{"pageNumber":"51","pageRowStart":"1250","pageSize":"25","recordCount":16446,"records":[{"id":70217195,"text":"70217195 - 2021 - Upland burning and grazing as strategies to offset climate-change effects on wetlands","interactions":[],"lastModifiedDate":"2021-04-08T14:28:20.228474","indexId":"70217195","displayToPublicDate":"2021-01-11T07:11:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Upland burning and grazing as strategies to offset climate-change effects on wetlands","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Wetland ecosystems perform a multitude of services valued by society and provide critical habitat for migratory birds and other wildlife. Despite their importance, wetlands have been lost to different local, regional, and global drivers. Remaining wetlands are extremely sensitive to changing temperature and precipitation regimes. Management of grassland areas in wetland catchments may be an effective strategy for counteracting potentially negative impacts of climate change on wetlands. Our objective was to estimate the effects of climate changes on wetland hydrology, and to explore strategies for increasing surface-water inputs to wetlands. We coupled a field study with process-based simulation modeling of wetland-water levels. We found that climate change could decrease the number of wetlands that hold ponded water during the waterfowl breeding season by 14% under a hot wet scenario or 29% under a hot dry scenario if no upland-management actions were taken. Upland burning reduced pond losses to 9% (hot wet) and 26% (hot dry). Upland grazing resulted in the smallest loss of ponded wetlands, 6% loss under the hot-and-wet scenario and 22% loss under the hot-and-dry scenario. Overall, water inputs could be increased by either burning or grazing of upland vegetation thereby reducing pond losses during the waterfowl breeding season. While field results suggest that both grazing and burning can reduce the vegetative structure that could lead to increases in runoff in grassland catchments, our model simulations indicated that additional actions may be needed for managers to minimize future meteorologically driven water losses.</p></div></div><div id=\"Sec1-section\" class=\"c-article-section\"><br></div>","language":"English","publisher":"Springer","doi":"10.1007/s11273-020-09778-1","usgsCitation":"McKenna, O.P., Renton, D.A., Mushet, D.M., and DeKeyser, E.S., 2021, Upland burning and grazing as strategies to offset climate-change effects on wetlands: Wetlands Ecology and Management, v. 29, https://doi.org/10.1007/s11273-020-09778-1.","productDescription":"16 p.","startPage":"208","ipdsId":"IP-112405","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":453876,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11273-020-09778-1","text":"Publisher Index Page"},{"id":382084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","edition":"193","noUsgsAuthors":false,"publicationDate":"2021-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":807933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renton, David A. drenton@usgs.gov","contributorId":247571,"corporation":false,"usgs":false,"family":"Renton","given":"David","email":"drenton@usgs.gov","middleInitial":"A.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":807934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":807935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeKeyser, Edward S.","contributorId":247572,"corporation":false,"usgs":false,"family":"DeKeyser","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":807936,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70217093,"text":"ds1132 - 2021 - Quality of surface water in Missouri, water year 2019","interactions":[],"lastModifiedDate":"2021-01-11T12:55:18.624014","indexId":"ds1132","displayToPublicDate":"2021-01-08T12:15:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1132","displayTitle":"Quality of Surface Water in Missouri, Water Year 2019","title":"Quality of surface water in Missouri, water year 2019","docAbstract":"<p>The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network (AWQMN). During water year 2019 (October 1, 2018, through September 30, 2019), water-quality data were collected at 73 stations: 71 AWQMN and alternate AWQMN stations, and 2 U.S. Geological Survey National Water Quality Monitoring Program stations. Among the stations in this report, four stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, <i>Escherichia coli</i> bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticides are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed monitored by the station. Some stations have been classified based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) they monitor. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented for representative streamflow-gaging stations in the State.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ds1132","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kay, R.T., 2021, Quality of surface water in Missouri, water year 2019: U.S. Geological Survey Data Series 1132, 26 p., https://doi.org/10.3133/ds1132.","productDescription":"Report: v, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119904","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381906,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1132/ds1132.pdf","text":"Report","size":"1.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1132"},{"id":381905,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1132/coverthb.jpg"},{"id":382023,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"National Water Information System"}],"country":"United States","state":"Missouri","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.545006,36.336809],[-89.605668,36.342234],[-89.615841,36.336085],[-89.620255,36.323006],[-89.611819,36.309088],[-89.578492,36.288317],[-89.554289,36.277751],[-89.539487,36.277368],[-89.534507,36.261802],[-89.539229,36.248821],[-89.562206,36.250909],[-89.577544,36.242262],[-89.602374,36.238106],[-89.642182,36.249486],[-89.678046,36.248284],[-89.695235,36.252766],[-89.705328,36.239898],[-89.69263,36.224959],[-89.607004,36.171179],[-89.591605,36.144096],[-89.59307,36.129699],[-89.601936,36.11947],[-89.666598,36.095802],[-89.678821,36.084636],[-89.688577,36.029238],[-89.706932,36.000981],[-90.37789,35.995683],[-90.351732,36.025347],[-90.34909,36.040131],[-90.339343,36.047112],[-90.333261,36.067504],[-90.320746,36.071326],[-90.320662,36.087138],[-90.29991,36.098236],[-90.294492,36.112949],[-90.266256,36.120559],[-90.235585,36.139474],[-90.231386,36.147348],[-90.23537,36.159153],[-90.220425,36.184764],[-90.21128,36.183392],[-90.188189,36.20536],[-90.152497,36.215582],[-90.14224,36.227522],[-90.126366,36.229367],[-90.130114,36.240307],[-90.118219,36.253491],[-90.114922,36.265595],[-90.086471,36.271531],[-90.06398,36.303038],[-90.081961,36.322097],[-90.074074,36.342895],[-90.077695,36.348478],[-90.066297,36.3593],[-90.064514,36.382085],[-90.078671,36.399116],[-90.138512,36.413952],[-90.134231,36.422827],[-90.143743,36.424433],[-90.143798,36.428483],[-90.134136,36.436602],[-90.137323,36.455411],[-90.141101,36.461791],[-90.155804,36.463555],[-90.152888,36.47093],[-90.142222,36.470554],[-90.143683,36.476029],[-90.158838,36.479558],[-90.159305,36.492446],[-90.152481,36.497952],[-94.617919,36.499414],[-94.617975,37.722176],[-94.607354,39.113444],[-94.589933,39.140403],[-94.591933,39.155003],[-94.608834,39.160503],[-94.640035,39.153103],[-94.662435,39.157603],[-94.663835,39.179103],[-94.680336,39.184303],[-94.714137,39.170403],[-94.741938,39.170203],[-94.763138,39.179903],[-94.781518,39.206146],[-94.811663,39.206594],[-94.831679,39.215938],[-94.835056,39.220658],[-94.825663,39.241729],[-94.831471,39.256273],[-94.84632,39.268481],[-94.887056,39.28648],[-94.905329,39.311952],[-94.910017,39.352543],[-94.88136,39.370383],[-94.879281,39.37978],[-94.885026,39.389801],[-94.901823,39.392798],[-94.92311,39.384492],[-94.942039,39.389499],[-94.946293,39.405646],[-94.972952,39.421705],[-94.982144,39.440552],[-95.0375,39.463689],[-95.045716,39.472459],[-95.052177,39.499996],[-95.082714,39.516712],[-95.109304,39.542285],[-95.113077,39.559133],[-95.103228,39.577783],[-95.089515,39.581028],[-95.064519,39.577115],[-95.049277,39.589583],[-95.046361,39.599557],[-95.055152,39.621657],[-95.053367,39.630347],[-95.027644,39.665454],[-95.018318,39.672869],[-94.984149,39.67785],[-94.971317,39.68641],[-94.971206,39.729305],[-94.965318,39.739065],[-94.948726,39.745593],[-94.902612,39.724202],[-94.875643,39.730494],[-94.862943,39.742994],[-94.860743,39.763094],[-94.869644,39.772894],[-94.912293,39.759338],[-94.934262,39.773642],[-94.935206,39.78313],[-94.929654,39.788282],[-94.884084,39.794234],[-94.875944,39.813294],[-94.878677,39.826522],[-94.886933,39.833098],[-94.916918,39.836138],[-94.942567,39.856602],[-94.928466,39.876344],[-94.929574,39.888754],[-94.95154,39.900533],[-94.986975,39.89667],[-95.00844,39.900596],[-95.024389,39.891202],[-95.027931,39.871522],[-95.037767,39.865542],[-95.085003,39.861883],[-95.128166,39.874165],[-95.140601,39.881688],[-95.143802,39.901918],[-95.149657,39.905948],[-95.179453,39.900062],[-95.199347,39.902709],[-95.206326,39.912121],[-95.20069,39.928155],[-95.204428,39.938949],[-95.250254,39.948644],[-95.269886,39.969396],[-95.302507,39.984357],[-95.315271,40.01207],[-95.356876,40.031522],[-95.387195,40.02677],[-95.40726,40.033112],[-95.416824,40.043235],[-95.42164,40.058952],[-95.409856,40.07432],[-95.407591,40.09803],[-95.394216,40.108263],[-95.39284,40.115887],[-95.398667,40.126419],[-95.428749,40.135577],[-95.436348,40.15872],[-95.460746,40.169173],[-95.479193,40.185652],[-95.482757,40.197346],[-95.469718,40.227908],[-95.477501,40.24272],[-95.490333,40.248966],[-95.521925,40.24947],[-95.552473,40.261904],[-95.556325,40.267714],[-95.550966,40.285947],[-95.562157,40.297359],[-95.581787,40.29958],[-95.610439,40.31397],[-95.642262,40.306025],[-95.657328,40.310856],[-95.653729,40.322582],[-95.625204,40.334288],[-95.623728,40.346567],[-95.641027,40.366399],[-95.643934,40.386849],[-95.659134,40.40869],[-95.65819,40.44188],[-95.693133,40.469396],[-95.699969,40.505275],[-95.661687,40.517309],[-95.652262,40.538114],[-95.655848,40.546609],[-95.671754,40.562626],[-95.678718,40.56256],[-95.694147,40.556942],[-95.69505,40.533124],[-95.708591,40.521551],[-95.722444,40.528118],[-95.75711,40.52599],[-95.769281,40.536656],[-95.763366,40.550797],[-95.773549,40.578205],[-95.765645,40.585208],[-94.632035,40.571186],[-94.080463,40.572899],[-92.689854,40.589884],[-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 \"}}]}","contact":"<p>Director, <a href=\"http://www.usgs.gov/centers/cm-water/\" data-mce-href=\"http://www.usgs.gov/centers/cm-water/\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>1400 Independence Road<br>Rolla, MO 65401</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>The Ambient Water-Quality Monitoring Network</li><li>Laboratory Reporting Conventions</li><li>Surface-Water Quality Data Analysis Methods</li><li>Station Classification for Data Analysis</li><li>Hydrologic Conditions</li><li>Distribution, Concentration, and Detection Frequency of Selected Constituents</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2021-01-08","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kay, Robert T. 0000-0002-6281-8997","orcid":"https://orcid.org/0000-0002-6281-8997","contributorId":205367,"corporation":false,"usgs":true,"family":"Kay","given":"Robert T.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807597,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70217094,"text":"sir20205119 - 2021 - Trends in groundwater levels in and near the Rosebud Indian Reservation, South Dakota, water years 1956–2017","interactions":[],"lastModifiedDate":"2021-01-08T12:48:31.039196","indexId":"sir20205119","displayToPublicDate":"2021-01-07T15:35:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5119","displayTitle":"Trends in Groundwater Levels in and near the Rosebud Indian Reservation, South Dakota, Water Years 1956–2017","title":"Trends in groundwater levels in and near the Rosebud Indian Reservation, South Dakota, water years 1956–2017","docAbstract":"<p>The U.S. Geological Survey (USGS), in cooperation with the Rosebud Sioux Tribe, completed a study to characterize water-level fluctuations in observation wells to examine driving factors that affect water levels in and near the Rosebud Indian Reservation, which comprises all of Todd County. The study investigates concerns regarding potential effects of groundwater withdrawals and climate conditions on groundwater levels within an area that includes Todd County and a surrounding area that extends 10 miles north, east, and west of the county border. Characterization of water-level fluctuations in observation wells and relative driving factors was accomplished by statistical trend analysis.</p><p>Two statistical methods were used for analysis of temporal trends for climatic and hydrologic data. To determine which trend analysis to use, applicable datasets were tested for statistically significant short-term persistence (STP). In the absence of significant STP, existence of statistical trends was determined using the standard Mann-Kendall test for probability values less than or equal to 0.10 (90-percent confidence level); however, a modified Mann-Kendall test was used for datasets where statistically significant STP was detected. Trend magnitudes were computed using the Sen’s slope estimator.</p><p>Monthly data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) were aggregated to obtain annual and seasonal datasets for total precipitation, minimum air temperature (<i>T<sub>min</sub></i>), and maximum air temperature (<i>T<sub>max</sub></i>) for the study area and a surrounding buffer area. Trend tests for total precipitation,<i> T<sub>min</sub></i>, and <i>T<sub>max</sub></i> were completed for annual and seasonal time series for water years 1956–2017, which is about 2 years before the earliest available water-level measurements. A 2-year offset was arbitrarily selected because scrutiny of water-level and precipitation data indicated that responses of groundwater levels for many of the observation wells lagged major changes in precipitation patterns by about 2 years. Statistically significant upward trends were detected for annual precipitation and annual <i>T<sub>min</sub></i> for almost all of the study area and the surrounding buffer area. Statistically significant downward trends in <i>T<sub>max</sub></i> were detected for a very small part of the study area; however, the sparse spatial coverage reduces confidence that these are true trends. Spatial distributions of statistically significant trends in seasonal climate data were generally similar to the annual trends, but with substantial differences in the spatial density of the trends.