{"pageNumber":"180","pageRowStart":"4475","pageSize":"25","recordCount":46666,"records":[{"id":70226342,"text":"ofr20211065 - 2021 - Turbidity–suspended-sediment concentration regression equations for monitoring stations in the upper Esopus Creek watershed, Ulster County, New York, 2016–19","interactions":[],"lastModifiedDate":"2021-11-22T12:06:29.314733","indexId":"ofr20211065","displayToPublicDate":"2021-11-19T13:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1065","displayTitle":"Turbidity–Suspended-Sediment Concentration Regression Equations for Monitoring Stations in the Upper Esopus Creek Watershed, Ulster County, New York, 2016–19","title":"Turbidity–suspended-sediment concentration regression equations for monitoring stations in the upper Esopus Creek watershed, Ulster County, New York, 2016–19","docAbstract":"<p>Upper Esopus Creek is the primary tributary to the Ashokan Reservoir, part of the New York City water-supply system. Elevated concentrations of suspended sediment and turbidity in the watershed of the creek are of concern for the system.</p><p>Water samples were collected through a range of streamflow and turbidity at 14 monitoring sites in the upper Esopus Creek watershed for analyses of suspended-sediment concentration (SSC) and measurements of turbidity. Analyses of the samples provided data that were used to develop cross-section coefficients and turbidity-SSC regression equations for the monitoring sites for the period October 2016 through September 2019. The equations can be used to estimate SSC at a 15-minute timestep for the monitored sites. The equations can be validated for future use by the collection and analysis of additional data.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211065","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Siemion, J., Bonville, D.B., McHale, M.R., and Antidormi, M.R., 2021, Turbidity–suspended-sediment concentration regression equations for monitoring stations in the upper Esopus Creek watershed, Ulster County, New York, 2016–19: U.S. Geological Survey Open-File Report 2021–1065, 27 p., https://doi.org/10.3133/ofr20211065.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120199","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":391805,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MV3NZ8","text":"USGS data release","linkHelpText":"Suspended-sediment concentration and turbidity data for sites in the upper Esopus Creek watershed New York, 2016–19"},{"id":391807,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1065/images"},{"id":391804,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1065/ofr20211065.pdf","text":"Report","size":"2.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1065"},{"id":391806,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1065/ofr20211065.XML"},{"id":391803,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1065/coverthb.jpg"}],"country":"United States","state":"New York","county":"Ulster County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-73.9109,42.1271],[-73.916,42.1199],[-73.9245,42.1019],[-73.9311,42.082],[-73.93,42.0765],[-73.9302,42.0679],[-73.9341,42.0575],[-73.937,42.0398],[-73.9347,42.0293],[-73.9331,42.0216],[-73.9436,41.9913],[-73.9504,41.9664],[-73.9556,41.9528],[-73.9551,41.9464],[-73.954,41.9401],[-73.9567,41.9301],[-73.9625,41.9179],[-73.9639,41.9138],[-73.9609,41.9088],[-73.9423,41.8827],[-73.9389,41.8704],[-73.939,41.8654],[-73.9423,41.8596],[-73.9448,41.8559],[-73.9461,41.851],[-73.9477,41.8346],[-73.9463,41.8142],[-73.9504,41.7979],[-73.9488,41.7847],[-73.946,41.7719],[-73.9414,41.7592],[-73.9408,41.7592],[-73.938,41.7469],[-73.9389,41.7337],[-73.9424,41.7142],[-73.9439,41.6993],[-73.9411,41.6884],[-73.9513,41.6149],[-73.9525,41.59],[-73.9999,41.5855],[-74.0521,41.5816],[-74.0575,41.5926],[-74.0677,41.604],[-74.0886,41.5988],[-74.0983,41.6089],[-74.1246,41.6133],[-74.1325,41.6152],[-74.1282,41.5833],[-74.1858,41.5944],[-74.187,41.5908],[-74.1907,41.5913],[-74.2458,41.6036],[-74.25,41.6059],[-74.2502,41.6291],[-74.2606,41.6337],[-74.2667,41.6324],[-74.2754,41.6284],[-74.281,41.6257],[-74.2989,41.6182],[-74.3156,41.6115],[-74.3187,41.6084],[-74.3404,41.5954],[-74.3521,41.5982],[-74.3583,41.5938],[-74.3675,41.5916],[-74.3681,41.5961],[-74.3705,41.597],[-74.3736,41.5975],[-74.376,41.5994],[-74.3772,41.6044],[-74.3807,41.6117],[-74.3843,41.6167],[-74.3873,41.6217],[-74.3884,41.6299],[-74.392,41.6345],[-74.3926,41.6399],[-74.3943,41.6458],[-74.4004,41.6486],[-74.4449,41.6726],[-74.4833,41.6942],[-74.5755,41.7453],[-74.4892,41.8377],[-74.4573,41.8747],[-74.5124,41.8992],[-74.6363,41.9542],[-74.7235,41.9915],[-74.78,42.0182],[-74.667,42.0697],[-74.5538,42.1212],[-74.5312,42.1464],[-74.504,42.1449],[-74.4516,42.1694],[-74.3077,42.1142],[-74.2496,42.1095],[-74.0767,42.0968],[-74.0424,42.1682],[-74.0259,42.1621],[-74.0054,42.1642],[-74.0038,42.18],[-73.9189,42.1286],[-73.9109,42.1271]]]},\"properties\":{\"name\":\"Ulster\",\"state\":\"NY\"}}]}","contact":"<p><a href=\"mailto:dc_ny@usgs.gov\" data-mce-href=\"mailto:dc_ny@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/ny-water\" data-mce-href=\"https://www.usgs.gov/centers/ny-water\">New York Water Science Center</a><br>U.S. Geological Survey<br>425 Jordan Road<br>Troy, NY 12180–8349</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Study Methods</li><li>Development of Cross-Section Coefficients and Regression Equations</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Streamflow Duration Curves</li><li>Appendix 2. Turbidity Duration Curves</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-11-19","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Siemion, Jason 0000-0001-5635-6469 jsiemion@usgs.gov","orcid":"https://orcid.org/0000-0001-5635-6469","contributorId":127562,"corporation":false,"usgs":true,"family":"Siemion","given":"Jason","email":"jsiemion@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonville, Donald B. 0000-0003-4480-9381","orcid":"https://orcid.org/0000-0003-4480-9381","contributorId":248849,"corporation":false,"usgs":true,"family":"Bonville","given":"Donald","email":"","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael R. 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":1735,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826928,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Antidormi, Michael R. 0000-0002-3967-1173 mantidormi@usgs.gov","orcid":"https://orcid.org/0000-0002-3967-1173","contributorId":150722,"corporation":false,"usgs":true,"family":"Antidormi","given":"Michael","email":"mantidormi@usgs.gov","middleInitial":"R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826929,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70226471,"text":"pp1868 - 2021 - Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud","interactions":[],"lastModifiedDate":"2021-11-22T12:09:52.710721","indexId":"pp1868","displayToPublicDate":"2021-11-19T10:43:51","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":"1868","displayTitle":"Global Cropland-Extent Product at 30-m Resolution (GCEP30) Derived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud","title":"Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud","docAbstract":"<h1>Executive Summary</h1><p>Global food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations; and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product.</p><p>Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface; (2) composition of analysis-ready data (ARD) cubes; (3) creation of a large global-reference data hub for machine learning; (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud; and (5) Google Earth Engine (GEE) cloud computing.</p><p>The five key steps involved nine distinct phases. First, the world was segmented into 74 agroecological zones (AEZs). Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. Third, more than 100,000 reference-training data samples were collected using ground data (some of which were collected using a mobile application), as well as submeter- to 5-m-resolution, very high-resolution imagery sourced from other reliable sources. Fourth, reference-training data were used to create a knowledge base for separating cropland from noncropland. Fifth, MLAs such as the pixel-based supervised random forest and support-vector machines were written on the GEE using Python and JavaScript. Sixth, object-based recursive hierarchical segmentation algorithm was used, in addition to MLAs, to overcome uncertainties. Seventh, MLAs used the knowledge base to classify and separate cropland from noncropland. Eighth, accuracy assessment was conducted by generating error matrices for each of the 74 AEZs using 19,171 independent validation-data samples. Ninth, cropland areas were computed for all countries of the world and compared with United Nation’s (UN’s) Food and Agricultural Organization (FAO) and other national statistics.</p><p>The outcome was a Landsat-derived global cropland-extent product at 30-m resolution (GCEP30), which has an overall accuracy of 91.7 percent. For the cropland class, producer’s accuracy was 83.4 percent, and user’s accuracy was 78.3 percent. GCEP30 calculated (using direct pixel count) the global net-cropland area (GNCA) for the year 2015 as 1.873 billion hectares (~12.6 percent of the Earth’s terrestrial area). The continental cropland distribution as a percentage of GNCA was Asia, 33 percent; Europe, 25.5 percent; Africa, 16.7 percent; North America, 14.4 percent; South America, 8.1 percent; and Australia and Oceania, 2.4 percent. The worldwide cropland areas in GCEP30 for 2015 were higher by 236 to 299 million hectares (Mha) compared to national statistics reported elsewhere for the same year (for example, in Food and Agriculture Organization’s corporate statistical database [FAOSTAT] and in the monthly irrigated and rainfed crop areas [MIRCA] database). The global cropland area reported for 2015 increased by 344 Mha (22.5 percent), compared to the year 2000. During the same period (2000–2015), the world’s population increased by 20 percent. Whereas some of these areal increases are real increases in cropland areas, others are due to the types of data, methods, and approaches used. Using the highest known resolution (compared to previous coarse-resolution global products) enabled this study to capture fragmented croplands. Coarse-resolution data compute areas on the basis of subpixels, which, for a large proportion of certain land use–land cover classes, will show only a certain percentage of the total pixel area as actual area. Subpixel areas can lead to substantial uncertainties in area computation, as determining the exact fraction of cropland areas within a coarse-resolution pixel is resource intensive and subject to errors. Other innovations in GCEP30 include reference-data hubs, machine learning, and cloud computing.</p><p>Cropland areas in 214 countries, territories, departments, and regions were calculated for the year 2015 using GCEP30, on the basis of UN’s global administrative unit layers (GAUL) boundaries. The 10 leading countries in terms of cropland area (as a percentage of the GNCA) were India (9.6 percent), United States (8.95 percent), China (8.82 percent), Russia (8.32 percent), Brazil (3.42 percent), Ukraine (2.32 percent), Canada (2.29 percent), Argentina (2.05 percent), Indonesia (2 percent), and Nigeria (1.91 percent). Together, these 10 countries occupy 50 percent of the global cropland, and they have 52 percent of the global population. Their combined cropland area increased by 2 percent between 2000 and 2015, compared to the substantial increase in population of 517 million (15.5 percent). Together, India, United States, China, and Russia encompass 36 percent of the total area. In the United States and Canada, from 2000 to 2015, cropland decreased by about 2 percent, whereas their populations increased by 14 and 13 percent, respectively. The additional food requirements in these 10 countries, which are caused by increased populations, as well as increasing nutritional demands, are met by production increases in existing cropland or through virtual food trade, or both.</p><p>More than 18 countries, territories, departments, or regions had 60 percent or more of their geographic area as cropland: Republic of Moldova, San Marino, and Hungary had more than 80 percent of the country’s area as cropland; Denmark, Ukraine, Ireland, and Bangladesh, 70 to 80 percent; and Uruguay, Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czechia, Italy, India, and Azerbaijan, 60 to 70 percent. Europe and South Asia can be considered agricultural capitals of the world, on the basis of their percentages of geographic area as cropland. United States, China, and Russia, which all have high cropland areas, are ranked second, third, and fourth in the world; India is ranked first. However, the amount of cropland as a percentage of the country’s geographic area is relatively very low for United States (18.3 percent), China (17.7 percent), and Russia (9.5 percent), whereas it is 60.5 percent for India. Most African and South American countries, territories, departments, or regions have less than 15 percent of their geographic area as cropland.</p><p>China and India together house 36 percent of the world’s population; however, between 2000 and 2015, the amount of China’s cropland area fell by 18.9 percent, owing to urban expansion and the abandonment of farmlands caused by demographic changes (that is, the movement of population from villages to cities). In contrast, China’s population grew by 10 percent. The amount of India’s cropland increased by 8.5 percent, whereas its population grew by 20 percent.</p><p>This study showed that, out of the 10 leading cropland countries, Ukraine, Nigeria, Russia, and Indonesia showed an 18 to 31 percent increase in cropland areas, on the basis of GCEP30 by the year 2015, compared to 2000. Nigeria’s cropland area increased by 25 percent, and its population increased by 31 percent in the same period. In these countries, food security is maintained by cropland expansion, productivity increases, and virtual food trade. Nevertheless, this trend of increasing net-cropland area and productivity will likely become difficult to maintain, owing to diminishing arable lands and plateauing of 50 years of continual yield increases, requiring policymakers to explore novel and data-supported approaches to solving future food security issues.</p><p>The GCEP30 product, which can be browsed at full resolution at <a data-mce-href=\"https://www.croplands.org\" href=\"https://www.croplands.org\" target=\"_blank\" rel=\"noopener\">www.croplands.org</a>, has been released for public download and use through U.S. Geological Survey (USGS)–National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (see <a rel=\"noopener\" href=\"https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/\" target=\"_blank\" data-mce-href=\"https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/\">https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/</a>).</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1868","usgsCitation":"Thenkabail, P.S., Teluguntla, P.G., Xiong, J., Oliphant, A., Congalton, R.G., Ozdogan, M., Gumma, M.K., Tilton, J.C., Giri, C., Milesi, C., Phalke, A., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., and Foley, D., 2021, Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud: U.S. Geological Survey Professional Paper 1868, 63 p., https://doi.org/10.3133/pp1868.","productDescription":"Report: ix, 63 p.; Dataset","numberOfPages":"63","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119164","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":391888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1868/pp1868.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391887,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1868/covrthb.jpg"},{"id":391890,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/","text":"Associated data","linkHelpText":"- Release of GFSAD 30 meter Cropland Extent Products"}],"contact":"<p><a data-mce-href=\"https://www.usgs.gov/centers/wgsc/connect\" href=\"https://www.usgs.gov/centers/wgsc/connect\" target=\"_blank\" rel=\"noopener\">Director</a>, <br><a data-mce-href=\"https://www.usgs.gov/centers/wgsc/\" href=\"https://www.usgs.gov/centers/wgsc/\" target=\"_blank\" rel=\"noopener\">Western Geographic Science Center&nbsp;</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>350 N. Akron Rd.