The autumn and winter flood season of western Washington coincides with the incubation period of many Pacific salmon (Onchorhynchus spp.) populations. During this period, salmon embryos incubating within gravel nests called “redds” are vulnerable to mobilization of surrounding sediment during floods. As overlying sediment is transported downstream, the vertical position of the streambed can be lowered, a process termed streambed scour; thus developing salmon embryos may be destroyed resulting in decreasing egg-to-fry survival rates. The Sauk River, which drains a 1,900 km2 (733.5 mi2) area of the central Cascade Range of Washington State, provides spawning and rearing habitat for several species of Pacific salmon including Chinook salmon (O. tshawytscha), which were listed as threatened under the Endangered Species Act (ESA) in 1999. In order to assess the hydrologic conditions when streambed scour and concomitant geomorphic changes occur, accelerometer scour monitors (ASMs), which record the time when streambed scour lowers the streambed to the level of salmon egg pockets, were deployed in two geomorphically different reaches of the Sauk River to monitor scour during water year 2018. Nineteen ASMs were deployed in an upstream reach, which was largely confined by valley walls with vegetated, stable banks and low channel-migration rates near the confluence of the Sauk and White Chuck Rivers. Twelve additional ASMs were deployed in a downstream reach within an unconfined valley with unvegetated, unstable banks and high channel-migration rates between the town of Darrington and the confluence of the Sauk and Suiattle Rivers. During the ASM deployment, discharge measured at the U.S. Geological Survey (USGS) streamgage Sauk River above White Chuck River, near Darrington, Washington (12186000), peaked at 479 m3/s (16,900 ft3/s) with an estimated 0.18 probability of annual exceedance (5.7-year recurrence interval). During the flood season, large-scale geomorphic changes, including channel migration and bar deposition, were measured at the downstream reach, but only minimal geomorphic changes were measured at the upstream reach. ASMs deployed at the downstream reach were not recovered after the flood season and total scour depth was presumed to have exceeded ASM anchor depth. At the upstream reach, 7 of the 19 deployed ASMs were recovered after the flood season and all recovered ASMs recorded scour at discharges that equaled or exceeded 204 m3/s (7,210 ft3/s). The remaining 12 ASMs deployed at the upstream reach were not recovered and total scour depth was presumed to have exceeded ASM anchor depth. Collectively, this analysis enhances the ability of fisheries managers to forecast egg-to-fry survival rates of salmonids by determining the hydrologic conditions at which scour at the level of salmon redds initiates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215133","collaboration":"Prepared in cooperation with the Sauk-Suiattle Indian Tribe","usgsCitation":"Gendaszek, A.S., 2021, Streambed scour of salmon (Oncorhynchus spp.) redds in the Sauk River, Northwestern Washington: U.S. Geological Survey Scientific Investigations Report 2021–5133, 19 p., https://doi.org/10.3133/sir20215133.","productDescription":"Report: iv, 19 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-124695","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":392362,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95KOMTC","text":"USGS data release","description":"USGS data release.","linkHelpText":"Accelerometer scour monitor data on the Sauk River, Washington, Water Year 2018"},{"id":392360,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5133/coverthb.jpg"},{"id":392361,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5133/sir20215133.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5133"}],"country":"United States","state":"Washington","otherGeospatial":"Sauk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.80816650390625,\n 48.31060120649363\n ],\n [\n -121.36871337890625,\n 48.31060120649363\n ],\n [\n -121.36871337890625,\n 48.6927734325279\n ],\n [\n -121.80816650390625,\n 48.6927734325279\n ],\n [\n -121.80816650390625,\n 48.31060120649363\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
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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.
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.
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.
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.
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.
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.
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.
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.
The GCEP30 product, which can be browsed at full resolution at www.croplands.org, 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 https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/).
","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":"Director,
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350 N. Akron Rd.
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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.
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.
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.
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.
","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 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U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911
Mayon is a basaltic andesitic, open-vent volcano characterized by persistent passive degassing from the summit at 2463 m above sea level. Mid-size (<0.1 km3) and mildly explosive eruptions and occasional phreatic eruptions have occurred approximately every 10 years for over a hundred years. Mayon’s plumbing system structure, processes, and time scales driving its eruptions are still not well-known, despite being the most active volcano in the Philippines. We investigated the petrology and geochemistry of its crystal-rich lavas (~50 vol% phenocrysts) from nine historical eruptions between 1928 and 2009 and propose a conceptual model of the processes and magmatic architecture that led to the eruptions. The whole-rock geochemistry and mineral assemblage (plagioclase + orthopyroxene + clinopyroxene + Fe-Ti oxide ± olivine) of the lavas have remained remarkably homogenous (54 wt% SiO2,~4 wt% MgO) from 1928 to 2009. However, electron microscope images and microprobe analyses of the phenocrysts and the existence of three types of glomerocrysts testify to a range of magmatic processes, including long-term magma residence, magma mixing, crystallization, volatile fluxing, and degassing. Multiple mineral-melt geothermobarometers suggest a relatively thermally buffered system at 1050±25 °C, with several magma residence zones, ranging from close to the surface, through reservoirs at ~4–5 km, and as deep as ~ 20 km. Diffusion chronometry on >200 orthopyroxene crystals reveal magma mixing timescales that range from a few days to about 65 years, but the majority are shorter than the decadal inter-eruptive repose period. This implies that magma intrusion at Mayon has been nearly continuous over the studied time period, with limited crystal recycling from one eruption to the next. The variety of plagioclase textures and zoning patterns reflect fluxing of volatiles from depth to shallower melts through which they eventually reach the atmosphere through an open conduit. The crystal-rich nature of the erupted magmas may have developed during each inter-eruptive period. We propose that Mayon has behaved over almost 100 years as a steady state system, with limited variations in eruption frequency, degassing flux, magma composition, and crystal content that are mainly determined by the amount and composition of deep magma and volatile input in the system. We explore how Mayon volcano’s processes and working model can be related to other open-vent mafic and water-rich systems such as Etna, Stromboli, Villarrica, or Llaima. Finally, our understanding of open-vent, persistently active volcanoes is rooted in historical observations, but volcano behavior can evolve over longer time frames. We speculate that these volcanoes produce specific plagioclase textures that can be used to identify similar volcanic behavior in the geologic record.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-021-01486-9","usgsCitation":"Ruth, D.C., and Costa, F., 2021, A petrological and conceptual model of Mayon volcano (Philippines) as an example of an open-vent volcano: Bulletin of Volcanology, v. 83, 62, 28 p., https://doi.org/10.1007/s00445-021-01486-9.","productDescription":"62, 28 p.","ipdsId":"IP-123082","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467226,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-021-01486-9","text":"Publisher Index Page"},{"id":463239,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","otherGeospatial":"Mayon volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 123.5101731400274,\n 13.501020877721444\n ],\n [\n 123.5101731400274,\n 13.33999591750549\n ],\n [\n 123.70654204522504,\n 13.33999591750549\n ],\n [\n 123.70654204522504,\n 13.501020877721444\n ],\n [\n 123.5101731400274,\n 13.501020877721444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationDate":"2021-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruth, Dawn Catherine Sweeney 0000-0001-9369-9364","orcid":"https://orcid.org/0000-0001-9369-9364","contributorId":334908,"corporation":false,"usgs":true,"family":"Ruth","given":"Dawn","email":"","middleInitial":"Catherine Sweeney","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Costa, Fidel","contributorId":184169,"corporation":false,"usgs":false,"family":"Costa","given":"Fidel","email":"","affiliations":[],"preferred":false,"id":916875,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225724,"text":"70225724 - 2021 - Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise","interactions":[],"lastModifiedDate":"2021-11-05T11:58:08.77405","indexId":"70225724","displayToPublicDate":"2021-09-02T06:57:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise","docAbstract":"Tilting of the ground due to loading by the variable atmosphere is known to corrupt very long period horizontal seismic records (below 10 mHz) even at the quietest stations. At BFO (Black Forest Observatory, SW-Germany), the opportunity arose to study these disturbances on a variety of simultaneously operated state-of-the-art broad-band sensors. A series of time windows with clear atmospherically caused effects was selected and attempts were made to model these ‘signals’ in a deterministic way. This was done by simultaneously least-squares fitting the locally recorded barometric pressure and its Hilbert transform to the ground accelerations in a bandpass between 100 and 3600 s periods. Variance reductions of up to 97 per cent were obtained. We show our results by combining the ‘specific pressure induced accelerations’ for the two horizontal components of the same sensor as vectors on a horizontal plane, one for direct pressure and one for its Hilbert transform. It turned out that at BFO the direct pressure effects are large, strongly position dependent and largely independent of atmospheric events for instruments installed on piers, while three post-hole sensors are only slightly affected. The infamous ‘cavity effects’ are invoked to be responsible for these large effects on the pier sensors. On the other hand, in the majority of cases all sensors showed very similar magnitudes and directions for the vectors obtained for the regression with the Hilbert transform, but highly variable from event to event especially in direction. Therefore, this direction most certainly has to do with the gradient of the pressure field moving over the station which causes a larger scale deformation of the crust. The observations are very consistent with these two fundamental mechanisms of how fluctuations of atmospheric surface pressure causes tilt noise. The results provide a sound basis for further improvements of the models for these mechanisms. The methods used here can already help to reduce atmospherically induced noise in long-period horizontal seismic records.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab336","usgsCitation":"Zurn, W., Forbriger, T., Widmer-Schnidrig, R., Duffner, P., and Ringler, A.T., 2021, Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise: Geophysical Journal International, v. 228, no. 2, p. 927-943, https://doi.org/10.1093/gji/ggab336.","productDescription":"17 p.","startPage":"927","endPage":"943","ipdsId":"IP-131609","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":450964,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5445/ir/1000140172","text":"External Repository"},{"id":391423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"228","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Zurn, W.","contributorId":268322,"corporation":false,"usgs":false,"family":"Zurn","given":"W.","affiliations":[{"id":55624,"text":"Black Forest Observatory (Schiltach)","active":true,"usgs":false}],"preferred":false,"id":826410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forbriger, T.","contributorId":268323,"corporation":false,"usgs":false,"family":"Forbriger","given":"T.","email":"","affiliations":[{"id":55625,"text":"Black Forest Observatory (Schiltach); Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":826411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Widmer-Schnidrig, R.","contributorId":221153,"corporation":false,"usgs":false,"family":"Widmer-Schnidrig","given":"R.","email":"","affiliations":[{"id":40338,"text":"Black Forest Observatory, Institute of Geodesy, Stuttgart University, Wolfach, Germany","active":true,"usgs":false}],"preferred":false,"id":826412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duffner, P.","contributorId":268324,"corporation":false,"usgs":false,"family":"Duffner","given":"P.","email":"","affiliations":[{"id":55625,"text":"Black Forest Observatory (Schiltach); Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":826413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":826414,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227199,"text":"70227199 - 2021 - U–Pb zircon eruption age of the Old Crow tephra and review of extant age constraints","interactions":[],"lastModifiedDate":"2022-01-04T13:52:32.345028","indexId":"70227199","displayToPublicDate":"2021-02-27T07:48:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3216,"text":"Quaternary Geochronology","active":true,"publicationSubtype":{"id":10}},"title":"U–Pb zircon eruption age of the Old Crow tephra and review of extant age constraints","docAbstract":"Eruption of the Old Crow tephra deposited ~200 km3 of volcanic ash throughout Alaska and the northwestern Yukon (eastern Beringia), providing an isochronous marker across the region on a scale unique in the Pleistocene. The Old Crow tephra represents a critical temporal piercing point used extensively to link geographically disparate stratigraphic sections and the paleo-environmental records they contain. Although the canonical age of the Old Crow suggests eruption during the transition between the glacial and interglacial periods of Marine Isotope Stages (MIS) 5 and 6 at ~125 ka, recent U–Th–Pb and (U–Th)/He zircon dating of the tephra suggests eruption at ~200 ka, within MIS 7. If accurate, this revised eruption age begets significant change to existing models describing the geologic and biotic evolution of Beringia in the Pleistocene. Thus, confidently knowing the age of the tephra is critical to its time-stratigraphic utility and for past and future work in the region where the tephra has been found. With this contribution, we review existing Old Crow age constraints and present an eruption age for the tephra determined via high spatial resolution ion microprobe U–Pb surface analysis on zircon crystals isolated from source-proximal (<500 km from plausible source) pumiceous pyroclasts of the tephra. By dating only glass-mantled crystals isolated from discrete pumice clasts, we limit the potential for sample contamination from exotic crystals and resulting age bias. The young population of dates from this dataset corroborate previous radiometric dates and confirm Old Crow eruption within late MIS 7 at 207 ± 13 ka.
