting a suite of habitats to maximise fitness. Here, we explore the influence of geomorphology on stream channel attributes and assess Broad Whitefish (Coregonus nasus) spawning habitat potential in the Colville River in Arctic Alaska. Using high-resolution digital surface models (5 m2), we quantified the stream network extent and summarised channel habitat attributes continuously across the drainage network. Next, we developed an intrinsic potential (IP) model for Broad Whitefish by using geomorphic channel parameters previously understood to be associated with spawning habitats (channel width, median substrate size and channel braiding) to estimate the potential of streams across the Colville River watershed to provide spawning habitat. Our model results show the majority of habitat with high IP (≥0.6) was located within the braided sections of the main channel, which encompass >1548 km, but only 2% of the total channel network. The IP model was tested by tracking radio-tagged Broad Whitefish using aerial surveys. Prespawn fish moved into the watershed starting mid-July and mostly used habitat with moderate to very high IP in the middle and lower watershed. Several individuals were relocated in smaller multichannels with vegetated bars that contained very low IP (≤0.2), suggesting that other factors, such as hyporheic flow, may also influence spawning habitat selection. Our study demonstrates that IP modelling offers a useful method to quantify spawning habitat potential in data-poor riverscapes, providing useful information for managers to assess potential anthropogenic impacts and develop conservation plans to protect essential Broad Whitefish habitat.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12657","usgsCitation":"Leppi, J., Falke, J.A., Rinella, D., Wipfli, M.S., Seitz, A., and Whitman, M.S., 2022, Landscape geomorphology and local-riverine features influence Broad Whitefish (Coregonus nasus) spawning habitat suitability in Arctic Alaska: Ecology of Freshwater Fish, v. 31, no. 4, p. 622-639, https://doi.org/10.1111/eff.12657.","productDescription":"18 p.","startPage":"622","endPage":"639","ipdsId":"IP-126724","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12657","text":"Publisher Index Page"},{"id":489404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163.185772718535,\n 70.98799175474022\n ],\n [\n -163.185772718535,\n 66.54828717076046\n ],\n [\n -142.02222047339154,\n 66.54828717076046\n ],\n [\n -142.02222047339154,\n 70.98799175474022\n ],\n [\n -163.185772718535,\n 70.98799175474022\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Leppi, Jason C.","contributorId":338571,"corporation":false,"usgs":false,"family":"Leppi","given":"Jason C.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rinella, Daniel J.","contributorId":355579,"corporation":false,"usgs":false,"family":"Rinella","given":"Daniel J.","affiliations":[{"id":81169,"text":"Fish and Wildlife Field Conservation Office","active":true,"usgs":false}],"preferred":false,"id":938901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Seitz, Andrew C.","contributorId":264890,"corporation":false,"usgs":false,"family":"Seitz","given":"Andrew C.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":938902,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whitman, Matthew S.","contributorId":338574,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":81170,"text":"Arctic Field Office","active":true,"usgs":false}],"preferred":false,"id":938903,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230100,"text":"dr1151 - 2022 - Bed-material transport in the upper Esopus Creek watershed, Ulster and Greene Counties, New York, 2017–20","interactions":[],"lastModifiedDate":"2026-03-16T19:59:16.748023","indexId":"dr1151","displayToPublicDate":"2022-03-30T13:30:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1151","displayTitle":"Bed-Material Transport in the Upper Esopus Creek Watershed, Ulster and Greene Counties, New York, 2017–20","title":"Bed-material transport in the upper Esopus Creek watershed, Ulster and Greene Counties, New York, 2017–20","docAbstract":"The U.S. Geological Survey, in cooperation with the Ashokan Watershed Stream Management Program, investigated the feasibility of bedload monitoring in the upper Esopus Creek watershed, Ulster and Greene Counties, New York, from 2017 to 2020. Traditional bedload samples were collected at two locations: Birch Creek at Big Indian, New York (station 013621955), and Stony Clove Creek at Jansen Road at Lanesville, New York (station 01362336), during two storms. Measured bedload-transport rates ranged from less than 1 to 37.2 short tons per day during the study period. Active and passive tracers were deployed in Stony Clove Creek at Jansen Road to measure bed-material displacement during storms. Accelerometers in the active tracers provided data on the initiation and duration of motion of bed material in the 128- to 190-millimeter size class (B-axis measurement of 175 millimeters). The active tracers were loosely placed on the streambed and were generally mobilized at streamflows of 130–375 cubic feet per second. Displacement of the passive tracers was measured five times and provided data on the variability of displacement of multiple-size classes of bed material by different streamflows. Passive-tracer data also indicated that particles larger than the opening of the Elwha sampler deployed for traditional sampling may have been in transport during sampling. Sediment-generated noise was not distinguishable from background stream noise in hydrophone recordings.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1151","collaboration":"Prepared in cooperation with the Ashokan Watershed Stream Management Program","usgsCitation":"Siemion, J., Antidormi, M.R., Bonville, D.B., Finkelstein, J., and Marineau, M., 2022, Bed-material transport in the upper Esopus Creek watershed, Ulster and Greene Counties, New York, 2017–20: U.S. Geological Survey Data Report 1151, 20 p., https://doi.org/10.3133/dr1151.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130089","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":397770,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1151/coverthb.jpg"},{"id":397771,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1151/dr1151.pdf","text":"Report","size":"3.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1151"},{"id":397773,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1151/dr1151.XML"},{"id":501203,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112753.htm","linkFileType":{"id":5,"text":"html"}},{"id":397883,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1151/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397774,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1151/images/"},{"id":397772,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KDJIPO","text":"USGS data release","linkHelpText":"Bed material transport data in the upper Esopus Creek watershed, Ulster and Greene Counties, NY, 2017-2020"}],"country":"United States","state":"New York","county":"Greene County, Ulster County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-74.253,42.4071],[-73.9845,42.4411],[-73.9415,42.4465],[-73.8181,42.4613],[-73.7751,42.4653],[-73.7722,42.4571],[-73.7722,42.4325],[-73.7901,42.3841],[-73.7923,42.371],[-73.7908,42.3555],[-73.7808,42.3336],[-73.7777,42.3113],[-73.7779,42.3008],[-73.7839,42.2864],[-73.7936,42.2701],[-73.8056,42.2576],[-73.8195,42.25],[-73.8345,42.2425],[-73.8421,42.2367],[-73.846,42.2285],[-73.8505,42.22],[-73.8553,42.2028],[-73.8573,42.196],[-73.8593,42.1896],[-73.8656,42.1834],[-73.8732,42.1789],[-73.8795,42.174],[-73.8863,42.1732],[-73.8932,42.1683],[-73.8965,42.1606],[-73.8998,42.1511],[-73.9044,42.1389],[-73.9109,42.1271],[-73.916,42.1199],[-73.9245,42.1019],[-73.9311,42.082],[-73.93,42.0765],[-73.9302,42.0679],[-73.9341,42.0575],[-73.937,42.0398],[-73.9347,42.0293],[-73.9331,42.0216],[-73.9436,41.9913],[-73.9504,41.9664],[-73.9556,41.9528],[-73.9551,41.9464],[-73.954,41.9401],[-73.9567,41.9301],[-73.9625,41.9179],[-73.9639,41.9138],[-73.9609,41.9088],[-73.9423,41.8827],[-73.9389,41.8704],[-73.939,41.8654],[-73.9423,41.8596],[-73.9448,41.8559],[-73.9461,41.851],[-73.9477,41.8346],[-73.9463,41.8142],[-73.9504,41.7979],[-73.9488,41.7847],[-73.946,41.7719],[-73.9414,41.7592],[-73.9408,41.7592],[-73.938,41.7469],[-73.9389,41.7337],[-73.9424,41.7142],[-73.9439,41.6993],[-73.9411,41.6884],[-73.9513,41.6149],[-73.9525,41.59],[-73.9999,41.5855],[-74.0521,41.5816],[-74.0575,41.5926],[-74.0677,41.604],[-74.0886,41.5988],[-74.0983,41.6089],[-74.1246,41.6133],[-74.1325,41.6152],[-74.1282,41.5833],[-74.1858,41.5944],[-74.187,41.5908],[-74.1907,41.5913],[-74.2458,41.6036],[-74.25,41.6059],[-74.2502,41.6291],[-74.2606,41.6337],[-74.2667,41.6324],[-74.2754,41.6284],[-74.281,41.6257],[-74.2989,41.6182],[-74.3156,41.6115],[-74.3187,41.6084],[-74.3404,41.5954],[-74.3521,41.5982],[-74.3583,41.5938],[-74.3675,41.5916],[-74.3681,41.5961],[-74.3705,41.597],[-74.3736,41.5975],[-74.376,41.5994],[-74.3772,41.6044],[-74.3807,41.6117],[-74.3843,41.6167],[-74.3873,41.6217],[-74.3884,41.6299],[-74.392,41.6345],[-74.3926,41.6399],[-74.3943,41.6458],[-74.4004,41.6486],[-74.4449,41.6726],[-74.4833,41.6942],[-74.5755,41.7453],[-74.4892,41.8377],[-74.4573,41.8747],[-74.5124,41.8992],[-74.6363,41.9542],[-74.7235,41.9915],[-74.78,42.0182],[-74.667,42.0697],[-74.5538,42.1212],[-74.5312,42.1464],[-74.504,42.1449],[-74.4516,42.1694],[-74.5348,42.201],[-74.4937,42.2579],[-74.4278,42.3492],[-74.4432,42.3547],[-74.3977,42.3675],[-74.389,42.3697],[-74.3778,42.3701],[-74.3685,42.3704],[-74.3617,42.3699],[-74.358,42.3676],[-74.3513,42.3617],[-74.3469,42.3594],[-74.3426,42.3584],[-74.3389,42.3602],[-74.3314,42.3601],[-74.3227,42.3605],[-74.3141,42.3595],[-74.3078,42.359],[-74.306,42.3599],[-74.2997,42.363],[-74.2954,42.363],[-74.2861,42.3629],[-74.2804,42.3647],[-74.2748,42.3673],[-74.2666,42.3732],[-74.2591,42.3795],[-74.2547,42.3812],[-74.2497,42.3807],[-74.2472,42.3807],[-74.246,42.3816],[-74.246,42.3839],[-74.253,42.4071]]]},\"properties\":{\"name\":\"Greene\",\"state\":\"NY\"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The 2019 Mw 7.1 Ridgecrest California earthquake rupture passed within 4 km of the Trona Pinnacles, a large group of tufa rock pillars. Reconnaissance following the Ridgecrest mainshock documented fresh damage to several of the Pinnacles. Repeated aerial photogrammetric surveys also documented damage during subsequent aftershocks. Here, we describe the photogrammetric data with emphasis on a specific rock spire that toppled during an Mw5.5 aftershock. We calculate the volumes of the intact spire and of its subsequent debris. To explore the utility of the pinnacles as fragile geologic features for constraining past earthquake shaking intensity, we calculate the quasi static, horizontal acceleration required to break the spire at its base. We also examine the response of this feature to observed shaking using a dynamic model of the spire. In this case, we find that the quasi‐static estimate provides a conservative maximum constraint on fragility. The dynamic model of the spire suggests that shaking during the Mw 7.1 mainshock likely generated tensile stresses in excess of the spire’s bulk strength, thereby making it vulnerable to collapse in subsequent aftershocks.