&nbsp; <br>Moffett Field, CA 94035&nbsp; </p>","tableOfContents":"<ul><li>Acknowledgments&nbsp;&nbsp;</li><li>Executive Summary&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Data&nbsp;&nbsp;</li><li>Methods&nbsp;&nbsp;</li><li>Results and Discussions&nbsp;&nbsp;</li><li>Significant Findings&nbsp;&nbsp;</li><li>Conclusions&nbsp;&nbsp;</li><li>References Cited&nbsp;</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-11-19","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science 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0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":827018,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Congalton, Russell G.","contributorId":84646,"corporation":false,"usgs":true,"family":"Congalton","given":"Russell G.","affiliations":[],"preferred":false,"id":827019,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ozdogan, Mutlu","contributorId":32060,"corporation":false,"usgs":true,"family":"Ozdogan","given":"Mutlu","affiliations":[],"preferred":false,"id":827020,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gumma, Murali Krishna","contributorId":50426,"corporation":false,"usgs":true,"family":"Gumma","given":"Murali Krishna","affiliations":[],"preferred":false,"id":827021,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tilton, James C.","contributorId":214482,"corporation":false,"usgs":false,"family":"Tilton","given":"James","email":"","middleInitial":"C.","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":827022,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":827023,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Milesi, 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Managers often use creel and other angler survey data to inform state- and waterbody-level management efforts. Despite the broad implementation of angler surveys and their importance to fisheries management at state scales, regional and national coordination among these activities is minimal, limiting data applicability for larger-scale management practices and research. Here, we introduce the U.S. Inland Creel and Angler Survey Catalog (CreelCat), a first-of-its-kind, publicly available national database of angler survey data that establishes a baseline of national inland recreational fishing metrics. We highlight research and management applications to help support sustainable inland recreational fishing practices, consider cautions, and make recommendations for implementation.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/fsh.10671","usgsCitation":"Lynch, A.J., Sievert, N., Embke, H.S., Robertson, A., Myers, B.J., Allen, M.S., Feiner, Z., Hoogakker, F., Knoche, S., Krogman, R., Midway, S.R., Nieman, C.L., Paukert, C., Pope, K.L., Rogers, M.W., Wszola, L.S., and Beard, 2021, The U.S. Inland Creel and Angler Survey Catalog (CreelCat): Development, applications, and opportunities: Fisheries Magazine, v. 46, no. 11, p. 574-583, https://doi.org/10.1002/fsh.10671.","productDescription":"10 p.","startPage":"574","endPage":"583","ipdsId":"IP-122018","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":500804,"rank":1,"type":{"id":41,"text":"Open Access External Repository 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,{"id":70225535,"text":"sir20215069 - 2021 - Depth of groundwater used for drinking-water supplies in the United States","interactions":[],"lastModifiedDate":"2021-11-18T23:19:01.5908","indexId":"sir20215069","displayToPublicDate":"2021-11-18T06:53:01","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":"2021-5069","displayTitle":"Depth of Groundwater Used for Drinking-Water Supplies in the United States","title":"Depth of groundwater used for drinking-water supplies in the United States","docAbstract":"<p class=\"p1\">Groundwater supplies 35 percent of drinking water in the United States. Mapping the quantity and quality of groundwater at the depths used for potable supplies requires an understanding of locational variation in the characteristics of drinking-water wells (depth and open interval). Typical depths of domestic- and public-drinking-water supply wells vary by and within aquifer across the United States. The depths to the top and bottom of the zones from which drinking water is withdrawn are important predictor variables in regional- and national-scale statistical water models, but spatially extensive maps of the depths to drinking-water-supply sources are not consistently available in modeled regions. Therefore, it was necessary to generate a set of grids representing surfaces of the approximate common depth and length of open intervals in the wells from which water is withdrawn for domestic- and public-drinking-water supply (withdrawal zones) within the conterminous United States.</p><p class=\"p1\">Well data (about 7.6 million records) were compiled from several sources, including the U.S. Geological Survey’s National Water Information System (600,922 records), the U.S. Environmental Protection Agency’s Safe Drinking Water Information System dataset (66,540 records, primarily public-supply wells), a groundwater ambient monitoring dataset (31,448 records, primarily domestic-supply wells), individual State data (6,096,503 records), a national brackish aquifer study (96,885 records), and a glacial aquifer study (729,564 records).</p><p class=\"p1\">Fifty-seven principal aquifers and 65 secondary hydrogeologic regions have been designated in the conterminous United States. The principal aquifers and secondary hydrogeologic regions vary in depth, thickness, lithology, and transmissivity characteristics. Some principal aquifers underlie secondary hydrogeologic regions, and may in turn be overlain by glacial sediment or basin and valley fill aquifers, which may also be used as drinking-water sources. The principal aquifer and secondary hydrogeologic region polygons were merged with overlying sediment polygons, where present, including glacial sediment, coarse glacial sediment, and stream valley alluvium (alluvium) polygons, to generate unique hydrogeologic settings across the conterminous United States. A total of 288 distinct hydrogeologic settings resulted from the merging of principal aquifer, secondary hydrogeologic region, glacial sediment, coarse glacial sediment, and alluvium polygons.</p><p class=\"p2\">Each well was assigned to a hydrogeologic setting on the basis of location. Hydrogeologic setting well groupings were used to guide calculations of the median value for well depth and depth to and length of open intervals across the hydrogeologic setting. Where well data were sparse or missing, wells from hydrogeologic settings with similar well construction properties, geology, physiography, and topography were grouped and used to calculate the moving median depth (if less than five wells in a 100-kilometer [62.1-mile] radius) and to estimate open interval length (if not available within hydrogeologic setting). Grids were generated to represent what might be considered as the “typical” or “median” domestic- and public-supply well in an area. The well properties are defined with moving median grids of top depth, bottom depth, and length of open interval at a 1-square-kilometer (0.38-square-mile) grid cell scale.</p><p class=\"p2\">Median depths and open intervals of domestic- and public-supply wells varied by lithology of the hydrogeologic setting and overlying sediment. Overall, the median depths were 142 feet (43.3 meters) for all domestic-supply wells and 202 feet (61.6 meters) for all public-supply wells. The median open intervals were 21 feet (6.4 meters) for domestic-supply wells and 49 feet (14.9 meters) for public-supply wells. The shallowest median bottom open interval depths for domestic-supply wells were in the secondary hydrogeologic regions with coarse glacial sediment, which suggests that the wells are most commonly completed in the permeable coarse glacial sediment and not in the underlying secondary hydrogeologic region. Public-supply wells were completed at relatively shallow median depths when drilled in permeable sediment that overlie secondary hydrogeologic regions. When public-supply wells were completed in principal aquifers, the median depths were typically greater than wells completed in secondary hydrogeologic regions.</p><p class=\"p2\">Well data used in this study were limited to those available from national or State digital databases. Several quality-assurance checks were performed during data compilation, but a comprehensive quality assurance inspection for each of the data sources was outside the scope of this study. Grids defining typical open intervals in domestic- and public-supply wells are presented. Although there are many places where multiple aquifers are stacked, these results correspond primarily to the aquifer with the highest documented number of wells for each use.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215069","programNote":"National Water Quality Program","usgsCitation":"Degnan, J.R., Kauffman, L.J., Erickson, M.L., Belitz, K., and Stackelberg, P.E., 2021, Depth of groundwater used for drinking-water supplies in the United States: U.S. Geological Survey Scientific Investigations Report 2021–5069, 69 p., https://doi.org/10.3133/sir20215069.","productDescription":"Report: iv, 69 p.; Data Release; Interactive Maps","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122329","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":390686,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94640EM","text":"USGS data 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-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}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/new-england-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Study Methods</li><li>Results of Analyses</li><li>Discussion</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1</li></ul>","publishedDate":"2021-11-18","noUsgsAuthors":false,"publicationDate":"2021-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Degnan, James R. 0000-0002-5665-9010 jrdegnan@usgs.gov","orcid":"https://orcid.org/0000-0002-5665-9010","contributorId":498,"corporation":false,"usgs":true,"family":"Degnan","given":"James","email":"jrdegnan@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauffman, Leon J. 0000-0003-4564-0362 lkauff@usgs.gov","orcid":"https://orcid.org/0000-0003-4564-0362","contributorId":1094,"corporation":false,"usgs":true,"family":"Kauffman","given":"Leon","email":"lkauff@usgs.gov","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":825486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X pestack@usgs.gov","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":1069,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","email":"pestack@usgs.gov","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825488,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70226569,"text":"70226569 - 2021 - Growing as slow as a turtle: Unexpected maturational differences in a small, long-lived species","interactions":[],"lastModifiedDate":"2021-11-29T11:52:54.779651","indexId":"70226569","displayToPublicDate":"2021-11-18T05:50:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Growing as slow as a turtle: Unexpected maturational differences in a small, long-lived species","docAbstract":"<div class=\"abstract toc-section abstract-type-\"><div class=\"abstract-content\"><p>Turtle body size is associated with demographic and other traits like mating success, reproductive output, maturity, and survival. As such, growth analyses are valuable for testing life history theory, demographic modeling, and conservation planning. Two important but unsettled research areas relate to growth after maturity and growth rate variation. If individuals exhibit indeterminate growth after maturity, older adults may have an advantage in fecundity, survival, or both over younger/smaller adults. Similarly, depending on how growth varies, a portion of the population may mature earlier, grow larger, or both. We used 23-years of capture-mark-recapture data to study growth and maturity in the Spotted Turtle (<i>Clemmys guttata</i>), a species suffering severe population declines and for which demographic data are needed for development of effective conservation and management strategies. There was strong support for models incorporating sex as a factor, with the interval growth model reparametrized for capture-mark-recapture data producing later mean maturation estimates than the age-based growth model. We found most individuals (94%) continued growing after maturity, but the instantaneous relative annual plastral growth rate was low. We recommend future studies examine the possible contribution of such slow, continued adult growth to fecundity and survival. Even seemingly negligible amounts of annual adult growth can have demographic consequences affecting the population vital rates for long-lived species.</p></div></div>","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0259978","usgsCitation":"Edmonds, D., Dreslik, M.J., Lovich, J.E., Wilson, T., and Ernst, C., 2021, Growing as slow as a turtle: Unexpected maturational differences in a small, long-lived species: PLoS ONE, v. 16, no. 11, e0259978, 12 p., https://doi.org/10.1371/journal.pone.0259978.","productDescription":"e0259978, 12 p.","ipdsId":"IP-129601","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450183,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0259978","text":"Publisher Index Page"},{"id":392171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Edmonds, Devin","contributorId":269528,"corporation":false,"usgs":false,"family":"Edmonds","given":"Devin","email":"","affiliations":[{"id":55975,"text":"Illinois Natural History Survey, University of Illinois Urbana-Champaign, 1816 South Oak Street, Champaign, Illinois, USA 61820","active":true,"usgs":false}],"preferred":false,"id":827364,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dreslik, Michael J.","contributorId":269529,"corporation":false,"usgs":false,"family":"Dreslik","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":55975,"text":"Illinois Natural History Survey, University of Illinois Urbana-Champaign, 1816 South Oak Street, Champaign, Illinois, USA 61820","active":true,"usgs":false}],"preferred":false,"id":827365,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":827366,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Thomas P.","contributorId":269530,"corporation":false,"usgs":false,"family":"Wilson","given":"Thomas P.","affiliations":[{"id":55978,"text":"Department of Biological and Environmental Sciences, 615 McCallie Avenue, University of Tennessee, Chattanooga, Tennessee, USA, 37403","active":true,"usgs":false}],"preferred":false,"id":827367,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ernst, Carl H.","contributorId":269531,"corporation":false,"usgs":false,"family":"Ernst","given":"Carl H.","affiliations":[{"id":27990,"text":"Deceased","active":true,"usgs":false}],"preferred":false,"id":827368,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70225532,"text":"sir20215109 - 2021 - Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","interactions":[],"lastModifiedDate":"2021-11-23T13:06:28.021637","indexId":"sir20215109","displayToPublicDate":"2021-11-17T07:15:00","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":"2021-5109","displayTitle":"Documentation and Mapping of Flooding From the January and March 2018 Nor’easters in Coastal New England","title":"Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","docAbstract":"<p>In January and March 2018, coastal Massachusetts experienced flooding from two separate nor’easters. To put the January and March floods into historical context, the USGS computed statistical stillwater elevations. Stillwater elevations recorded in January 2018 in Boston (9.66 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 2 and 1 percent (between a 50- and 100-year recurrence interval). Stillwater elevations recorded in March 2018 in Boston (9.17 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 4 and 2 percent (between a 25- and 50-year recurrence interval). Flood maps show that the area inundated by the January storm is slightly more extensive than that of the March storm, reflecting the respective profiles of the two storms. On the basis of a limited dataset, the attenuation of peak water levels was estimated as a function of the hydraulic distance inland and the starting stillwater elevation computed for the flood within 0.6 foot of what was measured in the field. A simple one-dimensional model was calibrated using flood elevation data collected after the January flood, and the results of the model were validated using flood elevation data collected after the March flood to model the attenuation of the flood elevations as the storms move inland.