</p><p>Groundwater trends for 58 observation wells were analyzed for three separate water-level parameters (minimum, median, and maximum) because wells are measured sporadically and data are biased towards more frequent measurements during periods of heaviest irrigation demand. Trends in the time series of annual precipitation (from PRISM) starting 2 years earlier than for the associated water-level trend also were analyzed for the location of each individual observation well. Sen’s slope and Mann-Kendall probability values (p-values) were computed for the three water-level parameters and for the annual precipitation time series. Graphs showing results of trend analyses for each observation well also showed changes over time in the sum of licensed groundwater withdrawals within six specified radii (0.5, 1, 2, 3, 4, and 5 miles) of each well as a qualitative indicator of proximal groundwater demand.</p><p>Of all 58 observation wells considered, 28 wells had significant upward trends for at least one of the three water-level parameters, 11 wells had significant downward trends for at least one water-level parameter, and 19 wells did not have any significant trends. Significant upward trends in annual precipitation were detected for 48 of the 58 wells.</p><p>Results of trend analyses likely show the effects of groundwater withdrawals on water levels in the Ogallala aquifer in areas of substantial demand. Precipitation trends are significantly upward for 43 of the 48 wells completed in the Ogallala aquifer that were analyzed. Of the 48 Ogallala aquifer wells, 24 had significant upward trends for at least one water-level parameter (17 with all 3); however, 10 wells had statistically significant downward trends for at least one water-level parameter (8 with all 3 parameters). All but one of the wells with significant downward trends are located in the south-central part of the study area where licensed irrigation withdrawals are concentrated.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205119","collaboration":"Prepared in cooperation with the Rosebud Sioux Tribe","usgsCitation":"Valseth, K.J., and Driscoll, D.G., 2021, Trends in groundwater levels in and near the Rosebud Indian Reservation, South Dakota, water years 1956–2017: U.S. Geological Survey Scientific Investigations Report 2020–5119, 46 p., https://doi.org/10.3133/sir20205119.","productDescription":"Report: v, 46 p.; 2 Appendixes; Data Release","onlineOnly":"Y","ipdsId":"IP-111377","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":382008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"National Water Information System"},{"id":381910,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119_appendix2.pdf","text":"Appendix 2","size":"132 kB","description":"SIR 2020-5119 Appendix 2"},{"id":381909,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119_appendix1.pdf","text":"Appendix 1","size":"404 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5119 Appendix 1"},{"id":381908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119.pdf","text":"Report","size":"4.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5119"},{"id":381907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5119/coverthb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Rosebud Indian Reservation","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -101.612548828125,\n              43.01268088642034\n            ],\n            [\n              -99.8492431640625,\n              43.01268088642034\n            ],\n            [\n              -99.8492431640625,\n              43.600284023536325\n            ],\n            [\n              -101.612548828125,\n              43.600284023536325\n            ],\n            [\n              -101.612548828125,\n              43.01268088642034\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/dakota-water/\" data-mce-href=\"https://www.usgs.gov/centers/dakota-water/\">Dakota Water Science Center</a><br>U.S. Geological Survey<br>821 East Interstate Avenue, Bismarck, ND 58503<br>1608 Mountain View Road, Rapid City, SD 57702</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Data Sources and Analytical Methods</li><li>Analysis of Trends</li><li>Summary</li><li>References Cited</li><li>Appendix 1</li><li>Appendix 2</li></ul>","publishedDate":"2021-01-07","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Valseth, Kristen J. 0000-0003-4257-6094","orcid":"https://orcid.org/0000-0003-4257-6094","contributorId":203447,"corporation":false,"usgs":true,"family":"Valseth","given":"Kristen","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driscoll, Daniel G. 0000-0003-0016-8535 dgdrisco@usgs.gov","orcid":"https://orcid.org/0000-0003-0016-8535","contributorId":207583,"corporation":false,"usgs":true,"family":"Driscoll","given":"Daniel","email":"dgdrisco@usgs.gov","middleInitial":"G.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807599,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70217134,"text":"sir20205115 - 2021 - Water-resource management monitoring needs, State of Hawai‘i","interactions":[],"lastModifiedDate":"2021-01-08T12:57:15.296601","indexId":"sir20205115","displayToPublicDate":"2021-01-07T11:29:06","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5115","displayTitle":"Water-Resource Management Monitoring Needs, State of Hawai‘i","title":"Water-resource management monitoring needs, State of Hawai‘i","docAbstract":"<p>In cooperation with the State of Hawai‘i Commission on Water Resource Management and in collaboration with the University of Hawaiʻi Water Resources Research Center, the U.S. Geological Survey developed a water-resource monitoring program—a rainfall, surface-water, and groundwater data-collection program—that is required to meet State needs for water-resource assessment, management, and protection in Hawai‘i. Current and foreseeable issues related to water-resource management and climate-change effects guided the evaluation of data-collection sites within the monitoring program. Data-collection sites currently (2018) being operated in Hawai‘i were evaluated, and additional data-collection sites were selected on the basis of their usefulness for characterizing anthropogenic effects on water resources or representing natural conditions. Data-collection strategies consist of a combination of continuous long-term monitoring to evaluate trends and climate-change effects and occasional and periodic intensive monitoring to enhance spatial understanding of hydrologic conditions and to address current issues in priority areas—areas that currently have water-availability issues or are expected to have the greatest socioeconomic or ecological effects because of climate change.</p><p>Priority areas for rainfall monitoring consist of urban and agricultural lands, areas with high rainfall and high-rainfall gradient, and areas within the trade-wind inversion band. Surface-water priority areas consist of streams with major surface-water diversions, with established interim instream-flow standards, in a surface-water management area, that support water leases, and with uncertainties in hydrogeologic characteristics. Priority areas for groundwater monitoring consist of areas with high withdrawal, declining water levels, reduced recharge, limited alternative sources, and uncertainties in hydrogeologic characteristics.</p><p>Data-quality objectives for the rainfall, surface-water, and groundwater monitoring programs that describe anticipated uses of the data were established with the goal of producing useful, reliable, and accurate water-resource information of&nbsp;sufficient precision to support decision making. The data-quality objectives also consider quality-assurance and quality-control programs that ensure defensible data. Establishment of common data-quality objectives not only assures comparability of data collected by multiple agencies but also allows data from academic, private, and public organizations to be useful for meeting State monitoring needs, provided the data meet appropriate data-quality objectives and data-accessibility requirements.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205115","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management and in collaboration with the University of Hawai‘i Water Resources Research Center","usgsCitation":"Cheng, C.L., Izuka, S.K., Kennedy, J.J., Frazier, A.G., and Giambelluca, T.W., 2021, Water-resource management monitoring needs, State of Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2020-5115, 114 p., https://doi.org/10.3133/sir20205115.","productDescription":"xviii, 114 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 \"}}]}","contact":"<p><a href=\"mailto:dc_hi@usgs.gov\" data-mce-href=\"mailto:dc_hi@usgs.gov\">Director</a>,<br><a href=\"https://www.usgs.gov/piwsc\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/piwsc\">Pacific Islands Water Science Center</a><br><a data-mce-href=\"https://www.usgs.gov\" href=\"https://www.usgs.gov\" target=\"_blank\" rel=\"noopener\">U.S. Geological Survey</a><br>Inouye Regional Center<br>1845 Wasp Blvd., B176<br>Honolulu, HI 96818</p>","tableOfContents":"<ul><li>Executive Summary</li><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Setting</li><li>Approach</li><li>Rainfall</li><li>Surface Water</li><li>Groundwater</li><li>Data-Quality Objectives</li><li>Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-01-07","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Cheng, Chui Ling 0000-0003-2396-2571 ccheng@usgs.gov","orcid":"https://orcid.org/0000-0003-2396-2571","contributorId":3926,"corporation":false,"usgs":true,"family":"Cheng","given":"Chui","email":"ccheng@usgs.gov","middleInitial":"Ling","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izuka, Scot K. 0000-0002-8758-9414 skizuka@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-9414","contributorId":2645,"corporation":false,"usgs":true,"family":"Izuka","given":"Scot","email":"skizuka@usgs.gov","middleInitial":"K.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Joseph 0000-0002-6608-2366","orcid":"https://orcid.org/0000-0002-6608-2366","contributorId":203317,"corporation":false,"usgs":true,"family":"Kennedy","given":"Joseph","email":"","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frazier, Abby G.","contributorId":221112,"corporation":false,"usgs":false,"family":"Frazier","given":"Abby","email":"","middleInitial":"G.","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":807716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giambelluca, Thomas W.","contributorId":70069,"corporation":false,"usgs":true,"family":"Giambelluca","given":"Thomas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":807717,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","interactions":[{"subject":{"id":70217131,"text":"pp1867A - 2021 - Kīlauea’s 2008–2018 summit lava lake—Chronology and eruption insights","indexId":"pp1867A","publicationYear":"2021","noYear":false,"chapter":"A","displayTitle":"Kīlauea’s 2008–2018 Summit Lava Lake—Chronology and Eruption Insights","title":"Kīlauea’s 2008–2018 summit lava lake—Chronology and eruption insights"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":1},{"subject":{"id":70217132,"text":"pp1867B - 2021 - Views of a century of activity at Kīlauea Caldera—A visual essay","indexId":"pp1867B","publicationYear":"2021","noYear":false,"chapter":"B","displayTitle":"Views of a Century of Activity at Kīlauea Caldera—A Visual Essay","title":"Views of a century of activity at Kīlauea Caldera—A visual essay"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":2},{"subject":{"id":70217133,"text":"pp1867F - 2021 - Groundwater dynamics at Kīlauea Volcano and vicinity, Hawaiʻi","indexId":"pp1867F","publicationYear":"2021","noYear":false,"chapter":"F","displayTitle":"Groundwater Dynamics at Kīlauea Volcano and Vicinity, Hawaiʻi","title":"Groundwater dynamics at Kīlauea Volcano and vicinity, Hawaiʻi"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":3},{"subject":{"id":70218198,"text":"pp1867E - 2021 - Patterns of bubble bursting and weak explosive activity in an active lava lake—Halema‘uma‘u, Kīlauea, 2015","indexId":"pp1867E","publicationYear":"2021","noYear":false,"chapter":"E","displayTitle":"Patterns of Bubble Bursting and Weak Explosive Activity in an Active Lava Lake—Halema‘uma‘u, Kīlauea, 2015","title":"Patterns of bubble bursting and weak explosive activity in an active lava lake—Halema‘uma‘u, Kīlauea, 2015"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":4},{"subject":{"id":70225534,"text":"pp1867G - 2021 - A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","indexId":"pp1867G","publicationYear":"2021","noYear":false,"chapter":"G","displayTitle":"A Decade of Geodetic Change at Kīlauea’s Summit— Observations, Interpretations, and Unanswered Questions  from Studies of the 2008–2018 Halema‘uma‘u Eruption","title":"A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":5},{"subject":{"id":70226828,"text":"pp1867C - 2021 - Crater growth and lava-lake dynamics revealed through multitemporal terrestrial lidar scanning at Kīlauea Volcano, Hawaiʻi","indexId":"pp1867C","publicationYear":"2021","noYear":false,"chapter":"C","displayTitle":"Crater Growth and Lava-Lake Dynamics Revealed Through Multitemporal Terrestrial Lidar Scanning at Kīlauea Volcano, Hawaiʻi","title":"Crater growth and lava-lake dynamics revealed through multitemporal terrestrial lidar scanning at Kīlauea Volcano, Hawaiʻi"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":6}],"lastModifiedDate":"2021-01-15T01:44:44.053426","indexId":"pp1867","displayToPublicDate":"2021-01-07T10:06:28","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1867","displayTitle":"The 2008–2018 Summit Lava Lake at Kīlauea Volcano, Hawai‘i","title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","docAbstract":"<p>The 2008–2018 lava lake at the summit of Kīlauea marked the longest sustained period of lava lake activity at the summit in decades and provided a