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220210275","usgsCitation":"Donnellan, A., Garcia-Suarez, J., McPhillips, D., Asimaki, D., Goulet, C., Meng, X., Devine, S., and Lyzanga, G., 2022, Toppling of a Trona Pinnacles Spire following the M5.5 RidgecrestaAftershock of June 2020: Seismological Research Letters, v. 93, no. 3, p. 1768-1776, https://doi.org/10.1785/0220210275.","productDescription":"9 p.","startPage":"1768","endPage":"1776","ipdsId":"IP-137323","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":488946,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://infoscience.epfl.ch/record/294243","text":"External Repository"},{"id":416120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Trona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.37290967993692,\n 35.61140365258099\n ],\n [\n -117.37290967993692,\n 35.61036568121271\n ],\n [\n -117.37105357870811,\n 35.61036568121271\n ],\n [\n -117.37105357870811,\n 35.61140365258099\n ],\n [\n -117.37290967993692,\n 35.61140365258099\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"93","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Donnellan, Andrea","contributorId":304295,"corporation":false,"usgs":false,"family":"Donnellan","given":"Andrea","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":870100,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia-Suarez, Joaquin","contributorId":304296,"corporation":false,"usgs":false,"family":"Garcia-Suarez","given":"Joaquin","email":"","affiliations":[{"id":66021,"text":"Swiss Federal Institute","active":true,"usgs":false}],"preferred":false,"id":870101,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McPhillips, Devin 0000-0003-1987-9249","orcid":"https://orcid.org/0000-0003-1987-9249","contributorId":217362,"corporation":false,"usgs":true,"family":"McPhillips","given":"Devin","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":870102,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Asimaki, Domniki","contributorId":304297,"corporation":false,"usgs":false,"family":"Asimaki","given":"Domniki","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":870103,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goulet, Christine","contributorId":304298,"corporation":false,"usgs":false,"family":"Goulet","given":"Christine","affiliations":[{"id":54387,"text":"SCEC","active":true,"usgs":false}],"preferred":false,"id":870104,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meng, Xiaofeng","contributorId":304299,"corporation":false,"usgs":false,"family":"Meng","given":"Xiaofeng","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":870105,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Devine, Savannah","contributorId":304300,"corporation":false,"usgs":false,"family":"Devine","given":"Savannah","email":"","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":870106,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lyzanga, Gregory","contributorId":304301,"corporation":false,"usgs":false,"family":"Lyzanga","given":"Gregory","email":"","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":870107,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70230103,"text":"70230103 - 2022 - Poplar Island: Understanding the development of a beneficial use restoration site","interactions":[],"lastModifiedDate":"2022-03-30T14:39:14.395853","indexId":"70230103","displayToPublicDate":"2022-03-29T09:26:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1462,"text":"Ecological Restoration","active":true,"publicationSubtype":{"id":10}},"title":"Poplar Island: Understanding the development of a beneficial use restoration site","docAbstract":"Poplar Island, like many other islands throughout the Chesapeake Bay, eroded from 460 hectares in 1847 to only 1.5 hectares by the 1990’s. However, the U.S. Army Corps of Engineers, Maryland Department of Transportation, and numerous other state and federal agencies selected this site as the location of a beneficial use project aimed at restoring remote island habitat in the Chesapeake Bay using clean dredge material. While monitoring efforts since the beginning of restoration efforts have documented extensive use of Poplar Island by numerous species across multiple taxa, these efforts have previously lacked context regarding underlying habitat patterns. However, such information is especially important on a site like Poplar Island where habitat distribution and availability can change dramatically between years due to ongoing construction efforts. To address this information gap we digitized annual aerial imagery of Poplar Island from 2006-2017 into 20 habitat classes. The resulting data layers demonstrate the transition of cells along the eastern side of the island from undeveloped cells to planted marsh cells, which matches trends seen in avian monitoring data. Similarly, our data display changes in the distribution of specific resources such as sand across the island, and how individual locations of interest such as constructed habitat islands have evolved over time. We believe that these data will provide critical insight into the factors influencing wildlife distribution patterns on Poplar Island, and will allow for the identification of management actions that may either be targeted or avoided in the planning of future beneficial use projects.","language":"English","publisher":"University of Wisconsin Press","doi":"10.3368/er.40.1.17","usgsCitation":"Prosser, D., Sullivan, J.D., Wall, J.L., Buck, E., Taylor, J.F., Callahan, C.R., and McGowan, P.C., 2022, Poplar Island: Understanding the development of a beneficial use restoration site: Ecological Restoration, v. 40, p. 17-24, https://doi.org/10.3368/er.40.1.17.","productDescription":"8 p.","startPage":"17","endPage":"24","ipdsId":"IP-128662","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":397857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Talbot","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.39360427856445,\n 38.74250272111668\n ],\n [\n -76.3553237915039,\n 38.74250272111668\n ],\n [\n -76.3553237915039,\n 38.78339443129763\n ],\n [\n -76.39360427856445,\n 38.78339443129763\n ],\n [\n -76.39360427856445,\n 38.74250272111668\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":839037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":839038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wall, Jennifer L.","contributorId":205845,"corporation":false,"usgs":false,"family":"Wall","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":839039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buck, Evan J","contributorId":265821,"corporation":false,"usgs":false,"family":"Buck","given":"Evan J","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":839040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, John F.","contributorId":80890,"corporation":false,"usgs":false,"family":"Taylor","given":"John","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":839041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Callahan, Carl R.","contributorId":205289,"corporation":false,"usgs":false,"family":"Callahan","given":"Carl","email":"","middleInitial":"R.","affiliations":[{"id":37073,"text":"USFWS, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":839042,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGowan, Peter C.","contributorId":13867,"corporation":false,"usgs":false,"family":"McGowan","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":839043,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230841,"text":"70230841 - 2022 - Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain","interactions":[],"lastModifiedDate":"2022-04-26T14:00:19.627318","indexId":"70230841","displayToPublicDate":"2022-03-29T08:51:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain","docAbstract":"A previously completed mineral resources assessment of the Texas Coastal Plain indicated the potential for the future discovery of uranium resources. Geoenvironmental assessments that include the hydrogeologic framework can be used as a tool to understand the potential effects of mining operations. The hydrogeologic framework for this study focused on the composite hydrogeologic unit of the tract permissive for the occurrence of uranium consisting of the upper part of the Miocene-age Fleming Formation/Lagarto Clay, Pliocene-age Goliad and Pleistocene-age Willis Sands, Pleistocene-age Lissie and Beaumont Formations, and Holocene-age alluvial sediments (fluvial alluvium and eolian sand deposits). This composite hydrogeologic unit, which contains the Chicot and Evangeline aquifers of the Gulf Coast aquifer system, is intended for inclusion in a regional-scale geoenvironmental assessment of as yet undiscovered uranium resources. This article provides (1) a brief literature review describing the geologic and hydrogeologic settings, (2) the methodology used to develop a composite hydrogeologic framework, and (3) descriptions and maps of the land-surface altitude, composite hydrogeologic unit base and midpoint depth, water-level altitude, depth of water, unsaturated and saturated zone thickness, and transmissivity and hydraulic conductivity. A composite hydrogeologic unit, created by combining geologic and hydrogeologic data and maps for individual geologic and hydrogeologic units, is intended for use as a tool in a geoenvironmental assessment to evaluate potential contaminant migration through various avenues. Potential applications include using the hydrogeologic framework as an input into a geoenvironmental assessment to help estimate the potential for (1) runoff of contaminants into surface water, (2) infiltration of contaminants into the groundwater (aquifers), or (3) movement of contaminants from the mining area through wind, groundwater-flow, or streamflow in a given permissive tract. The procedures outlined in this paper also provide a method for developing hydrogeologic frameworks that can be applied in other areas where mining may occur.