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215109","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Lombard, P.J., Olson, S.A., Sturtevant, L.P., and Kalmon, R.D., 2021, Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England: U.S. Geological Survey Scientific Investigations Report 2021–5109, 13 p., https://doi.org/10.3133/sir20215109.","productDescription":"Report: iv, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125348","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":390667,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RINQ4B","text":"USGS data release","linkHelpText":"Data and shapefiles used to document the floods associated with the January and March 2018 nor’easters for coastal areas of New England"},{"id":390669,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5109/sir20215109.XML"},{"id":390668,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5109/images/"},{"id":390666,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://wim.usgs.gov/geonarrative/newenglandnoreaster2018dashboard","text":"USGS web page","linkHelpText":"- Nor’easter storm events in coastal New England—January 4 and March 2–4, 2018"},{"id":390665,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://wim.usgs.gov/geonarrative/newenglandnoreaster2018","text":"USGS web page","linkHelpText":"- The January and March 2018 nor'easters—Flood documentation and mapping of two large storm events in coastal Massachusetts"},{"id":390664,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5109/sir20215109.pdf","text":"Report","size":"5.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5109"},{"id":390663,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5109/coverthb.jpg"}],"country":"United States","state":"Connecticut, Massachusetts, Maine, New Hampshire,  Rhode 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 \"}}]}","contact":"<p><a href=\"mailto:dc_nweng@usgs.gov\" data-mce-href=\"mailto:dc_nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Stillwater Elevations</li><li>Mapping of Coastal Flooding</li><li>Attenuation of Flood Water-Surface Elevations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-11-17","noUsgsAuthors":false,"publicationDate":"2021-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":203509,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olson, Scott A. 0000-0002-1064-2125 solson@usgs.gov","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":2059,"corporation":false,"usgs":true,"family":"Olson","given":"Scott","email":"solson@usgs.gov","middleInitial":"A.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sturtevant, Luke P. 0000-0001-8983-8210 lsturtevant@usgs.gov","orcid":"https://orcid.org/0000-0001-8983-8210","contributorId":4969,"corporation":false,"usgs":true,"family":"Sturtevant","given":"Luke","email":"lsturtevant@usgs.gov","middleInitial":"P.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalmon, Rena D. 0000-0002-3210-3210","orcid":"https://orcid.org/0000-0002-3210-3210","contributorId":206320,"corporation":false,"usgs":true,"family":"Kalmon","given":"Rena","email":"","middleInitial":"D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825468,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70226145,"text":"sir20215121 - 2021 - Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18","interactions":[],"lastModifiedDate":"2021-11-19T21:06:31.076465","indexId":"sir20215121","displayToPublicDate":"2021-11-16T13:40:00","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":"2021-5121","displayTitle":"Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence Among Selected Large River Sites of the Conterminous United States, 2017–18","title":"Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18","docAbstract":"<p>The U.S. Geological Survey measured cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins at 11 river sites throughout the conterminous United States in a multiyear pilot study during 2017–19 through the National Water Quality Assessment Project to better understand the occurrence of cyanobacteria and cyanotoxins in large inland and coastal rivers. This report focuses on the first 2 years of data collection (2017 and 2018) and describes occurrence of anatoxin-, cylindrospermopsin-, microcystin-, and saxitoxin-producing cyanobacteria, cyanotoxin synthetase genes (<i>anaC</i>, <i>cyrA</i>, taxa specific <i>mcyE</i>, and <i>sxtA</i>), and cyanotoxins (anatoxins, cylindrospermopsins, microcystins, and saxitoxins). Study findings demonstrate that cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins are present in large U.S rivers under ambient conditions and show that downstream transport and flushing likely affect relative abundance of potential cyanotoxin-producing cyanobacteria. Additionally, the results agree with existing literature that support the importance of water temperature, light, and nutrients—as moderated by hydrologic conditions—in shaping the structure of riverine cyanobacterial communities.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215121","programNote":"National Water Quality Program","usgsCitation":"Zuellig, R.E., Graham, J.L., Stelzer, E.A., Loftin, K.A., and Rosen, B.H., 2021, Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5121, 22 p., https://doi.org/10.3133/sir20215121.","productDescription":"Report: v, 22 p.; 4 Data Releases; Related Work","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122244","costCenters":[{"id":191,"text":"Colorado Water Science 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data-mce-href=\"mailto:dc_ny@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/ny-water\" data-mce-href=\"https://www.usgs.gov/centers/ny-water\">New York Water Science Center</a><br>U.S. Geological Survey<br>425 Jordan Road<br>Troy, NY 12180–8349</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results of Quality Assurance and Quality Control Analysis</li><li>Potential Cyanotoxin-Producing Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence</li><li>Concordance Between Potential Cyanotoxin-Producing Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence</li><li>Association Between Biological Response and Selected Environmental Variables</li><li>Descriptive Association Between Cyanobacteria and Streamflow</li><li>Limitations</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke 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,{"id":70225731,"text":"fs20213055 - 2021 - Landsat Collection 2 Level-2 Science Products","interactions":[],"lastModifiedDate":"2021-11-16T17:09:17.289209","indexId":"fs20213055","displayToPublicDate":"2021-11-16T11:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3055","displayTitle":"Landsat Collection 2 Level-2 Science Products","title":"Landsat Collection 2 Level-2 Science Products","docAbstract":"<p>The U.S. Geological Survey produces research quality, applications ready, Level-2 Science Products derived from Landsat Collection 2 Level-1 data. These products are used to monitor, assess, and project changes in land use, land cover, and environmental conditions affecting the human condition, natural processes, and biological habitats. Landsat Collection 2 Level-2 Science Products are time-series observational data processed for consistency and continuity to measure effects of environmental change and serve as input into Landsat essential climate variable Level-3 Science Products.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213055","usgsCitation":"U.S. Geological Survey, 2021, Landsat Collection 2 Level-2 Science Products: U.S. Geological Survey Fact Sheet 2021–3055, 2 p., https://doi.org/10.3133/fs20213055.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-130375","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":391441,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3055/fs20213055.pdf","text":"Report","size":"2.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3055"},{"id":391440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3055/coverthb.jpg"}],"contact":"<p>Director, <a href=\"http://www.usgs.gov/centers/eros/\" data-mce-href=\"http://www.usgs.gov/centers/eros/\"> Earth Resources Observation and Science Center</a><br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p>","tableOfContents":"<ul><li>Surface Reflectance</li><li>Surface Temperature</li><li>Data Access</li></ul>","publishedDate":"2021-11-16","noUsgsAuthors":false,"publicationDate":"2021-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128215,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":826445,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70267758,"text":"70267758 - 2021 - Space-for-time is not necessarily a substitution when monitoring the distribution of pelagic fishes in the San Francisco Bay-Delta","interactions":[],"lastModifiedDate":"2025-05-30T15:27:55.94088","indexId":"70267758","displayToPublicDate":"2021-11-16T10:24:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Space-for-time is not necessarily a substitution when monitoring the distribution of pelagic fishes in the San Francisco Bay-Delta","docAbstract":"<p><span>Occupancy models are often used to analyze long-term monitoring data to better understand how and why species redistribute across dynamic landscapes while accounting for incomplete capture. However, this approach requires replicate detection/non-detection data at a sample unit and many long-term monitoring programs lack temporal replicate surveys. In such cases, it has been suggested that surveying subunits within a larger sample unit may be an efficient substitution (i.e., space-for-time substitution). Still, the efficacy of fitting occupancy models using a space-for-time substitution has not been fully explored and is likely context dependent. Herein, we fit occupancy models to Delta Smelt (</span><i>Hypomesus transpacificus</i><span>) and Longfin Smelt (</span><i>Spirinchus thaleichthys</i><span>) catch data collected by two different monitoring programs that use the same sampling gear in the San Francisco Bay-Delta, USA. We demonstrate how our inferences concerning the distribution of these species changes when using a space-for-time substitution. Specifically, we found the probability that a sample unit was occupied was much greater when using a space-for-time substitution, presumably due to the change in the spatial scale of our inferences. Furthermore, we observed that as the spatial scale of our inferences increased, our ability to detect environmental effects on system dynamics was obscured, which we suspect is related to the tradeoffs associated with spatial grain and extent. Overall, our findings highlight the importance of considering how the unique characteristics of monitoring programs influences inferences, which has broad implications for how to appropriately leverage existing long-term monitoring data to understand the distribution of species.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8292","usgsCitation":"Duarte, A., and Peterson, J., 2021, Space-for-time is not necessarily a substitution when monitoring the distribution of pelagic fishes in the San Francisco Bay-Delta: Ecology and Evolution, v. 11, no. 23, p. 16727-16744, https://doi.org/10.1002/ece3.8292.","productDescription":"18 p.","startPage":"16727","endPage":"16744","ipdsId":"IP-123647","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8292","text":"Publisher Index Page"},{"id":489260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Calfornia","otherGeospatial":"San Francisco Bay-Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121.21496769714439,\n              38.746434916955366\n            ],\n            [\n              -122.69121998415443,\n              38.746434916955366\n            ],\n            [\n              -122.69121998415443,\n              37.862368554497834\n            ],\n            [\n              -121.21496769714439,\n              37.862368554497834\n            ],\n            [\n              -121.21496769714439,\n              38.746434916955366\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"11","issue":"23","noUsgsAuthors":false,"publicationDate":"2021-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":79822,"corporation":false,"usgs":true,"family":"Duarte","given":"Adam","email":"","affiliations":[],"preferred":false,"id":938750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938749,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
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            43.27\n              ],\n              [\n                -79.17167,\n                43.46634\n              ],\n              [\n                -78.72028,\n                43.62509\n              ],\n              [\n                -77.73789,\n                43.62906\n              ],\n              [\n                -76.82003,\n                43.62878\n              ],\n              [\n                -76.5,\n                44.01846\n              ],\n              [\n                -76.375,\n                44.09631\n              ],\n              [\n                -75.31821,\n                44.81645\n              ],\n              [\n                -74.867,\n                45.00048\n              ],\n              [\n                -73.34783,\n                45.00738\n              ],\n              [\n                -71.50506,\n                45.0082\n              ],\n              [\n                -71.405,\n                45.255\n              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-68.03252,\n                44.3252\n              ],\n              [\n                -69.06,\n                43.98\n              ],\n              [\n                -70.11617,\n                43.68405\n              ],\n              [\n                -70.64548,\n                43.09024\n              ],\n              [\n                -70.81489,\n                42.8653\n              ],\n              [\n                -70.825,\n                42.335\n              ],\n              [\n                -70.495,\n                41.805\n              ],\n              [\n                -70.08,\n                41.78\n              ],\n              [\n                -70.185,\n                42.145\n              ],\n              [\n                -69.88497,\n                41.92283\n              ],\n              [\n                -69.96503,\n                41.63717\n              ],\n              [\n                -70.64,\n                41.475\n              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             29.18\n              ],\n              [\n                -80.53558,\n                28.47213\n              ],\n              [\n                -80.53,\n                28.04\n              ],\n              [\n                -80.05654,\n                26.88\n              ],\n              [\n                -80.08801,\n                26.20576\n              ],\n              [\n                -80.13156,\n                25.81677\n              ],\n              [\n                -80.38103,\n                25.20616\n              ],\n              [\n                -80.68,\n                25.08\n              ],\n              [\n                -81.17213,\n                25.20126\n              ],\n              [\n                -81.33,\n                25.64\n              ],\n              [\n                -81.71,\n                25.87\n              ],\n              [\n                -82.24,\n                26.73\n              ],\n              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             [\n                -93.22637,\n                29.78375\n              ],\n              [\n                -93.84842,\n                29.71363\n              ],\n              [\n                -94.69,\n                29.48\n              ],\n              [\n                -95.60026,\n                28.73863\n              ],\n              [\n                -96.59404,\n                28.30748\n              ],\n              [\n                -97.14,\n                27.83\n              ],\n              [\n                -97.37,\n                27.38\n              ],\n              [\n                -97.38,\n                26.69\n              ],\n              [\n                -97.33,\n                26.21\n              ],\n              [\n                -97.14,\n                25.87\n              ],\n              [\n                -97.53,\n                25.84\n              ],\n              [\n                -98.24,\n                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          32.72083\n              ],\n              [\n                -115.99135,\n                32.61239\n              ],\n              [\n                -117.12776,\n                32.53534\n              ],\n              [\n                -117.29594,\n                33.04622\n              ],\n              [\n                -117.944,\n                33.62124\n              ],\n              [\n                -118.4106,\n                33.74091\n              ],\n              [\n                -118.51989,\n                34.02778\n              ],\n              [\n                -119.081,\n                34.078\n              ],\n              [\n                -119.43884,\n                34.34848\n              ],\n              [\n                -120.36778,\n                34.44711\n              ],\n              [\n                -120.62286,\n                34.60855\n              ],\n              [\n                -120.74433,\n                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          ],\n              [\n                -124.02053,\n                44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/core-science-systems/ngp/3dep\" data-mce-href=\"https://www.usgs.gov/core-science-systems/ngp/3dep\">National Geospatial Program</a><br>U.S. Geological Survey<br>MS 511<br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Rural Broadband Access</li><li>Determining Broadband Coverage</li><li>Reference Cited</li><li>3D Elevation Program (3DEP)</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2021-11-16","noUsgsAuthors":false,"publicationDate":"2021-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thatcher, Cindy A. 0000-0003-0331-071X","orcid":"https://orcid.org/0000-0003-0331-071X","contributorId":218872,"corporation":false,"usgs":true,"family":"Thatcher","given":"Cindy","email":"","middleInitial":"A.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":826628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lukas, Vicki 0000-0002-3151-6689 vlukas@usgs.gov","orcid":"https://orcid.org/0000-0002-3151-6689","contributorId":2890,"corporation":false,"usgs":true,"family":"Lukas","given":"Vicki","email":"vlukas@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":826629,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70238819,"text":"70238819 - 2021 - Are drought indices and climate data good indicators of ecologically relevant soil moisture dynamics in drylands?","interactions":[],"lastModifiedDate":"2022-12-13T13:06:04.143849","indexId":"70238819","displayToPublicDate":"2021-11-16T07:01:42","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":"Are drought indices and climate data good indicators of ecologically relevant soil moisture dynamics in drylands?","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab010\" class=\"abstract author\" lang=\"en\"><div id=\"as010\"><p id=\"sp0010\">Droughts are disproportionately impacting global dryland regions where ecosystem health and function are tightly coupled to moisture availability. Drought severity is commonly estimated using algorithms such as the standardized precipitation-evapotranspiration index (SPEI), which can estimate climatic water balance impacts at various hydrologic scales by varying computational length. However, the performance of these metrics as indicators of soil moisture dynamics at ecologically relevant scales, across soil depths, and in consideration of broader scale ecohydrological processes, requires more attention. In this study, we tested components of climatic water balance, including SPEI and SPEI computation lengths, to recreate multi-decadal and periodic soil-moisture patterns across soil profiles at 866 sites in the western United States. Modeling results show that SPEI calculated over the prior 12-months was the most predictive computation length and could recreate changes in moisture availability within the soil profile over longer periods of time and for annual recharge of deeper soil moisture stores. SPEI was slightly less successful with recreating spring surface-soil moisture availability, which is key to dryland ecosystems dominated by winter precipitation. Meteorological drought indices like SPEI are intended to be convenient and generalized indicators of meteorological water deficit. However, the inconsistent ability of SPEI to recreate ecologically relevant patterns of soil moisture at regional scales suggests that process-based models, and the larger data requirements they involve, remain an important tool for dryland ecohydrology</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108379","usgsCitation":"Barnard, D., Germino, M., Bradford, J., O’Connor, R., Andrews, C.M., and Shriver, R.K., 2021, Are drought indices and climate data good indicators of ecologically relevant soil moisture dynamics in drylands?: Ecological Indicators, v. 133, 108379, 8 p., https://doi.org/10.1016/j.ecolind.2021.108379.","productDescription":"108379, 8 p.","ipdsId":"IP-123393","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450195,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108379","text":"Publisher Index Page"},{"id":436116,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MZKCWZ","text":"USGS data release","linkHelpText":"Standardized Precipitation-Evapotranspiration Index for western United States, 2001-2014, derived from gridMET climate estimates"},{"id":410357,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.6058382513936,\n              39.23869657680433\n            ],\n            [\n              -111.6058382513936,\n              45.4634532299672\n            ],\n            [\n              -121.44540957944166,\n              45.4634532299672\n            ],\n            [\n              -121.44540957944166,\n              39.23869657680433\n            ],\n            [\n              -111.6058382513936,\n              39.23869657680433\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"133","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnard, David 0000-0003-1877-3151","orcid":"https://orcid.org/0000-0003-1877-3151","contributorId":218008,"corporation":false,"usgs":true,"family":"Barnard","given":"David","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":858785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Connor, Rory 0000-0002-6473-0032","orcid":"https://orcid.org/0000-0002-6473-0032","contributorId":222832,"corporation":false,"usgs":true,"family":"O’Connor","given":"Rory","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":858786,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":858787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shriver, Robert K 0000-0002-4590-4834","orcid":"https://orcid.org/0000-0002-4590-4834","contributorId":222834,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert","email":"","middleInitial":"K","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":858788,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70226146,"text":"sir20215082 - 2021 - Factors affecting uncertainty of public supply, self-supplied domestic, irrigation, and thermoelectric water-use data, 1985–2015—Evaluation of information sources, estimation methods, and data variability","interactions":[],"lastModifiedDate":"2022-01-24T16:51:46.274711","indexId":"sir20215082","displayToPublicDate":"2021-11-15T17:10:00","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":"2021-5082","displayTitle":"Factors Affecting Uncertainty of Public Supply, Self-Supplied Domestic, Irrigation, and Thermoelectric Water-Use Data, 1985–2015—Evaluation of Information Sources, Estimation Methods, and Data Variability","title":"Factors affecting uncertainty of public supply, self-supplied domestic, irrigation, and thermoelectric water-use data, 1985–2015—Evaluation of information sources, estimation methods, and data variability","docAbstract":"<p>The U.S. Geological Survey (USGS) Water-Use Program is responsible for compiling and disseminating the Nation's water-use data. Working in cooperation with local, State, and Federal agencies, the USGS has collected and published national water-use estimates every 5 years, beginning in 1950. These water-use data may vary because of actual changes in water use, because of changes in estimation methods, or because of errors. Comparison and interpretation of these data is difficult without first determining the factors that contribute to data variability. This report describes factors that may affect data quality and documents ways to investigate the variability of public supply, self-supplied domestic, irrigation, and thermoelectric water-use data for the 1985–2015 compilations.</p><p>The USGS produces national water-use estimates for various categories of water use for every county in the United States. Knowledge about the sources of data for county estimates is important because factors such as estimation methodology and reporting affect data uncertainty Determination of meaningful patterns and trends in the data are contingent on the use of consistent methodology throughout the period of interest. With the many ways that water-use data have been collected, assembled, and estimated, multiple factors likely contribute to data uncertainty, Data used to produce these estimates may be furnished from agencies that collect information from entities who report water use; gaps in reported data are typically estimated to achieve a comprehensive county estimate. For example, public supply and thermoelectric category data are based primarily on furnished site-specific data; whereas crop irrigation is often furnished or estimated at the county scale. Public supply deliveries for domestic use and self-supplied domestic withdrawals are most often estimated by USGS personnel using per capita use rate coefficients. Irrigation may be estimated using crop water requirements, application rates, or other soil water balance methods when furnished reported data are not available.</p><p>Rates, percentages, medians, and interquartile ranges were used to investigate variability in the water-use data among States, regions, and years. The purposes of these evaluations were to (1) identify extreme values that may reflect changes in information sources, estimation methods, or errors; (2) indicate areas of variable or consistent values that are unexpected; and (3) indicate areas where values change because of local climate or other factors. Where factors are identified that contribute to data variability, such as a change in methodology, additional work could determine uncertainty because of these factors.</p><p>These evaluations identified the availability of information that is needed to address data limitations. Factors such as estimation methodology affect data quality. Some updates to method codes assigned in 2015 and assignment of method codes to earlier compilation datasets for all categories would provide much needed metadata for users of the data. Improvements in data documentation describing sources of information and estimation methods and additional metadata information from agencies and entities that furnish water-use data, would enable a more complete understanding and depiction of water-use patterns and trends. Additional metadata are needed for users of the data to better understand the water-use data and interpret changes in water use across the United States and with time.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215082","usgsCitation":"Luukkonen, C.L., Belitz, K., Sullivan, S.L., and Sargent, P., 2021, Factors affecting uncertainty of public supply, self-supplied domestic, irrigation, and thermoelectric water-use data, 1985–2015—Evaluation of information sources, estimation methods, and data variability: U.S. Geological Survey Scientific Investigations Report 2021–5082, 78 p., https://doi.org/10.3133/sir20215082.","productDescription":"Report: ix, 78 p.; Database; Data Release","onlineOnly":"Y","ipdsId":"IP-123556","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391626,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TA1DI9","text":"USGS data release","linkHelpText":"Public supply, self-supplied domestic, irrigation, and thermoelectric water-use data from 5-year compilation datasets from 1985 to 2015 used to assess data variability and uncertainty"},{"id":391625,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5082/sir20215082.pdf","text":"Report","size":"26.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5082"},{"id":391624,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5082/coverthb.jpg"},{"id":391627,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed July 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Information Relevant to the Water-Use Data Elements for the 2015 Compilation</li><li>Assessment of the Variability of Water-Use Data Values by State and Category</li><li>Assessment of the Variability of Water-Use Data by Region and Compilation Year</li><li>Guidance for Additional Uncertainty Assessments and Water-Use Compilations</li><li>Summary</li><li>References Cited</li><li>Glossary</li><li>Appendix 1</li></ul>","publishedDate":"2021-11-15","noUsgsAuthors":false,"publicationDate":"2021-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Luukkonen, Carol L. 0000-0001-7056-8599","orcid":"https://orcid.org/0000-0001-7056-8599","contributorId":208181,"corporation":false,"usgs":true,"family":"Luukkonen","given":"Carol","email":"","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826638,"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":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Samantha L. 0000-0002-9462-0029","orcid":"https://orcid.org/0000-0002-9462-0029","contributorId":205316,"corporation":false,"usgs":true,"family":"Sullivan","given":"Samantha","email":"","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sargent, Pierre","contributorId":268785,"corporation":false,"usgs":false,"family":"Sargent","given":"Pierre","email":"","affiliations":[{"id":55660,"text":"U.S. Geological Survey, retired","active":true,"usgs":false}],"preferred":false,"id":826641,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70226182,"text":"70226182 - 2021 - Context dependency of disease-mediated competitive release in bat assemblages following white-nose syndrome","interactions":[],"lastModifiedDate":"2021-11-16T12:58:22.531567","indexId":"70226182","displayToPublicDate":"2021-11-14T06:56:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Context dependency of disease-mediated competitive release in bat assemblages following white-nose syndrome","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>White-nose syndrome (WNS) has caused dramatic declines of several cave-hibernating bat species in North America since 2006, which has increased the activity of non-susceptible species in some geographic areas or during times of night formerly occupied by susceptible species—indicative of disease-mediated competitive release (DMCR). Yet, this pattern has not been evaluated across multiple bat assemblages simultaneously or across multiple years since WNS onset. We evaluated whether WNS altered spatial and temporal niche partitioning in bat assemblages at four locations in the eastern United States using long-term datasets of bat acoustic activity collected before and after WNS arrival. Activity of WNS-susceptible bat species decreased by 79–98% from pre-WNS levels across the four study locations, but only one of our four study sites provided strong evidence supporting the DMCR hypothesis in bats post-WNS. These results suggest that DMCR is likely dependent on the relative difference in activity by susceptible and non-susceptible species groups pre-WNS and the relative decline of susceptible species post-WNS allowing for competitive release, as well as the amount of time that had elapsed post-WNS. Our findings challenge the generality of WNS-mediated competitive release between susceptible and non-susceptible species and further highlight declining activity of some non-susceptible species, especially<span>&nbsp;</span><i>Lasiurus borealis</i>, across three of four locations in the eastern United States. These results underscore the broader need for conservation efforts to address the multiple potential interacting drivers of bat declines on both WNS-susceptible and non-susceptible species.