In this study, we captured how a river channel responds to a sediment pulse originating from a dam removal using multiple lines of evidence derived from streamflow gages along the Patapsco River, Maryland, USA. Gages captured characteristics of the sediment pulse, including travel times of its leading edge (~7.8 km yr−1) and peak (~2.6 km yr−1) and suggest both translation and increasing dispersion. The pulse also changed local hydraulics and energy conditions, increasing flow velocities and Froude number, due to bed fining, homogenization and/or slope adjustment. Immediately downstream of the dam, recovery to pre‐pulse conditions occurred within the year, but farther downstream recovery was slower, with the tail of the sediment pulse working through the lower river by the end of the study 7 years later.
The patterns and timing of channel change associated with the sediment pulse were not driven by large flow or suspended sediment‐transporting events, with change mostly occurring during lower flows. This suggests pulse mobility was controlled by process‐factors largely independent of high flow.
In contrast, persistent changes occurred to out‐of‐channel flooding dynamics. Stage associated with flooding increased during the arrival of the sediment pulse, 1 to 2 years after dam removal, suggesting persistent sediment deposition at the channel margins and nearby floodplain. This resulted in National Weather Service‐indicated flood stages being attained by 3–43% smaller discharges compared to earlier in the study period.
This study captured a two‐signal response from the sediment pulse: (1) short‐ to medium‐term (weeks to months) translation and dispersion within the channel, resulting in aggradation and recovery of bed elevations and changing local hydraulics; and (2) dispersion and persistent longer‐term (years) effects of sediment deposition on overbank surfaces. This study further demonstrated the utility of US Geological Survey gage data to quantify geomorphic change, increase temporal resolution, and provide insights into trajectories of change over varying spatial and temporal scales.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5083","usgsCitation":"Cashman, M.J., Gellis, A.C., Boyd, E.L., Collins, M.J., Anderson, S.W., Mcfarland, B.D., and Ryan, A.M., 2021, Channel response to a dam‐removal sediment pulse captured at high‐temporal resolution using routine gage data: Earth Surfaces Processes and Landforms, v. 46, no. 6, p. 1145-1159, https://doi.org/10.1002/esp.5083.","productDescription":"15 p.","startPage":"1145","endPage":"1159","ipdsId":"IP-113441","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":436533,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9REXNQ9","text":"USGS data release","linkHelpText":"Data for Specific Gage Analysis on the Patapsco River, 2010-2017"},{"id":384751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patapsco River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.9317626953125,\n 39.38738660316804\n ],\n [\n -76.98257446289062,\n 39.35394512666976\n ],\n [\n -76.88438415527344,\n 39.31198794598777\n ],\n [\n -76.8218994140625,\n 39.29976783250087\n ],\n [\n -76.7999267578125,\n 39.26043647112078\n ],\n [\n -76.75666809082031,\n 39.216295294574024\n ],\n [\n -76.68937683105469,\n 39.21097520599528\n ],\n [\n -76.60697937011719,\n 39.22480659786848\n ],\n [\n -76.9317626953125,\n 39.38738660316804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - 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2021 - Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States","interactions":[],"lastModifiedDate":"2021-09-10T13:45:18.247157","indexId":"70223853","displayToPublicDate":"2020-12-14T08:35:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States","docAbstract":"Over half of global rivers and streams lack perennial flow, and understanding the distribution and drivers of their flow regimes is critical for understanding their hydrologic, biogeochemical, and ecological functions. We analyzed nonperennial flow regimes using 540 U.S. Geological Survey watersheds across the contiguous United States from 1979 to 2018. Multivariate analyses revealed regional differences in no-flow fraction, date of first no flow, and duration of the dry-down period, with further divergence between natural and human-altered watersheds. Aridity was a primary driver of no-flow metrics at the continental scale, while unique combinations of climatic, physiographic and anthropogenic drivers emerged at regional scales. Dry-down duration showed stronger associations with nonclimate drivers compared to no-flow fraction and timing. Although the sparse distribution of nonperennial gages limits our understanding of such streams, the watersheds examined here suggest the important role of aridity and land cover change in modulating future stream drying.
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Nathan","affiliations":[{"id":48727,"text":"The National Socio-Environmental Synthesis Center, University of Maryland, Annapolis, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":822952,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Krabbenhoft, Corey 0000-0002-2630-8287","orcid":"https://orcid.org/0000-0002-2630-8287","contributorId":225163,"corporation":false,"usgs":false,"family":"Krabbenhoft","given":"Corey","email":"","affiliations":[{"id":41059,"text":"College of Arts and Sciences and Research and Education in Energy, Environment and Water (RENEW) Institute, University at Buffalo, Buffalo, NY 14228","active":true,"usgs":false}],"preferred":false,"id":822953,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Boersma, Kate 0000-0002-0707-3283","orcid":"https://orcid.org/0000-0002-0707-3283","contributorId":225167,"corporation":false,"usgs":false,"family":"Boersma","given":"Kate","email":"","affiliations":[{"id":41063,"text":"Department of Biology, University of San Diego, San Diego, CA 92105, USA","active":true,"usgs":false}],"preferred":false,"id":822954,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Datry, Thibault 0000-0003-1390-6736","orcid":"https://orcid.org/0000-0003-1390-6736","contributorId":225166,"corporation":false,"usgs":false,"family":"Datry","given":"Thibault","email":"","affiliations":[{"id":41062,"text":"Centre de Lyon-Villeurbanne, 69626 Villeurbanne CEDEX, France","active":true,"usgs":false}],"preferred":false,"id":822955,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Olden, Julian D.","contributorId":202893,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":822956,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Allen, George H.","contributorId":257248,"corporation":false,"usgs":false,"family":"Allen","given":"George H.","affiliations":[{"id":51991,"text":"Department of Geography, Texas A&M University, College Station, TX, USA","active":true,"usgs":false}],"preferred":false,"id":822957,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Price, Adam N.","contributorId":265572,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":822958,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Costigan, Katie H.","contributorId":166700,"corporation":false,"usgs":false,"family":"Costigan","given":"Katie H.","affiliations":[],"preferred":false,"id":822959,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Hale, Rebecca 0000-0002-3552-3691","orcid":"https://orcid.org/0000-0002-3552-3691","contributorId":195753,"corporation":false,"usgs":false,"family":"Hale","given":"Rebecca","email":"","affiliations":[{"id":12865,"text":"Smithsonian Institute","active":true,"usgs":false}],"preferred":false,"id":822960,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Ward, Adam S","contributorId":191363,"corporation":false,"usgs":false,"family":"Ward","given":"Adam","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":822961,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Allen, Daniel C. 0000-0002-0451-0564","orcid":"https://orcid.org/0000-0002-0451-0564","contributorId":225169,"corporation":false,"usgs":false,"family":"Allen","given":"Daniel","middleInitial":"C.","affiliations":[{"id":41064,"text":"Department of Biology, University of Oklahoma, Norman OK, 73019","active":true,"usgs":false}],"preferred":false,"id":822962,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70218021,"text":"70218021 - 2020 - Geologic map of the Dog River and northern part of the Badger Lake 7.5′ quadrangles, Hood River County, Oregon","interactions":[],"lastModifiedDate":"2021-04-14T14:37:25.364879","indexId":"70218021","displayToPublicDate":"2020-12-31T09:33:36","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":8123,"text":"Geological Map","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"126","title":"Geologic map of the Dog River and northern part of the Badger Lake 7.5′ quadrangles, Hood River County, Oregon","docAbstract":"The Dog River and northern part of the Badger Lake 7.5' quadrangles encompasses an area of ~201 km2 (77.6 mi2) of the High Cascades of north-central Oregon, lying across the eastern slopes of Mount Hood volcano (Figure 1-1; Plate 1; referred to herein as Dog River–Badger Lake area). Mount Hood, known as Wy’east to Native Americans, is Oregon’s tallest peak (3,427 m [11,241 ft]). The volcano has erupted episodically for the past 500,000 years, experiencing two major eruptive periods during the last 1,500 years (Scott and others, 1997a; Scott and others, 2003; Scott and Gardner, 2017). Cascade Range volcanism and structural development in the area dates back longer, with eruptive activity dating from latest Miocene to recent time; part of that volcano-tectonic record is detailed by new high-resolution geologic mapping presented here.