","language":"English","publisher":"MDPI","doi":"10.3390/min12040420","usgsCitation":"Teeple, A., Becher, K.D., Walton-Day, K., Humberson, D.G., and Gallegos, T., 2022, Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain: Minerals, v. 12, no. 4, 420, 29 p., https://doi.org/10.3390/min12040420.","productDescription":"420, 29 p.","ipdsId":"IP-136336","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":448338,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min12040420","text":"Publisher Index Page"},{"id":435904,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AZMEU0","text":"USGS data release","linkHelpText":"Data used for developing a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain"},{"id":399663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.84521484375,\n 29.707139348134145\n ],\n [\n -93.69140625,\n 30.29701788337205\n ],\n [\n -93.55957031249999,\n 30.996445897426373\n ],\n [\n -93.84521484375,\n 31.74685416292141\n ],\n [\n -94.0869140625,\n 32.287132632616384\n ],\n [\n -94.19677734375,\n 32.97180377635759\n ],\n [\n -95.537109375,\n 32.62087018318113\n ],\n [\n -96.7236328125,\n 30.86451022625836\n ],\n [\n -97.9541015625,\n 29.34387539941801\n ],\n [\n -99.11865234374999,\n 27.994401411046148\n ],\n [\n -99.36035156249999,\n 27.332735136859146\n ],\n [\n -99.29443359375,\n 26.745610382199022\n ],\n [\n -98.67919921875,\n 26.254009699865737\n ],\n [\n -98.06396484375,\n 26.03704188651584\n ],\n [\n -97.42675781249999,\n 25.958044673317843\n ],\n [\n -97.00927734375,\n 25.760319754713887\n ],\n [\n -97.1630859375,\n 26.54922257769204\n ],\n [\n -97.1630859375,\n 27.547241546253268\n ],\n [\n -95.8447265625,\n 28.5941685062326\n ],\n [\n -94.41650390625,\n 29.305561325527698\n ],\n [\n -93.84521484375,\n 29.707139348134145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becher, Kent D 0000-0002-3947-0793","orcid":"https://orcid.org/0000-0002-3947-0793","contributorId":290642,"corporation":false,"usgs":false,"family":"Becher","given":"Kent","email":"","middleInitial":"D","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":841446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humberson, Delbert G 0000-0001-6789-9135","orcid":"https://orcid.org/0000-0001-6789-9135","contributorId":240891,"corporation":false,"usgs":false,"family":"Humberson","given":"Delbert","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":841448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":841449,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230714,"text":"70230714 - 2022 - Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?","interactions":[],"lastModifiedDate":"2022-05-13T15:19:01.582298","indexId":"70230714","displayToPublicDate":"2022-03-29T06:42:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?","docAbstract":"The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub-daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state-of-the art applications of ML for water quality models and discuss opportunities to improve the use of ML for emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model-data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge-guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision-relevant predictions of riverine water quality.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14565","usgsCitation":"Varadharajan, C., Appling, A.P., Arora, B., Christianson, D., Hendrix, V., Kumar, V., Lima, A.R., Mueller, J., Oliver, S.K., Ombadi, M., Perciano, T., Sadler, J.M., Weierbach, H., Willard, J., Xu, Z., and Zwart, J.A., 2022, Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?: Hydrological Processes, v. 36, e14565, 22 p., https://doi.org/10.1002/hyp.14565.","productDescription":"e14565, 22 p.","ipdsId":"IP-133065","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":448340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14565","text":"Publisher Index Page"},{"id":399487,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","noUsgsAuthors":false,"publicationDate":"2022-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Varadharajan, Charuleka","contributorId":242712,"corporation":false,"usgs":false,"family":"Varadharajan","given":"Charuleka","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":841218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arora, Bhavna 0000-0001-7841-886X","orcid":"https://orcid.org/0000-0001-7841-886X","contributorId":290532,"corporation":false,"usgs":false,"family":"Arora","given":"Bhavna","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christianson, Danielle","contributorId":265829,"corporation":false,"usgs":false,"family":"Christianson","given":"Danielle","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":841220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hendrix, Valerie 0000-0001-9061-8952","orcid":"https://orcid.org/0000-0001-9061-8952","contributorId":290533,"corporation":false,"usgs":false,"family":"Hendrix","given":"Valerie","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":841222,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lima, Aranildo R.","contributorId":290536,"corporation":false,"usgs":false,"family":"Lima","given":"Aranildo","email":"","middleInitial":"R.","affiliations":[{"id":25337,"text":"Aquatic Informatics","active":true,"usgs":false}],"preferred":false,"id":841223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Juliane 0000-0001-8627-1992","orcid":"https://orcid.org/0000-0001-8627-1992","contributorId":290539,"corporation":false,"usgs":false,"family":"Mueller","given":"Juliane","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841224,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841225,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ombadi, Mohammed","contributorId":290542,"corporation":false,"usgs":false,"family":"Ombadi","given":"Mohammed","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841226,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Perciano, Talita 0000-0002-2388-1803","orcid":"https://orcid.org/0000-0002-2388-1803","contributorId":290546,"corporation":false,"usgs":false,"family":"Perciano","given":"Talita","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841227,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":841228,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Weierbach, Helen","contributorId":290549,"corporation":false,"usgs":false,"family":"Weierbach","given":"Helen","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841229,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":841230,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Xu, Zexuan","contributorId":290552,"corporation":false,"usgs":false,"family":"Xu","given":"Zexuan","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841231,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":841232,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70230092,"text":"ofr20221007 - 2022 - Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16","interactions":[],"lastModifiedDate":"2026-03-27T19:43:51.579154","indexId":"ofr20221007","displayToPublicDate":"2022-03-28T13:10:41","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1007","displayTitle":"Uncertainty Analysis of Index-Velocity Meters and Discharge Computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, Water Years 2006–16","title":"Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16","docAbstract":"Monitoring discharge in the Chicago Sanitary and Ship Canal is critical for the accounting done by the U.S. Army Corps of Engineers of the diversion of water from Lake Michigan to the Mississippi River Basin by the State of Illinois. The primary streamgage used for this discharge monitoring, the Chicago Sanitary and Ship Canal near Lemont, Illinois (U.S. Geological Survey station 05536890), is operated by the U.S. Geological Survey as an index-velocity station and at the time of this study (water years 2006–16) had two continuous velocity meters (an acoustic Doppler velocity meter and an acoustic velocity meter) and a water-level sensor, among other instruments. Discharge is computed at the streamgage using an index-velocity rating developed by linear regression of the velocity meter values fitted to discharges intermittently measured with an acoustic Doppler current profiler. In this study, the uncertainties of the velocity meters and stage sensors were estimated using a type B (judgment-based) approach, and measured discharge uncertainties were taken from those provided by a common acoustic Doppler current profiler data processing software tool, QRev. The velocity meter uncertainties, expressed as standard deviations, were estimated to be about 2.5 percent of velocity except near zero, where they exceeded that fraction, whereas for the acoustic Doppler current profiler uncertainties, when converted to mean channel velocity, 2.5 percent of velocity was determined to be a lower bound. The estimated velocity meter and measured discharge uncertainties were compared to index-velocity ratings developed from regression analyses of two types: (1) those that allow specification of measurement uncertainties and (2) ordinary least squares (OLS) regression, which does not. Based on the linearity of the index-velocity rating and the approximate agreement of the distributions of the fitting and prediction velocities, the assumptions required for unbiased prediction by OLS regression were determined to be approximately satisfied. From the regression residuals, it was determined that the estimated measurement uncertainties are too small, too similar between acoustic velocity meter and acoustic Doppler velocity meter velocities, and possibly too strongly dependent on velocity. Large, non-Gaussian OLS regression residuals also were observed. The uncertainty of annual mean discharge computed using the different regressions also was considered and was determined to be strongly dependent on the assumed measurement uncertainty. Because the assumptions required for OLS regression to give unbiased and variance-maintaining predictions were determined to be approximately satisfied, the results of discharge computation using the index-velocity rating based on OLS regression were deemed to be reliable. These results indicate about 0.8-percent uncertainty in the computed discharge as measured by the coefficient of variation at the annual time scale when using the acoustic Doppler velocity meter and 1.2-percent uncertainty with the acoustic velocity meter. It may be possible to improve the accuracy of the computed discharge and its uncertainty by further examining the measurement uncertainties and addressing differences in the distributions of the velocities used in fitting the index-velocity ratings and those used in prediction. Although the index-velocity ratings and computed discharges presented in this study are similar to those used in computing the published discharge at the study streamgage, the values presented in this report are not intended to replace the published discharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221007","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Chicago District","usgsCitation":"Over, T.M., Muste, M., Duncker, J.J., Tsai, H., Jackson, P.R., Johnson, K.K., Engel, F.L., and Prater, C.D., 2022, Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16: U.S. Geological Survey Open-File Report 2022–1007, 35 p., https://doi.org/10.3133/ofr20221007.","productDescription":"Report: viii, 35 p.; Appendix; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-125889","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501754,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112755.htm","linkFileType":{"id":5,"text":"html"}},{"id":397713,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7X63K41","text":"USGS data release","linkHelpText":"Discharge measurements at U.S. Geological Survey streamgage 05536890 Chicago Sanitary and Ship Canal near Lemont, Illinois, 2005–2013"},{"id":397709,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007_appendix2.pdf","text":"Appendix 2","size":"7.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1007 appendix 2","linkHelpText":"—Slides"},{"id":397714,"rank":7,"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":397708,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1007"},{"id":397712,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1007/images"},{"id":397711,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007.XML"},{"id":397707,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1007/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Lemont","otherGeospatial":"Chicago Sanitary and Ship Canal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.06846618652344,\n 41.66573093599398\n ],\n [\n -88.04718017578125,\n 41.64469659784919\n ],\n [\n -87.86796569824217,\n 41.699063978799174\n ],\n [\n -87.75672912597656,\n 41.789744876718984\n ],\n [\n -87.7979278564453,\n 41.83068856472101\n ],\n [\n -87.92701721191406,\n 41.75645886225854\n ],\n [\n -88.06709289550781,\n 41.6908605241911\n ],\n [\n -88.06846618652344,\n 41.66573093599398\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin Ave.