</p></div></div>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3825","usgsCitation":"Bombaci, S., Russell, R., St. Germain, M.J., Dobony, C., Ford, W., Loeb, S., and Jachowski, D., 2021, Context dependency of disease-mediated competitive release in bat assemblages following white-nose syndrome: Ecosphere, v. 12, no. 11, e03825, 15 p., https://doi.org/10.1002/ecs2.3825.","productDescription":"e03825, 15 p.","ipdsId":"IP-111761","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":450204,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecs2.3825","text":"External Repository"},{"id":391739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Bombaci, Sara","contributorId":268816,"corporation":false,"usgs":false,"family":"Bombaci","given":"Sara","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":826736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":826740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"St. Germain, Michael J.","contributorId":25959,"corporation":false,"usgs":false,"family":"St. Germain","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":33131,"text":"Dept of Fish and Wildlife Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":826737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dobony, Christopher A.","contributorId":264897,"corporation":false,"usgs":false,"family":"Dobony","given":"Christopher A.","affiliations":[{"id":54576,"text":"DoD","active":true,"usgs":false}],"preferred":false,"id":826741,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":826738,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loeb, Susan","contributorId":204263,"corporation":false,"usgs":false,"family":"Loeb","given":"Susan","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":826739,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jachowski, David S.","contributorId":228814,"corporation":false,"usgs":false,"family":"Jachowski","given":"David S.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":826742,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70226134,"text":"sir20215127 - 2021 - Total phosphorus loadings for the Cedar River at Palo, Iowa, 2009–20","interactions":[],"lastModifiedDate":"2021-11-15T11:55:16.375506","indexId":"sir20215127","displayToPublicDate":"2021-11-12T18:05:00","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":"2021-5127","displayTitle":"Total Phosphorus Loadings for the Cedar River at Palo, Iowa, 2009–20","title":"Total phosphorus loadings for the Cedar River at Palo, Iowa, 2009–20","docAbstract":"<p>In support of nutrient reduction efforts, total phosphorus loads and yields were computed using turbidity-surrogate and LOAD ESTimator (LOADEST) models for the Cedar River at Palo, Iowa, for January 1, 2009, to December 15, 2020. Sample data were used to create a total phosphorus concentration turbidity-surrogate model. Total phosphorus loads also were computed from two streamflow-based LOADEST load models for the periods 2009–20 and 2016–20. The 2009–20 model was used for comparison with previously published loads at this site. The 2016–20 LOADEST model was used with the turbidity-surrogate model before sensor deployment and during periods of missing sensor data to obtain a more complete annual total phosphorus load. This report presents computed loads and methods needed to compute site-specific loads accurately and track annual progress toward nutrient reduction goals within the State.</p><p>A comparison of loads from Weighted Regressions on Time, Discharge, and Season; LOADEST; and surrogate models indicated substantial differences at this site among these methods. Changes in both monitoring approaches (high-frequency sensor and surrogate data) and changes in load-calculation methods present potential challenges in assessing trends, such as assessment of load reduction.</p><p>Annual total phosphorus loads for the Cedar River at Palo, Iowa, ranged from 1,370 to 2,360 U.S. short tons per year for 2018–20, based on the turbidity-surrogate model with gaps in sensor data filled with the 2016–20 LOADEST model. Annual total phosphorus yields for the Cedar River ranged from 0.67 to 1.16 pounds per acre per year for 2018–20. Although this load estimate is lower than previous estimates for the benchmark period of 2006–10, when normalized by streamflow, nearly all the apparent reduction can be attributed to differences in the load-calculation methods.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215127","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Garrett, J.D., 2021, Total phosphorus loadings for the Cedar River at Palo, Iowa, 2009–20: U.S. Geological Survey Scientific Investigations Report 2021–5127, 15 p., https://doi.org/10.3133/sir20215127.","productDescription":"Report vi, 15 p.: Database; Related Work","onlineOnly":"Y","ipdsId":"IP-127065","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391620,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5127/coverthb.jpg"},{"id":391621,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5127/sir20215127.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5127"},{"id":391622,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":391623,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20185090","text":"Transport of nitrogen and phosphorus in the Cedar River Basin, Iowa and Minnesota, 2000–15"}],"country":"United States","state":"Palo","otherGeospatial":"Cedar River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.83197021484375,\n              42.02889410108475\n            ],\n            [\n              -91.71180725097655,\n              42.02889410108475\n            ],\n            [\n              -91.71180725097655,\n              42.09312731992276\n            ],\n            [\n              -91.83197021484375,\n              42.09312731992276\n            ],\n            [\n              -91.83197021484375,\n              42.02889410108475\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","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>400 South Clinton Street, Suite 269<br>Iowa City, IA 52240</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods for Data Collection and Computation</li><li>Water-Quality Sample and Sensor Data</li><li>Continuous Water-Quality Time-Series Data to Compute Nutrient Loadings</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2021-11-12","noUsgsAuthors":false,"publicationDate":"2021-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Garrett, Jessica D. 0000-0002-4466-3709 jgarrett@usgs.gov","orcid":"https://orcid.org/0000-0002-4466-3709","contributorId":4229,"corporation":false,"usgs":true,"family":"Garrett","given":"Jessica","email":"jgarrett@usgs.gov","middleInitial":"D.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826587,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70225933,"text":"sir20215106 - 2021 - Water and sediment chemistry of selected existing and potential habitats of the Mohave tui chub, Mojave National Preserve, California, 2018","interactions":[],"lastModifiedDate":"2021-11-15T11:47:37.497179","indexId":"sir20215106","displayToPublicDate":"2021-11-12T09:28:27","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":"2021-5106","displayTitle":"Water and Sediment Chemistry of Selected Existing and Potential Habitats of the Mohave Tui Chub, Mojave National Preserve, California, 2018","title":"Water and sediment chemistry of selected existing and potential habitats of the Mohave tui chub, Mojave National Preserve, California, 2018","docAbstract":"<p>The Mohave tui chub (<i>Siphateles bicolor mohavensis</i>) was nearly extirpated from the Mojave River drainage in California by the mid-twentieth century and was listed as endangered in 1970. A source population of Mohave tui chub exists at MC Spring in Zzyzx, California, and has been used for several re-establishment efforts in previous decades. Two potential habitats in the Mojave National Preserve with perennial sources of water were identified by the National Park Service as candidates for additional Mohave tui chub re-establishment: West Pond and Rainbow Wells Pond. West Pond, an artificial pond at Zzyzx near MC Spring, contained a population of Mohave tui chub that died off in 1985 because of changes in water quality. The pond was rehabilitated in the past several years through re-excavation and by pumping fresh groundwater into the pond. Rainbow Wells Pond is an abandoned excavated mine site in the Cima Dome area. The bottom of the excavation intersects the water table, forming a pond. In cooperation with the National Park Service, the U.S. Geological Survey monitored water-quality conditions at West Pond and Rainbow Wells Pond for 1 year to characterize the suitability of spring habitat for re-establishment of Mohave tui chub populations. Data were also collected at three existing Mohave tui chub habitats in Mojave National Preserve to provide further information on the range of acceptable physical and chemical conditions. Initial water-quality results at West Pond indicate the pond has similar water quality as existing Mohave tui chub habitats. Initial water-quality results at Rainbow Wells Pond indicate the dissolved oxygen concentrations and springtime water temperatures are less than the long-term tolerable ranges for Mohave tui chub.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215106","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Earp, K.J., and Paul, A.P., 2021, Water and sediment chemistry of selected existing and potential habitats of the Mohave tui chub, Mojave National Preserve, California, 2018: U.S. Geological Survey Scientific Investigations Report 2021–5106, 26 p., https://doi.org/10.3133/sir20215106.","productDescription":"Report: v, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-099414","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":391580,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"National Water Information System"},{"id":391576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5106/covrthb.jpg"},{"id":391577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5106/sir20215106.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391578,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5106/sir20215106.xml"},{"id":391579,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5106/images"}],"country":"United States","state":"California","otherGeospatial":"Mojave National Preserve","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.2301025390625,\n              35.47409160773029\n            ],\n            [\n              -115.36193847656249,\n              35.54116627999815\n            ],\n            [\n              -115.59814453125001,\n              35.55457449014312\n            ],\n            [\n              -115.806884765625,\n              35.567980458012094\n            ],\n            [\n              -116.43859863281249,\n              35.38457160381764\n            ],\n            [\n              -116.55944824218749,\n              35.074964853989556\n            ],\n            [\n              -116.54296874999999,\n              34.79576153473033\n            ],\n            [\n              -116.16943359374999,\n              34.56085936708384\n            ],\n            [\n              -115.7080078125,\n              34.36611072883117\n            ],\n            [\n              -115.224609375,\n              34.261756524459805\n            ],\n            [\n              -114.72473144531251,\n              34.30260622622907\n            ],\n            [\n              -114.58740234375,\n              34.58347505599177\n            ],\n            [\n              -114.6368408203125,\n              34.84536693184101\n            ],\n            [\n              -114.6533203125,\n              35.016500995886005\n            ],\n            [\n              -115.2301025390625,\n              35.47409160773029\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_nv@usgs.gov\" data-mce-href=\"mailto:dc_nv@usgs.gov\">Director</a>,<br><a href=\"https://www.usgs.gov/centers/nv-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/nv-water\">Nevada Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>2730 N. Deer Run Road<br>Carson City, Nevada 95819</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods of Study</li><li>Water and Sediment Chemistry</li><li>Suitability of Potential Habitats: Rainbow Wells Pond and West Pond</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-11-12","noUsgsAuthors":false,"publicationDate":"2021-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Earp, Katherine J. 0000-0002-5291-6737 kjearp@usgs.gov","orcid":"https://orcid.org/0000-0002-5291-6737","contributorId":223704,"corporation":false,"usgs":true,"family":"Earp","given":"Katherine","email":"kjearp@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paul, Angela P. 0000-0003-3909-1598 appaul@usgs.gov","orcid":"https://orcid.org/0000-0003-3909-1598","contributorId":2305,"corporation":false,"usgs":true,"family":"Paul","given":"Angela","email":"appaul@usgs.gov","middleInitial":"P.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826578,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70225680,"text":"cir1486 - 2021 - Nitrogen in the Chesapeake Bay watershed—A century of change, 1950–2050","interactions":[],"lastModifiedDate":"2026-01-26T22:36:29.876041","indexId":"cir1486","displayToPublicDate":"2021-11-10T14:05:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1486","displayTitle":"Nitrogen in the Chesapeake Bay Watershed—A Century of Change, 1950–2050","title":"Nitrogen in the Chesapeake Bay watershed—A century of change, 1950–2050","docAbstract":"<h1>Foreword</h1><p>Sustaining the quality of the Nation’s water resources and the health of our diverse ecosystems depends on the availability of sound water-resources data and information to develop effective, science-based policies. Effective management of water resources also brings more certainty and efficiency to important economic sectors. Taken together, these actions lead to immediate and long-term economic, social, and environmental benefits that will make a difference to the lives of the almost 400 million people projected to live in the United States by 2050.</p><p>The Chesapeake Bay is the largest and most productive estuary in the United States and is a vital environmental and economic resource. Approximately half of the water volume of the Chesapeake Bay originates from streams and rivers that drain the 64,243 mi<sup>2</sup> Chesapeake Bay watershed. The Bay and its tributaries have been degraded by excessive nutrients, such as nitrogen, from contributing watersheds. Inputs of nitrogen to the Bay lead to increased algal growth, decreased dissolved oxygen, and declining fisheries. In 2000, the Chesapeake Bay was listed as impaired under the Clean Water Act and Total Maximum Daily Loads (TMDLs) for nutrients and sediment have been established to assist with management actions aimed at nutrient reductions. Effective nutrient management requires an understanding of past, present, and future nutrient sources, fate, and transport in the watershed.</p><p>The Chesapeake Bay community has been a pioneer in science, management, and regulation to improve water quality. Factors like climate, hydrology, source inputs, and management controls play a vital role in determining the delivery and magnitude of nitrogen inputs to the Bay. Science in the form of monitoring data, predictive tools, and interpretive reports can help inform decisions to better balance the use and control of nitrogen in coastal areas. The findings in this report can contribute to effective management of the Bay and its watershed by providing a synthesis of the understanding of how human activities and environmental change in the watershed in the past, present, and future will influence the export of nitrogen to the Bay.