new opportunity for observing and understanding lava lake behavior. The individual chapters of this Professional Paper volume cover the basic chronology of the eruption, rich historical background, observations and measurements of lake activity, hydrological setting, as well as geophysical and other monitoring data that tracked the activity. &nbsp;</p><p>The primary focus of this Professional Paper is the 2008–2018 lava lake activity, ending with the draining of the lake in May 2018. The 2018 summit collapse events that followed the lake draining, and which dramatically altered the topography of the summit region, are published elsewhere. &nbsp;</p><p>As this volume was published online in January 2021, a new lava lake had just formed in Halemaʻumaʻu. &nbsp;This new activity is further testament to the dynamic nature of Halemaʻumaʻu and the proclivity for lava lake activity at the summit of Kīlauea. </p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1867","usgsCitation":"Patrick, M., Orr, T., Swanson, D., and Houghton, B., eds., The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i: U.S. Geological Survey Professional Paper 1867, variously paged, https://doi.org/10.3133/pp1867.","productDescription":"Multi-Chaptered report","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"links":[{"id":382006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1867/a/covrthb.jpg"}],"contact":"<p><a href=\"mailto:askHVO@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:askHVO@usgs.gov\">Contact HVO</a><br><a href=\"https://www.usgs.gov/observatories/hawaiian-volcano-observatory\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/observatories/hawaiian-volcano-observatory\">Hawaiian Volcano Observatory</a><br><a href=\"https://www.usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov\">U.S. Geological Survey</a><br>1266 Kamehameha Avenue<br>Suite A-8<br>Hilo, HI 96720</p>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-01-07","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"editors":[{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":807676,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Orr, Tim R. 0000-0003-1157-7588 torr@usgs.gov","orcid":"https://orcid.org/0000-0003-1157-7588","contributorId":149803,"corporation":false,"usgs":true,"family":"Orr","given":"Tim","email":"torr@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":807677,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Swanson, Don 0000-0002-1680-3591 donswan@usgs.gov","orcid":"https://orcid.org/0000-0002-1680-3591","contributorId":168817,"corporation":false,"usgs":true,"family":"Swanson","given":"Don","email":"donswan@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":807678,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Houghton, Bruce F. 0000-0002-7532-9770","orcid":"https://orcid.org/0000-0002-7532-9770","contributorId":140077,"corporation":false,"usgs":false,"family":"Houghton","given":"Bruce","email":"","middleInitial":"F.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":807679,"contributorType":{"id":2,"text":"Editors"},"rank":4}]}}
,{"id":70238836,"text":"70238836 - 2021 - Simulating water and heat transport with freezing and cryosuction in unsaturated soil: Comparing an empirical, semi-empirical and physically-based approach","interactions":[],"lastModifiedDate":"2022-12-14T15:25:50.400448","indexId":"70238836","displayToPublicDate":"2021-01-07T09:05:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Simulating water and heat transport with freezing and cryosuction in unsaturated soil: Comparing an empirical, semi-empirical and physically-based approach","docAbstract":"<p><span>Freezing of unsaturated soil is an important process that influences runoff and infiltration in cold-climate regions. We used a simple numerical model to simulate water and heat transport with phase change in unsaturated soil via three different approaches: empirical, semi-empirical and physically based. We compared the performance and parameterization of each approach through testing on three experimental datasets. All approaches reproduced the observed unsaturated freezing process satisfactorily. The empirical cryosuction equation used in this study managed to capture observed cryosuction with a fixed empirical parameter value. The semi-empirical version therefore does not require calibration of a specific frozen soil related parameter. In view of simplicity, small computational demand and accurate performance, all three approaches are suitable for implementation in land-use schemes, catchment scale hydrological models, or multi-dimensional thermo-hydrological models.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2021.103846","usgsCitation":"Stuurop, J.C., van der Zee, S.E., Voss, C., and French, H.K., 2021, Simulating water and heat transport with freezing and cryosuction in unsaturated soil: Comparing an empirical, semi-empirical and physically-based approach: Advances in Water Resources, v. 149, 103846, 16 p., https://doi.org/10.1016/j.advwatres.2021.103846.","productDescription":"103846, 16 p.","ipdsId":"IP-125325","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453908,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.advwatres.2021.103846","text":"Publisher Index Page"},{"id":410474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"149","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stuurop, Joris C","contributorId":299855,"corporation":false,"usgs":false,"family":"Stuurop","given":"Joris","email":"","middleInitial":"C","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":858860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van der Zee, Sjoerd E. A. T. M","contributorId":299856,"corporation":false,"usgs":false,"family":"van der Zee","given":"Sjoerd","email":"","middleInitial":"E. A. T. M","affiliations":[{"id":64966,"text":"Wageningen University, Monash University","active":true,"usgs":false}],"preferred":false,"id":858861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voss, Clifford I. 0000-0001-5923-2752","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":211844,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":858862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"French, Helen K","contributorId":299857,"corporation":false,"usgs":false,"family":"French","given":"Helen","email":"","middleInitial":"K","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":858863,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70217200,"text":"70217200 - 2021 - Modeling hydrologic processes associated with soil saturation and debris flow initiation during the September 2013 storm, Colorado Front Range","interactions":[],"lastModifiedDate":"2021-05-13T15:55:57.045834","indexId":"70217200","displayToPublicDate":"2021-01-07T07:11:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Modeling hydrologic processes associated with soil saturation and debris flow initiation during the September 2013 storm, Colorado Front Range","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Seven days of extreme rainfall during September 2013 produced more than 1100 debris flows in the Colorado Front Range, about 78% of which occurred on south-facing slopes (SFS). Previously published soil moisture (volumetric water content) observations suggest that SFS were wetter than north-facing slopes (NFS) during the event, which contrasts with soil moisture patterns observed during normal conditions. Various causes have been hypothesized for the preferential saturation of SFS, but those hypotheses remain largely untested. Here, we analyze the soil moisture patterns using additional soil moisture observations, determine the hydrologic processes controlling the preferential saturation of SFS, and evaluate the importance of soil moisture in predicting the debris flow initiation sites. Soil moisture patterns are simulated using the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT + VS) model. Five hypotheses are tested that may have influenced the soil moisture reversal including higher rainfall rates, lower interception rates, lower saturated water content, thinner soils, and reduced deep drainage on SFS. The EMT + VS model is coupled with an infinite slope stability model to produce factor of safety maps. The hypotheses are tested by comparing the modeled soil moisture to soil moisture observations and the debris flow initiation sites. The results suggest that differences in interception and deep drainage between SFS and NFS were primarily responsible for producing wetter SFS, but the soil moisture pattern likely played a smaller role than vegetation and slope in determining where debris flows initiated. The final model predicts instability at approximately 72% of the observed debris flow initiation sites.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s10346-020-01582-5","usgsCitation":"Timilsina, S., Niemann, J.D., Rathburn, S.L., Rengers, F.K., and Nelson, P.A., 2021, Modeling hydrologic processes associated with soil saturation and debris flow initiation during the September 2013 storm, Colorado Front Range: Landslides, v. 18, p. 1741-1759, https://doi.org/10.1007/s10346-020-01582-5.","productDescription":"19 p.","startPage":"1741","endPage":"1759","ipdsId":"IP-122076","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":467260,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10217/197344","text":"External Repository"},{"id":382086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"Colorado","city":"Ft. Collins, Boulder","otherGeospatial":"Boulder River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.72143554687499,\n              39.90973623453719\n            ],\n            [\n              -104.8974609375,\n              39.90973623453719\n            ],\n            [\n              -104.8974609375,\n              40.66397287638688\n            ],\n            [\n              -105.72143554687499,\n              40.66397287638688\n            ],\n            [\n              -105.72143554687499,\n              39.90973623453719\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2021-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Timilsina, Sujana","contributorId":247584,"corporation":false,"usgs":false,"family":"Timilsina","given":"Sujana","email":"","affiliations":[{"id":49584,"text":"Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, USA","active":true,"usgs":false}],"preferred":false,"id":807962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niemann, Jeffrey D. 0000-0002-2988-0879","orcid":"https://orcid.org/0000-0002-2988-0879","contributorId":247585,"corporation":false,"usgs":false,"family":"Niemann","given":"Jeffrey","email":"","middleInitial":"D.","affiliations":[{"id":49584,"text":"Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, USA","active":true,"usgs":false}],"preferred":false,"id":807963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rathburn, Sara L.","contributorId":140606,"corporation":false,"usgs":false,"family":"Rathburn","given":"Sara","email":"","middleInitial":"L.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":807964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":807965,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, Peter A.","contributorId":195598,"corporation":false,"usgs":false,"family":"Nelson","given":"Peter","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":807966,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70229118,"text":"70229118 - 2021 - Metamorphosis in an era of increasing climate variability","interactions":[],"lastModifiedDate":"2022-03-01T17:59:37.990363","indexId":"70229118","displayToPublicDate":"2021-01-04T11:47:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5980,"text":"Trends in Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Metamorphosis in an era of increasing climate variability","docAbstract":"Most animals have complex life cycles including metamorphosis or other discrete life stage transitions during which individuals may be particularly vulnerable to environmental stressors. With climate change, individuals will be exposed to increasing thermal and hydrologic variability during metamorphosis, which may affect survival and performance through physiological, behavioral, and ecological mechanisms. Furthermore, because metamorphosis entails changes in traits and vital rates, it is likely to play an important role in how populations respond to increasing climate variability. To identify mechanisms underlying population responses and associated trait and life history evolution, we need new approaches to estimating changes in individual traits and performance throughout metamorphosis, and we need to integrate metamorphosis as an explicit life stage in analytical models.","language":"English","publisher":"CellPress","doi":"10.1016/j.tree.2020.11.012","usgsCitation":"Lowe, W., Martin, T.E., Skelly, D.K., and Woods, H.A., 2021, Metamorphosis in an era of increasing climate variability: Trends in Ecology & Evolution, v. 36, no. 4, p. 360-375, https://doi.org/10.1016/j.tree.2020.11.012.","productDescription":"16 p.","startPage":"360","endPage":"375","ipdsId":"IP-122666","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487883,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tree.2020.11.012","text":"Publisher Index Page"},{"id":396608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lowe, Winsor H.","contributorId":64532,"corporation":false,"usgs":false,"family":"Lowe","given":"Winsor H.","affiliations":[{"id":5097,"text":"University of Montana, Division of Biological Sciences","active":true,"usgs":false}],"preferred":false,"id":836562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Thomas E. 0000-0002-4028-4867 tmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-4028-4867","contributorId":1208,"corporation":false,"usgs":true,"family":"Martin","given":"Thomas","email":"tmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skelly, David K.","contributorId":181900,"corporation":false,"usgs":false,"family":"Skelly","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":836563,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woods, H. Arthur","contributorId":287211,"corporation":false,"usgs":false,"family":"Woods","given":"H.","email":"","middleInitial":"Arthur","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":836564,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70254305,"text":"70254305 - 2021 - Regional crop water use assessment using Landsat-derived evapotranspiration","interactions":[],"lastModifiedDate":"2024-05-17T14:43:46.916845","indexId":"70254305","displayToPublicDate":"2021-01-01T09:40:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7176,"text":"Hydrologic Processes","active":true,"publicationSubtype":{"id":10}},"title":"Regional crop water use assessment using Landsat-derived evapotranspiration","docAbstract":"<p><span>Reliable information on water use and availability at basin and field scales are important to ensure the optimized constructive uses of available water resources. This study was conducted with the specific objective to estimate Landsat-based actual evapotranspiration (ETa) using the Operational Simplified Surface Energy Balance (SSEBop) model across the state of South Dakota (SD), USA for the 1986–2018 (33-year) period. Validated ETa estimations (r</span><sup>2</sup><span>&nbsp;= 0.91, PBIAS = −4%, and %RMSE = 11.8%) were further used to understand the crop water-use characteristics and existing historic mono-directional (increasing/decreasing) trends over the eastern (ESD) and western (WSD) regions of SD. The crop water-use characteristics indicated that the annual cropland water uses across the ESD and WSD were more or less met by the precipitation amounts in the area. The ample water supply and distribution have led to high rainfed and low percentage of irrigated cropland (~2.5%) in the state. The WSD faced greater crop-water use reductions than the ESD during drought periods. The landscape ETa responses across the state were found to be more sensitive than precipitation for the drought impact assessments. The Mann Kendall trend analysis revealed the absence of a significant trend (</span><i>p</i><span> &gt; 0.05) in annual ETa at a regional scale due to the varying weather conditions in the state. However, about 12% and 9% cropland areas in the ESD and WSD, respectively, revealed a significant mono-directional trend at pixel scale ETa. Most of the pixels under significant trend showed an increasing trend that can be explained by the shift in agricultural practices, increased irrigated cropland area, higher productions, moisture regime shifts, and decreased risk of farming in the dry areas. The decreasing trend pixels were clustered in mid-eastern SD and could be the result of dynamic conversion of wetlands to croplands and decreased irrigation practices in the region. This study also demonstrates the tremendous potential and robustness of the SSEBop model, Landsat imagery, and remote sensing-based ETa modelling approaches in estimating consistent spatially distributed evapotranspiration.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14015","usgsCitation":"Bawa, A., Senay, G.B., and Kumar, S., 2021, Regional crop water use assessment using Landsat-derived evapotranspiration: Hydrologic Processes, v. 35, no. 1, e14015, 13 p., https://doi.org/10.1002/hyp.14015.","productDescription":"e14015, 13 p.","ipdsId":"IP-124142","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":428803,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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Dakota\",\"nation\":\"USA  \"}}]}","volume":"35","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Bawa, Arun 0000-0003-1226-0320","orcid":"https://orcid.org/0000-0003-1226-0320","contributorId":336731,"corporation":false,"usgs":false,"family":"Bawa","given":"Arun","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":900997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":900947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kumar, Sandeep 0000-0002-2717-5455","orcid":"https://orcid.org/0000-0002-2717-5455","contributorId":336732,"corporation":false,"usgs":false,"family":"Kumar","given":"Sandeep","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":900998,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70217087,"text":"70217087 - 2021 - Rapid sensitivity analysis for reducing uncertainty in landslide hazard assessments","interactions":[],"lastModifiedDate":"2021-01-05T13:47:14.328525","indexId":"70217087","displayToPublicDate":"2020-12-23T07:45:34","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Rapid sensitivity analysis for reducing uncertainty in landslide hazard assessments","docAbstract":"<p id=\"Par1\" class=\"Para\">One of the challenges in assessing temporal and spatial aspects of landslide hazard using process-based models is estimating model input parameters, especially in areas where limited measurements of soil and rock properties are available. In an effort to simplify and streamline parameter estimation, development of a simple, rapid approach to sensitivity analysis relies on field measurements of landslide characteristics, especially slope and depth. This method is demonstrated for a case study in Puerto Rico where widespread destruction resulted from tens of thousands of debris flows induced by Hurricanes Irma and María in Puerto Rico in 2017. The approach can be applied to estimation of shear strength as well as hydrologic parameters that control infiltration and flow of water in the subsurface and ultimately the timing of landslides resulting from heavy rainfall. Results narrow the possible range of cohesion and friction parameters as well as hydraulic conductivity and other soil water parameters by counting the fraction of field observations that can be explained by each combination of parameters. For cases studied in Puerto Rico, the method identified combinations of cohesion and friction values that explain more than 80–90% of observed landslide source areas.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"WLF 2020: Understanding and reducing landslide disaster risk","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-60227-7_37","usgsCitation":"Baum, R.L., 2021, Rapid sensitivity analysis for reducing uncertainty in landslide hazard assessments, <i>in</i> WLF 2020: Understanding and reducing landslide disaster risk, p. 329-335, https://doi.org/10.1007/978-3-030-60227-7_37.","productDescription":"7 p.","startPage":"329","endPage":"335","ipdsId":"IP-117901","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":381876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70217002,"text":"70217002 - 2021 - Assessing the hydrologic and physical conditions of a drainage basin","interactions":[],"lastModifiedDate":"2020-12-23T13:36:54.425907","indexId":"70217002","displayToPublicDate":"2020-12-22T07:35:53","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"Assessing the hydrologic and physical conditions of a drainage basin","docAbstract":"<p>An assessment of a drainage basin and its stream corridor will provide the data and information needed to understand current biophysical conditions and trends. Developing an understanding of the drivers of change is the next essential step for restoration success (Osterkamp and Toy, 1997; Corenbilt et al., 2007; Briggs and Osterkamp, 2003), Shields et al. 2003; Osterkamp et al., 2011). Establishing such a robust scientific foundation will allow stream practitioners to develop realistic restoration objectives and the tactics that will be effective to achieve them. Accomplishing this requires collecting data at watershed and reach scales and by drawing on scientific data from areas where conditions may be similar to, or applicable to, your site. Although decisions should be backed by conclusive data, to make progress we need to rely on the best available information, even if scientific uncertainty remains. Additional information will become available, so it is necessary to plan a project in a manner that incorporates available knowledge and permits goals, objectives, and tactics to be adjusted accordingly.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Renewing our rivers—Stream corridor restoration in dryland regions","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of Arizona Press","usgsCitation":"Osterkamp, W., Briggs, M.K., Dean, D.J., and Rodriquez, A., 2021, Assessing the hydrologic and physical conditions of a drainage basin, chap. 3 <i>of</i> Renewing our rivers—Stream corridor restoration in dryland regions, p. 43-103.","productDescription":"61 p.","startPage":"43","endPage":"103","ipdsId":"IP-060923","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381603,"type":{"id":15,"text":"Index Page"},"url":"https://uapress.arizona.edu/book/renewing-our-rivers"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Osterkamp, Waite","contributorId":245868,"corporation":false,"usgs":false,"family":"Osterkamp","given":"Waite","affiliations":[{"id":49352,"text":"(Emeritus) National Research Program, Western Branch; University of Arizona Press, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":807215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Mark K.","contributorId":177076,"corporation":false,"usgs":false,"family":"Briggs","given":"Mark","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":807216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":131047,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":807217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodriquez, Alfredo","contributorId":245869,"corporation":false,"usgs":false,"family":"Rodriquez","given":"Alfredo","email":"","affiliations":[{"id":37767,"text":"World Wildlife Fund","active":true,"usgs":false}],"preferred":false,"id":807218,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70218024,"text":"70218024 - 2021 - Snowpack signals in North American tree rings","interactions":[],"lastModifiedDate":"2021-03-05T21:09:50.525601","indexId":"70218024","displayToPublicDate":"2020-12-22T07:22:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Snowpack signals in North American tree rings","docAbstract":"<p><span>Climate change has contributed to recent declines in mountain snowpack and earlier runoff, which in turn has intensified hydrological droughts in western North America. Climate model projections suggest that continued and severe snowpack reductions are expected over the 21st century, with profound consequences for ecosystems and human welfare. Yet the current understanding of trends and variability in mountain snowpack is limited by the relatively short and strongly temperature forced observational record. Motivated by the urgent need to better understand snowpack dynamics in a long-term, spatially coherent framework, here we examine snow-growth relationships in western North American tree-ring chronologies. We present an extensive network of snow-sensitive proxy data to support high space/time resolution paleosnow reconstruction, quantify and interpret the type and spatial density of snow related signals in tree-ring records, and examine the potential for regional bias in the tree-ring based reconstruction of different snow drought types (dry versus warm). Our results indicate three distinct snow-growth relationships in tree-ring chronologies: moisture-limited snow proxies that include a spring temperature signal, moisture-limited snow proxies lacking a spring temperature signal, and energy-limited snow proxies. Each proxy type is based on distinct physiological tree-growth mechanisms related to topographic and climatic site conditions, and provides unique information on mountain snowpack dynamics that can be capitalized upon within a statistical reconstruction framework. This work provides a platform and foundational background required for the accelerated production of high-quality annually-resolved snowpack reconstructions from regional to high (&lt;12 km) spatial scales in western North America, and by extension, will support an improved understanding of the vulnerability of snowmelt-derived water resources to natural variability and future climate warming.</span></p>","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/abd5de","usgsCitation":"Coulthard, B.L., Anchukaitis, K.J., Pederson, G.T., Cook, E.R., Littell, J., and Smith, D.J., 2021, Snowpack signals in North American tree rings: Environmental Research Letters, v. 16, no. 3, 034037, 13 p., https://doi.org/10.1088/1748-9326/abd5de.","productDescription":"034037, 13 p.","ipdsId":"IP-122302","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":454037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abd5de","text":"Publisher Index Page"},{"id":383252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Coulthard, Bethany L.","contributorId":250711,"corporation":false,"usgs":false,"family":"Coulthard","given":"Bethany","email":"","middleInitial":"L.","affiliations":[{"id":37455,"text":"University of Nevada","active":true,"usgs":false}],"preferred":false,"id":810246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anchukaitis, Kevin J.","contributorId":195005,"corporation":false,"usgs":false,"family":"Anchukaitis","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":810247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, Edward R","contributorId":218752,"corporation":false,"usgs":false,"family":"Cook","given":"Edward","email":"","middleInitial":"R","affiliations":[{"id":17701,"text":"Lamont-Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":810249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":810250,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Dan J.","contributorId":250712,"corporation":false,"usgs":false,"family":"Smith","given":"Dan","email":"","middleInitial":"J.","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":810251,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70217066,"text":"70217066 - 2021 - Development of genetic baseline information to support the conservation and management of wild Brook Trout in North Carolina","interactions":[],"lastModifiedDate":"2021-06-30T17:41:53.333707","indexId":"70217066","displayToPublicDate":"2020-12-20T06:35:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Development of genetic baseline information to support the conservation and management of wild Brook Trout in North Carolina","docAbstract":"<p><span>Following centuries of declines, there is growing interest in conserving extant wild populations and reintroducing Brook Trout (</span><i>Salvelinus fontinalis</i><span>) populations of native ancestry. A population genetic baseline can enhance conservation outcomes and promote restoration success. Consequently, it is important to document existing patterns of genetic variation across the landscape and translate these data into an approachable format for fisheries managers. We genotyped 9,507 Brook Trout representing 467 wild collections at 12 microsatellite loci to establish a genetic baseline for North Carolina, USA. Rarefied allelic richness and observed heterozygosity, which reflect within‐population diversity, were low to moderate relative to levels typically observed at higher latitudes (means = 3.12 and 0.42, respectively). Effective population sizes varied widely, but were often very low (151 collections with an estimated&nbsp;</span><i>N</i><sub>e</sub><span>&nbsp;&lt; 10). Despite decades of intensive stocking across the state, we found little to no evidence of hatchery introgression in most populations. Although genetic variation was significant at a variety of spatial scales (mean pairwise&nbsp;</span><i>F</i><span>’</span><sub>ST</sub><span>&nbsp;= 0.73), substantial genetic variation occurred between patches within individual watersheds. Analysis of molecular variance (AMOVA) found that a substantial portion (28.5%) of the observed genetic variation was attributed to differences among populations, with additional genetic variation among hydrological units (HUCs; 16.0%, 16.6%, 12.1%, and 9.4% of the overall variation among twelve‐, ten‐, eight‐, and six‐digit HUCs, respectively). We discuss a suite of potential applications for this type of genetic data to enhance management outcomes, such as conservation prioritization and selection of source stocks for reintroductions or genetic rescue.