Urbana, IL 61801
In-situ recovery (ISR) has been the only technique used to extract uranium from sandstone-hosted uranium deposits in the Pliocene Goliad Sand in the Texas Coastal Plain. Water plays a crucial role throughout the ISR lifecycle of production and groundwater restoration yet neither the water use nor other environmental footprints have been well documented. The goal of this study is to examine historical records for all six ISR operations completed in the Goliad Sand to identify and quantify parameters that indicate the surface and aquifer disturbances, water use, and radon emissions. Overall, the average mine area was 0.00023 ± 0.00006 acres per pound (ac/lb) U3O8. The average mine pore volume was 48.9 ± 50 gal/lb U3O8 with a minimum affected aquifer volume of 0.51 ± 0.08 cubic feet per pound (cu ft/lb) U3O8. An average of 258 ± 40 gallons (gal) of fluid were disposed per pound (lb) U3O8, with an average of 169 ± 26 gal/lb U3O8 attributed to restoration and 89 ± 36 gal/lb U3O8 attributed to the uranium production phase. The average radon emitted was 1.06 × 10−3 ± 7.4 × 10−4 curies per pound (Ci/lb) U3O8. Goodness-of-fit (R2) values are ≥0.79 for linear regressions of the amount of uranium produced versus mine area, mine pore volumes, mine aquifer volumes, water pumped, and total water disposed. The R2 value for radon emitted was 0.68. However, the water disposed only during the uranium production phase is more strongly correlated to the number of production days (R2 = 0.96) than to uranium production (R2 = 0.84), whereas the volume of water disposed during restoration is more strongly correlated to the “pore volume” (R2 = 0.97) than to uranium production (R2 = 0.90). Pore volume is an industry term used to describe the amount of fluid circulated through the aquifer during the uranium production period and stipulated in bond agreements in order to satisfy groundwater restoration requirements. Models constructed in this study can be used to estimate probable water use and the extent of surface and aquifer disturbances associated with ISR-amenable undiscovered uranium resources in the Goliad Sand. The historical perspective offered by the data compiled and correlations may prove useful to both industry and regulators.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/min12030369","usgsCitation":"Gallegos, T., Scott, A., Stengel, V.G., and Teeple, A., 2022, A methodology to assess the historical environmental footprint of in-situ recovery (ISR) of uranium: A demonstration in the Goliad Sand in the Texas Coastal Plain, USA: Minerals, v. 12, no. 3, 369, 29 p., https://doi.org/10.3390/min12030369.","productDescription":"369, 29 p.","ipdsId":"IP-132933","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":448350,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min12030369","text":"Publisher Index Page"},{"id":397704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Texas Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.69140625,\n 31.44741029142872\n ],\n [\n -96.328125,\n 31.22219703210317\n ],\n [\n -99.65698242187499,\n 27.61540601339959\n ],\n [\n -99.525146484375,\n 27.45953933271788\n ],\n [\n -99.60205078124999,\n 27.332735136859146\n ],\n [\n -99.503173828125,\n 27.225325836903373\n ],\n [\n -99.481201171875,\n 27.039556602163195\n ],\n [\n -99.2724609375,\n 26.686729520004036\n ],\n [\n -99.129638671875,\n 26.362342068998764\n ],\n [\n -98.843994140625,\n 26.303264239389534\n ],\n [\n -98.69018554687499,\n 26.165298896316042\n ],\n [\n -98.58032226562499,\n 26.194876675795218\n ],\n [\n -98.250732421875,\n 25.987674852384966\n ],\n [\n -97.85522460937499,\n 25.987674852384966\n ],\n [\n -97.3828125,\n 25.77021384896025\n ],\n [\n -97.174072265625,\n 26.007424156802212\n ],\n [\n -97.152099609375,\n 26.352497858154024\n ],\n [\n -97.31689453125,\n 26.755420897359123\n ],\n [\n -97.294921875,\n 27.01998400798257\n ],\n [\n -97.283935546875,\n 27.342494467201007\n ],\n [\n -97.03125,\n 27.819644755099446\n ],\n [\n -96.734619140625,\n 28.091366281406945\n ],\n [\n -95.965576171875,\n 28.565225490654658\n ],\n [\n -95.38330078125,\n 28.76765910569123\n ],\n [\n -95.108642578125,\n 29.084976575985912\n ],\n [\n -94.7021484375,\n 29.31514119318728\n ],\n [\n -93.98803710937499,\n 29.640320395351402\n ],\n [\n -93.812255859375,\n 29.640320395351402\n ],\n [\n -93.84521484375,\n 29.79298413547051\n ],\n [\n -93.702392578125,\n 29.964452850852005\n ],\n [\n -93.66943359374999,\n 30.07860131571654\n ],\n [\n -93.66943359374999,\n 30.287531589298727\n ],\n [\n -93.66943359374999,\n 30.552800413453546\n ],\n [\n -93.50463867187499,\n 30.911651004518244\n ],\n [\n -93.482666015625,\n 31.21280145833882\n ],\n [\n -93.69140625,\n 31.44741029142872\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":838857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, Annie 0000-0001-7286-3698 annescott@usgs.gov","orcid":"https://orcid.org/0000-0001-7286-3698","contributorId":223421,"corporation":false,"usgs":true,"family":"Scott","given":"Annie","email":"annescott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":838858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838976,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248721,"text":"70248721 - 2022 - Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density","interactions":[],"lastModifiedDate":"2023-09-18T14:05:04.028786","indexId":"70248721","displayToPublicDate":"2022-03-27T08:53:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density","docAbstract":"Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2590","usgsCitation":"Lofton, M., Brentrup, J.A., Beck, W.S., Zwart, J.A., Bhattacharya, R., Brighenti, L.S., Burnett, S.H., McCullough, I.M., Steele, B., Carey, C.C., Cottingham, K., Dietze, M., Ewing, H.A., Weathers, K.C., and LaDeau, S.L., 2022, Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density: Ecological Applications, v. 32, e2590, 24 p., https://doi.org/10.1002/eap.2590.","productDescription":"e2590, 24 p.","ipdsId":"IP-119852","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":448368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2590","text":"Publisher Index Page"},{"id":420889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire","otherGeospatial":"Lake Sunapee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.10639790180213,\n 43.45916503807919\n ],\n [\n -72.10639790180213,\n 43.3077735355308\n ],\n [\n -72.01805843117764,\n 43.3077735355308\n ],\n [\n -72.01805843117764,\n 43.45916503807919\n ],\n [\n -72.10639790180213,\n 43.45916503807919\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","noUsgsAuthors":false,"publicationDate":"2022-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Lofton, Mary","contributorId":329783,"corporation":false,"usgs":false,"family":"Lofton","given":"Mary","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":883298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brentrup, Jennifer A.","contributorId":194457,"corporation":false,"usgs":false,"family":"Brentrup","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beck, Whitney S.","contributorId":268335,"corporation":false,"usgs":false,"family":"Beck","given":"Whitney","email":"","middleInitial":"S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":883300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bhattacharya, Ruchi","contributorId":297412,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Ruchi","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":883302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brighenti, Ludmila S","contributorId":317713,"corporation":false,"usgs":false,"family":"Brighenti","given":"Ludmila","email":"","middleInitial":"S","affiliations":[{"id":69135,"text":"Universidade do Estado de Minas Gerais","active":true,"usgs":false}],"preferred":false,"id":883303,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burnett, Sarah H.","contributorId":288140,"corporation":false,"usgs":false,"family":"Burnett","given":"Sarah","email":"","middleInitial":"H.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":883304,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCullough, Ian M.","contributorId":149952,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":883305,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Steele, Bethel 0000-0003-4365-4103","orcid":"https://orcid.org/0000-0003-4365-4103","contributorId":329785,"corporation":false,"usgs":false,"family":"Steele","given":"Bethel","email":"","affiliations":[{"id":36248,"text":"Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":883306,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":883307,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cottingham, Kathryn L","contributorId":329786,"corporation":false,"usgs":false,"family":"Cottingham","given":"Kathryn L","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":883308,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Dietze, Michael","contributorId":248349,"corporation":false,"usgs":false,"family":"Dietze","given":"Michael","affiliations":[],"preferred":false,"id":883309,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ewing, Holly A.","contributorId":191962,"corporation":false,"usgs":false,"family":"Ewing","given":"Holly","email":"","middleInitial":"A.","affiliations":[{"id":33413,"text":"Bates College","active":true,"usgs":false}],"preferred":false,"id":883310,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Weathers, Kathleen C.","contributorId":202417,"corporation":false,"usgs":false,"family":"Weathers","given":"Kathleen","email":"","middleInitial":"C.","affiliations":[{"id":36424,"text":"Cary Institute of Ecosystems Studies","active":true,"usgs":false}],"preferred":false,"id":883311,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"LaDeau, Shannon L.","contributorId":172640,"corporation":false,"usgs":false,"family":"LaDeau","given":"Shannon","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":883312,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70230163,"text":"70230163 - 2022 - The wildland-urban interface in the United States based on 125 million building locations","interactions":[],"lastModifiedDate":"2022-07-07T16:44:38.536733","indexId":"70230163","displayToPublicDate":"2022-03-27T08:40:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"The wildland-urban interface in the United States based on 125 million building locations","docAbstract":"The wildland-urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human-wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous U.S. based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint dataset from Microsoft®. We classified WUI across the conterminous U.S. at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and census-based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6% – 18.8% of the conterminous U.S. as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail, and can be widely applied to take advantage of the growing availability of high-resolution building footprint datasets and classification methods.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2597","usgsCitation":"Carlson, A., Helmers, D., Hawbaker, T., Mockrin, M.H., and Radeloff, V.C., 2022, The wildland-urban interface in the United States based on 125 million building locations: Ecological Applications, v. 32, no. 5, e2597, 18 p., https://doi.org/10.1002/eap.2597.","productDescription":"e2597, 18 p.","ipdsId":"IP-129426","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":435908,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H094S2","text":"USGS data release","linkHelpText":"Lake trout hatch rates using adults collected in 2019 from Northern Refuge, Lake Michigan"},{"id":435907,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94BT6Q7","text":"USGS data release","linkHelpText":"Wildland-urban interface maps for the conterminous U.S. based on 125 million building locations"},{"id":397931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"32","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-06-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Amanda R. 0000-0002-0450-2636","orcid":"https://orcid.org/0000-0002-0450-2636","contributorId":195661,"corporation":false,"usgs":false,"family":"Carlson","given":"Amanda R.","affiliations":[],"preferred":false,"id":839345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helmers, David P.","contributorId":177497,"corporation":false,"usgs":false,"family":"Helmers","given":"David P.","affiliations":[],"preferred":false,"id":839346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":839347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mockrin, Miranda H.","contributorId":211622,"corporation":false,"usgs":false,"family":"Mockrin","given":"Miranda","email":"","middleInitial":"H.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":839348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Radeloff, Volker C.","contributorId":141124,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker","email":"","middleInitial":"C.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":839349,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239265,"text":"70239265 - 2022 - Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data","interactions":[],"lastModifiedDate":"2023-01-06T12:56:15.663693","indexId":"70239265","displayToPublicDate":"2022-03-27T06:51:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data","docAbstract":"Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost-effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non-feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite-based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground-based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species.
Prenatal exposure to drinking water with arsenic concentrations >50 μg/L is associated with adverse birth outcomes, with inconclusive evidence for concentrations ≤50 μg/L. In a collaborative effort by public health experts, hydrologists, and geologists, we used published machine learning model estimates to characterize arsenic concentrations in private wells—federally unregulated for drinking water contaminants—and evaluated associations with birth outcomes throughout the conterminous U.S.
Using several machine learning models, including boosted regression trees (BRT) and random forest classification (RFC), developed from measured groundwater arsenic concentrations of ∼20,000 private wells, we characterized the probability that arsenic concentrations occurred within specific ranges in groundwater. Probabilistic model estimates and private well usage data were linked by county to all live birth certificates from 2016 (n = 3.6 million). We evaluated associations with gestational age and term birth weight using mixed-effects models, adjusted for potential confounders and incorporated random intercepts for spatial clustering.
We generally observed inverse associations with term birth weight. For instance, when using BRT estimates, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 5 μg/L was associated with a −1.83 g (95% CI: −3.30, −0.38) lower term birth weight after adjusting for covariates. Similarly, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 10 μg/L was associated with a −2.79 g (95% CI: −4.99, −0.58) lower term birth weight. Associations with gestational age were null.
In this largest epidemiologic study of arsenic and birth outcomes to date, we did not observe associations of modeled arsenic estimates in private wells with gestational age and found modest inverse associations with term birth weight. Study limitations may have obscured true associations, including measurement error stemming from a lack of individual-level information on primary water sources, water arsenic concentrations, and water consumption patterns.