The geology of the Dog River–Badger Lake area was mapped by the Oregon Department of Geology and Mineral Industries (DOGAMI) between 2017 and 2020, in collaboration with geoscientists from the U. S. Geological Survey Cascade Volcano Observatory (USGS CVO) and Hamilton College, New York. The primary objective of this investigation is to provide an updated and spatially accurate geologic framework for the Dog River–Badger Lake area as part of a multi-year study of the geology of the larger Middle Columbia Basin (Figure 1-1, Figure 1-2). Additional key objectives of this project are to: 1) determine the geologic history of volcanic rocks in this part of the northern Oregon Cascade Range, including lava flows and volcaniclastic deposits erupted from Middle Pleistocene to Holocene Mount Hood volcano; 2) provide significant new details about the structure and fault history along the northern segment of the High Cascades intra-arc graben (Hood River graben); and 3) better understand geologic hazards in the region, related to earthquakes, volcanoes, and landslides. New detailed geologic data presented here also provides a basis for future geologic, geohydrologic, and geohazard studies in the greater Middle Columbia Basin. Detailed geologic mapping in this part of the Middle Columbia Basin is a high priority of the Oregon Geologic Map Advisory Committee (OGMAC), supported in part by grants from the STATEMAP component of the USGS National Cooperative Geologic Mapping Program (G17AC00210, G19AC00160). Additional funds were provided by the State of Oregon.
The core products of this study are this report, an accompanying geologic map and cross sections (Plate 1), an Esri ArcGIS™ geodatabase, and Microsoft Excel® spreadsheets tabulating point data for geochemistry, geochronology, magnetic polarity, orientation points, and well data. The geodatabase presents the new geologic mapping in a digital format consistent with the USGS National Cooperative Geologic Mapping Program Geologic Map Schema (GeMS) (U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020). This geodatabase contains spatial information, including geologic polygons, contacts, structures, geochemistry, geochronology, magnetic observation, orientation points, and well data, as well as data about each geologic unit such as age, lithology, mineralogy, and structure. Digitization at scales of 1:8,000 or better was accomplished using a combination of high-resolution lidar topography and imagery. Surficial and bedrock geologic units contained in the geodatabase are depicted on the Plate 1 at a scale of 1:24,000. Both the geodatabase and geologic map are supported by this report describing the geology in detail.
","language":"English","publisher":"Oregon Department of Geology and Mineral Industries","usgsCitation":"McClaughry, J.D., Scott, W., Duda, C.J., and Conrey, R.M., 2020, Geologic map of the Dog River and northern part of the Badger Lake 7.5′ quadrangles, Hood River County, Oregon: Geological Map 126, Report: 154 p.; 1 Plate 48 x 52 inches; Database; Metadata.","productDescription":"Report: 154 p.; 1 Plate 48 x 52 inches; Database; Metadata","ipdsId":"IP-126371","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":385093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383245,"type":{"id":15,"text":"Index Page"},"url":"https://www.oregongeology.org/pubs/gms/p-GMS-126.htm"}],"scale":"24000","country":"United States","state":"Oregon","county":"Hood River County","otherGeospatial":"Dog River and Northern Part of the Badger Lake 7.5' Quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.87683105468749,\n 44.97839955494438\n ],\n [\n -120.5914306640625,\n 44.97839955494438\n ],\n [\n -120.5914306640625,\n 45.73494252455993\n ],\n [\n -121.87683105468749,\n 45.73494252455993\n ],\n [\n -121.87683105468749,\n 44.97839955494438\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McClaughry, Jason D.","contributorId":194544,"corporation":false,"usgs":false,"family":"McClaughry","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":810242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, William E. 0000-0001-8156-979X","orcid":"https://orcid.org/0000-0001-8156-979X","contributorId":250706,"corporation":false,"usgs":true,"family":"Scott","given":"William E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Carlie J. M.","contributorId":250707,"corporation":false,"usgs":false,"family":"Duda","given":"Carlie","email":"","middleInitial":"J. M.","affiliations":[{"id":32397,"text":"Oregon Department of Geology and Mineral Industries","active":true,"usgs":false}],"preferred":false,"id":810244,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conrey, Richard M.","contributorId":194345,"corporation":false,"usgs":false,"family":"Conrey","given":"Richard","email":"","middleInitial":"M.","affiliations":[{"id":13203,"text":"School of the Environment, Washington State University","active":true,"usgs":false}],"preferred":false,"id":810245,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216871,"text":"sir20205091 - 2020 - Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15","interactions":[],"lastModifiedDate":"2021-04-08T21:42:55.915848","indexId":"sir20205091","displayToPublicDate":"2020-12-16T09:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5091","displayTitle":"Simulation of Groundwater Flow in the Regional Aquifer System on Long Island, New York, for Pumping and Recharge Conditions in 2005–15","title":"Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15","docAbstract":"A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses.
The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations.
A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period.
These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15.
About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205091","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., Masterson, J.P., Finkelstein, J.S., Monti, J., Jr., Misut, P.E., and Fienen, M.N., 2020, Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15: U.S. Geological Survey Scientific Investigations Report 2020–5091, 75 p., https://doi.org/10.3133/sir20205091.","productDescription":"Report: ix, 75 p.; 3 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112206","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381521,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5091/images/"},{"id":381195,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5091"},{"id":381194,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5091/coverthb2.jpg"},{"id":381192,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":381191,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWQSEJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to simulate groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15"},{"id":381190,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens Counties, New York, October-November 2017"},{"id":381520,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.XML"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.102783203125,\n 40.55554790286311\n ],\n [\n -73.7017822265625,\n 40.49709237269567\n ],\n [\n -72.8778076171875,\n 40.65147128144057\n ],\n [\n -72.037353515625,\n 40.91351257612758\n ],\n [\n -71.6143798828125,\n 41.16211393939692\n ],\n [\n -71.9384765625,\n 41.178653972331674\n ],\n [\n -72.191162109375,\n 41.236511201246216\n ],\n [\n -72.65808105468749,\n 41.11660732012896\n ],\n [\n -73.4490966796875,\n 40.967455873296714\n ],\n [\n -73.751220703125,\n 40.89275342420696\n ],\n [\n -74.0203857421875,\n 40.718119379753446\n ],\n [\n -74.102783203125,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The headwaters of the Snake River are in the mountains of northwestern Wyoming. Maintaining the recognized high quality of water in Grand Teton National Park is a National Park Service (NPS) priority. To characterize and understand the water resources of Grand Teton National Park, the NPS established a monitoring program to monitor the quality of area surface waters. Beginning in 2006, water was sampled by the NPS and analyzed for a range of chemical species at the Snake River above Jackson Lake at Flagg Ranch streamgage 13010065 (hereafter referred to as “Snake River at Flagg Ranch”), a site where the U.S. Geological Survey (USGS) previously sampled and analyzed water from 1986 through 2004. The USGS, in cooperation with the NPS, evaluated water-quality data collected by both entities to determine if discharge and total dissolved solids (referred to as dissolved solids) have changed in the Snake River at the Flagg Ranch.
To understand potential changes with time in dissolved solids, discharge was analyzed between January 1986 and December 2018, which corresponds with the time period when water-quality data were collected. Mean annual discharge varied during this time, with high, low, mean, and median flows generally increasing from 1986 through 1998, decreasing through 2005, and then generally increasing through 2018.
Combining water-quality data collected by the USGS and NPS provides a longer, more complete dataset for analyses. During the period of time when NPS was the sampling agency, specific conductance data were collected, but dissolved-solids data were not. The specific conductance data from both agencies were evaluated to determine if combining the data was justified. The interquartile ranges of data collected by both agencies are similar, and rapid, large changes in values during the period of transition between USGS and NPS sampling do not occur. The USGS and NPS datasets are not statistically different in the spring, summer, or fall, but are statistically different in the winter. The winter differences could be a function of the lack of wintertime NPS sampling, which excludes higher-concentration, lower-discharge data or a function of changes in the actual concentration in the stream. Although there is some difference in the winter datasets, the similarity in sampling methods and general overall data characteristics justifies combining the data for trend analyses.
Because the dissolved-solids parameter is useful for managers, it is often calculated from specific conductance using a linear regression model when dissolved-solids data are absent. For this study, creating a modeled dataset of dissolved solids for the NPS data collection period of time provided a longer, more complete dataset of dissolved-solids concentrations.
The concentrations of dissolved solids over time are identified by season and indicate that samples collected in the fall and winter have higher concentrations than samples collected in spring and summer. Specifically, the mean dissolved-solids concentrations in fall and winter are around 188 milligrams per liter (mg/L), whereas the mean concentrations are around 130 mg/L in spring and summer. This difference is generally attributed to the dilution of spring and summer samples by snowmelt generated runoff during the high-flow period of the year.
Trend analyses of dissolved-solids concentrations and loads indicate that an upward trend in concentration from 1986 to 2018 is likely, and a downward trend in load is highly likely. Comparing 1986 to 2018, dissolved-solids concentration is estimated to have increased by 2.25 mg/L (1.4 percent). During that same period, the dissolved-solids load is estimated to have decreased 11.8 million kilograms per year (12-percent decrease). This decrease is consistent with the estimated decrease in annual mean of daily mean discharge. Because 10 percent of the total change in dissolved-solids load is related to a change in the concentration-discharge relationship and 2 percent is related to changes in discharge, the decreased load is related less to changes in discharge and more to landscape scale processes that are affecting the concentration-discharge relationship.