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10569","usgsCitation":"Kazyak, D., Lubinski, B.A., Rash, J.M., Johnson, T.C., and King, T.L., 2021, Development of genetic baseline information to support the conservation and management of wild Brook Trout in North Carolina: North American Journal of Fisheries Management, v. 41, no. 3, p. 626-638, https://doi.org/10.1002/nafm.10569.","productDescription":"13 p.","startPage":"626","endPage":"638","ipdsId":"IP-101589","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":381793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"North Carolina","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.860595703125,\n              36.53612263184686\n            ],\n            [\n              -81.7822265625,\n              36.63316209558658\n            ],\n            [\n              -83.84765625,\n              35.460669951495305\n            ],\n            [\n              -84.375,\n              35.02999636902566\n            ],\n            [\n              -81.03515625,\n              35.28150065789119\n            ],\n            [\n              -80.7275390625,\n              34.813803317113155\n            ],\n            [\n              -79.4970703125,\n              34.813803317113155\n            ],\n            [\n              -78.44238281249999,\n              33.797408767572485\n            ],\n            [\n              -76.2451171875,\n              34.994003757575776\n            ],\n            [\n              -75.7177734375,\n              35.817813158696616\n            ],\n            [\n              -75.860595703125,\n              36.53612263184686\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":807465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":807466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rash, Jacob M","contributorId":218128,"corporation":false,"usgs":false,"family":"Rash","given":"Jacob","email":"","middleInitial":"M","affiliations":[{"id":39760,"text":"Division of Inland Fisheries, North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":807467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Thomas C","contributorId":245999,"corporation":false,"usgs":false,"family":"Johnson","given":"Thomas","email":"","middleInitial":"C","affiliations":[{"id":36454,"text":"North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":807468,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, Timothy L.","contributorId":199023,"corporation":false,"usgs":false,"family":"King","given":"Timothy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":807469,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70217175,"text":"70217175 - 2021 - Identification of seasonal streamflow regimes and streamflow drivers for daily and peak flows in Alaska","interactions":[],"lastModifiedDate":"2021-02-17T22:12:21.749487","indexId":"70217175","displayToPublicDate":"2020-12-17T08:18:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Identification of seasonal streamflow regimes and streamflow drivers for daily and peak flows in Alaska","docAbstract":"<p>Alaska is among northern high‐latitude regions where accelerated climate change is expected to impact streamflow properties, including seasonality and primary flow drivers. Evaluating changes to streamflow, including flood characteristics, across this large and diverse environment can be improved by identifying the distribution and influence of flow drivers. Using metrics of mean monthly streamflow data from 253 streamgages, seasonal flow regimes were clustered to guide identification of seasonal‐flow drivers and form hydrologic groups for identification of peak‐flow populations. Nine seasonally distinct subclasses described variability within three classes dominated by (mostly fall) rainfall, (spring) snowmelt, and (summer) high‐elevation melt. The most glacierized basins exclusively grouped into high‐elevation melt subclasses, and less glacierized basins sometimes exhibited seasonal patterns aligned with rainfall‐ and snowmelt‐dominated regimes. Peak‐flow populations varied by subclass from dominant rainfall or dominant snowmelt to mixed rainfall‐snowmelt or mixed rainfall, snowmelt, and high‐elevation melt. Within subclasses, rainfall generated higher mean peak flows (relative to mean annual flow) than snowmelt or high‐elevation melt. Seasonal flow regimes showed clear but complex associations with basin characteristics, primarily elevation and winter temperature, and with geographic location. These dependencies provided elevation‐based analogies for changes associated with warming and insights for seasonal flow regime prediction and hydrologic region delineation. These results provide a spatially comprehensive perspective on seasonal streamflow drivers across Alaska from historical data and serve as an important historical basis for analysis.</p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028425","usgsCitation":"Curran, J.H., and Biles, F.E., 2021, Identification of seasonal streamflow regimes and streamflow drivers for daily and peak flows in Alaska: Water Resources Research, v. 57, no. 2, ee2020WR028425, https://doi.org/10.1029/2020WR028425.","productDescription":"ee2020WR028425","ipdsId":"IP-119457","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":436611,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13TMJUP","text":"USGS data release","linkHelpText":"Selected Basin Boundaries for USGS Streamgages in Alaska through 2019"},{"id":436610,"rank":0,"type":{"id":30,"text":"Data 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,{"id":70223853,"text":"70223853 - 2021 - Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States","interactions":[],"lastModifiedDate":"2021-09-10T13:45:18.247157","indexId":"70223853","displayToPublicDate":"2020-12-14T08:35:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States","docAbstract":"<p><span>Over half of global rivers and streams lack perennial flow, and understanding the distribution and drivers of their flow regimes is critical for understanding their hydrologic, biogeochemical, and ecological functions. We analyzed nonperennial flow regimes using 540 U.S. Geological Survey watersheds across the contiguous United States from 1979 to 2018. Multivariate analyses revealed regional differences in no-flow fraction, date of first no flow, and duration of the dry-down period, with further divergence between natural and human-altered watersheds. Aridity was a primary driver of no-flow metrics at the continental scale, while unique combinations of climatic, physiographic and anthropogenic drivers emerged at regional scales. Dry-down duration showed stronger associations with nonclimate drivers compared to no-flow fraction and timing. Although the sparse distribution of nonperennial gages limits our understanding of such streams, the watersheds examined here suggest the important role of aridity and land cover change in modulating future stream drying.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL090794","usgsCitation":"Hammond, J., Zimmer, M., Shanafield, M., Kaiser, K.E., Godsey, S., Mims, M.C., Zipper, S., Burrow, R., Kampf, S.K., Dodds, W., Jones, C., Krabbenhoft, C., Boersma, K., Datry, T., Olden, J., Allen, G., Price, A.N., Costigan, K., Hale, R., Ward, A.S., and Allen, D., 2021, Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States: Geophysical Research Letters, v. 48, no. 2, e2020GL090794, 11 p., https://doi.org/10.1029/2020GL090794.","productDescription":"e2020GL090794, 11 p.","ipdsId":"IP-119781","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":454097,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020gl090794","text":"External Repository"},{"id":436624,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D8VDHI","text":"USGS data release","linkHelpText":"Mean annual no flow, climate and watershed properties for 540 non-perennial USGS gages in the contiguous U.S."},{"id":436623,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D8VDHI","text":"USGS data release","linkHelpText":"Mean annual no flow, climate and watershed properties for 540 non-perennial USGS gages in the contiguous U.S."},{"id":389054,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"contiguous United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      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     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  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              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":"48","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmer, Margaret 0000-0001-8287-1923","orcid":"https://orcid.org/0000-0001-8287-1923","contributorId":225158,"corporation":false,"usgs":false,"family":"Zimmer","given":"Margaret","affiliations":[{"id":41054,"text":"Earth and Planetary Sciences, University of California, Santa Cruz, CA, 95064, USA","active":true,"usgs":false}],"preferred":false,"id":822943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shanafield, Margaret","contributorId":106772,"corporation":false,"usgs":true,"family":"Shanafield","given":"Margaret","affiliations":[],"preferred":false,"id":822944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaiser, Kendra E. 0000-0003-1773-6236","orcid":"https://orcid.org/0000-0003-1773-6236","contributorId":211475,"corporation":false,"usgs":false,"family":"Kaiser","given":"Kendra","email":"","middleInitial":"E.","affiliations":[{"id":38255,"text":"Boise State Unviersity","active":true,"usgs":false}],"preferred":false,"id":822945,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Godsey, Sarah E","contributorId":223120,"corporation":false,"usgs":false,"family":"Godsey","given":"Sarah E","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":822946,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mims, Meryl C.","contributorId":29253,"corporation":false,"usgs":true,"family":"Mims","given":"Meryl","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":822947,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zipper, Samuel 0000-0002-8735-5757","orcid":"https://orcid.org/0000-0002-8735-5757","contributorId":225160,"corporation":false,"usgs":false,"family":"Zipper","given":"Samuel","email":"","affiliations":[{"id":41056,"text":"Kansas Geological Survey, University of Kansas, Lawrence KS 66047, USA","active":true,"usgs":false}],"preferred":false,"id":822948,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Burrow, Ryan 0000-0002-3296-1864","orcid":"https://orcid.org/0000-0002-3296-1864","contributorId":225162,"corporation":false,"usgs":false,"family":"Burrow","given":"Ryan","email":"","affiliations":[{"id":41058,"text":"Australian Rivers Institute, Griffith University, Brisbane, Queensland, Australia 4111","active":true,"usgs":false}],"preferred":false,"id":822949,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kampf, Stephanie K. 0000-0001-8991-2679","orcid":"https://orcid.org/0000-0001-8991-2679","contributorId":225146,"corporation":false,"usgs":false,"family":"Kampf","given":"Stephanie","email":"","middleInitial":"K.","affiliations":[{"id":41048,"text":"Associate Professor, Department of Ecosystem Science and Sustainability, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":822950,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dodds, Walter 0000-0002-6666-8930","orcid":"https://orcid.org/0000-0002-6666-8930","contributorId":225164,"corporation":false,"usgs":false,"family":"Dodds","given":"Walter","email":"","affiliations":[{"id":41060,"text":"Division of Biology, Kansas State University, Manhattan, KS 66502","active":true,"usgs":false}],"preferred":false,"id":822951,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jones, C. Nathan","contributorId":243549,"corporation":false,"usgs":false,"family":"Jones","given":"C. Nathan","affiliations":[{"id":48727,"text":"The National Socio-Environmental Synthesis Center, University of Maryland, Annapolis, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":822952,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Krabbenhoft, Corey 0000-0002-2630-8287","orcid":"https://orcid.org/0000-0002-2630-8287","contributorId":225163,"corporation":false,"usgs":false,"family":"Krabbenhoft","given":"Corey","email":"","affiliations":[{"id":41059,"text":"College of Arts and Sciences and Research and Education in Energy, Environment and Water (RENEW) Institute, University at Buffalo, Buffalo, NY 14228","active":true,"usgs":false}],"preferred":false,"id":822953,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Boersma, Kate 0000-0002-0707-3283","orcid":"https://orcid.org/0000-0002-0707-3283","contributorId":225167,"corporation":false,"usgs":false,"family":"Boersma","given":"Kate","email":"","affiliations":[{"id":41063,"text":"Department of Biology, University of San Diego, San Diego, CA 92105, USA","active":true,"usgs":false}],"preferred":false,"id":822954,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Datry, Thibault 