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 3 (July–September), 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/ofr20221025","usgsCitation":"Micijevic, E., Rengarajan, R., Haque, M.O., Lubke, M., Tuli, F.T.Z., Shaw, J.L., Hasan, N., Denevan, A., Franks, S., 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 3, 2021: U.S. Geological Survey Open-File Report 2022–1025, 38 p., https://doi.org/10.3133/ofr20221025.","productDescription":"Report: vii, 38 p.; Dataset","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-134677","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":397609,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221025/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397606,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"U.S. Geological Survey database","linkHelpText":"—EarthExplorer"},{"id":397605,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1025/images"},{"id":397604,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1025/ofr20221025.XML"},{"id":397603,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1025/ofr20221025.pdf","text":"Report","size":"2.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1025"},{"id":397602,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1025/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Spectroscopic data are rich in information and are commonly used in planetary research. Many mission teams, research labs, and individual research scientists derive thematic products from multi- and hyperspectral data sets and apply spectroscopic analysis techniques to derive new understanding. The PyHAT is a powerful and versatile, free, and open-source Python library designed to support exploratory spectral data analysis, the derivation of mission generated thematic products, and the application of statistical learning methods to spectral data. We present a general overview of the software architecture and identify the classes of users we seek to support. Four case studies demonstrate the use for both orbital and in-situ (landed) hyperspectral data.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Machine Learning for Planetary Science","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-818721-0.00012-4","usgsCitation":"Laura, J., Gaddis, L., Anderson, R.B., and Aneece, I.P., 2022, Introduction to the Python Hyperspectral Analysis Tool (PyHAT), chap. 4 of Machine Learning for Planetary Science, p. 55-90, https://doi.org/10.1016/B978-0-12-818721-0.00012-4.","productDescription":"36 p.","startPage":"55","endPage":"90","ipdsId":"IP-122322","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":421821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":885549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaddis, Lisa R. 0000-0001-9953-5483","orcid":"https://orcid.org/0000-0001-9953-5483","contributorId":93178,"corporation":false,"usgs":true,"family":"Gaddis","given":"Lisa R.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":885550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":885551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":885552,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230014,"text":"fs20223017 - 2022 - Kentucky and Landsat","interactions":[],"lastModifiedDate":"2023-01-21T15:56:29.665852","indexId":"fs20223017","displayToPublicDate":"2022-03-24T14:18:40","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3017","displayTitle":"Kentucky and Landsat","title":"Kentucky and Landsat","docAbstract":"From its rolling pastures to its forested Appalachian peaks, Kentucky’s scenery offers beauty along with contrast. Rivers, including the Mississippi and the Ohio, border much of the State, and more rivers and hundreds of lakes are inside its borders. Kentucky is also home to the world’s longest known cave system, Mammoth Cave National Park, and its residents maintain long-held traditions of coal mining, farming, horse racing, and bourbon making.
Although residents and visitors have a lot to explore up close, viewing Kentucky through a long lens—one extending into space—can offer even more information about its environmental and natural resources. Landsat satellite data can help State and Federal governments monitor the quality and health of Kentucky’s lands and waters.
Here is a closer look at some of the many ways that Landsat benefits Kentucky.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223017","usgsCitation":"U.S. Geological Survey, 2022, Kentucky and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3017, 2 p., https://doi.org/10.3133/fs20223017.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-127727","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412029,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223017/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":411899,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3017/images"},{"id":411898,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3017/fs20223017.XML"},{"id":411897,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3017/versionHist.txt","size":"2 kB","linkFileType":{"id":2,"text":"txt"}},{"id":397493,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3017/coverthb2.jpg"},{"id":411896,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3017/fs20223017.pdf","text":"Report","size":"8.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3017"}],"country":"United States","state":"Kentucky","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-89.485106,36.497692],[-89.5391,36.498201],[-89.570071,36.544387],[-89.571509,36.552569],[-89.563185,36.568749],[-89.546113,36.579989],[-89.527583,36.581147],[-89.480893,36.569771],[-89.465445,36.536163],[-89.47246,36.513741],[-89.485106,36.497692]]],[[[-82.333044,37.740969],[-82.319686,37.734404],[-82.307235,37.707669],[-82.298074,37.704143],[-82.301964,37.696223],[-82.297126,37.684228],[-82.303867,37.678392],[-82.296724,37.678071],[-82.291773,37.669143],[-82.284687,37.675277],[-82.257111,37.656749],[-82.23939,37.661465],[-82.226111,37.653092],[-82.209691,37.625103],[-82.187298,37.626935],[-82.191444,37.644378],[-82.174688,37.646529],[-82.172762,37.634008],[-82.18143,37.621842],[-82.164191,37.620192],[-82.168137,37.608495],[-82.158554,37.609546],[-82.156718,37.59279],[-82.131977,37.593537],[-82.127321,37.586667],[-82.127303,37.572681],[-82.144648,37.568315],[-82.133954,37.562245],[-82.133299,37.552996],[-82.121985,37.552763],[-82.116584,37.559588],[-82.103127,37.560097],[-82.098924,37.5533],[-82.07503,37.555824],[-82.064792,37.539021],[-82.049584,37.535222],[-82.048205,37.528972],[-82.042397,37.533916],[-82.04478,37.546713],[-82.038972,37.547926],[-82.028826,37.537667],[-82.021006,37.540526],[-82.009194,37.533243],[-81.999844,37.542579],[-81.992597,37.538323],[-81.970147,37.546504],[-81.964971,37.543026],[-82.309415,37.300066],[-82.324619,37.28318],[-82.341849,37.280886],[-82.342068,37.274109],[-82.350948,37.267077],[-82.449164,37.243908],[-82.486439,37.231204],[-82.491486,37.225086],[-82.498858,37.227044],[-82.520117,37.212906],[-82.528746,37.213742],[-82.592451,37.182847],[-82.633493,37.154264],[-82.651646,37.151908],[-82.676765,37.134965],[-82.722097,37.120168],[-82.726201,37.115882],[-82.721617,37.101276],[-82.724954,37.091905],[-82.717204,37.079544],[-82.727022,37.073019],[-82.722254,37.057948],[-82.724714,37.042758],[-82.742454,37.04298],[-82.747981,37.025214],[-82.759175,37.027333],[-82.782144,37.008242],[-82.828592,37.005707],[-82.836008,36.988837],[-82.866019,36.978272],[-82.870274,36.965993],[-82.855705,36.953808],[-82.861282,36.944848],[-82.857965,36.929529],[-82.876215,36.910218],[-82.873213,36.897263],[-82.878569,36.889585],[-82.910315,36.874055],[-82.970253,36.857686],[-82.998376,36.85663],[-83.006086,36.847889],[-83.021887,36.849989],[-83.025887,36.855289],[-83.07259,36.854589],[-83.07519,36.840889],[-83.101792,36.829089],[-83.098492,36.814289],[-83.103092,36.806689],[-83.131694,36.781488],[-83.131245,36.767105],[-83.125655,36.761407],[-83.127833,36.750828],[-83.136395,36.743088],[-83.194597,36.739487],[-83.311403,36.710287],[-83.386099,36.686589],[-83.423707,36.667385],[-83.466483,36.6647],[-83.498011,36.670485],[-83.531912,36.664984],[-83.577312,36.641784],[-83.607913,36.637083],[-83.628913,36.624083],[-83.648314,36.622683],[-83.649513,36.616683],[-83.673114,36.604682],[-83.690714,36.582581],[-84.543138,36.596277],[-84.843091,36.605127],[-85.024627,36.619354],[-85.195372,36.625498],[-85.290627,36.62645],[-85.488353,36.614994],[-85.677789,36.618157],[-86.03277,36.630367],[-86.333051,36.648778],[-86.507771,36.652445],[-86.543777,36.640536],[-86.550054,36.644817],[-86.551292,36.637985],[-86.564143,36.633472],[-86.589906,36.652486],[-87.853204,36.633247],[-87.849567,36.663701],[-88.070532,36.678118],[-88.068208,36.659747],[-88.045127,36.602939],[-88.032489,36.540662],[-88.037822,36.51385],[-88.053205,36.497129],[-89.300284,36.507147],[-89.417293,36.499033],[-89.382762,36.583603],[-89.376367,36.613868],[-89.365548,36.625059],[-89.327589,36.632194],[-89.27171,36.571387],[-89.259994,36.565149],[-89.236542,36.566824],[-89.213563,36.580119],[-89.202607,36.601576],[-89.197654,36.628936],[-89.15908,36.666352],[