As noted above, the data collected by the USGS and NPS are generally comparable with regards to sampling and analytical methods, and data collected by both agencies were used as one dataset for trend analyses. The current NPS sampling schedule, however, is creating a dataset biased towards lower concentration dissolved-solids data, which occurs during higher summer flows, by only sampling during April through November. From 1986 to 2018, the percentage of NPS samples is small enough that the effect on trends is expected to be minimal. Because of the importance of low flow (winter season) data, it is likely that an April through November sampling regime may affect the ability to detect trends or determine seasonality in the future. Collection of winter data in particular is important based on the findings that the changes in the modeled concentration-discharge relationship over time have been most pronounced during the winter (represented by February) months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205062","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Miller, O.L., and Eddy-Miller, C.A., 2020, Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018: U.S. Geological Survey Scientific Investigations Report 2020–5062, 19 p., https://doi.org/10.3133/sir20205062.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-116863","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":376357,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5062/coverthb.jpg"},{"id":376358,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5062/sir20205062.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5062"}],"country":"United States","state":"Wyoming","otherGeospatial":"Flagg Ranch watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.05804443359375,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.081666311450526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Boseman Avenue
Helena, MT 59601
Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
West Valley City, UT 84119–2047
The St. Louis River estuary is a large freshwater estuary, next to Duluth, Minnesota, that encompasses the headwaters of Lake Superior. The St. Louis River estuary is one of the most complex and compromised near-shore systems in the upper Great Lakes with a long history of environmental contamination caused by logging, mining, paper mills, and other heavy industrial activities. Presently (2020), a widely available, science-based assessment tool capable of evaluating ecosystem-level responses to remediation and restoration projects has not existed for the estuary. To address this need, the U.S. Geological Survey (USGS) built a predictive, mechanistic, three-dimensional hydrodynamic model for the estuary using the Environmental Fluid Dynamics Code framework. In the current version, the model can simulate continuous discharge, water-surface elevations, water temperature, and flow velocity, although the modular framework allows for future additions of water-quality modeling.
The model was calibrated using data collected from April 2016 through November 2016 and validated with data collected from April 2017 through November 2017. The four types of data used to evaluate model performance were water-surface elevations, discharge, water temperature, and flow velocities. Streamflow and temperature boundary condition data included a mixture of USGS streamgage data, Minnesota Department of Natural Resources gage data, and estimates derived from the gage data.
The model was able to simulate the water-surface elevations with generally good agreement between the simulated and measured values for both years at the daily time step. Specifically, the model was able to demonstrate excellent
agreement with the measured data with Nash-Sutcliffe efficiency coefficients greater than 0.8 for all three locations; however, the model was unable to produce hourly water-surface elevations with such accuracy for 2016–17.
Discharge was more dynamic than the water-surface elevations, both for the measured and simulated data. Generally, most of the discharge ranged from −650 to 1,200 cubic meters per second, but the constantly changing flux exiting the estuary into Lake Superior (positive flows) and entering the estuary from Lake Superior (negative flows) occurred throughout the year. Even upstream at the St. Louis River at Oliver, Wisconsin, gage (USGS station 0402403250), the effect of flows into the estuary from Lake Superior did occur, demonstrating the strong effect of the Lake Superior seiche on flows for the estuary.
From a performance standpoint, the model was able to simulate discharge with generally good agreement in both years, although the 2017 validation was better than the 2016 calibration period. For the daily Nash-Sutcliffe efficiency coefficients, the simulated values were 0.98, 0.62, 0.49, and 0.71 for the Oliver gage; the Superior Bay entry channel at Superior, Wisc., (USGS station 464226092005600); the Superior Bay Duluth Ship Canal at Duluth, Minn., (USGS station 464646092052900); and total entries (combination of the Superior entry and Duluth entry), respectively. For the hourly evaluation criteria, the model performed poorly, with Nash-Sutcliffe efficiency coefficients less than 0 for the two entries into Lake Superior; therefore, as a predictor of discharge at the hourly scale, the model performed worse than using the measured data average. Similar to discharge, the model was a good predictor of flow velocity at the daily time scale but had difficulty matching the measured data at the hourly scale. For discharge and flow velocity, matching at subdaily time steps for a system as complicated as the St. Louis River estuary is considered difficult because the match is highly sensitive to coordinating the exact measurement location to the simulated value.
The final calibration target was water temperature, calibrated for the Oliver gage and the Duluth entry. For calibration purposes, the Duluth entry was the more important water temperature target because the Oliver gage was more of an internal check on the model. The Nash-Sutcliffe efficiency coefficients for the Duluth entry were high; hourly Nash-Sutcliffe efficiency coefficients at the Duluth entry were either at or greater than 0.7 for both years, and daily values were 0.84 and 0.82 for 2016 and 2017, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205028","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Smith, E.A., Kiesling, R.L., and Hayter, E.J., 2020, Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17: U.S. Geological Survey Scientific Investigations Report 2020–5028, 31 p., https://doi.org/10.3133/sir20205028.","productDescription":"Report: viii, 31 p.; Data Release; Dataset","onlineOnly":"Y","ipdsId":"IP-113167","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":437002,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U1XXG0","text":"USGS data release","linkHelpText":"St. Louis River estuary (Minnesota-Wisconsin) EFDC model scenarios for velocity profiles around Munger Landing, selected years (2012-2019)"},{"id":374450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5028/coverthb.jpg"},{"id":374451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5028/sir20205028.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5028"},{"id":374452,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990OUS6","text":"USGS data release","linkHelpText":"St. Louis River estuary (Minnesota-Wisconsin) EFDC hydrodynamic model for discharge and temperature simulations: 2016–17"},{"id":374455,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System—","linkHelpText":"USGS Water Data for the Nation"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Louis River estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.548828125,\n 46.62869257083747\n ],\n [\n -92.0050048828125,\n 46.62869257083747\n ],\n [\n -92.0050048828125,\n 47.07199249565323\n ],\n [\n -92.548828125,\n 47.07199249565323\n ],\n [\n -92.548828125,\n 46.62869257083747\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
The Bi-State Distinct Population Segment (Bi-State DPS) of greater sage-grouse (Centrocercus urophasianus, hereinafter “sage-grouse”) represents a genetically distinct and geographically isolated population that straddles the border between Nevada and California. The primary threat to these sage-grouse populations is the expansion of single-leaf pinyon (Pinus monophylla) and Utah juniper (Juniperus osteosperma) into sagebrush ecosystems, which fragments and reduces population connectivity and survival. Other important threats include low water availability during brood-rearing, particularly during drought, and increased predation by common ravens (Corvus corax), a generalist predator often associated with anthropogenic resource subsidies. Although the Bi-State DPS occurs at high elevations relative to sage-grouse range-wide, changes in historical wildfire cycles and the conversion of native shrubs to invasive annual grasslands still threaten these populations. The Bi-State DPS has undergone multiple federal status assessments and associated litigation. For example, in October of 2013, the Bi-State DPS was proposed for listing as threatened under the Endangered Species Act of 1973 by the U.S. Fish and Wildlife Service (USFWS), then withdrawn in April 2015. The withdrawal decision was challenged, and in May 2018, a Federal district court ordered the withdrawal decision to be vacated, and USFWS was required to re-open the October 2013 listing evaluation.
In response, the U.S. Geological Survey (USGS), with State and Federal collaborators, embarked on a multipronged analysis to provide current and best available science regarding population status of sage-grouse within the Bi-State DPS. Using data from a long-term monitoring program, we carried out four analytical study objectives, and here, we provide preliminary results of these analyses. First, we used integrated population modeling (IPM) to predict annual population abundance and annual finite rate of population change for the Bi-State DPS, as a whole, and for each subpopulation between 1995 and 2018. Because sage-grouse exhibit population cycles (periodic increases and decreases in abundance across approximately 6- to 10-year wavelengths), we estimated trends across three nested temporal scales that represent one (11 years), two (18 years), and three (24 years) complete population cycles. These estimates of relatively long-term averaged population change account for temporal (that is, interannual) variation. Our model predicted population abundance for the Bi-State DPS during 2018 at 3,305 individuals (2,247–4,683), with the majority occupying Bodie Hills and Long Valley. The model also predicted cyclic dynamics in abundance through time with evidence of 24-year population growth and slight trends of decline over the past 18 years. Specifically, across the Bi-State DPS as a whole, we estimated annual average at 0.99, 0.99, and 1.02 over the one, two, and three population cycles, which equated to a 10.5 percent, 16.6 percent decrease, and 60.0 percent increase in abundance over the 11-, 18-, and 24-year cycles. Estimated abundance in 2018 had not reached numbers lower than those predicted during 1995. However, we found spatial variation in population trends across the three cycles. Bodie Hills subpopulation comprised the greatest (1,521) and exhibited average annual greater than 1.0 across all periods resulting in average annual increases of 7 percent. This relatively large subpopulation has grown 5 times larger than what was predicted in 1995 while experiencing cyclical dynamics within that period.
Conversely, other smaller subpopulations within the Bi-State DPS exhibited average annual equal to or less than 1.0 resulting in estimated 10-year risks of extirpation ranging from 2.0 to 76.1 percent. In general, evidence of decline among smaller subpopulations was greatest for the most recent period (2008–18) compared to a period that encompassed three full population cycles (24-year). This difference coincides with an intense period of drought that began in 2012.
For comparative purposes as part of this first objective, we conducted a similar analysis for populations of sage-grouse within Nevada and California but outside the Bi-State DPS. We developed a region-wide and distance-weighted IPM using lek count from Nevada Department of Wildlife (NDOW) and California Department of Fish and Wildlife (CDFW) databases and with telemetry data collected by USGS across 12 sage-grouse subpopulations. Our models predicted similar patterns in population cycling outside the Bi-State DPS but with much stronger evidence of long-term declines across 24 years. Specifically, median averaged across each year of the 11-, 18-, and 24-year periods resulted in average annual values of 0.94, 0.97, and 0.99, respectively. These values equate to 41.0 percent, 38.5 percent, and 21.3 percent declines over the corresponding periods.