</p><p>We hope this publication will provide you with insights and information to meet your water resource needs and will foster increased civilian awareness and involvement in the protection and restoration of our Nation’s waters. The information in this report is intended primarily for those interested or involved in resource management and protection, conservation, regulation, and policymaking at the regional and national levels.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1486","programNote":"National Water-Quality Program","usgsCitation":"Clune, J.W., and Capel, P.D., eds., 2021, Nitrogen in the Chesapeake Bay watershed—A century of change, 1950–2050 (ver. 1.2, 2024): U.S. Geological Survey Circular 1486, 168 p., https://doi.org/10.3133/cir1486.","productDescription":"vi, 168 p.","numberOfPages":"168","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-109208","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":499071,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111889.htm","linkFileType":{"id":5,"text":"html"}},{"id":391297,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1486/coverthb4.jpg"},{"id":391298,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1486/cir1486.pdf","text":"Report","size":"70.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1486"},{"id":396026,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://www.usgs.gov/media/videos/nitrogen-chesapeake-bay-watershed-century-change","text":"Video","linkHelpText":"- Nitrogen in the Chesapeake Bay Watershed: A Century of 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data-mce-href=\"https://www.usgs.gov/centers/pa-water\">Pennsylvania Water Science Center</a><br>U.S. Geological Survey<br>215 Limekiln Road<br>New Cumberland, PA 17070</p>","tableOfContents":"<ul><li>Foreword</li><li>Overview of Major Findings</li><li>Environmental Setting of the Chesapeake Bay Watershed</li><li>Nitrogen Setting of the Chesapeake Bay Watershed</li><li>Historical Setting of the Chesapeake Bay Watershed</li><li>Chapter 1. Changes in Nitrogen, Water Quality, and Management</li><li>Chapter 2. Nitrogen in Streams and Groundwater</li><li>Chapter 3. Changes in Climate</li><li>Chapter 4. Changes in Hydrology</li><li>Chapter 5. Changes in Atmospheric Deposition of Nitrogen</li><li>Chapter 6. Changes in Land Use</li><li>Chapter 7. Changes in Agricultural Water-Quality Management</li><li>Chapter 8. Changes in Water-Quality Management in Developed Areas</li><li>Chapter 9. Modeling the Effect of Nitrogen Loads from Multiple Changes in the Watershed</li><li>Chapter 10. Watershed Scale Changes in Nitrogen Export: Past and Future</li><li>Excess Nitrogen Impacts on Coastal Areas Across the Nation and the World</li><li>Final Thoughts</li><li>References Cited</li><li>Glossary</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2021-11-10","revisedDate":"2024-01-09","noUsgsAuthors":false,"publicationDate":"2021-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Clune, John W. 0000-0002-3563-1975 jclune@usgs.gov","orcid":"https://orcid.org/0000-0002-3563-1975","contributorId":173410,"corporation":false,"usgs":true,"family":"Clune","given":"John","email":"jclune@usgs.gov","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capel, Paul D. 0000-0003-1620-5185 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,{"id":70225749,"text":"sir20215092 - 2021 - Continuous monitoring and Bayesian estimation of nutrient and sediment loads from Illinois watersheds, for water years 2016–2020","interactions":[],"lastModifiedDate":"2021-11-10T15:38:37.861327","indexId":"sir20215092","displayToPublicDate":"2021-11-10T10:20:00","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":"2021-5092","displayTitle":"Continuous Monitoring and Bayesian Estimation of Nutrient and Sediment Loads from Illinois Watersheds, for Water Years 2016–2020","title":"Continuous monitoring and Bayesian estimation of nutrient and sediment loads from Illinois watersheds, for water years 2016–2020","docAbstract":"<p>The State of Illinois is one of the leading contributors of nitrogen, phosphorus, and suspended sediment to the Mississippi River and the Gulf of Mexico. During water years 2016–20, the U.S. Geological Survey, in cooperation with the Illinois Environmental Protection Agency, operated continuous monitoring stations on eight major rivers in Illinois to better quantify nutrient and sediment loadings from the State of Illinois to the Mississippi River. This report estimates nitrate, phosphorus, and suspended-sediment loadings over that period, which can provide a benchmark against which to assess future changes in loading.</p><p>In addition, this report develops a new method for incorporating the uncertainty created by gaps in continuous datasets based on Bayesian machine learning. Data gaps are a common problem in continuous monitoring, and gap filling is necessary to quantify loadings and the uncertainty in loadings, which is essential if these results are to provide a benchmark for future studies. The uncertainty estimates may also be useful in an operational context, and this report provides examples of how uncertainty can be used in monitoring-network design and potentially reducing monitoring costs.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215092","collaboration":"Prepared in cooperation with the Illinois Environmental Protection Agency","usgsCitation":"Hodson, T.O., Terrio, P.J., Peake, C.S., and Fazio, D.J., 2021, Continuous monitoring and Bayesian estimation of nutrient and sediment loads from Illinois watersheds, for water years 2016–2020: U.S. Geological Survey Scientific Investigations Report 2021–5092, 40 p., https://doi.org/ 10.3133/ sir20215092.","productDescription":"Report: vii, 40 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-126875","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436121,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T6W30D","text":"USGS data release","linkHelpText":"Estimated nutrient and sediment concentrations from major rivers in Illinois based on continuous monitoring from October 1, 2015, through September 30, 2021"},{"id":391519,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5092/coverthb.jpg"},{"id":391520,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5092/sir20215092.pdf","text":"Report","size":"5.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5092"},{"id":391521,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MS8OIH","text":"USGS data release","linkHelpText":"Modeled nutrient and sediment concentrations from major rivers in Illinois based on continuous monitoring from October 1, 2015, through September 30, 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 \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/cm-water\" data-mce-href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>405 N. Goodwin Ave.<br>Urbana, IL 61801</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Methods</li><li>Data Coverage</li><li>Streamflow and Discrete Water-Quality Data</li><li>Imputation Results</li><li>Comparison Among Model Forms</li><li>Loads and Yields</li><li>Continuous Monitoring and Discrete Sampling</li><li>Network Improvements</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Station Descriptions</li></ul>","publishedDate":"2021-11-10","noUsgsAuthors":false,"publicationDate":"2021-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodson, Timothy O. 0000-0003-0962-5130","orcid":"https://orcid.org/0000-0003-0962-5130","contributorId":78634,"corporation":false,"usgs":true,"family":"Hodson","given":"Timothy","email":"","middleInitial":"O.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terrio, Paul J. 0000-0002-1515-9570 pjterrio@usgs.gov","orcid":"https://orcid.org/0000-0002-1515-9570","contributorId":3313,"corporation":false,"usgs":true,"family":"Terrio","given":"Paul","email":"pjterrio@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peake, Colin S. 0000-0001-9712-1623","orcid":"https://orcid.org/0000-0001-9712-1623","contributorId":268354,"corporation":false,"usgs":true,"family":"Peake","given":"Colin","email":"","middleInitial":"S.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fazio, David J. 0000-0003-0254-5162","orcid":"https://orcid.org/0000-0003-0254-5162","contributorId":268355,"corporation":false,"usgs":true,"family":"Fazio","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826487,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70225748,"text":"sir20215050 - 2021 - Preliminary geohydrologic assessment of Buenos Aires National Wildlife Refuge, Altar Valley, southeastern Arizona","interactions":[],"lastModifiedDate":"2021-11-10T19:08:22.752141","indexId":"sir20215050","displayToPublicDate":"2021-11-10T09:09:24","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":"2021-5050","displayTitle":"Preliminary Geohydrologic Assessment of Buenos Aires National Wildlife Refuge, Altar Valley, Southeastern Arizona","title":"Preliminary geohydrologic assessment of Buenos Aires National Wildlife Refuge, Altar Valley, southeastern Arizona","docAbstract":"<p>The Buenos Aires National Wildlife Refuge is located in the southern part of Altar Valley, southwest of Tucson in southeastern Arizona. The primary water-supply well at the Buenos Aires National Wildlife Refuge has experienced a two-decade decrease in groundwater levels in the well, as have other wells in the southern part of Altar Valley. In part to understand this trend, a study was undertaken by the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, to summarize what is known about the geohydrologic system on the refuge and analyze groundwater-level trends and precipitation-groundwater correlations. In addition, available data were compiled where possible on the climate, land cover, soils, geology, and hydrology to provide a foundation for future modeling of the system.</p><p>Altar Valley is a sedimentary basin bounded by a mixture of Paleozoic to Tertiary sedimentary, volcanic, granitic, and metamorphic rocks. The valley fill is undifferentiated Tertiary to Quaternary sediments underlain by middle Miocene to Pliocene rocks that consist of moderately to strongly consolidated conglomerate and sandstone. Surface water, when present in the predominantly ephemeral streams of the valley, flows from south to north. Arivaca Creek has a cienega (or wetland) where groundwater surfaces before it flows as a short perennial reach out of Arivaca Basin. Groundwater maps compiled between 1934 and 2016 showed groundwater flowing from south to north. Before the 1980s, temporal patterns of groundwater levels in wells in Altar Valley varied substantially from one well to another. In the mid-1980s, comparatively high levels of precipitation occurred: the 1980s median value was 15.3 inches, whereas the median for the period of record was 13.2 inches. In addition, apparently corresponding groundwater level increases were seen in nearly all wells studied. After this initial increase, two different groundwater-level trends began to be observed in two spatially distinct sets of wells: in the northern part, groundwater levels were relatively steady, whereas in the southern part, groundwater levels declined from 10 to 20 feet between 1990 and 2019. Annual groundwater pumpage declined substantially in the northern part of the valley beginning in the early 1980s, but it began to increase again in the 1990s. Pumpage in the southern part has remained low and relatively steady compared to the northern part. Although the precise reasons for the declining groundwater levels in the southern part remain unclear, groundwater levels may be affected by factors such as climate cycles, long-term drought, and temperature-induced declines in recharge, resulting in increased evapotranspiration.</p><p>Preliminary analyses of two wells, one selected from each part of the valley, using linear regression and lag correlation to investigate correlation between annual precipitation and groundwater levels, showed a maximum correlation at a lag of about 17 years in the southern part of the valley and about 25 years in the northern part, indicating that, although variable sources and traveltimes of recharged water may be needed to propagate to each location, the strongest correlation at each well is with precipitation that was recharged 17 and 25 years prior to the groundwater response in that well. Assuming a constant flow of groundwater from the southern to the northern part of the valley, a decrease in recharge is expected to lead to a decrease in aquifer storage. As to the comparatively stable groundwater levels in the northern part, pumpage is still only about one-half what it was in the early 1980s, even though pumpage has increased there since the 1990s. Water levels in most wells in the northern part were drawn down prior to the decrease in pumping in the early 1980s, possibly owing to a combination of pumping and the nearly 20-year midcentury drought that occurred between 1940 and 1960. Water levels were in the process of recovering when the increase in pumping occurred in the 1990s. Because the water levels were recovering (increasing) instead of remaining static, the increased pumping may have only limited the recovery rather than causing a decrease in water levels, as a new quasi-equilibrium state may have been reached. Additional possible causes for the stable groundwater levels include (1) upgradient aquifer transmissivity that was high enough to offset pumping, (2) a low-permeability barrier, such as bedrock or clay, at the north end of the valley that caused groundwater pooling, (3) higher lateral inflow of groundwater in the northern part of the valley, (4) a delay in the effect of storage declines propagating from the south, or (5) some combination thereof.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215050","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Owen-Joyce, S.J., Callegary, J.B., and Rosebrough, A.E., 2021, Preliminary geohydrologic assessment of Buenos Aires National Wildlife Refuge, Altar Valley, southeastern Arizona: U.S. Geological Survey Scientific Investigations Report 2021–5050, 29 p., https://doi.org/10.3133/sir20215050.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-118417","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":391517,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5050/sir20215050.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391518,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QST8OX","linkHelpText":"Groundwater well data and annual groundwater pumpage data (1984–2019) in Altar Valley, Arizona"},{"id":391516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5050/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Altar Valley, Buenos Aires National Wildlife Refuge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.56341552734375,\n              31.459125370764387\n            ],\n            [\n              -111.34780883789062,\n              31.459125370764387\n            ],\n            [\n              -111.34780883789062,\n              31.81864727496152\n            ],\n            [\n              -111.56341552734375,\n              31.81864727496152\n            ],\n            [\n              -111.56341552734375,\n              31.459125370764387\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_az@usgs.gov\" data-mce-href=\"mailto:dc_az@usgs.gov\">Director</a>,<br><a href=\"https://www.usgs.gov/centers/az-water\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/az-water\">Arizona Water Science Center</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>520 N. Park Avenue<br>Tucson, AZ 85719</p>","tableOfContents":"<ul><li>Acknowledgments&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Aquifer Assessment&nbsp;&nbsp;</li><li>Altar Valley Precipitation–Groundwater Level Correlation&nbsp;&nbsp;</li><li>Summary&nbsp;&nbsp;</li><li>Selected References&nbsp;&nbsp;</li><li>Appendix 1. Selected Well Data in the Altar Valley, Arizona, Groundwater Area&nbsp;&nbsp;</li><li>Appendix 2. Annual Groundwater Pumpage in Altar Valley, Arizona, Between 1984 and 2019</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-11-10","noUsgsAuthors":false,"publicationDate":"2021-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Owen-Joyce, Sandra J. 0000-0002-4400-5618 sjowen@usgs.gov","orcid":"https://orcid.org/0000-0002-4400-5618","contributorId":5215,"corporation":false,"usgs":true,"family":"Owen-Joyce","given":"Sandra","email":"sjowen@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":826481,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Callegary, James B. 0000-0003-3604-0517 jcallega@usgs.gov","orcid":"https://orcid.org/0000-0003-3604-0517","contributorId":2171,"corporation":false,"usgs":true,"family":"Callegary","given":"James","email":"jcallega@usgs.gov","middleInitial":"B.