-89.168723,36.671892],[-89.169522,36.688878],[-89.19948,36.716045],[-89.199798,36.734217],[-89.184523,36.753638],[-89.169106,36.759473],[-89.130399,36.751702],[-89.119198,36.759802],[-89.116067,36.772423],[-89.123481,36.785258],[-89.155891,36.789126],[-89.171069,36.798119],[-89.179229,36.812915],[-89.178888,36.831368],[-89.1704,36.841522],[-89.137969,36.847349],[-89.117567,36.887356],[-89.099007,36.961389],[-89.11503,36.980335],[-89.132685,36.9822],[-89.17112,37.008072],[-89.180849,37.026843],[-89.181369,37.046305],[-89.168087,37.074218],[-89.154504,37.088907],[-89.14132,37.093865],[-89.111189,37.119052],[-89.086526,37.165602],[-89.029981,37.211144],[-89.000968,37.224401],[-88.966831,37.229891],[-88.933077,37.227749],[-88.80572,37.188595],[-88.732105,37.143956],[-88.693983,37.141155],[-88.625889,37.119458],[-88.545403,37.070003],[-88.504437,37.065265],[-88.458948,37.073796],[-88.424776,37.149901],[-88.447764,37.203527],[-88.471753,37.220155],[-88.487277,37.244077],[-88.508031,37.260261],[-88.515939,37.284043],[-88.484462,37.345609],[-88.476592,37.386875],[-88.456,37.408482],[-88.408808,37.425216],[-88.365471,37.401663],[-88.312585,37.440591],[-88.281667,37.452596],[-88.135142,37.471626],[-88.087664,37.471059],[-88.064234,37.484548],[-88.061292,37.505232],[-88.069018,37.525297],[-88.131622,37.572968],[-88.139973,37.586451],[-88.142225,37.603737],[-88.156827,37.632801],[-88.159372,37.661847],[-88.122412,37.709685],[-88.059588,37.742608],[-88.02803,37.799224],[-87.997102,37.797672],[-87.95259,37.771742],[-87.944506,37.775256],[-87.932554,37.797672],[-87.90681,37.807624],[-87.903804,37.817762],[-87.910276,37.843416],[-87.936228,37.867937],[-87.941021,37.879168],[-87.938365,37.890802],[-87.904789,37.924892],[-87.892471,37.92793],[-87.87254,37.920999],[-87.830578,37.876516],[-87.7909,37.875714],[-87.76226,37.890906],[-87.717971,37.89257],[-87.67573,37.90193],[-87.666481,37.895786],[-87.66282,37.881449],[-87.681633,37.855917],[-87.679188,37.836321],[-87.666522,37.827455],[-87.635806,37.827015],[-87.612426,37.83384],[-87.588729,37.860984],[-87.591582,37.887194],[-87.620272,37.906922],[-87.62896,37.926714],[-87.606216,37.949642],[-87.601416,37.972542],[-87.585916,37.975442],[-87.574715,37.967742],[-87.57203,37.947466],[-87.559342,37.931146],[-87.511499,37.906426],[-87.447786,37.942427],[-87.418585,37.944763],[-87.380247,37.935596],[-87.344933,37.911164],[-87.302599,37.898558],[-87.220944,37.849134],[-87.158878,37.837871],[-87.14195,37.816176],[-87.129629,37.786608],[-87.111133,37.782512],[-87.090636,37.787808],[-87.067836,37.806065],[-87.043854,37.870796],[-87.045101,37.893775],[-87.033444,37.906593],[-86.969044,37.932858],[-86.919329,37.936664],[-86.85595,37.987292],[-86.820071,37.999392],[-86.794985,37.988982],[-86.765054,37.93251],[-86.73146,37.89434],[-86.718462,37.893123],[-86.680929,37.91501],[-86.647081,37.908621],[-86.644754,37.894806],[-86.661233,37.862761],[-86.661637,37.849714],[-86.655286,37.842505],[-86.638265,37.842718],[-86.609163,37.855408],[-86.598108,37.867382],[-86.599848,37.906754],[-86.588581,37.921159],[-86.534156,37.917007],[-86.507831,37.928829],[-86.50939,37.942492],[-86.525174,37.968228],[-86.521825,38.038327],[-86.51176,38.044448],[-86.452192,38.05049],[-86.432789,38.067171],[-86.430091,38.078638],[-86.434046,38.086763],[-86.463858,38.101177],[-86.463248,38.119278],[-86.449793,38.127223],[-86.431749,38.126121],[-86.401653,38.105396],[-86.379775,38.129274],[-86.335145,38.129242],[-86.323453,38.139032],[-86.321274,38.147418],[-86.325941,38.154317],[-86.37174,38.164183],[-86.377434,38.171379],[-86.373801,38.193352],[-86.360377,38.198796],[-86.287773,38.15805],[-86.271802,38.137874],[-86.27872,38.089303],[-86.273584,38.067443],[-86.261273,38.052721],[-86.220371,38.027922],[-86.178983,38.011308],[-86.12757,38.016011],[-86.095766,38.00893],[-86.075393,37.996948],[-86.053912,37.963571],[-86.038188,37.95935],[-86.029509,37.99264],[-85.951467,38.005608],[-85.925418,38.023456],[-85.906163,38.08617],[-85.908764,38.161169],[-85.894764,38.188469],[-85.845464,38.23027],[-85.829364,38.276769],[-85.780963,38.288469],[-85.761062,38.27257],[-85.744862,38.26717],[-85.683561,38.295469],[-85.653641,38.327108],[-85.638777,38.361443],[-85.632937,38.395666],[-85.607629,38.439295],[-85.587758,38.450495],[-85.536542,38.456083],[-85.498866,38.468242],[-85.474354,38.504074],[-85.423077,38.531581],[-85.4156,38.546341],[-85.415821,38.563558],[-85.437446,38.601724],[-85.438742,38.659319],[-85.456978,38.689135],[-85.452114,38.709348],[-85.434065,38.729455],[-85.410925,38.73708],[-85.363827,38.730477],[-85.306049,38.741649],[-85.275454,38.741172],[-85.246505,38.731821],[-85.213257,38.695446],[-85.172528,38.688082],[-85.13868,38.699168],[-85.103313,38.725323],[-84.990006,38.778383],[-84.941071,38.775627],[-84.887919,38.794652],[-84.814641,38.784488],[-84.813939,38.800209],[-84.829958,38.830632],[-84.791002,38.860572],[-84.785234,38.880439],[-84.812746,38.895132],[-84.867778,38.899133],[-84.877029,38.909016],[-84.877762,38.920357],[-84.83516,38.957961],[-84.829857,38.969385],[-84.83712,38.988059],[-84.889065,39.04082],[-84.897364,39.057378],[-84.831197,39.10192],[-84.78768,39.115297],[-84.766749,39.138558],[-84.750749,39.147358],[-84.718548,39.137059],[-84.684847,39.100459],[-84.657246,39.09546],[-84.632446,39.07676],[-84.620112,39.073457],[-84.572144,39.08206],[-84.550844,39.09936],[-84.524644,39.09216],[-84.510076,39.093606],[-84.470542,39.12146],[-84.449793,39.117754],[-84.435541,39.102261],[-84.427913,39.054962],[-84.406941,39.045662],[-84.346039,39.036963],[-84.326539,39.027463],[-84.304698,39.006455],[-84.288164,38.955789],[-84.234453,38.893226],[-84.231306,38.830552],[-84.212904,38.805707],[-84.071491,38.770475],[-84.044486,38.770572],[-83.962123,38.787384],[-83.917217,38.769665],[-83.873168,38.762418],[-83.852085,38.751433],[-83.836696,38.717857],[-83.787113,38.699489],[-83.769347,38.65522],[-83.720779,38.646704],[-83.679484,38.630036],[-83.663911,38.62793],[-83.649737,38.632753],[-83.637377,38.66793],[-83.626922,38.679387],[-83.520953,38.703045],[-83.493342,38.694187],[-83.468059,38.67547],[-83.384755,38.663171],[-83.356445,38.654009],[-83.327636,38.637489],[-83.319101,38.612233],[-83.307832,38.600824],[-83.294193,38.596588],[-83.264011,38.621535],[-83.245572,38.627936],[-83.202453,38.616956],[-83.142836,38.625076],[-83.112372,38.671685],[-83.053104,38.695831],[-83.027917,38.727143],[-82.979395,38.725976],[-82.923694,38.750076],[-82.894193,38.756576],[-82.879492,38.751476],[-82.869892,38.728177],[-82.877592,38.690177],[-82.859391,38.660378],[-82.854291,38.613454],[-82.844306,38.590862],[-82.820161,38.572703],[-82.789776,38.559951],[-82.724846,38.5576],[-82.696621,38.542112],[-82.657051,38.496816],[-82.608202,38.468049],[-82.593673,38.421809],[-82.599737,38.39037],[-82.593008,38.375082],[-82.597979,38.344909],[-82.576936,38.328275],[-82.572691,38.318801],[-82.583056,38.296829],[-82.574656,38.263873],[-82.581796,38.248592],[-82.60423,38.247303],[-82.61226,38.236087],[-82.608944,38.22366],[-82.600353,38.218949],[-82.599326,38.197231],[-82.611343,38.171548],[-82.642997,38.16956],[-82.637306,38.13905],[-82.622125,38.133414],[-82.621164,38.123239],[-82.606589,38.120843],[-82.587782,38.108879],[-82.584039,38.090663],[-82.551259,38.070799],[-82.517351,38.001204],[-82.48978,37.998869],[-82.483871,37.984505],[-82.464257,37.983412],[-82.46938,37.973059],[-82.483836,37.971566],[-82.484758,37.965752],[-82.472669,37.960721],[-82.475096,37.954906],[-82.48512,37.946044],[-82.495294,37.946612],[-82.491182,37.93581],[-82.501948,37.934756],[-82.49814,37.9283],[-82.480338,37.925836],[-82.487616,37.919905],[-82.475534,37.911945],[-82.474574,37.900295],[-82.469058,37.90222],[-82.464297,37.915038],[-82.421484,37.885652],[-82.417679,37.870658],[-82.409799,37.865392],[-82.422127,37.863952],[-82.423513,37.860313],[-82.414651,37.85626],[-82.420484,37.847496],[-82.39968,37.829935],[-82.39871,37.808785],[-82.385259,37.81741],[-82.377393,37.803009],[-82.340455,37.786058],[-82.335981,37.7745],[-82.323004,37.773907],[-82.333816,37.765391],[-82.331162,37.763125],[-82.312824,37.765027],[-82.310893,37.762005],[-82.333044,37.740969]]]]},\"properties\":{\"name\":\"Kentucky\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: March 24, 2022; Version 1.1: January 18, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
In 2000, the U.S. Geological Survey published a distinctive map, entitled “A Tapestry of Time and Terrain,” which showed a generalized depiction of the geology in the conterminous United States, draped over shaded-relief topography. In 2003, that map concept was extended geographically, and the resulting new map was published at 1:8,000,000 scale as “The North America Tapestry of Time and Terrain” (NATTT).
The NATTT map showed the wide range of ages of the bedrock that underlies North America, as well as the distribution of the principal rock types— sedimentary, igneous (as two subtypes, volcanic and plutonic), and metamorphic. Regional processes active at the land surface, as well as continental-scale tectonic events, are exposed on the maps, in the three dimensions of space and in the fourth dimension, geologic time.