0000-0003-1390-6736","orcid":"https://orcid.org/0000-0003-1390-6736","contributorId":225166,"corporation":false,"usgs":false,"family":"Datry","given":"Thibault","email":"","affiliations":[{"id":41062,"text":"Centre de Lyon-Villeurbanne, 69626 Villeurbanne CEDEX, France","active":true,"usgs":false}],"preferred":false,"id":822955,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Olden, Julian D.","contributorId":202893,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":822956,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Allen, George H.","contributorId":257248,"corporation":false,"usgs":false,"family":"Allen","given":"George H.","affiliations":[{"id":51991,"text":"Department of Geography, Texas A&M University, College Station, TX, USA","active":true,"usgs":false}],"preferred":false,"id":822957,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Price, Adam N.","contributorId":265572,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":822958,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Costigan, Katie H.","contributorId":166700,"corporation":false,"usgs":false,"family":"Costigan","given":"Katie H.","affiliations":[],"preferred":false,"id":822959,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Hale, Rebecca 0000-0002-3552-3691","orcid":"https://orcid.org/0000-0002-3552-3691","contributorId":195753,"corporation":false,"usgs":false,"family":"Hale","given":"Rebecca","email":"","affiliations":[{"id":12865,"text":"Smithsonian Institute","active":true,"usgs":false}],"preferred":false,"id":822960,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Ward, Adam S","contributorId":191363,"corporation":false,"usgs":false,"family":"Ward","given":"Adam","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":822961,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Allen, Daniel C. 0000-0002-0451-0564","orcid":"https://orcid.org/0000-0002-0451-0564","contributorId":225169,"corporation":false,"usgs":false,"family":"Allen","given":"Daniel","middleInitial":"C.","affiliations":[{"id":41064,"text":"Department of Biology, University of Oklahoma, Norman OK, 73019","active":true,"usgs":false}],"preferred":false,"id":822962,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}}
,{"id":70218730,"text":"70218730 - 2021 - Seasonal periphyton response to low-level nutrient exposure in a least disturbed mountain stream, the Buffalo River, Arkansas","interactions":[],"lastModifiedDate":"2021-03-10T13:14:06.685619","indexId":"70218730","displayToPublicDate":"2020-12-11T07:06:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal periphyton response to low-level nutrient exposure in a least disturbed mountain stream, the Buffalo River, Arkansas","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab010\" class=\"abstract author\" lang=\"en\"><div id=\"as010\"><p id=\"sp0010\">Like most streams located in the Ozark Plateaus, the Buffalo River in Arkansas generally has excellent water quality. Water-quality conditions in Big Creek, however, a major tributary of the middle Buffalo River, have been less favorable than that of other Buffalo River tributaries. Concerns regarding the influence of water quality in Big Creek on the Buffalo River magnified in 2013 when a large confined animal feeding operation (CAFO) began operating in the watershed. In response to these concerns, the U.S. Geological Survey compared monthly nutrient concentrations and seasonal periphyton assemblage metrics of a site on Big Creek downstream of the CAFO, two Buffalo River control sites upstream of the confluence with Big Creek, and three Buffalo River test sites downstream of the confluence with Big Creek. In addition to identifying potential nutrient patterns and periphyton responses along a low-level nutrient exposure gradient, the study determined how nutrient contributions from Big Creek (and the CAFO) are affecting ecological conditions and consequent ecosystem services in the Buffalo River. Nutrient and periphyton data exhibited more temporal than spatial variability. Nutrient concentrations were generally highest of all sites at the Big Creek site. Concentrations at the five sites on the Buffalo River were typically low (near laboratory reporting limits), and concentrations at the three test sites rarely exceeded those of the two control sites. An index developed with three ecologically relevant periphyton metrics (oligotrophic taxa and<span>&nbsp;</span><i>Homoeothrix</i><span>&nbsp;</span>percent relative abundance and mesotrophic diatoms percent taxa richness) suggested that nutrient uptake at sites downstream of the Big Creek-Buffalo River confluence resulted in subtle shifts in downstream periphyton assemblages. The periphyton index of biological integrity at control sites was slightly and generally more favorable compared to test sites. Even so, when periphyton data were considered in conjunction with both hydrology and water-quality data, the negative consequences of antecedent high flows and associated scouring exceeded the potential positive effects that low-level nutrients had on algal productivity. These findings emphasize the importance of comparing biological and chemical data across extended temporal scales, particularly when working with low-level nutrient gradients.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2020.107150","usgsCitation":"Justus, B., Driver, L., and Burge, D.R., 2021, Seasonal periphyton response to low-level nutrient exposure in a least disturbed mountain stream, the Buffalo River, Arkansas: Ecological Indicators, v. 121, 107150, 17 p., https://doi.org/10.1016/j.ecolind.2020.107150.","productDescription":"107150, 17 p.","ipdsId":"IP-115738","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":454114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2020.107150","text":"Publisher Index Page"},{"id":384261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Buffalo River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -94.00451660156249,\n              35.68407153314097\n            ],\n            [\n              -91.9281005859375,\n              35.68407153314097\n            ],\n            [\n              -91.9281005859375,\n              36.465471886798134\n            ],\n            [\n              -94.00451660156249,\n              36.465471886798134\n            ],\n            [\n              -94.00451660156249,\n              35.68407153314097\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"121","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Justus, Billy 0000-0002-3458-9656 bjustus@usgs.gov","orcid":"https://orcid.org/0000-0002-3458-9656","contributorId":202148,"corporation":false,"usgs":true,"family":"Justus","given":"Billy","email":"bjustus@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811554,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driver, Lucas 0000-0003-2549-1849","orcid":"https://orcid.org/0000-0003-2549-1849","contributorId":219176,"corporation":false,"usgs":true,"family":"Driver","given":"Lucas","email":"","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burge, David R. L.","contributorId":254961,"corporation":false,"usgs":false,"family":"Burge","given":"David","email":"","middleInitial":"R. L.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":811556,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70217587,"text":"70217587 - 2021 - Machine learning predictions of pH in the Glacial Aquifer System, Northern USA","interactions":[],"lastModifiedDate":"2021-05-13T15:55:05.573687","indexId":"70217587","displayToPublicDate":"2020-12-11T06:59:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning predictions of pH in the Glacial Aquifer System, Northern USA","docAbstract":"<p><span>A boosted regression tree model was developed to predict pH conditions in three dimensions throughout the glacial aquifer system of the contiguous United States using pH measurements in samples from 18,386 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and, when coupled with long flowpaths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate‐poor, pH conditions remain acidic. At depths typical of drinking‐water supplies, predicted pH &gt;7.5—which is associated with arsenic mobilization—occurs more frequently than predicted pH &lt;6—which is associated with water corrosivity and the mobilization of other trace elements. A novel aspect of this model was the inclusion of numerically based estimates of groundwater flow characteristics (age and flowpath length) as predictor variables. The sensitivity of pH predictions to these variables was consistent with hydrologic understanding of groundwater flow systems and the geochemical evolution of groundwater quality. The model was not developed to provide precise estimates of pH at any given location. Rather, it can be used to more generally identify areas where contaminants may be mobilized into groundwater and where corrosivity issues may be of concern to prioritize areas for future groundwater monitoring.</span></p>","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.13063","usgsCitation":"Stackelberg, P.E., Belitz, K., Brown, C., Erickson, M., Elliott, S.M., Kauffman, L.J., Ransom, K.M., and Reddy, J., 2021, Machine learning predictions of pH in the Glacial Aquifer System, Northern USA: Groundwater, v. 37, no. 4, p. 531-543, https://doi.org/10.1111/gwat.13063.","productDescription":"13 p.","startPage":"531","endPage":"543","ipdsId":"IP-122702","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":454116,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gwat.13063","text":"External Repository"},{"id":436626,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RF0R6E","text":"USGS data release","linkHelpText":"Data for machine learning predictions of pH in the glacial aquifer system, northern USA"},{"id":382483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":808740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808744,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kauffman, Leon J. 0000-0003-4564-0362","orcid":"https://orcid.org/0000-0003-4564-0362","contributorId":206428,"corporation":false,"usgs":true,"family":"Kauffman","given":"Leon","email":"","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808745,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808746,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reddy, James E. 0000-0002-6998-7267","orcid":"https://orcid.org/0000-0002-6998-7267","contributorId":206426,"corporation":false,"usgs":true,"family":"Reddy","given":"James E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808747,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70216892,"text":"70216892 - 2021 - The impact of ventilation patterns on calcite dissolution rates within karst conduits","interactions":[],"lastModifiedDate":"2020-12-30T14:53:23.957547","indexId":"70216892","displayToPublicDate":"2020-12-10T08:47:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"The impact of ventilation patterns on calcite dissolution rates within karst conduits","docAbstract":"<p><span>Erosion rates in streams vary dramatically over time, as differences in streamflow and sediment load enhance or inhibit erosion processes. Within cave streams, and other bedrock channels incising soluble rocks, changes in water chemistry are an important factor in determining how erosion rates will vary in both time and space. Prior studies in surface streams, springs, and caves suggest that variation in dissolved </span><span class=\"math\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=\"><span class=\"MJX_Assistive_MathML\">CO<sub>2</sub></span></span></span><span> is the strongest control on variation in calcite dissolution rates. However, the controls on&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=\"><span class=\"MJX_Assistive_MathML\">CO<sub>2</sub></span></span></span><span> variation remain poorly quantified. Limited data suggest that ventilation of karst systems can substantially influence dissolved&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-3-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=\"><span class=\"MJX_Assistive_MathML\">CO<sub>2</sub></span></span></span><span> within karst conduits. However, the interactions among cave ventilation, air-water&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-4-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=\"><span class=\"MJX_Assistive_MathML\">CO<sub>2</sub></span></span></span><span> exchange, and dissolution dynamics have not been studied in detail. In this study, three years of time series measurements of dissolved and gaseous&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-5-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=\"><span class=\"MJX_Assistive_MathML\">CO<sub>2</sub></span></span></span><span>, cave airflow velocity, and specific conductance from Blowing Springs Cave, Arkansas, were analyzed and used to estimate continuous calcite dissolution rates and quantify the correlations between those rates and potential physical and chemical drivers. We find that chimney effect airflow creates temperature-driven switches in airflow direction, and that the resulting seasonal changes in airflow regulate both gaseous and dissolved </span><span class=\"math\"><span id=\"MathJax-Element-6-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=\"><span class=\"MJX_Assistive_MathML\">CO<sub>2</sub></span></span></span><span> within the cave. As in previous studies, partial pressure of&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-7-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=\"><span class=\"MJX_Assistive_MathML\">CO<sub>2</sub></span></span></span><span>&nbsp;(</span><span class=\"math\"><span id=\"MathJax-Element-8-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=\">p<span class=\"MJX_Assistive_MathML\">CO<sub>2</sub></span></span></span><span>) is the strongest chemical control of dissolution rate variability. However, we also show that cave airflow direction, rather than streamflow, is the strongest physical driver of changes in dissolution rate, contrary to the typical situation in surface channel erosion where floods largely determine the timing and extent of geomorphic work. At the study site, chemical erosion is typically active in the summer, during periods of cave downdraft (airflow from upper to lower entrances), and inactive in the winter, during updraft (airflow from lower to upper entrances). Storms provide only minor perturbations to this overall pattern. We also find that airflow direction modulates dissolution rate variation during storms, with higher storm variability during updraft than during downdraft. Finally, we compare our results with the limited set of other studies that have examined dissolution rate variation within cave streams and draw an initial hypothesis that evolution of cave ventilation patterns strongly impacts how dissolution rate dynamics evolve over the lifetime of karst conduits.