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosebrough, Amy Elizabeth","contributorId":268353,"corporation":false,"usgs":false,"family":"Rosebrough","given":"Amy","email":"","middleInitial":"Elizabeth","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":true,"id":826483,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70226173,"text":"70226173 - 2021 - Multilayer perceptrons (MLPs)","interactions":[],"lastModifiedDate":"2021-11-16T13:14:51.29369","indexId":"70226173","displayToPublicDate":"2021-11-10T07:14:03","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Multilayer perceptrons (MLPs)","docAbstract":"<div id=\"body\"><div class=\"content\"><p id=\"Par1\" class=\"Para\">Artificial neural networks (ANNs) are adaptable systems that can solve problems that are difficult to describe with a mathematical relationship. They seek relationships between different types of datasets with their abilities to learn either with supervision or without. ANNs recognize patterns between input and output space and generalize solutions, in a way simulating the human brain’s learning experience with many relatively simple individual processing elements, called neurons. Neurons are networked (network topology) in a number of ways depending on the problem type and complexity. One of the most widely used ANN learning techniques is supervised learning coupled with a multilayer perceptron (MLP) topology due to its flexible applicability to a wide range of modeling problems involving both general classification and regression. ANNs, due to this flexibility, have been applied to many fields since the 1990s and their theory, types (such as radial basis functions, random...</p></div></div>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Mathematical Geosciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-26050-7_455-1","usgsCitation":"Karacan, C.O., 2021, Multilayer perceptrons (MLPs), chap. <i>of</i> Encyclopedia of Mathematical Geosciences, 3 p., https://doi.org/10.1007/978-3-030-26050-7_455-1.","productDescription":"3 p.","ipdsId":"IP-124707","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":391746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":826715,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70226717,"text":"70226717 - 2021 - Strong evidence for two disjunct populations of Black Scoters Melanitta americana in North America","interactions":[],"lastModifiedDate":"2021-12-07T13:05:27.13515","indexId":"70226717","displayToPublicDate":"2021-11-10T07:05:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3764,"text":"Wildfowl","onlineIssn":"2052-6458","printIssn":"0954-6324","active":true,"publicationSubtype":{"id":10}},"title":"Strong evidence for two disjunct populations of Black Scoters Melanitta americana in North America","docAbstract":"<div>Black Scoters<span>&nbsp;</span><i>Melanitta americana</i><span>&nbsp;</span>were marked with satellite transmitters on Atlantic and Pacific coasts of North America to examine continental-scale population delineation. Scoters marked on the different coasts did not overlap at any stage of the annual cycle, suggesting that birds in the two regions could be monitored and managed as separate populations: 1) an Atlantic population, which winters along the Atlantic coast and Great Lakes and breeds from northeast continental Canada westward to the Northwest Territories, and 2) a Pacific population, which winters along the Pacific coasts of Alaska, British Columbia and the Pacific northwest states, and breeds in western Alaska. Range maps for Black Scoter could reflect these distributions revealed by satellite telemetry. Our data provide new information on the distribution of Black Scoters in North America, which can be used to improve the design of future surveys.</div>","language":"English","publisher":"Wildfowl Journal","usgsCitation":"Bowman, T.D., Gilliland, S.G., Schamber, J.L., Flint, P.L., Esler, D., Boyd, W., Rosenberg, D.H., Savard, J.L., Perry, M., and Osenkowski, J.E., 2021, Strong evidence for two disjunct populations of Black Scoters Melanitta americana in North America: Wildfowl, v. 71, p. 179-192.","productDescription":"14 p.","startPage":"179","endPage":"192","ipdsId":"IP-121283","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":392567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":392559,"type":{"id":15,"text":"Index Page"},"url":"https://wildfowl.wwt.org.uk/index.php/wildfowl/article/view/2759"}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.57812499999999,\n              48.45835188280866\n            ],\n            [\n              -58.35937499999999,\n              48.45835188280866\n            ],\n            [\n              -58.35937499999999,\n              65.94647177615738\n            ],\n            [\n              -107.57812499999999,\n              65.94647177615738\n            ],\n            [\n              -107.57812499999999,\n              48.45835188280866\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -166.2890625,\n              57.136239319177434\n            ],\n            [\n              -155.390625,\n              57.136239319177434\n            ],\n            [\n              -155.390625,\n              69.03714171275197\n            ],\n            [\n              -166.2890625,\n              69.03714171275197\n            ],\n            [\n              -166.2890625,\n              57.136239319177434\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"71","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bowman, Timothy D.","contributorId":80779,"corporation":false,"usgs":false,"family":"Bowman","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":827939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilliland, Scott G.","contributorId":216936,"corporation":false,"usgs":false,"family":"Gilliland","given":"Scott","email":"","middleInitial":"G.","affiliations":[{"id":12590,"text":"Canadian Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":827940,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schamber, Jason L","contributorId":269800,"corporation":false,"usgs":false,"family":"Schamber","given":"Jason","email":"","middleInitial":"L","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":827941,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":827942,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":827943,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boyd, W. Sean","contributorId":241002,"corporation":false,"usgs":false,"family":"Boyd","given":"W. Sean","affiliations":[{"id":48188,"text":"Environment Canada","active":true,"usgs":false}],"preferred":false,"id":827944,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rosenberg, Daniel H.","contributorId":42774,"corporation":false,"usgs":false,"family":"Rosenberg","given":"Daniel","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":827945,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Savard, Jean-Pierre L.","contributorId":101776,"corporation":false,"usgs":false,"family":"Savard","given":"Jean-Pierre","email":"","middleInitial":"L.","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":827946,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Perry, Matthew 0000-0001-6452-9534 mperry@usgs.gov","orcid":"https://orcid.org/0000-0001-6452-9534","contributorId":179173,"corporation":false,"usgs":true,"family":"Perry","given":"Matthew","email":"mperry@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":827947,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Osenkowski, Jason E.","contributorId":216934,"corporation":false,"usgs":false,"family":"Osenkowski","given":"Jason","email":"","middleInitial":"E.","affiliations":[{"id":39552,"text":"Rhode Island Department of Environmental Management","active":true,"usgs":false}],"preferred":false,"id":827948,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70225730,"text":"ofr20201084 - 2021 - Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species","interactions":[],"lastModifiedDate":"2021-11-10T12:31:36.129608","indexId":"ofr20201084","displayToPublicDate":"2021-11-09T15:40:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1084","displayTitle":"Decision-Support Framework for Linking Regional-Scale Management Actions to Continental-Scale Conservation of Wide-Ranging Species","title":"Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species","docAbstract":"<p><i>Anas acuta</i> (Northern pintail; hereafter pintail) was selected as a model species on which to base a decision-support framework linking regional actions to continental-scale population and harvest objectives. This framework was then used to engage stakeholders, such as Landscape Conservation Cooperatives’ (LCCs’) habitat management partners within areas of importance to pintails, while maximizing cross-taxa effects from the framework. The mathematical framework for the model had been previously developed for pintails. A key assumption incorporated into the model is that density dependence in survival occurs during the post-hunting (winter) period, where resources are hypothesized to be limiting. Because few data are available to directly inform this process, the approach used was to build a hierarchical Bayesian integrated population model (IPM) that simultaneously uses data from bird-band recoveries, breeding population counts, and harvest surveys to estimate values of parameters of an annual population projection model, including population size, survival rate, reproductive rate, and process and observation error variances, that are logically consistent with each other, given the mathematical structure imposed through the IPM.</p><p>The main accomplishments of this study are (1) development of an IPM for pintail to guide harvest and habitat management, (2) development of a Prairie Parkland Region breeding submodel to predict pintail productivity, (3) development of statistical methodology to estimate pintail productivity (as measured by the ratio of juvenile to adults in hunter-collected wing samples) and winter survival and to relate these estimates to covariates, and (4) illustration of how to use a model and estimated parameter values to predict pintail population size and sustainable harvest as a function of habitat.</p><p>Estimation of pintail survival from bird-banding data shows that there has been relatively little variation in survival over the period 1960–2013. A productivity model showed strong effects of breeding ground conditions, wintering-ground precipitation, and density dependence on pintail productivity. Thus, most temporal variation in pintail demographic rates has been due to effects on reproduction and not survival, including effects of breeding or wintering-ground habitat. These results indicate that habitat conservation efforts may be most effective if they focus on maintaining or increasing breeding and wintering-ground habitat to increase pintail productivity rather than pintail survival. Environmental perturbations in excess of historical experience, such as what could occur under climate change, might have meaningful effects on survival but cannot be estimated with current data. Direct effects of climate, land use, or management are likely to be greater on productivity than survival, but substantial uncertainty remains about predictions of equilibrium population size and sustainable yield.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201084","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Osnas, E.E., Boomer, G.S., Devries, J.H., and Runge, M.C., 2021, Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species: U.S. Geological Survey Open-File Report 2020–1084, 31 p., https://doi.org/10.3133/ofr20201084.","productDescription":"Report: vi, 31 p.; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-083951","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":391433,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93YTR3X","text":"USGS data release","linkHelpText":"Data release—Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species"},{"id":391431,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1084/coverthb.jpg"},{"id":391432,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1084/ofr20201084.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1084"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/eesc\" data-mce-href=\"https://www.usgs.gov/centers/eesc\">Eastern Ecological Science Center</a><br>U.S. Geological Survey<br>12100 Beech Forest Road<br>Laurel, MD 20708</p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Objectives</li><li>Methods</li><li>Decision-Support Framework Results</li><li>Discussion</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2021-11-09","noUsgsAuthors":false,"publicationDate":"2021-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Osnas, Erik E. 0000-0001-9528-0866 eosnas@usgs.gov","orcid":"https://orcid.org/0000-0001-9528-0866","contributorId":5586,"corporation":false,"usgs":true,"family":"Osnas","given":"Erik","email":"eosnas@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":826432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boomer, G. Scott 0000-0001-5854-3604","orcid":"https://orcid.org/0000-0001-5854-3604","contributorId":261408,"corporation":false,"usgs":false,"family":"Boomer","given":"G.","email":"","middleInitial":"Scott","affiliations":[{"id":7199,"text":"US FWS","active":true,"usgs":false}],"preferred":true,"id":826433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Devries, James H.","contributorId":268336,"corporation":false,"usgs":false,"family":"Devries","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":7182,"text":"Ducks Unlimited Canada","active":true,"usgs":false}],"preferred":true,"id":826434,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":826435,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70225545,"text":"ofr20211091 - 2021 - Digital Shoreline Analysis System (DSAS) version 5.1 user guide","interactions":[],"lastModifiedDate":"2024-05-16T14:04:20.434812","indexId":"ofr20211091","displayToPublicDate":"2021-11-09T12:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1091","displayTitle":"Digital Shoreline Analysis System (DSAS) Version 5.1 User Guide","title":"Digital Shoreline Analysis System (DSAS) version 5.1 user guide","docAbstract":"<p>The Digital Shoreline Analysis System version 5 software is an add-in to Esri ArcGIS Desktop version 10.4–10.7 that enables a user to calculate rate-of-change statistics from a time series of vector shoreline positions. The Digital Shoreline Analysis System provides an automated method for establishing measurement locations, performs rate calculations, provides the statistical data necessary to assess the reliability of the rates, and includes a beta model for forecasting shoreline position. The Digital Shoreline Analysis System version 5.1 includes updates to the interface and the application of proxy-datum bias. This in-depth user guide provides comprehensive instruction on the installation and use of the program, including how to create a reference baseline for measurements, steps needed to generate measurement transects and metadata, guidelines on how to manually add or edit existing transects, and an explanation of the visualization options to display calculated rates of shoreline change.</p><p><strong>Note:</strong> As of May 2024, the current version of the Digital Shoreline Analysis System (DSAS), version 6.0, is a standalone desktop application for calculating shoreline or boundary change over time. The user guide for DSAS version 5.1 is applicable to many aspects of version 6.0. The user guide provides relevant information on the DSAS workflow, including how to define a reference baseline for measurements, attribute requirements for baselines and shorelines, and supporting information on rate calculations and statistics.