This new publication contains the geographic information system (GIS) data from the NATTT map, which, in turn, includes map data from the earlier Tapestry map. The GIS files contain over 20,000 polygons that represent geologic age and rock class. In addition, layer files are provided to facilitate a symbolized display of the map that is similar to that of the original NATTT map publication. The data are intended to be used at the scale of the published source map (1:8,000,000).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1150","usgsCitation":"Cahan, S.M., Garrity, C.P., Soller, D.R., and Vigil, J.L., 2022, Database of the \"North America Tapestry of Time and Terrain\" map: U.S. Geological Survey Data Series 1150, 2 p., https://doi.org/10.3133/ds1150.","productDescription":"Report: 2 p.; GIS Database","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-118780","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":397503,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/imap/i2781/","text":"Geologic Investigations Series I-2781","linkHelpText":"- The North America Tapestry of Time and Terrain (USGS I-2781)"},{"id":397502,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112670.htm","text":"GIS Database","linkHelpText":"- Database of the \"North America Tapestry of Time and Terrain\" map"},{"id":397501,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1150/ds1150.pdf","text":"Report","size":"150 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":397500,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1150/covrthb.jpg"},{"id":501273,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112670.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director,
Geology, Energy & Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS-954
Reston, VA 20192
Trees are bioindicators of global climate change and regional urbanization, but available monitoring tools are ineffective for fine-scale observation of many species. Using six accelerometers mounted on two urban ash trees (Fraxinus americana), we looked at high-frequency tree vibrations, or change in periodicity of tree sway as a proxy for mass changes, to infer seasonal patterns of flowering and foliage (phenophases). We compared accelerometer-estimated phenophases to those derived from digital repeat photography using Green Chromatic Coordinates (GCC) and visual observation of phenophases defined by the USA National Phenology Network (NPN). We also drew comparisons between two commercial accelerometers and assessed how placement height influenced the ability to extract seasonal transition dates. Most notably, tree sway data showed a greenness signal in an urban environment and produced a clear flowering time-series and peak flowering signal (PF), marking the first observations of a flower phenophase using accelerometer data. Estimated start of spring (SOS) from accelerometers and time-lapse GCC were more similar than start of autumn (SOA); accelerometers lagged behind the time-lapse camera dates by three and four days for SOS and 13 and 14 days for SOA for each tree. Estimates for SOS and SOA from accelerometers and time-lapse cameras aligned closely with different NPN phenophases. The two commercial accelerometers produced similar season onset: a difference of 2.4 to 3.8 days for SOS, 2.1 days for SOA, and 0.5 to 2.0 days for PF. Accelerometers placed at the main crown branch point versus higher in the canopy showed a difference of 0.2 to 4.9 days for SOS and -1.5 to 1.7 days for PF. Our results suggest accelerometers present a novel opportunity to objectively monitor reproductive tree biology and fill gaps in phenology observations. Furthermore, widely available accelerometers show promise for scaling up from individual trees to the landscape level to aid forest management and assessing climate change impacts to tree phenology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2022.108900","usgsCitation":"Jaeger, D.M., Looze, A.M., Raleigh, M.S., Miller, B.W., Friedman, J.M., and Wessman, C.A., 2022, From flowering to foliage: Accelerometers track tree sway to provide high-resolution insights into tree phenology: Agricultural and Forest Meteorology, v. 318, 108900, 13 p., https://doi.org/10.1016/j.agrformet.2022.108900.","productDescription":"108900, 13 p.","ipdsId":"IP-132166","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":448377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.library.noaa.gov/view/noaa/68189","text":"Publisher Index Page"},{"id":398113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Boulder","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.27408599853516,\n 39.99053629940934\n ],\n [\n -105.23735046386719,\n 39.99053629940934\n ],\n [\n -105.23735046386719,\n 40.01525729596965\n ],\n [\n -105.27408599853516,\n 40.01525729596965\n ],\n [\n -105.27408599853516,\n 39.99053629940934\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"318","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jaeger, Deidre M.","contributorId":289672,"corporation":false,"usgs":false,"family":"Jaeger","given":"Deidre","email":"","middleInitial":"M.","affiliations":[{"id":62228,"text":"University of Colorado Department of Ecology and Evolutionary Biology","active":true,"usgs":false}],"preferred":false,"id":839588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Looze, A. M. C.","contributorId":289673,"corporation":false,"usgs":false,"family":"Looze","given":"A.","email":"","middleInitial":"M. C.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":839589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Raleigh, M. S.","contributorId":289674,"corporation":false,"usgs":false,"family":"Raleigh","given":"M.","email":"","middleInitial":"S.","affiliations":[{"id":62230,"text":"Oregon State University, Corvallis","active":true,"usgs":false}],"preferred":false,"id":839590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":839591,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":839592,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wessman, C. A.","contributorId":289675,"corporation":false,"usgs":false,"family":"Wessman","given":"C.","email":"","middleInitial":"A.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":839593,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230017,"text":"70230017 - 2022 - FluOil: A novel tool for modeling the transport of oil-particle aggregates in inland waterways","interactions":[],"lastModifiedDate":"2022-03-25T13:25:57.234236","indexId":"70230017","displayToPublicDate":"2022-03-24T09:14:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"FluOil: A novel tool for modeling the transport of oil-particle aggregates in inland waterways","docAbstract":"Spilled oil in inland waterways can aggregate with mineral and organic particles to form oil-particle aggregates (OPAs). OPAs can be transported in suspension or deposited to the bed. Modeling the fate and transport of OPAs can provide useful information for making mitigation decisions. A novel open-source tool, FluOil, is developed to predict where OPAs may deposit and when they arrive in affected river/lake reaches by implementing the random walk particle tracking algorithm to represent the advection, diffusion, deposition, and resuspension of OPAs. The usability of FluOil is demonstrated with the 2010 Kalamazoo River oil spill case study. An unsteady hydrodynamic model simulates the river hydraulics and provides hydraulic data for use in FluOil. Settling velocity and critical shear stress for resuspension are the most important OPA properties concerning the transport and deposition of OPAs. Settling velocity determines the vertical distribution of OPAs and, thus, the travel speed, whereas critical shear stress determines where and when OPAs are deposited and resuspended.","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2021.771764","usgsCitation":"Li, Y., Zhu, Z., Soong, D., Khorasani, H., Wang, S., Fitzpatrick, F.A., and Garcia, M., 2022, FluOil: A novel tool for modeling the transport of oil-particle aggregates in inland waterways: Frontiers in Water, v. 3, 771764, 14 p., https://doi.org/10.3389/frwa.2021.771764.","productDescription":"771764, 14 p.","ipdsId":"IP-119066","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":448381,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2021.771764","text":"Publisher Index Page"},{"id":397523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Kalamazoo River, Talmadge Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.55465698242188,\n 42.239702056572334\n ],\n [\n -84.96757507324219,\n 42.239702056572334\n ],\n [\n -84.96757507324219,\n 42.3468728197949\n ],\n [\n -85.55465698242188,\n 42.3468728197949\n ],\n [\n -85.55465698242188,\n 42.239702056572334\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2022-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Yilan","contributorId":289194,"corporation":false,"usgs":false,"family":"Li","given":"Yilan","email":"","affiliations":[{"id":62064,"text":"Department of Civil, Structural, and Environmental Engineering, University at Buffalo, NY","active":true,"usgs":false}],"preferred":false,"id":838679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Zhenduo","contributorId":206524,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhenduo","email":"","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":838680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soong, David 0000-0003-0404-2163","orcid":"https://orcid.org/0000-0003-0404-2163","contributorId":206523,"corporation":false,"usgs":true,"family":"Soong","given":"David","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Khorasani, Hamed","contributorId":289241,"corporation":false,"usgs":false,"family":"Khorasani","given":"Hamed","email":"","affiliations":[],"preferred":false,"id":838782,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Shu","contributorId":289195,"corporation":false,"usgs":false,"family":"Wang","given":"Shu","email":"","affiliations":[{"id":62065,"text":"School of Civil and Resource Engineering, University of Science and Technology Beiijing, Beijing, 100083 PR China","active":true,"usgs":false}],"preferred":false,"id":838682,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":196543,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","email":"fafitzpa@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":false,"id":838785,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garcia, Marcelo H.","contributorId":74236,"corporation":false,"usgs":false,"family":"Garcia","given":"Marcelo H.","affiliations":[{"id":33106,"text":"University of Illinois at Urbana Champaign","active":true,"usgs":false}],"preferred":false,"id":838684,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230036,"text":"70230036 - 2022 - Submarine landslide susceptibility mapping in recently deglaciated terrain, Glacier Bay, Alaska","interactions":[],"lastModifiedDate":"2022-04-01T21:51:54.317121","indexId":"70230036","displayToPublicDate":"2022-03-24T08:46:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Submarine landslide susceptibility mapping in recently deglaciated terrain, Glacier Bay, Alaska","docAbstract":"Submarine mass wasting events have damaged underwater structures and propagated waves that have inundated towns and affected human populations in nearby coastal areas. Susceptibility to submarine landslides can be pronounced in degrading cryospheric environments, where existing glaciers can provide high volumes of sediment, while cycles of glaciation and ice-loss can damage and destabilize slopes. Despite their contribution to potential tsunami hazard, submarine landslides can be difficult to study because of limited access and data collection in underwater environments. Here we present a method to quantify and map the submarine landslide susceptibility of sediment-covered slopes in Glacier Bay, Glacier Bay National Park and Preserve, Alaska, using multibeam-sonar bathymetric digital elevation models (DEMs) and historical maps of glacial extents over the last ∼250 years. After mapping an inventory of >7,000 landslide scarps in submarine sediments, we filtered the inventory by size to account for limitations in DEM resolution and spatial scales relevant to tsunami hazards. We then assessed landslide concentration, accounting for the age of the initial exposure of submarine slopes by deglaciation. We found a positive correlation between landslide concentration and deglaciation age, which we interpreted as a mean landslide accumulation rate over the period of record. Local deviations from this rate indicated differences in susceptibility. Additionally, we accounted for some of the effect of material and morphometric properties by estimating the submarine bedrock-sediment distribution using a morphometric model and assessing the relationship between slope angle and landslide incidence. Finally, we supplemented our susceptibility assessment with a geomorphic component based on the propensity of active submarine fans and deltas to produce landslides. Thus, our map of submarine landslide susceptibility incorporates three components: age-adjusted landslide concentration, slope angle, and geomorphology. We find that areas of mapped high susceptibility correlate broadly with areas of high sediment input and availability, locations of fans and deltas, and steep sediment-covered glacially carved fjords and troughs. Areas of high submarine landslide susceptibility in Glacier Bay moderately correspond with locations of known high-hazard subaerial slopes, but more research on submarine and subaerial landslides in degrading cryospheric environments would be beneficial to better understand landslide and tsunami hazards.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.821188","usgsCitation":"Avdievitch, N.N., and Coe, J.A., 2022, Submarine landslide susceptibility mapping in recently deglaciated terrain, Glacier Bay, Alaska: Frontiers in Earth Science, v. 10, 821188, 10 p., https://doi.org/10.3389/feart.2022.821188.","productDescription":"821188, 10 p.","ipdsId":"IP-129361","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":448383,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.821188","text":"Publisher Index Page"},{"id":397597,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -137.120361328125,\n 58.07787626787517\n ],\n [\n -135.75,\n 58.07787626787517\n ],\n [\n -135.75,\n 59.226555635719215\n ],\n [\n -137.120361328125,\n 59.226555635719215\n ],\n [\n -137.120361328125,\n 58.07787626787517\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Avdievitch, Nikita N. 0000-0002-2507-2962","orcid":"https://orcid.org/0000-0002-2507-2962","contributorId":225492,"corporation":false,"usgs":true,"family":"Avdievitch","given":"Nikita","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":838822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":838823,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229681,"text":"sir20225019 - 2022 - Bedload and suspended-sediment transport in lower Vance Creek, western Washington, water years 2019–20","interactions":[],"lastModifiedDate":"2026-04-09T16:38:11.743054","indexId":"sir20225019","displayToPublicDate":"2022-03-23T15:38:15","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5019","displayTitle":"Bedload and Suspended-Sediment Transport in Lower Vance Creek, Western Washington, Water Years 2019–20","title":"Bedload and suspended-sediment transport in lower Vance Creek, western Washington, water years 2019–20","docAbstract":"Vance Creek drains a 24 square mile area of the Olympic Mountains in western Washington. The lower 4 miles of the creek often go dry in discontinuous patches during the summer, limiting salmon rearing success. To better understand sediment transport dynamics in the creek and aid in potential restoration design, bedload and suspended-sediment concentration samples were collected for water years 2019–20 at a site about 2 miles upstream from the creek’s confluence with the South Fork Skokomish River.
Fifty bedload samples and 7 suspended-sediment concentration samples were collected over 7 sampling days. These samples were used to develop rating curves relating bedload flux or suspended-sediment concentration to discharge. Mean annual bedload flux was estimated to be 12,200 ± 2,300 tons per year for water years 1930–2020 period of record, based on application of the derived bedload rating curve to an extrapolated daily discharge record. The mean annual suspended-sediment load over the same period was estimated to be 9,000 tons per year with large, but unquantified, uncertainty. Bedload material was predominantly gravel from 0.08 to 2.5 inches (2 to 64 millimeters) in diameter. At the highest sampled discharges, approximately equivalent to a 50 percent annual exceedance probability (2-year peak-flow event), the bedload grain-size distribution was similar to that of the local channel bed. Bedload grain-size distributions generally coarsened as discharge increased. The suspended-sediment load was consistently one-half sand and one-half silt and clay, regardless of discharge. Bedload constituted about 60 percent of the total sediment flux (bedload plus suspended load). This is near the upper limit of values observed in a global compilation of long-term load partitioning data.