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.125824","usgsCitation":"Covington, M.D., Knierim, K.J., Young, H.H., Rodriguez, J., and Gnoza, H., 2021, The impact of ventilation patterns on calcite dissolution rates within karst conduits: Journal of Hydrology, v. 593, 125824, 17 p., https://doi.org/10.1016/j.jhydrol.2020.125824.","productDescription":"125824, 17 p.","ipdsId":"IP-118284","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":381252,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri","city":"Bella Vista","otherGeospatial":"Blowing Springs Cave","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -94.47624206542969,\n              36.380937621825886\n            ],\n            [\n              -94.13360595703125,\n              36.380937621825886\n            ],\n            [\n              -94.13360595703125,\n              36.61111838494165\n            ],\n            [\n              -94.47624206542969,\n              36.61111838494165\n            ],\n            [\n              -94.47624206542969,\n              36.380937621825886\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"593","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Covington, Matthew D.","contributorId":192015,"corporation":false,"usgs":false,"family":"Covington","given":"Matthew","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":806758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Holly H","contributorId":222433,"corporation":false,"usgs":false,"family":"Young","given":"Holly","email":"","middleInitial":"H","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":806760,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodriguez, Josue","contributorId":245654,"corporation":false,"usgs":false,"family":"Rodriguez","given":"Josue","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":806761,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gnoza, Hannah","contributorId":245655,"corporation":false,"usgs":false,"family":"Gnoza","given":"Hannah","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":806762,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70217147,"text":"70217147 - 2021 - Effective hydrological events in an evolving mid‐latitude mountain river system following cataclysmic disturbance—A saga of multiple influences","interactions":[],"lastModifiedDate":"2021-02-18T12:41:01.836757","indexId":"70217147","displayToPublicDate":"2020-12-09T07:25:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Effective hydrological events in an evolving mid‐latitude mountain river system following cataclysmic disturbance—A saga of multiple influences","docAbstract":"<p>Cataclysmic eruption of Mount St. Helens (USA) in 1980 reset 30 km of upper North Fork Toutle River (NFTR) valley to a zero‐state fluvial condition. Consequently, a new channel system evolved. Initially, a range of streamflows eroded channels (tens of meters incision, hundreds of meters widening) and transported immense sediment loads. Now, single, large‐magnitude or multiple moderate‐magnitude events are needed to accomplish substantial channel modification. Three large floods (two ≥100‐year events; one ∼10–25‐year event along lower Toutle River) from 1996 to 2015 indicate flood effectiveness in this environment is affected by both geomorphic and environmental factors. The largest and smallest of these floods (February 1996, November 2006) transported the most sediment by single floods since 1982; erosion and sediment transport by an ∼100‐year flood in December 2015 was not exceptional. Strong coupling between NFTR and its tall corridor banks, local geologic and hydraulic conditions promoting threshold erosion, event sequencing, and possibly a longitudinal gradient in stream power are important factors affecting event effectiveness on channel modification. In addition, environmental factors have also been influential, as variations in snowpack, storm trajectories and rainfall distributions, and episodic mobilization of debris flows have also influenced geomorphic response. Other factors such as vegetation anchoring, strong channel–hillside coupling, disparities between flood frequencies and perturbation relaxation times, and large variations in flood duration do not appear to be critical influences. Climate forecasts for warmer temperatures and a shift from snowfall to rainfall at high elevations may promote further acute geomorphic responses.</p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR026851","usgsCitation":"Major, J.J., Spicer, K.R., and Mosbrucker, A.R., 2021, Effective hydrological events in an evolving mid‐latitude mountain river system following cataclysmic disturbance—A saga of multiple influences: Water Resources Research, v. 57, no. 2, e2019WR026851, https://doi.org/10.1029/2019WR026851.","productDescription":"e2019WR026851","ipdsId":"IP-123125","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":381991,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Major, Jon J. 0000-0003-2449-4466 jjmajor@usgs.gov","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":439,"corporation":false,"usgs":true,"family":"Major","given":"Jon","email":"jjmajor@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":807738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spicer, Kurt R. 0000-0001-5030-3198 krspicer@usgs.gov","orcid":"https://orcid.org/0000-0001-5030-3198","contributorId":2684,"corporation":false,"usgs":true,"family":"Spicer","given":"Kurt","email":"krspicer@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":807740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mosbrucker, Adam R. 0000-0003-0298-0324 amosbrucker@usgs.gov","orcid":"https://orcid.org/0000-0003-0298-0324","contributorId":4968,"corporation":false,"usgs":true,"family":"Mosbrucker","given":"Adam","email":"amosbrucker@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":807739,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70217061,"text":"70217061 - 2021 - Temporal and spatial variations in river specific conductivity: Implications for understanding sources of river water and hydrograph separations","interactions":[],"lastModifiedDate":"2020-12-31T12:52:12.442417","indexId":"70217061","displayToPublicDate":"2020-12-09T06:48:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal and spatial variations in river specific conductivity: Implications for understanding sources of river water and hydrograph separations","docAbstract":"<p><span>Specific conductivity (SC) is commonly used to estimate the proportion of baseflow (i.e., waters from within catchments such as groundwater, interflow, or bank return flows) contributing to rivers. Reach-scale SC comparisons are also useful for identifying where multiple water stores contribute to baseflow. Daily SC values of adjacent gauges in Australian (the Barwon, Glenelg, and Campaspe Rivers) and North American (the Upper Colorado River) catchments are commonly not well correlated (R</span><sup>2</sup><span>&nbsp;=&nbsp;0.32 to 0.82). Smoothed inter-gauge SC values averaged over 7 to 45&nbsp;days are better correlated and define a series of hysteresis loops. The variable SC patterns between adjacent gauges probably reflect varying proportions of groundwater, bank return waters, interflow, and soil water contributing to baseflow. In some rivers using SC values to compare baseflow along river reaches on sub-annual timescales may be not be feasible.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.125895","usgsCitation":"Cartwright, I., and Miller, M., 2021, Temporal and spatial variations in river specific conductivity: Implications for understanding sources of river water and hydrograph separations: Journal of Hydrology, v. 593, 125895, 8 p., https://doi.org/10.1016/j.jhydrol.2020.125895.","productDescription":"125895, 8 p.","ipdsId":"IP-120526","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":381795,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"593","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cartwright, Ian 0000-0001-5300-4716","orcid":"https://orcid.org/0000-0001-5300-4716","contributorId":245985,"corporation":false,"usgs":false,"family":"Cartwright","given":"Ian","email":"","affiliations":[{"id":27278,"text":"Monash University","active":true,"usgs":false}],"preferred":false,"id":807452,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807453,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70229501,"text":"70229501 - 2021 - Eolian sediments","interactions":[],"lastModifiedDate":"2022-03-09T14:20:27.61191","indexId":"70229501","displayToPublicDate":"2020-12-02T08:17:11","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Eolian sediments","docAbstract":"<p><span>The origin and nature of eolian (wind-blown) sediments are reviewed, with an emphasis on the occurrence of these features in the Quaternary. Eolian sediments consist of windblown sand,&nbsp;loess, and long-range-transported (LRT) dust, in order of decreasing particle size. Eolian sand forms some of the most dramatic landscapes in the world, particularly when these sediments are deposited as&nbsp;dunes&nbsp;in sand seas. The largest eolian sand seas are found in subtropical deserts and in mid-latitude basins that are arid because of rainshadow effects. Dunes can be helpful in interpreting past climates, both for understanding past moisture balance and paleowinds (past wind directions). Loess is windblown silt that can be recognized in the field and mapped as a geologic body. It can be many tens of meters thick, but usually decreases systematically with distance from its source or sources. Much loess is glaciogenic, the result of glacial grinding of bedrock into “rock flour” that is easily entrained by the wind, but some loess owes its origins to nonglacial processes or is simply inherited from silt-rich rocks. The geologic record shows that both glacial loess and non-glacial loess accumulated mostly during glacial periods, suggesting that particular environmental conditions are favorable for loess accumulation. These conditions include increased source sediments, a dry, windy environment with minimal vegetation cover, and a decreased intensity of the&nbsp;hydrological cycle. The same conditions apparently enhance the production of LRT dust, which consists of particles generally less than 10</span><span>&nbsp;</span><span>μm. At present, most dust sources are in the same regions where the largest eolian sand seas occur, although sandy sediments are not the only sources of finer-grained dust. LRT dust can be transported across oceans, from continent to continent, and may play important roles in the overall planetary radiation balance, as&nbsp;fertilizer&nbsp;to the world's primary producers in the oceans, and as a soil parent material. Geologic records of LRT dust transport can be found in&nbsp;deep-sea sediments, ice caps, lakes, distal loess deposits, and soils. These records indicate that, like loess, the flux of dust was greater during glacial periods. Although eolian sand, loess, and LRT dust all have rich geologic records in the Quaternary, there is an increasing recognition of the importance of all these features in the longer, pre-Quaternary geologic record.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Geology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-409548-9.12491-1","usgsCitation":"Muhs, D.R., 2021, Eolian sediments, chap. <i>of</i> Encyclopedia of Geology, p. 348-369, https://doi.org/10.1016/B978-0-12-409548-9.12491-1.","productDescription":"22 p.","startPage":"348","endPage":"369","ipdsId":"IP-108752","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":396901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"2nd editionE","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":1857,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel","email":"dmuhs@usgs.gov","middleInitial":"R.","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":true,"id":837629,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70216733,"text":"70216733 - 2021 - Small mammal responses to wetland restoration in the Greater Everglades ecosystem","interactions":[],"lastModifiedDate":"2021-04-08T14:17:17.479018","indexId":"70216733","displayToPublicDate":"2020-11-22T07:56:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Small mammal responses to wetland restoration in the Greater Everglades ecosystem","docAbstract":"<p>Wetlands have experienced dramatic losses in extent around the world, disrupting ecosystem function, habitat, and biodiversity. In Florida’s Greater Everglades, a massive restoration effort costing billions of dollars and spanning multiple decades is underway. As Everglades restoration is implemented in incremental projects, scientists and planners monitor the outcomes of projects. In this study, we evaluated the progress of a restoration project in the southwestern Everglades. We aimed to determine whether the presence and density of small mammals differed between areas with hydrologic restoration of the ecosystem and areas without restoration. Our three focal species were: marsh rice rat (<i>Oryzomys palustris</i>), hispid cotton rat (<i>Sigmodon hispidus</i>), and cotton mouse (<i>Peromyscus gossypinus</i>). Using spatially explicit capture‐recapture models, we found greater densities of cotton mouse in restored habitat and lower densities of hispid cotton rat in sites with higher water levels. Additionally, we found an increase in the presence of the marsh rice rat in restored areas compared to unrestored, but captures were too low to reliably assess significance. Our study provides evidence that ongoing restoration in the southwestern Everglades is already impacting the small mammal community.