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211091","usgsCitation":"Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S., 2021, Digital Shoreline Analysis System (DSAS) version 5.1 user guide: U.S. Geological Survey Open-File Report 2021–1091, 104 p., https://doi.org/10.3133/ofr20211091.","productDescription":"Report: xi, 104 p.; Software Release","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123671","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":390774,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/centers/whcmsc/science/digital-shoreline-analysis-system-dsas","text":"Digital Shoreline Analysis System (DSAS)"},{"id":390775,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P13WIZ8M","text":"Digital Shoreline Analysis System version 6.0"},{"id":390767,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1091/ofr20211091.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1091"},{"id":390766,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1091/coverthb.jpg"}],"contact":"<p><a href=\"mailto:WHSC_science_director@usgs.gov\" data-mce-href=\"mailto:WHSC_science_director@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/whcmsc\" data-mce-href=\"https://www.usgs.gov/centers/whcmsc\">Woods Hole Coastal and Marine Science Center</a><br>U.S. Geological Survey<br>384 Woods Hole Road<br>Quissett Campus<br>Woods Hole, MA 02543–1598</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>1. Introduction</li><li>2. Installation Steps</li><li>3. Sample Data</li><li>4. DSAS Toolbar</li><li>5. Required Inputs</li><li>6. DSAS Workflow</li><li>7. Statistics</li><li>8. Beta Shoreline Forecasting</li><li>9. Summary Report</li><li>10. Metadata</li><li>11. References Cited</li><li>12. Appendix 1. Troubleshooting</li><li>13.Appendix 2. Calculating and Applying the Proxy-Datum Bias Between High-Water Line and Mean High Water Shorelines</li><li>14. Appendix 3. Summary Report Text</li><li>15. Appendix 4. Sample Data Workflows</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-11-09","noUsgsAuthors":false,"publicationDate":"2021-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Himmelstoss, Emily A. 0000-0002-1760-5474 ehimmelstoss@usgs.gov","orcid":"https://orcid.org/0000-0002-1760-5474","contributorId":194838,"corporation":false,"usgs":true,"family":"Himmelstoss","given":"Emily","email":"ehimmelstoss@usgs.gov","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":825525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henderson, Rachel E. 0000-0001-5810-7941 rehenderson@contractor.usgs.gov","orcid":"https://orcid.org/0000-0001-5810-7941","contributorId":196870,"corporation":false,"usgs":true,"family":"Henderson","given":"Rachel","email":"rehenderson@contractor.usgs.gov","middleInitial":"E.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":825526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kratzmann, Meredith G. 0000-0002-2513-2144 mkratzmann@usgs.gov","orcid":"https://orcid.org/0000-0002-2513-2144","contributorId":4950,"corporation":false,"usgs":true,"family":"Kratzmann","given":"Meredith","email":"mkratzmann@usgs.gov","middleInitial":"G.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":825527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farris, Amy S. 0000-0002-4668-7261 afarris@usgs.gov","orcid":"https://orcid.org/0000-0002-4668-7261","contributorId":196866,"corporation":false,"usgs":true,"family":"Farris","given":"Amy","email":"afarris@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":825528,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70226154,"text":"70226154 - 2021 - Carbon and ecohydrological priorities in managing woody encroachment: UAV perspective 63 years after a control treatment","interactions":[],"lastModifiedDate":"2021-12-10T17:42:20.47679","indexId":"70226154","displayToPublicDate":"2021-11-08T06:17:29","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":"Carbon and ecohydrological priorities in managing woody encroachment: UAV perspective 63 years after a control treatment","docAbstract":"<div class=\"article-text wd-jnl-art-abstract cf\"><p>Woody encroachment, including both woody species expansion and density increase, is a globally observed phenomenon that deteriorates arid and semi-arid rangeland health, biodiversity, and ecosystem services. Mechanical and chemical control treatments are commonly performed to reduce woody cover and restore ecohydrologic function. While the immediate impacts of woody control treatments are well documented in short-term studies, treatment impacts at decadal scales are not commonly studied. Using a controlled herbicide treatment from 1954 in the Sierra Ancha Experimental Forest in central Arizona, USA, we quantify woody encroachment and associated aboveground carbon accumulation in treated and untreated watersheds. Woody encroachment and aboveground carbon are estimated using high resolution multispectral images and photogrammetric data from a fixed-wing unmanned aerial vehicle (UAV). We then combine the contemporary UAV image-derived estimates with historical records from immediately before and after the treatment to consider long-term trends in woody vegetation cover, aboveground carbon, water yield, and sedimentation. Our results indicate that the treatment has had a lasting impact. More than six decades later, woody cover in two treated watersheds are still significantly lower compared to two control watersheds, even though woody cover increased in all four drainages. Aboveground woody carbon in the treated watersheds is approximately one half that accumulated in the control watersheds. The historical records indicate that herbicide treatment also increased water yield and reduced annual sedimentation. Given the sustained reduction in woody cover and aboveground woody biomass in treated watersheds, we infer that the herbicide treatment has had similarly long lasting impacts on ecohydrological function. Land managers can consider legacy impacts from control treatments to better balance carbon and ecohydrological consequences of woody encroachment and treatment activities.</p></div>","language":"English","publisher":"IOPScience","doi":"10.1088/1748-9326/ac3796","usgsCitation":"Sankey, T.T., Leonard, J., Moore, M., Sankey, J., and Belmonte, A., 2021, Carbon and ecohydrological priorities in managing woody encroachment: UAV perspective 63 years after a control treatment: Environmental Research Letters, 124053, 14 p., https://doi.org/10.1088/1748-9326/ac3796.","productDescription":"124053, 14 p.","ipdsId":"IP-129603","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450259,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac3796","text":"Publisher Index Page"},{"id":391674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-12-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":826670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leonard, Jackson","contributorId":268790,"corporation":false,"usgs":false,"family":"Leonard","given":"Jackson","email":"","affiliations":[{"id":55664,"text":"USDA Forest Service RMRS","active":true,"usgs":false}],"preferred":false,"id":826671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moore, Margaret","contributorId":268791,"corporation":false,"usgs":false,"family":"Moore","given":"Margaret","email":"","affiliations":[{"id":52178,"text":"Northern Arizona University, Flagstaff, AZ 86011","active":true,"usgs":false}],"preferred":false,"id":826672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":826673,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belmonte, Adam","contributorId":222546,"corporation":false,"usgs":false,"family":"Belmonte","given":"Adam","email":"","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":826674,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70225753,"text":"70225753 - 2021 - Exposure of predatory and scavenging birds to anticoagulant rodenticides in France: Exploration of data from French surveillance programs","interactions":[],"lastModifiedDate":"2022-01-25T17:12:08.987756","indexId":"70225753","displayToPublicDate":"2021-11-05T07:24:58","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":"Exposure of predatory and scavenging birds to anticoagulant rodenticides in France: Exploration of data from French surveillance programs","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0060\">Wild raptors are widely used to assess exposure to different environmental contaminants, including anticoagulant rodenticides (ARs). ARs are used on a global scale for rodent control, and act by disruption of the vitamin K cycle that results in haemorrhage usually accompanied by death within days. Some ARs are highly persistent and bioaccumulative, which can cause significant exposure of non-target species. We characterized AR exposure in a heterogeneous sample of dead raptors collected over 12 years (2008–2019) in south-eastern France. Residue analysis of 156 liver samples through LC-MS/MS revealed that 50% (78/156) were positive for ARs, with 13.5% (21/156) having summed second-generation AR (SGAR) concentrations &gt;100 ng/g ww. While SGARs were commonly detected (97.4% of positive samples), first-generation ARs were rarely found (7.7% of positive samples). ARs were more frequently detected and at greater concentration in predators (prevalence: 82.5%) than in scavengers (38.8%). Exposure to multiple ARs was common (64.1% of positive samples). While chlorophacinone exposure decreased over time, an increasing exposure trend was observed for the SGAR brodifacoum, suggesting that public policies may not be efficient at mitigating risk of exposure for non-target species. Haemorrhage was observed in 88 birds, but AR toxicosis was suspected in only 2 of these individuals, and no difference in frequency of haemorrhage was apparent in birds displaying summed SGAR levels above or below 100 ng/g ww. As for other contaminants, 17.2% of liver samples (11/64) exhibited Pb levels compatible with sub-clinical poisoning (&gt;6 μg/g dw), with 6.3% (4/64) above the threshold for severe/lethal poisoning (&gt;30 μg/g dw). Nine individuals with Pb levels &gt;6 μg/g dw also had AR residues, demonstrating exposure to multiple contaminants. Broad toxicological screening for other contaminants was positive for 18 of 126 individuals, with carbofuran and mevinphos exposure being the suspected cause of death of 17 birds. Our findings demonstrate lower but still substantial AR exposure of scavenging birds compared to predatory birds, and also illustrate the complexity of diagnosing AR toxicosis through forensic investigations.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.151291","usgsCitation":"Moriceau, M., Lefebvre, S., Fourel, I., Benoit, E., Buronfosse, F., Orabi, P., Rattner, B.A., and Lattard, V., 2021, Exposure of predatory and scavenging birds to anticoagulant rodenticides in France: Exploration of data from French surveillance programs: Science of the Total Environment, v. 810, 151291, 13 p., https://doi.org/10.1016/j.scitotenv.2021.151291.","productDescription":"151291, 13 p.","ipdsId":"IP-130715","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450265,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://hal.science/hal-03419591","text":"Publisher Index Page"},{"id":391567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -5.9765625,\n              42.13082130188811\n            ],\n            [\n              8.26171875,\n              42.13082130188811\n            ],\n            [\n              8.26171875,\n              51.39920565355378\n            ],\n            [\n              -5.9765625,\n              51.39920565355378\n            ],\n            [\n              -5.9765625,\n              42.13082130188811\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"810","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moriceau, Meg-Anne","contributorId":268361,"corporation":false,"usgs":false,"family":"Moriceau","given":"Meg-Anne","email":"","affiliations":[{"id":55634,"text":"USC1233 RS2GP, INRA, VetAgro Sup, Univ Lyon, F69 280 Marcy-l’Étoile, FR","active":true,"usgs":false}],"preferred":false,"id":826500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lefebvre, Sebastien","contributorId":228855,"corporation":false,"usgs":false,"family":"Lefebvre","given":"Sebastien","email":"","affiliations":[{"id":41519,"text":"USC1233 RS2GP, INRA, VetAgro Sup, Univ Lyon, France","active":true,"usgs":false}],"preferred":false,"id":826528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fourel, Isabelle","contributorId":228856,"corporation":false,"usgs":false,"family":"Fourel","given":"Isabelle","email":"","affiliations":[{"id":41519,"text":"USC1233 RS2GP, INRA, VetAgro Sup, Univ Lyon, France","active":true,"usgs":false}],"preferred":false,"id":826529,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Benoit, Etienne","contributorId":228857,"corporation":false,"usgs":false,"family":"Benoit","given":"Etienne","email":"","affiliations":[{"id":41519,"text":"USC1233 RS2GP, INRA, VetAgro Sup, Univ Lyon, France","active":true,"usgs":false}],"preferred":false,"id":826530,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buronfosse, Florence","contributorId":268365,"corporation":false,"usgs":false,"family":"Buronfosse","given":"Florence","email":"","affiliations":[{"id":55636,"text":"CNITV, VetAgro Sup, 1 avenue Bourgelat, 69 280 Marcy-l’Étoile, FR","active":true,"usgs":false}],"preferred":false,"id":826531,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Orabi, Pascal","contributorId":268366,"corporation":false,"usgs":false,"family":"Orabi","given":"Pascal","email":"","affiliations":[{"id":55638,"text":"French Bird Protection League (LPO France)","active":true,"usgs":false}],"preferred":false,"id":826532,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":826533,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lattard, Virginie","contributorId":228858,"corporation":false,"usgs":false,"family":"Lattard","given":"Virginie","email":"","affiliations":[{"id":41519,"text":"USC1233 RS2GP, INRA, VetAgro Sup, Univ Lyon, France","active":true,"usgs":false}],"preferred":false,"id":826534,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70231396,"text":"70231396 - 2021 - The Boreal-Arctic Wetland and Lake Dataset (BAWLD)","interactions":[],"lastModifiedDate":"2022-05-10T11:50:54.064361","indexId":"70231396","displayToPublicDate":"2021-11-05T06:47:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"The Boreal-Arctic Wetland and Lake Dataset (BAWLD)","docAbstract":"<p>Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (&gt;10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (&lt;0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12  × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).</p>","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-13-5127-2021","usgsCitation":"Olefeldt, D., Hovemyr, M., Kuhn, M., Bastviken, D., Bohn, T., Connolly, J., Crill, P., Euskirchen, E., Finkelstein, S., Genet, H., Grosse, G., Harris, L., Heffernan, L., Helbig, M., Hugelium, G., Hutchins, R., Juutinen, S., Lara, M., Malhotra, A., Manies, K.L., McGuire, A., Natali, S., O’Donnell, J.A., Parmentier, F., Rasanen, A., Schaedel, C., Sonnentag, O., Strack, M., Tank, S., Treat, C., Varner, R., Virtanen, T., Watts, J., and Warren, R., 2021, The Boreal-Arctic Wetland and Lake Dataset (BAWLD): Earth System Science Data, v. 13, p. 5127-5149, https://doi.org/10.5194/essd-13-5127-2021.","productDescription":"23 p.","startPage":"5127","endPage":"5149","ipdsId":"IP-129170","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":450274,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5194/essd-13-5127-2021","text":"External Repository"},{"id":400379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2021-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Olefeldt, David","contributorId":169408,"corporation":false,"usgs":false,"family":"Olefeldt","given":"David","affiliations":[{"id":32365,"text":"Department of Renewable Resources, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":842473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hovemyr, Mikael","contributorId":291509,"corporation":false,"usgs":false,"family":"Hovemyr","given":"Mikael","email":"","affiliations":[],"preferred":false,"id":842474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuhn, M.A.","contributorId":291510,"corporation":false,"usgs":false,"family":"Kuhn","given":"M.A.","email":"","affiliations":[],"preferred":false,"id":842475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bastviken, D","contributorId":264953,"corporation":false,"usgs":false,"family":"Bastviken","given":"D","affiliations":[{"id":54595,"text":"Department of Thematic Studies - 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