Second, we used lek count data in a state-space modeling framework to compare trends in population abundance across different spatial scales (that is, leks versus Bi-State DPS). This hierarchical framework allowed us to disentangle declines associated with climate conditions as opposed to other local level factors that might signal the need for management intervention. Specifically, we identified 7 leks that were both declining and recently decoupled from larger spatial scale trends, typically governed by climatic conditions (referred to as soft or hard signals). The goal of this analysis was to provide an early warning system that might have implications for conservation actions at local scales.
Third, we developed phenological (spring, summer–fall, and winter) and reproductive life stage (nesting, early brood-rearing, and late-brood rearing) based resource selection functions using various environmental covariates. We report rankings of variable importance for each season and life stage, developed maps of habitat selection indices (HSI), binned categories representing low, moderate, and high classes of quality (where any category greater than or equal to low indicated selected habitat) for each phenological season and life stage, and produced composite maps by selected phenological and reproductive stage to estimate annual habitat.
Fourth, we used for each lek within the Bi-State DPS to carry out a spatial analysis that quantified substantial changes in the distribution of occupied habitat across long- (24-year) and short- (11-year) term periods. Owing to differences among available datasets, the long-term analysis primarily reflected spatial shifts among subpopulations comprising the majority of the Bi-State DPS (that is, Bodie Hills and Long Valley) while the short-term analysis also quantified changes among subpopulations along the periphery. Over long and short-term periods, the overall distribution of occupied habitat (as measured by 99 percent utilization distributions intersecting any quantified habitat) was reduced by 20,573 ha and 55,492 ha, respectively. Occupied core areas (as measured by 50 percent utilization distributions intersecting any quantified habitat) over long-term periods were solely located in Bodie Hills and Long Valley. Although nearly all subpopulations experienced contractions in occupied overall and core distribution, Bodie Hills experienced spatial expansion that occurred with concomitant spatial contraction at Long Valley over both periods. Subpopulations at the northern (Pine Nuts), central (Sagehen) and southern (White-Mountains) extents of the Bi-State DPS also experienced spatial contraction over the short-term period. These findings, coupled with those of population trends, indicate long-term patterns in redistribution of sage-grouse from Long Valley and peripheral subpopulations to Bodie Hills. That is, sage-grouse subpopulations at the periphery are declining while the largest population at the core is increasing, which could have meaningful impacts on overall metapopulation persistence. We provide evidence for loss of occupied habitat (reduced distribution) given local extirpation of subpopulations.
Fifth, we calculated percentages of selected phenological, life stage, and annual habitat that each subpopulation contributed to the Bi-State DPS. We then intersected these maps with a composite estimate of occupied habitat from the fourth objective and calculated percentages of selected habitat likely occupied by sage-grouse that each subpopulation contributed to the Bi-State DPS. These values provide evidence for loss of occupied habitat and subsequent reductions in spatial distribution given reductions in abundance and, in some cases, extirpation of leks within subpopulations.
Lastly, we carried out an initial in-depth analysis of selection for irrigated pastures and wet meadows during the brood-rearing stage for the Long Valley subpopulation. We chose this subpopulation because it represents a population core, representing 26.5 percent of total sage-grouse within the Bi-State DPS, and has exhibited long-term declines in abundance and distribution. This subpopulation is highly sensitive to precipitation and other factors that influence water availability. Models predicted higher use of the interior portions of irrigated pastures and wet meadows during late brood-rearing period, which represented a potentially risky use of habitat that was exacerbated during periods of low moisture (for example, drought, reduced water delivery, or both). Sage-grouse typically used edges of riparian areas and pastures, largely because the interior of these mesic areas consisted of considerably less overhead concealment cover (for example, shrubs) that likely resulted in a higher risk of mortality. We found that a lack of water delivery to pastures in the form of overwinter precipitation or diversion ditches increased the movements of sage-grouse to the interior of pastures. Although further investigation of water delivery impacts on chick survival are warrented, our initial findings regarding resource selection may explain recent declines in population growth at Long Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191149","collaboration":"Prepared in cooperation with the U. S. Fish and Wildlife Service, Bureau of Land Management, California Department of Fish and Wildlife, Nevada Department of Wildlife, and the U.S. Forest Service","usgsCitation":"Coates, P.S., Ricca, M.A., Prochazka, B.G., O’Neil, S,T., Severson, J.P., Mathews, S.R., Espinosa, S., Gardner, S., Lisius, S., and Delehanty, D.J., 2020, Population and habitat analyses for greater sage-grouse (Centrocercus urophasianus) in the bi-state distinct population segment—2018 update: U.S. Geological Survey Open-File Report 2019–1149, 122 p., https://doi.org/10.3133/ofr20191149.","productDescription":"x, 122 p.","onlineOnly":"Y","ipdsId":"IP-113768","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":371329,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1149/ofr20191149.pdf","text":"Report","size":"8.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1149"},{"id":371330,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1149/ofr20191149_hirez.pdf","text":"Report — High resolution graphics","size":"82.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1149 Hi Rez"},{"id":371328,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1149/coverthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 38.90813299596705\n ],\n [\n -119.92675781249999,\n 38.436379603\n ],\n [\n -119.72900390625001,\n 37.50972584293751\n ],\n [\n -118.85009765625,\n 36.90597988519294\n ],\n [\n -117.83935546874999,\n 36.12900165569652\n ],\n [\n -117.586669921875,\n 36.1733569352216\n ],\n [\n -117.31201171875001,\n 35.862343734896484\n ],\n [\n -116.806640625,\n 36.075742215627\n ],\n [\n -115.224609375,\n 35.79108281624994\n ],\n [\n -115.587158203125,\n 37.125286284966805\n ],\n [\n -117.23510742187501,\n 38.62545397209084\n ],\n [\n -119.5751953125,\n 39.16414104768742\n ],\n [\n -120.234375,\n 38.90813299596705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The city of Sioux Falls, in southeastern South Dakota, is the largest city in South Dakota. The U.S. Geological Survey (USGS), in cooperation with the city of Sioux Falls, completed a groundwater-flow model to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer in the model area.
The model area includes the Big Sioux aquifer and the underlying hydrogeologic units from Dell Rapids, South Dakota, to the confluence of the Big Sioux River and the outlet of the Sioux Falls Diversion Channel in eastern Sioux Falls, S. Dak. The Big Sioux aquifer is the primary aquifer in the model area and the focus of the groundwater-flow model. The Big Sioux River is the largest stream in the model area and is in hydraulic connection with the Big Sioux aquifer.
A conceptual model for the area was constructed and includes a characterization of the hydrogeologic framework, analysis and construction of potentiometric surfaces, and summary of estimated water budget components in the model area. The primary hydrogeologic units in the model area consist of (1) the Big Sioux aquifer, (2) a glacial till confining unit, and (3) bedrock aquifers (Split Rock Creek and Sioux Quartzite aquifers). Sources of groundwater recharge included infiltration of precipitation, stream seepage, and groundwater exchanges among the hydraulically connected Big Sioux aquifer, glacial till confining unit, and bedrock aquifers. Groundwater losses included evapotranspiration, groundwater discharge to streams, and groundwater withdrawal to supply water-use needs.
A numerical groundwater-flow model (numerical model) was constructed and was used to simulate all aspects of the conceptual model for predevelopment (steady-state) and time-varying (transient) monthly conditions for 1950–2017. The numerical model was constructed using the USGS modular hydrologic simulation program, MODFLOW–6, and was calibrated using the Parameter ESTimation software, PEST++.
The transient numerical model was calibrated for steady-state and transient monthly conditions for 1950–2017. Calibration targets were observations of hydraulic head, changes in hydraulic head, monthly mean streamflow (as a rate), and cumulative monthly stream discharge (as a volume). Parameters adjusted during model calibration were horizontal and vertical hydraulic conductivity, specific storage, specific yield, recharge and evapotranspiration multipliers, and streambed hydraulic conductivity. Horizontal and vertical hydraulic conductivity were estimated at pilot points distributed within the model area; specific storage and specific yield were assigned to uniform values in each layer in the model area; recharge and evapotranspiration multipliers were assigned uniformly for every stress period in the numerical model; and streambed hydraulic conductivity values were assigned uniformly between stream confluences.
The final calibrated parameter values of horizontal and vertical hydraulic conductivity, specific yield, specific storage, streambed hydraulic conductivity, recharge, and evapotranspiration were considered reasonable for the hydrogeologic materials and conditions in the model area for 1950–2017.
Overall, simulated hydraulic head altitudes had a linear regression coefficient of determination (R2) of 0.48. Hydraulic head altitude residuals for the glacial till confining unit and bedrock aquifers were typically greater in magnitude when compared to residuals in the Big Sioux aquifer, but simulated hydraulic head altitudes in the Big Sioux aquifer compared favorably with mean observed hydraulic head altitudes and had a linear regression R2 of 0.93.
Simulated streamflow hydrographs matched the general trends of observed increases and decreases in streamflow for USGS streamgages 06482000 (Big Sioux River at Sioux Falls, S. Dak.) and 06482020 (Big Sioux River at North Cliff Avenue at Sioux Falls, S. Dak.), but larger streamflows were overestimated at the first streamgage and underestimated at the second streamgage. The numerical model reasonably estimated cumulative monthly stream discharge for the first 10–15 years of available streamflow records at both USGS streamgages. After the first 10–15 years of available streamflow record, cumulative monthly stream discharge was closely estimated for USGS streamgage 06482000 and underestimated at USGS streamgage 06482020.
Composite sensitivities without regularization were calculated by PEST++ for the calibrated numerical model parameters and were averaged by parameter group. The parameter group with the highest mean composite sensitivity was the recharge multiplier parameter group.