Sediment transport at the Vance Creek sampling site was compared with sediment-transport data from five other watersheds in the region. To facilitate comparisons, mean annual loads were divided by mean annual runoff volume to obtain an effective average sediment concentration. This normalization accounts for differences in both drainage area and mean runoff depth between the comparison watersheds. At the three comparison watershed sites with relatively complete sediment-transport data, mean bedload concentrations ranged from 44 to 109 milligrams per liter (mg/L) and mean suspended-sediment concentrations ranged from 139 to 374 mg/L; bedload constituted 21 to 29 percent of the total sediment load. The mean bedload concentration at the Vance Creek sampling site (69 mg/L) fell in the middle of the range observed in comparison watersheds, whereas the mean suspended-sediment concentration (50 mg/L) was markedly lower. Bedload samples at the Vance Creek sampling site also were generally less sand rich (sample-average sand fraction was 13 percent at Vance Creek versus 20 to 37 percent for comparison waters). Bedload transport rates at the Vance Creek sampling site appear relatively average for the region, given the drainage basin area and average runoff. In contrast, the supply and transport of finer material, both in the suspended load and the sand fraction of the bedload, are relatively low.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225019","collaboration":"Prepared in cooperation with the Mason Conservation District","usgsCitation":"Anderson, S.W., 2022, Bedload and suspended-sediment transport in lower Vance Creek, western Washington, water\nyears 2019–20: U.S. Geological Survey Scientific Investigations Report 2022–5019, 25 p., https://doi.org/10.3133/sir20225019.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-119859","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":502372,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112712.htm","linkFileType":{"id":5,"text":"html"}},{"id":397070,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5019/images"},{"id":397068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5019/coverthb.jpg"},{"id":397069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5019/sir20225019.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5019"},{"id":397071,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5019/sir20225019.XML"}],"country":"United States","state":"Washington","otherGeospatial":"Vance Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.2889,\n 47.3208\n ],\n [\n -123.2833,\n 47.3208\n ],\n [\n -123.2833,\n 47.325\n ],\n [\n -123.2889,\n 47.325\n ],\n [\n -123.2889,\n 47.3208\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Understanding dispersion in rivers is critical for numerous applications, such as characterizing larval drift for endangered fish species and responding to spills of hazardous materials. Injecting a visible dye into the river can yield insight on dispersion processes, but conventional field instrumentation yields limited data on variations in dye concentration over time at a few, fixed points. Remote sensing can provide more detailed, spatially distributed information on the dye's motion, but this approach has only been tested in clear-flowing streams. The purpose of this study was to assess the potential of remote sensing to facilitate tracer studies in more turbid rivers. To pursue this objective, we injected Rhodamine WT dye into the Missouri River and collected field spectra from a boat, videos from a small unoccupied aircraft system (sUAS), and orthophotos from an airplane. Applying an optimal band ratio analysis (OBRA) algorithm to the field spectra revealed strong correlations (R2 = 0.936) between a spectrally based quantity and in situ concentration measurements. OBRA also performed well for broadband RGB (red, green, blue) images extracted from the sUAS-based videos; the resulting concentration maps were used to produce animations that captured movement of the dye pulse. Spectral mixture analysis of repeat orthophoto coverage yielded relative concentration estimates that provided a synoptic perspective on dispersion of the dye throughout the entire 13.8 km reach over the full 2.5-hr duration of the experiment. The results of this study demonstrate the potential to remotely sense tracer dye concentrations in large, highly turbid rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR031396","usgsCitation":"Legleiter, C.J., Sansom, B.J., and Jacobson, R., 2022, Remote sensing of visible dye concentrations during a tracer experiment on a large, turbid river: Water Resources Research, v. 58, no. 4, e2021WR031396, 23 p., https://doi.org/10.1029/2021WR031396.","productDescription":"e2021WR031396, 23 p.","ipdsId":"IP-133418","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448396,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr031396","text":"Publisher Index Page"},{"id":435912,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JDISO3","text":"USGS data release","linkHelpText":"Remotely sensed data and field measurements for mapping visible dye concentrations during a tracer experiment on the Missouri River near Columbia, MO, May 5, 2021"},{"id":398020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Columbia","otherGeospatial":"Missouri River, Searcy's Bend","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.50162124633789,\n 38.856552783257754\n ],\n [\n -92.45372772216797,\n 38.856552783257754\n ],\n [\n -92.45372772216797,\n 38.91467806459576\n ],\n [\n -92.50162124633789,\n 38.91467806459576\n ],\n [\n -92.50162124633789,\n 38.856552783257754\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":839523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sansom, Brandon James 0000-0001-7999-9547","orcid":"https://orcid.org/0000-0001-7999-9547","contributorId":289636,"corporation":false,"usgs":true,"family":"Sansom","given":"Brandon","email":"","middleInitial":"James","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":839524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jacobson, R. B. 0000-0002-8368-2064","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":92614,"corporation":false,"usgs":true,"family":"Jacobson","given":"R. B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":839525,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230024,"text":"70230024 - 2022 - Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome","interactions":[],"lastModifiedDate":"2023-03-24T16:54:57.706489","indexId":"70230024","displayToPublicDate":"2022-03-23T11:26:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome","docAbstract":"Invasions of native plant communities by non-native species present major challenges for ecosystem management and conservation. Invasive annual grasses such as cheatgrass, medusahead, and ventenata are pervasive and continue to expand their distributions across imperiled sagebrush-steppe communities of the western United States. These invasive grasses alter native plant communities, ecosystem function, and fire regimes, threatening sagebrush ecosystem persistence. Spatial data describing the distribution and abundance of invasive species are often used by resource managers to identify, target, and determine needed interventions. However, there are challenges associated with translating these datasets into management actions. We conducted a review of available spatial products to assess advances in, and barriers to, applying contemporary model-based maps to support rangeland management. We found dozens of regional data products describing cheatgrass or annual herbaceous cover and few maps describing ventenata or medusahead. Over the past decade, IAG spatial data increased in spatial and temporal resolution and increasingly used response variables that indicate the severity of infestation such as percent cover. Despite improvements, use of such data is limited by the time required to find, compare, understand, and translate model-based maps into management strategy. There is also a need for products with higher spatial resolution and accuracy. In collaboration with a multipartner stakeholder group, we identified key considerations that guide selection of IAG spatial data products for use by land managers and other users. On the basis of these considerations, we discuss issues that contribute to a research-implementation gap between users and product developers and suggest future directions for improved development of management-ready spatial products.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2022.01.006","usgsCitation":"Tarbox, B.C., Van Schmidt, N.D., Shyvers, J.E., Saher, D., Heinrichs, J., and Aldridge, C.L., 2022, Bridging the gap between spatial modeling and management of invasive annual grasses in the imperiled sagebrush biome: Rangeland Ecology & Management, v. 82, p. 104-115, https://doi.org/10.1016/j.rama.2022.01.006.","productDescription":"12 p.","startPage":"104","endPage":"115","ipdsId":"IP-129019","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":435913,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VW97AO","text":"USGS data release","linkHelpText":"Database of invasive annual grass spatial products for the western United States January 2010 to February 2021"},{"id":397530,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tarbox, Bryan C. 0000-0001-5040-3949","orcid":"https://orcid.org/0000-0001-5040-3949","contributorId":288930,"corporation":false,"usgs":true,"family":"Tarbox","given":"Bryan","email":"","middleInitial":"C.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Schmidt, Nathan D. 0000-0002-5973-7934","orcid":"https://orcid.org/0000-0002-5973-7934","contributorId":288931,"corporation":false,"usgs":true,"family":"Van Schmidt","given":"Nathan","email":"","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shyvers, Jessica E. 0000-0002-4307-0004","orcid":"https://orcid.org/0000-0002-4307-0004","contributorId":288929,"corporation":false,"usgs":true,"family":"Shyvers","given":"Jessica","email":"","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saher, D. Joanne 0000-0002-2452-2570","orcid":"https://orcid.org/0000-0002-2452-2570","contributorId":288928,"corporation":false,"usgs":false,"family":"Saher","given":"D. Joanne","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":838723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":838724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":838725,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267427,"text":"70267427 - 2022 - How lions move at night when they hunt?","interactions":[],"lastModifiedDate":"2025-05-23T16:02:05.636688","indexId":"70267427","displayToPublicDate":"2022-03-23T10:57:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"How lions move at night when they hunt?","docAbstract":"Movement patterns of lions (Panthera leo) reveal how they hunt large herbivores in heterogeneous landscapes such as the Kruger National Park in South Africa. Large herbivores are distributed differently on the landscape and therefore have different vulnerabilities as prey for lions. For instance, blue wildebeest (Connochaetes taurinus) occupy small grazing lawns at night but are difficult for lions to capture because open areas lack cover for stalking. African buffalo (Syncerus caffer) aggregate in large herds but are less available because these herds only intermittently enter the home ranges of individual lion prides. Unlike large herds of wildebeest and buffalo, plains zebra (Equus quagga) move widely in small herds while browsing greater kudus (Tragelaphus strepsiceros) and giraffes (Giraffa camelopardalis giraffa) generally occur in lower densities. We used spatial data derived from GPS collars to investigate several hypotheses regarding the movements of three lion prides in response to their prey. We found that lions were most active and moved longer distances during nighttime than during daytime. Lions remained within their core home ranges on 87% of nights and wandered to the outlying areas of the home ranges every second night. Lions visited grazing lawns, that is, area of short grass, where wildebeest herds resided every second night, and moved toward the direction of buffalo herds within 2 km of vicinity. Lions spent more time near riverbanks that provided dense woody cover at night than expected but concentrated only weakly near sites with surface water where herbivores drank in the dry season. Our study contributes to understanding how lions vary their movements in response to the spatial and temporal heterogeneity in the relative availability and vulnerability of multiple prey species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyac025","usgsCitation":"Yiu, S., Owen-Smith, N., and Cain, J.W., 2022, How lions move at night when they hunt?: Journal of Mammalogy, v. 103, no. 4, p. 855-864, https://doi.org/10.1093/jmammal/gyac025.","productDescription":"10 p.","startPage":"855","endPage":"864","ipdsId":"IP-109457","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"South Africa","otherGeospatial":"Kruger National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.72137474888291,\n -22.35002959432397\n ],\n [\n 31.220577742146247,\n -25.52321749917516\n ],\n [\n 32.13088908280099,\n -25.529841746852213\n ],\n [\n 31.940017350082087,\n -23.896450066908073\n ],\n [\n 31.323354828993274,\n -22.370399291095367\n ],\n [\n 30.72137474888291,\n -22.35002959432397\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Yiu, Sze-Wing","contributorId":355799,"corporation":false,"usgs":false,"family":"Yiu","given":"Sze-Wing","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":938170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Owen-Smith, Norman","contributorId":355800,"corporation":false,"usgs":false,"family":"Owen-Smith","given":"Norman","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":938171,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938169,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}