</p>","language":"English","publisher":"Wiley","doi":"10.1111/rec.13332","usgsCitation":"Romanach, S., D’Acunto, L., Chapman, J., and Hanson, M., 2021, Small mammal responses to wetland restoration in the Greater Everglades ecosystem: Restoration Ecology, v. 29, no. 3, e13332, 9 p., https://doi.org/10.1111/rec.13332.","productDescription":"e13332, 9 p.","ipdsId":"IP-114410","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":454216,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.13332","text":"Publisher Index Page"},{"id":436633,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BWA7RD","text":"USGS data release","linkHelpText":"Small mammal captures at the Picayune Strand State Forest, October 2014 - April 2016"},{"id":380947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -81.97448730468749,\n              25.035838555635017\n            ],\n            [\n              -79.903564453125,\n              25.035838555635017\n            ],\n            [\n              -79.903564453125,\n              26.59343927024179\n            ],\n            [\n              -81.97448730468749,\n              26.59343927024179\n            ],\n            [\n              -81.97448730468749,\n              25.035838555635017\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"29","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":220761,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":806009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":806010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapman, Julia","contributorId":245353,"corporation":false,"usgs":false,"family":"Chapman","given":"Julia","affiliations":[],"preferred":false,"id":806011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanson, Matthew R 0000-0002-2859-3878","orcid":"https://orcid.org/0000-0002-2859-3878","contributorId":245354,"corporation":false,"usgs":false,"family":"Hanson","given":"Matthew R","affiliations":[],"preferred":false,"id":806012,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70216906,"text":"70216906 - 2021 - Small atoll fresh groundwater lenses respond to a combination of natural climatic cycles and human modified geology","interactions":[],"lastModifiedDate":"2020-12-30T14:45:53.316375","indexId":"70216906","displayToPublicDate":"2020-11-20T07:04:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Small atoll fresh groundwater lenses respond to a combination of natural climatic cycles and human modified geology","docAbstract":"<p><span>Freshwater lenses underlying small ocean islands exhibit spatial variability and temporal fluctuations in volume, influencing ecologic management. For example, The Palmyra Atoll National Wildlife Refuge harbors one of the few surviving native stands of&nbsp;</span><i>Pisonia grandis</i><span>&nbsp;in the central Pacific Ocean, yet these trees face pressure from groundwater salinization, with little basic groundwater data to guide decision making. Adding to natural complexity, the geology of Palmyra was heavily altered by dredge and fill activities. Our study based at this atoll combines geophysical and hydrological field measurements from 2008 to 2019 with groundwater modeling to study the drivers of observed freshwater lens dynamics. Electromagnetic induction (EMI) field data were collected on the main atoll islands over repeat transects in 2008 following ‘strong’ La Niña conditions (wet) and in 2016 during ‘very strong’ El Niño conditions (dry). Shallow monitoring wells were installed adjacent to the geophysical transects in 2013 and screened within the fresh/saline groundwater transition zone. Temporal EMI and monitoring well data showed a strong contraction of the freshwater lens in response to El Niño conditions, and indicated a thicker lens toward the ocean side, an opposite spatial pattern to that observed for many other Pacific islands. On an outer islet where a stand of mature&nbsp;</span><i>Pisonia</i><span>&nbsp;trees exist, EMI surveys revealed only a thin (&lt;3&nbsp;m from land surface) layer of brackish groundwater during El Niño. Numerical groundwater simulations were performed for a range of permeability distributions and climate conditions at Palmyra. Results revealed that the observed atypical lens asymmetry is likely due to more efficient submarine groundwater discharge on the lagoon side as a result of lagoon dredging and filling with high-permeability material. Simulations also predict large decreases (40%) in freshwater lens volume during dry cycles and highlight threats to the&nbsp;</span><i>Pisonia</i><span>&nbsp;trees, yielding insight for atoll ecosystem management worldwide.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.143838","usgsCitation":"Briggs, M.A., Cantelon, J., Kurylyk, B., Kulongoski, J.T., Mills, A., and Lane, J., 2021, Small atoll fresh groundwater lenses respond to a combination of natural climatic cycles and human modified geology: Science of the Total Environment, v. 756, 143838, 14 p., https://doi.org/10.1016/j.scitotenv.2020.143838.","productDescription":"143838, 14 p.","ipdsId":"IP-124031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":454244,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.143838","text":"Publisher Index Page"},{"id":381317,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Palmyra Atoll","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -162.11434364318848,\n              5.865525058703975\n            ],\n            [\n              -162.04078674316406,\n              5.865525058703975\n            ],\n            [\n              -162.04078674316406,\n              5.896261485744235\n            ],\n            [\n              -162.11434364318848,\n              5.896261485744235\n            ],\n            [\n              -162.11434364318848,\n              5.865525058703975\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"756","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cantelon, J","contributorId":245723,"corporation":false,"usgs":false,"family":"Cantelon","given":"J","email":"","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":806901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kurylyk, B.","contributorId":222758,"corporation":false,"usgs":false,"family":"Kurylyk","given":"B.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":806902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mills, Audrey","contributorId":245724,"corporation":false,"usgs":false,"family":"Mills","given":"Audrey","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":806904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806905,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70216698,"text":"70216698 - 2021 - Mainstems: A logical data model implementing mainstem and drainage basin feature types based on WaterML2 Part 3: HY Features concepts","interactions":[],"lastModifiedDate":"2020-12-01T13:34:28.581683","indexId":"70216698","displayToPublicDate":"2020-11-13T07:32:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Mainstems: A logical data model implementing mainstem and drainage basin feature types based on WaterML2 Part 3: HY Features concepts","docAbstract":"<div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\">The Mainstems data model implements the<span>&nbsp;</span><i>catchment</i><span>&nbsp;</span>and<span>&nbsp;</span><i>flowpath</i><span>&nbsp;</span>concepts from WaterML2 Part 3: Surface Hydrology Features (HY_Features) for persistent, cross-scale, identification of hydrologic features. The data model itself provides a focused and lightweight method to describe hydrologic networks with minimum but sufficient information. The design is intended to provide a model for data integration that can be used for network navigation and persistent hydrologic indexing (hydrographic addressing) functionality. Mainstems is designed to provide long-term stability with minimal maintenance requirements. The data model is not meant to advance hydrologic process representation or uniquely represent geomorphic characteristics. The principle assumption in Mainstems is that all<span>&nbsp;</span><i>drainage basins</i><span>&nbsp;</span>have one - and only one -<span>&nbsp;</span><i>headwater</i><span>&nbsp;</span>source area and a single<span>&nbsp;</span><i>mainstem</i><span>&nbsp;</span>that flows to a single<span>&nbsp;</span><i>outlet</i>. Using these base feature types, (<i>headwater</i>,<span>&nbsp;</span><i>outlet</i>,<span>&nbsp;</span><i>mainstem</i>, and<span>&nbsp;</span><i>drainage basin</i>) a nested set of<span>&nbsp;</span><i>drainage basins</i><span>&nbsp;</span>- and the associated dendritic network of<span>&nbsp;</span><i>mainstems</i><span>&nbsp;</span>- can be identified.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104927","usgsCitation":"Blodgett, D.L., Johnson, J.M., Sondheim, M., Wieczorek, M., and Frazier, N., 2021, Mainstems: A logical data model implementing mainstem and drainage basin feature types based on WaterML2 Part 3: HY Features concepts: Environmental Modelling and Software, v. 135, 104927, 11 p., https://doi.org/10.1016/j.envsoft.2020.104927.","productDescription":"104927, 11 p.","ipdsId":"IP-114535","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":454267,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2020.104927","text":"Publisher Index Page"},{"id":436644,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H0PTRH","text":"USGS data release","linkHelpText":"mainstems workflow: HU12 NHDPlusV2 NHDPlus HiRes Matching"},{"id":436643,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O15C70","text":"USGS data release","linkHelpText":"Mainstem Rivers of the World based on MERIT hydrography and Natural Earth names"},{"id":436642,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BTKP3T","text":"USGS data release","linkHelpText":"Mainstem Rivers of the Conterminous United States"},{"id":380907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"135","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, J. Micheal 0000-0002-5288-8350","orcid":"https://orcid.org/0000-0002-5288-8350","contributorId":245322,"corporation":false,"usgs":false,"family":"Johnson","given":"J.","email":"","middleInitial":"Micheal","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":805917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sondheim, Mark","contributorId":245323,"corporation":false,"usgs":false,"family":"Sondheim","given":"Mark","email":"","affiliations":[{"id":40433,"text":"NRCAN","active":true,"usgs":false}],"preferred":false,"id":805918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wieczorek, Michael 0000-0003-0999-5457","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":207911,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805919,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frazier, Nels","contributorId":245324,"corporation":false,"usgs":false,"family":"Frazier","given":"Nels","email":"","affiliations":[{"id":36995,"text":"NWS","active":true,"usgs":false}],"preferred":false,"id":805920,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70217153,"text":"70217153 - 2021 - From satellites to frogs: Quantifying ecohydrological change, drought mitigation, and population demography in desert meadows","interactions":[],"lastModifiedDate":"2021-01-07T13:34:01.062177","indexId":"70217153","displayToPublicDate":"2020-11-12T07:32:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"From satellites to frogs: Quantifying ecohydrological change, drought mitigation, and population demography in desert meadows","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0065\">Increasing frequency and severity of droughts have motivated natural resource managers to mitigate harmful ecological and hydrological effects of drought, but drought mitigation is an emerging science and evaluating its effectiveness is difficult. We examined ecohydrological responses of drought mitigation actions aimed at conserving populations of the Columbia spotted frog (<i>Rana luteiventris</i>) in a semi-arid valley in Nevada, USA. Abundance of this rare frog had declined precipitously after multiple droughts. Mitigation included excavating ponds to increase available surface water and installing earthen dams to raise water tables. We assessed responses of riparian vegetation to mitigation using a 30-year time series of satellite-derived Normalized Difference Vegetation Index (NDVI) and gridded weather data. We then analyzed a 23-year mark-recapture dataset to evaluate the effects of drought mitigation and NDVI on the probability of frog survival and rates of recruitment. After accounting for interannual precipitation variability, we found that NDVI increased significantly from before to after drought mitigation, suggesting that mitigation influenced the hydrology and vegetation of the meadows. Frog survival increased with NDVI, but mitigation had a stronger effect than NDVI suggesting that excavated mitigation ponds were particularly important for frog survival during drought. In contrast, frog recruitment was associated with NDVI more than mitigation, but only in meadows where NDVI was dependent on precipitation. At meadows with available groundwater, recruitment was associated with mitigation ponds. These findings suggest that mitigation ponds are critical for juvenile frogs to recruit into the adult population, but recruitment can also be increased by raising water tables in meadows lacking groundwater sources. Lagged recruitment (i.e., effects on larvae and juveniles) was negatively associated with NDVI. This study illustrates the ecohydrological complexity of drought mitigation and demonstrates novel ways to assess the effectiveness of drought mitigation using time series of readily available satellite imagery and organismal data.</p></div></div><div id=\"ab0010\" class=\"abstract graphical\" lang=\"en\"><br></div></div></div><div id=\"ab0010\" class=\"abstract graphical\" lang=\"en\"><br></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.143632","usgsCitation":"Pilliod, D., Hausner, M.B., and Scherer, R.D., 2021, From satellites to frogs: Quantifying ecohydrological change, drought mitigation, and population demography in desert meadows: Science of the Total Environment, v. 758, 143632, 15 p., https://doi.org/10.1016/j.scitotenv.2020.143632.","productDescription":"143632, 15 p.","ipdsId":"IP-118807","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":454274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.143632","text":"Publisher Index Page"},{"id":381995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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