Model simplifications, assumptions, and limitations were necessary for construction of the conceptual and numerical models and for calibration efficiency. Spatial simplification of hydraulic properties could cause the numerical model to misrepresent reactions to changes in localized stresses, such as additional demands for groundwater withdrawal. The numerical model was temporally discretized into monthly periods and required scaling daily rates into representative monthly rates for model input and calibration targets. Based on the comparison between the observed and simulated groundwater levels, monthly mean streamflow and cumulative monthly stream discharge, and general groundwater distribution and flow, the numerical model favorably simulated the flow in the Big Sioux aquifer.
Eventual capture was calculated in the model area using a steady-state numerical groundwater-flow model. The eventual capture map shows areas of higher streamflow capture adjacent to the Big Sioux River north of the city of Sioux Falls and along the lower part of the Sioux Falls Diversion Channel, and areas of lower streamflow capture along aquifer boundaries and near the southern Sioux Quartzite barrier.
The timing of capture was determined using a transient numerical groundwater-flow model to determine the likely captured water sources for 30 years of groundwater withdrawal at three hypothetical wells using three continuous withdrawal rates (112.5, 450.0, and 900.0 gallons per minute). Supply for all three hypothetical wells became capture-dominated after only a short period of continuous withdrawal. Capture stabilized after about 10–15 years for well A, and after 20–25 years for well B, and after about 10–15 years for well C.
The groundwater-flow model is a suitable tool to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer near Sioux Falls, S. Dak. The numerical model can be used to simulate hydrologic scenarios, advance understanding of groundwater budgets, compute system response to stress, and determine likely sources of water supplied to wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195117","collaboration":"Prepared in cooperation with the city of Sioux Falls","usgsCitation":"Davis, K.W., Eldridge, W.G., Valder, J.F., and Valseth, K.J., 2019, Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5117, 86 p., https://doi.org/10.3133/sir20195117.","productDescription":"Report: xi, 86 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-105956","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":369602,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20195013","text":"SIR 2019–5013","linkHelpText":"– Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota"},{"id":369600,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3393","text":"SIM 3393","linkHelpText":"– Delineation of the hydrogeologic framework of the Big Sioux aquifer near Sioux Falls, South Dakota, using airborne electromagnetic data"},{"id":369601,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F79885XC","text":"USGS data release for SIM 3393","linkHelpText":"– Airborne electromagnetic and magnetic survey data, Big Sioux aquifer, October 2015, Sioux Falls, South Dakota"},{"id":369603,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release for SIR 2019–5013","linkHelpText":"– Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"},{"id":369535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5117/coverthb.jpg"},{"id":369536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5117/sir20195117.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5117"},{"id":369537,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O59RO0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-6 model of the Big Sioux aquifer, Sioux Falls, South Dakota"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.06146240234375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.29919735147067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Trace-metal concentrations in sediment and in the clam Macoma petalum (formerly reported as Macoma balthica), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in south San Francisco Bay, Calif. This report includes the data collected by U.S. Geological Survey (USGS) scientists for the period January 2018 to December 2018. These append to long-term datasets extending back to 1974. A major focus of the report is an integrated description of the 2018 data within the context of the longer, multi-decadal dataset. This dataset supports the City of Palo Alto’s Near-Field Receiving-Water Monitoring Program, initiated in 1994.
Significant reductions in silver and copper concentrations in both sediment and M. petalum occurred at the site in the 1980s following the implementation by PARWQCP of advanced wastewater treatment and source control measures. Since the 1990s, concentrations of these elements appear to have stabilized at concentrations somewhat above (silver [Ag]) or near (copper [Cu]) regional background concentrations. Data for other metals, including chromium (Cr), mercury (Hg), nickel (Ni), selenium (Se), and zinc (Zn), have been collected since 1994. Over this period, concentrations of these elements have remained relatively constant, aside from seasonal variation that is common to all elements. In 2018, concentrations of silver and copper in M. petalum varied seasonally in response to a combination of site-specific metal exposures and annual growth and reproduction, as reported previously. Seasonal patterns for other elements, including Cr, Ni, Zn, Hg, and Se, were generally similar in timing and magnitude as those for Ag and Cu. This record suggests that legacy contamination and regional-scale factors now largely control sedimentary and bioavailable concentrations of silver and copper, as well as other elements of regulatory interest, at the Palo Alto site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191084","collaboration":"Prepared in cooperation with the City of Palo Alto, California","usgsCitation":"Cain, D.J., Thompson, J.K., Parchaso, F., Pearson, S., Stewart, R., Turner, M., Shrader, K.H., Zierdt Smith, E.L., and Luoma, S.N., 2019, Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2018: U.S. Geological Survey Open-File Report 2019–1084, 41 p., https://doi.org/10.3133/ofr20191084.","productDescription":"vi, 41 p.","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-109149","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":416180,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161118","text":"Open-File Report 2016-1118","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2015"},{"id":416181,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20171135","text":"Open-File Report 2017-1135","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016"},{"id":416182,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181107","text":"Open-File Report 2018-1107","linkHelpText":"- Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2017"},{"id":416184,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211079","text":"Open-File Report 2021-1079","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2019"},{"id":368964,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1084/ofr20191084.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-FIle Report 2019-1084"},{"id":368963,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1084/coverthb.jpg"},{"id":416185,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231017","text":"Open-File Report 2023-1017","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2020"}],"country":"United States","state":"California","otherGeospatial":"Palo Alto Regional Water Quality Control Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.14187622070311,\n 37.43179575348695\n ],\n [\n -122.08419799804689,\n 37.43179575348695\n ],\n [\n -122.08419799804689,\n 37.48085213924346\n ],\n [\n -122.14187622070311,\n 37.48085213924346\n ],\n [\n -122.14187622070311,\n 37.43179575348695\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Earth System Processes Division
U.S. Geological Survey
411 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
High-resolution, terrestrial laser scanning, also known as ground-based lidar (light detection and ranging), was used to quantify the volume of mercury-contaminated sediment eroded from an outcrop of historical placer-mining debris at the confluence of Humbug Creek and the South Yuba River in the Sierra Nevada foothills, about 17 kilometers northeast of Grass Valley, California, and delivered to a zone below an observed flood stage of the South Yuba River. Substantial quantities of mercury were used and lost to the environment from historical placer gold mining activities on the western slope of the Sierra Nevada, California, and recent studies have documented continued persistence of mercury and methylmercury concentrations in water, sediment, fish, and predatory invertebrates in the Yuba River drainage basin in relation to suspected mercury sources. To identify areas that have high levels of mercury contamination as possible remediation targets in the Yuba River drainage basin and other areas in the Sierra Nevada, the U.S. Geological Survey worked in cooperation with the Bureau of Land Management on this and other detailed studies. Malakoff Diggings, one of the largest hydraulic gold mines in the Sierra Nevada, is 3.5 kilometers north of the study site in the Humbug Creek subbasin.
Terrestrial laser scanning was used to produce centimeter-scale, three-dimensional maps of the complex outcrop surface, which was composed of an upper erosional area (cliff and over-steepened slope) and a lower depositional area (colluvial slope). The outcrop could not be mapped non-destructively or in sufficient detail by traditional surveying techniques. The study site, which was approximately 70 meters long, 30 meters wide and 20 meters high, was surveyed four times in 2 years (December 15, 2011; October 25, 2012; January 4, 2013; and November 22, 2013) to determine volumetric differences in the upper erosional and lower depositional areas between surveys. Measured changes in volume for the upper erosional area and lower depositional area were multiplied by the corresponding sediment density so that a mass-balance relationship, between the eroded and deposited sediment during each period, could be used to estimate the amount of mercury-contaminated sediment that was transported to below the base of the colluvial slope, where it could be mobilized by the South Yuba River during a flood having a 5-to-10-year recurrence interval. On December 2, 2012, a flood of this estimated magnitude reached the base of the colluvial slope.
Between the first and second surveys (December 15, 2011–October 25, 2012), an estimated mass of 18±9.2 kilograms of sediment was transported from steeper slopes to the gently sloping river bank below the base of the colluvial slope. Between the second and third surveys (October 25, 2012–January 4, 2013), an atmospheric river caused heavy precipitation at the study site during late November and early December 2012. This short-duration, high-intensity rain resulted in a large amount of erosion and deposition at the study site and also caused high streamflow (flood stage) in the South Yuba River. From October 2012 to January 2013, 51±31 kilograms of sediment was transported to below the base of the colluvial slope, that is, below the high-water mark of December 2, 2012. Between the third and fourth surveys (January 4, 2013–November 22, 2013), an additional 10±26 kilograms of sediment was transported to below the base of the colluvial slope. During the 24 months of the study, the total mass of sediment delivered below the base of the colluvial slope and the high-water mark of December 2, 2012, was 79±66 kilograms.
In any given year there is a 10–20-percent chance (5-to-10-year recurrence interval) of a flood equal to or greater than that of the December 2, 2012, flood, which could transport mercury-contaminated sediment at the study site into the South Yuba River. Hydraulically modeled estimates of the South Yuba River stage during floods having a 50- and 100-year recurrence interval (2- and 1-percent annual exceedance probability, respectively) indicated that resulting river stages could be 2.2–3.0 meters above the base of the colluvial slope, or 2.2–3.0 meters above the high-water mark of December 2, 2012. Such high river stages would be likely to inundate the lower half of the colluvial slope and mobilize a substantial volume of mercury-contaminated sediment to downstream areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195104","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Howle, J.F., Alpers, C.N., Kitchen, J., Bawden, G.W., and Bond, S., 2019, Quantifying the eroded and deposited mass of mercury-contaminated sediment by using terrestrial laser scanning at the confluence of Humbug Creek and the South Yuba River, Nevada County, California, 2011–13: U.S. Geological Survey Scientific Investigations Report 2019– 5104, 30 p., https://doi.org/10.3133/sir20195104.\n","productDescription":"Report: viii, 30 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-078829","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":368585,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5104/sir20195104.pdf","text":"Report","size":"6.5 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California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The study described in this report, initiated by the U.S. Geological Survey in 2014, was designed to evaluate fresh groundwater resources within the Ozark Plateaus, central United States, as an area within a broader national assessment of groundwater availability. The goals of the Ozark study were to evaluate historical effects of human activities on water levels and groundwater availability, quantify groundwater resources now and under probable future pumping and climate conditions, and evaluate existing monitoring networks for their value in making better predictions of future groundwater resources. Previous studies include simulation of local-scale groundwater flow under varying temporal scales, or simulation of the regional system under steady-state conditions. While these studies are useful, particularly for the problem for which they were designed, there is a need to look at the larger regional system under transient conditions to fully evaluate the water resource over time. This study focused on multiple spatial and temporal scales to examine changes in groundwater pumping, storage, and water-level declines. The regional scale provides a broad view of the sources and demands on the system with time.
The study area covers approximately 68,000 square miles in the central United States in parts of Missouri, Arkansas, Kansas, and Oklahoma and encompasses the Ozark Plateaus Physiographic Province (Ozark Plateaus), including the Salem Plateau, Springfield Plateau, and Boston Mountains. Groundwater is withdrawn from the Ozark Plateaus aquifer system (Ozark system) for public supply and for domestic, agriculture (including irrigation and aquaculture), livestock, and non-agricultural use (including industrial, thermoelectric power generation, mining, and commercial). The Ozark system provides an important drinking-water supply for people living in the Ozark Plateaus because public supply and domestic use combined constitute the largest groundwater use. Precipitation is the ultimate source of freshwater to the Ozark system; most rainfall occurs during April, May, and June, and precipitation increases generally from north to south across the study area.
Groundwater use currently accounts for only 10 percent of the total water use in the areas overlying the Ozark system, but provides a critical drinking-water resource because public supply and domestic groundwater withdrawals are largely from groundwater resources. The 380 million gallons per day of groundwater withdrawn from the Ozark system in 2010 accounts for approximately 2 percent of recharge. Although groundwater use represents a small component of the hydrologic budget, because of low storage in aquifer units, cones of depression with steep water-level gradients can develop quickly around pumping centers.
The amount of water entering and leaving the aquifer system from 1900 to about 1965 was relatively constant at a rate of about 13 billion gallons per day (Bgal/d). Much of this inflow of water is discharged through streams in the system to balance the hydrologic budget. Changes in storage over time (from outflows to inflows) reflect the large variability in recharge: if recharge decreases, water levels will decrease, resulting in less groundwater discharge to streams and more water released from aquifer storage. Conversely, when recharge increases, water levels increase, more groundwater discharges to streams, and aquifer storage is replenished. Although pumping generally increased from 1900 to 2016, it does not appear to correlate with the change in storage over the same time period. Regionally, simulated change in groundwater storage corresponds with changes in recharge, more so than with increases in pumping.
Average recharge was 11.6 Bgal/d for the period 1900 to 2016. Recharge was generally above average from predevelopment to 1965, followed by a period of below-average recharge from 1965 to about 1980. Recharge remained consistently above average from 1980 to about 1988, after which there was a period of average or below-average recharge, reflected by a decline through the mid-2000s.
The implications and potential effects of increased pumping and long-term climate change on the Ozark Plateaus hydrologic system and groundwater availability are a concern for communities and resource managers in the area. Pumping varies from year to year, but is generally expected to moderately increase with population, industrial, and agricultural needs. Most climate models predict warmer minimum and maximum air temperatures by midcentury in the Ozark Plateaus area, especially from midspring through early fall. Three scenarios were developed to simulate possible future conditions from 2016 to 2060 and assess the potential effects on the hydrologic system and availability of water resources. For each scenario, changes in water levels and hydrologic budget components were evaluated from predevelopment (1900) to present (2016) and 45 years into the future (2060). The baseline scenario represents an extension of the average (1996 to 2016) seasonal pumping and recharge values. The pumping scenario is an extension of the average (1996 to 2016) seasonal recharge values with increases in pumping following the historical trend for the period 2016–2060 of up to 120 percent of the 1996 to 2016 average seasonal pumping values. The general circulation model (GCM) scenario is an extension of the average (1996 to 2016) seasonal pumping values and variable recharge based on seasonal averages of soil water storage from a water-balance model using temperature and precipitation from multiple GCMs.
The general patterns of water-level decline are similar for each scenario. The areas of water-level decline in southwest Missouri and northeast Oklahoma are only marginally different by 2060 from those of 2009. In one area south of Springfield, Mo., water-level declines are less in the baseline and GCM scenarios than in 2009. This may be the result of a transition from groundwater use to surface-water supplies for a larger percentage of the demand in the area.
For all three scenarios, forecasted pumping, recharge, and aquifer properties play an important role in determining the uncertainty of water-level forecasts at 94 real-time observation wells. Simulated aquifer properties in the productive middle and lower Ozark aquifers and the St. Francois confining unit of the Ozark system contribute most to predictive uncertainty in water levels at approximately 35 percent of the real-time observation wells. Out of the 94 real-time observation wells, 82 are developed in the lower Ozark aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1854","collaboration":"Water Availability and Use Science Program","usgsCitation":"Clark, B.R., Duncan, L.L., and Knierim, K.J., 2019, Groundwater availability in the Ozark Plateaus aquifer system: U.S. Geological Survey Professional Paper 1854, 82 p., https://doi.org/10.3133/pp1854.","productDescription":"Report: x, 82 p.; Data Release","numberOfPages":"95","onlineOnly":"Y","ipdsId":"IP-097847","costCenters":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":368455,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1854/pp1854.pdf","text":"Report","size":"18.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1854"},{"id":368456,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H0PQ93","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model of groundwater flow in the Ozark Plateaus aquifer system"},{"id":368454,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1854/coverthb.jpg"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark Plateaus Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.1263427734375,\n 38.81831117374662\n ],\n [\n -90.3076171875,\n 38.89103282648846\n ],\n [\n -90.4559326171875,\n 38.8225909761771\n ],\n [\n -90.692138671875,\n 38.70694605159386\n ],\n [\n -90.8734130859375,\n 38.556757147352215\n ],\n [\n -91.0546875,\n 38.59970036588819\n ],\n [\n -91.2689208984375,\n 38.66835610151506\n ],\n [\n -91.3897705078125,\n 38.71551876930462\n ],\n [\n -91.571044921875,\n 38.68122173079789\n ],\n [\n -91.7083740234375,\n 38.70265930723801\n ],\n [\n -91.9171142578125,\n 38.65119833229951\n ],\n [\n -92.07092285156249,\n 38.57823196583313\n ],\n [\n -92.197265625,\n 38.586820096127674\n ],\n [\n -92.3345947265625,\n 38.6897975322717\n ],\n [\n -92.4884033203125,\n 38.90813299596705\n ],\n [\n -92.57080078125,\n 38.96368010198575\n ],\n [\n -92.74108886718749,\n 38.98503278695909\n ],\n [\n -92.87841796875,\n 38.997841307500714\n ],\n [\n -92.92236328125,\n 39.091699613104595\n ],\n [\n -92.9058837890625,\n 39.17691709496078\n ],\n [\n -92.8619384765625,\n 39.2407625100131\n ],\n [\n -92.9608154296875,\n 39.317300373271024\n ],\n [\n -93.0926513671875,\n 39.38101803294523\n ],\n [\n -93.2025146484375,\n 39.40224434029275\n ],\n [\n -93.350830078125,\n 39.30454987014581\n ],\n [\n -93.482666015625,\n 39.287545585410435\n ],\n [\n -93.70788574218749,\n 39.223742741391305\n ],\n [\n -93.98803710937499,\n 39.15136267949029\n ],\n [\n -95.2349853515625,\n 38.25112269630296\n ],\n [\n -95.2789306640625,\n 36.45221769643571\n ],\n [\n -95.0152587890625,\n 35.30840140169162\n ],\n [\n -94.19128417968749,\n 35.35321610123823\n ],\n [\n -93.834228515625,\n 35.47409160773029\n ],\n [\n -93.482666015625,\n 35.40696093270201\n ],\n [\n -93.16955566406249,\n 35.25459097465022\n ],\n [\n -93.065185546875,\n 35.15584570226544\n ],\n [\n -92.6641845703125,\n 35.08845057036537\n ],\n [\n -92.55432128906249,\n 34.95349314197422\n ],\n [\n -92.427978515625,\n 34.836349990763864\n ],\n [\n -92.1148681640625,\n 34.8183131456094\n ],\n [\n -90.3515625,\n 35.97800618085566\n ],\n [\n -89.35729980468749,\n 37.01132594307015\n ],\n [\n -89.4561767578125,\n 37.25656608611523\n ],\n [\n -89.4287109375,\n 37.37015718405753\n ],\n [\n -89.46716308593749,\n 37.45741810262938\n ],\n [\n -89.5166015625,\n 37.58811876638322\n ],\n [\n -89.56054687499999,\n 37.71859032558816\n ],\n [\n -89.70886230468749,\n 37.82280243352756\n ],\n [\n -89.8187255859375,\n 37.900865092570065\n ],\n [\n -89.9560546875,\n 37.97451499202459\n ],\n [\n -90.164794921875,\n 38.07836562996712\n ],\n [\n -90.2801513671875,\n 38.14751758025121\n ],\n [\n -90.3680419921875,\n 38.268375880204744\n ],\n [\n -90.32409667968749,\n 38.40194908237822\n ],\n [\n -90.252685546875,\n 38.53097889440024\n ],\n [\n -90.2032470703125,\n 38.62974534092597\n ],\n [\n -90.19775390625,\n 38.70694605159386\n ],\n [\n -90.1263427734375,\n 38.81831117374662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
640 Grassmere Park, Suite 100
Nashville, Tennessee, 37211
The city of Wichita’s water supply currently (2019) comes from two primary sources: Cheney Reservoir and the Equus Beds aquifer. The Equus Beds aquifer storage and recovery project was developed to help the city of Wichita meet increasing future water demands. Source water for artificial recharge comes from the Little Arkansas River during above-base-flow conditions, is treated using National Primary Drinking Water Regulations as a guideline, and is injected into the Equus Beds aquifer through recharge wells or surface spreading basins for later use. The Equus Beds aquifer storage and recovery project currently (2019) consists of two coexisting phases. Phase I began in 2007 and captures Little Arkansas River water and indirect streambank diversion well water for aquifer recharge using 4 wells and 2 recharge basins. Phase II began in 2013 and currently (2019) includes a surface-water treatment facility, a river intake facility, eight recharge injection wells, and a third recharge basin. The U.S. Geological Survey, in cooperation with the City of Wichita, completed this study to summarize water-quality and geochemical variability of the Equus Beds aquifer. Data in this report can be used to establish baseline conditions before implementing artificial aquifer recharge further, document groundwater quality, evaluate changing conditions, identify environmental factors affecting groundwater, provide science-based information for decision making, and help meet regulatory monitoring requirements.
Physicochemical properties were measured and water-quality data were collected from 2 Little Arkansas River surface-water sites and 63 Equus Beds aquifer groundwater sites, including 38 areal assessment index wells (IWs) during 2001 through 2016. Data collection included discrete samples and additional continuous measurements at selected sites. Discretely collected samples were analyzed for physicochemical properties, dissolved solids, primary ions, nutrients (nitrogen and phosphorus species), organic carbon, indicator bacteria, trace elements, arsenic species, organic compounds, and radioactivity. This report focuses discussion on aquifer water quality. Federal drinking-water criteria were used to evaluate aquifer water quality. Primary drinking-water criteria are those that are enforceable for public drinking water. Secondary criteria are those that can cause aesthetics or tastes that are unpleasant.
Continuously collected data at a subset of sites included streamflow, groundwater levels, water temperature, specific conductance, pH, oxidation-reduction potential (ORP), dissolved oxygen, turbidity, nitrate plus nitrite, and fluorescent dissolved organic matter. Continuous measurement of physicochemical properties in near-real time allowed characterization of Little Arkansas River surface water and Equus Beds aquifer groundwater during conditions and time scales that would not have been possible otherwise and served as a complement to discrete water-quality sampling. During 2001 through 2016, less than 1 percent of chloride and nitrate plus nitrite, 7 percent of dissolved iron, 48 percent of dissolved manganese, 12 percent of dissolved arsenic, and 39 percent of atrazine detections in surface-water samples exceeded their respective Federal primary or secondary drinking-water criteria. None of the surface-water samples collected exceeded the Federal sulfate criterion, and every sample had detections of total coliform bacteria during the study.
Constituents of concern in the Equus Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, Escherichia coli (E. coli), total coliforms, and dissolved iron and arsenic species. About 5 percent of shallow (less than 80 feet) and 7 percent of deep (greater than 80 feet) IW chloride sample concentrations exceeded the secondary Federal criterion of 250 milligrams per liter (mg/L). Chloride tended to exceed its criterion in shallow and deep wells along the Arkansas River and near Burrton, Kansas, an area with past oil and gas activities. Chloride concentrations near Burrton were larger in the deep parts of the aquifer. About 18 percent of shallow and 13 percent of deep IW sulfate sample concentrations exceeded the secondary Federal criterion of 250 mg/L. Mean sulfate concentrations tended to exceed the criterion in the central part of the study area. Shallow IW mean nitrate plus nitrite (hereafter referred to as “nitrate”) was substantially larger than mean deep IW nitrate. Geochemical conditions in the deeper aquifer reduced forms of nitrogen to species such as ammonia. About 15 percent of shallow and less than 1 percent of deep IW nitrate sample concentrations exceeded the Federal criterion of 10 mg/L. Mean shallow IW nitrate concentrations exceeded the criterion in the northeastern and southeastern parts of the study area; on average, deep IW nitrate concentrations did not exceed the criterion. E. coli and fecal coliform bacteria detections were usually at or near the detection limit. E. coli was detected in 3 percent of shallow and deep IWs, and fecal coliform bacteria were detected in 8 percent of shallow and 6 percent of deep IWs. Total coliforms were detected in 24 percent of shallow and 12 percent of deep IWs. E. coli coliphage was detected in two shallow IW samples (1 percent of samples) at the detection limit and was not detected in deep IW samples.
Dissolved iron was detected in 51 percent of shallow and 62 percent of deep IW samples. Dissolved iron concentrations exceeded the secondary Federal criterion of 0.3 mg/L in 38 percent of shallow and 46 percent of deep IW samples. Mean dissolved iron concentrations were largest mostly in the central and northwest part of the study area corresponding to an area of the aquifer where aquifer material is more clay-rich. The distribution of large dissolved iron concentrations was similar to that of large sulfate concentrations. About 55 percent of shallow and 92 percent of deep IW dissolved manganese samples exceeded the secondary Federal criterion of 0.05 mg/L. Almost all samples from the central and northern parts of the study area had mean dissolved manganese concentrations that exceeded the Federal criterion in the shallow part of the aquifer. Mean dissolved manganese concentrations in the shallow part of the aquifer were substantially large (greater than 1,000 micrograms per liter [μg/L]) in wells near the Little Arkansas River and in the central part of the study area because of chemically reducing conditions in the aquifer that likely related to larger percentages of clay in the aquifer material.
Concentrations of dissolved arsenic species generally were larger in the deep parts of the aquifer. Arsenite was the dominant form of arsenic on average in shallow (52 percent) and deep (55 percent) IWs. About 12 percent of shallow and 34 percent of deep IW dissolved arsenic sample concentrations exceeded the Federal primary drinking criterion of 10 μg/L. Shallow IW dissolved arsenic concentrations were larger near the Little Arkansas River and the center of the study area; large shallow IW dissolved arsenic concentrations (10–50 μg/L) in the center of the study area correspond to areas that have had the most water-level recovery since the historical low in 1993. Mean ORP in shallow IWs generally decreased with increasing water-level depths and were inversely related to mean dissolved arsenic concentrations because of more reducing conditions (smaller ORP) at larger depths below the land surface. Larger dissolved arsenic concentrations in the shallow parts of the aquifer were associated with decreases in water levels and a subsequent decrease in ORP and thus more reducing conditions.
Atrazine was detected in about 58 percent of shallow and 28 percent of deep IWs and did not exceed the primary Federal criterion of 3 μg/L in any groundwater samples. Atrazine concentrations in shallow IWs generally were largest in the northwest part of the study area near the North Branch Kisiwa Creek, and atrazine concentrations in deep IWs generally were largest most often in the southern part of the study area. Gross α radioactivity concentrations exceeded the primary Federal criterion of 15 picocuries per liter in 4 percent of shallow IW samples. Gross α and gross β radioactivity concentrations generally were larger in the southern third of the aquifer.
Most groundwater-sample-simulated minerals saturation indices (SIs) were consistently negative (undersaturated). Minerals that had SI values that were consistently or typically positive (oversaturated) included iron oxide, hydroxide, and quartz-group minerals. Several SI values for arsenic- and manganese-bearing minerals were consistently negative. Some manganese-bearing mineral SI values ranged from undersaturated to oversaturated in shallow and deep IWs during the study. Several carbonate minerals in shallow and deep IWs varied across their equilibrium state. Calcite SI values were larger more often in the deep parts of the aquifer and did not show a clear distributional pattern. Mean and median calcite SI values for shallow and deep IWs were negative (undersaturated) indicating the potential for calcite dissolution if calcite is present for a substantial part of the study period. However, some individual calcite SI values in this study indicated saturation and subsequent calcite precipitation may occur in the study area, potentially resulting in formation of calcite mineral deposits that may reduce efficiency of injection wells. SI values with respect to iron hydroxide varied across their equilibrium states. Mean and median SI values with respect to iron hydroxide were undersaturated in shallow and deep IWs; however, some samples had positive SI values indicating there is potential for iron hydroxide precipitation, possibly caused by leaching and oxidation of iron-containing minerals, like pyrite, in the aquifer material.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195026","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Scientific Investigations Report 2019–5026, 79 p., https://doi.org/10.3133/sir20195026.","productDescription":"Report: viii, 79 p.; Appendix Tables: Table 1.1 to Table 1.14; Companion Files","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097040","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5026/coverthb.jpg"},{"id":364761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026"},{"id":364771,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":" – Table 1.1 through Table 1.14"},{"id":364762,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Fact Sheet 2019–3017","size":"4.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and Equus Beds Aquifer, South-Central Kansas, 2001–16"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.83462524414062,\n 37.884608857503785\n ],\n [\n -97.82844543457031,\n 37.85859141570558\n ],\n [\n -97.76664733886719,\n 37.79296501804014\n ],\n [\n -97.57919311523438,\n 37.66805980224121\n ],\n [\n -97.33749389648438,\n 37.684907136008846\n ],\n [\n -97.33062744140625,\n 37.74248523826606\n ],\n [\n -97.35397338867188,\n 37.859675659210005\n ],\n [\n -97.34230041503906,\n 38.03619406237626\n ],\n [\n -97.3443603515625,\n 38.17829073458205\n ],\n [\n -97.40684509277344,\n 38.17613163876633\n ],\n [\n -97.8826904296875,\n 38.171273439283084\n ],\n [\n -97.89985656738281,\n 38.149137543764894\n ],\n [\n -97.83462524414062,\n 37.884608857503785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Dr.
Lawrence, KS 66049
Over the last half century, climate change, coral disease, and other anthropogenic disturbances have restructured coral-reef ecosystems on a global scale. The disproportionate loss of once-dominant, reef-building taxa has facilitated relative increases in the abundance of “weedy” or stress-tolerant coral species. Although the recent transformation of coral-reef assemblages is unprecedented on ecological timescales, determining whether modern coral reefs have truly reached a novel ecosystem state requires evaluating the dynamics of reef composition over much longer periods of time. Here, we provide a geologic perspective on the shifting composition of Florida's reefs by reconstructing the millennial-scale spatial and temporal variability in reef assemblages using 59 Holocene reef cores collected throughout the Florida Keys Reef Tract (FKRT). We then compare the relative abundances of reef-building species in the Holocene reef framework to data from contemporary reef surveys to determine how much Florida's modern reef assemblages have diverged from long-term baselines. We show that the composition of Florida's reefs was, until recently, remarkably stable over the last 8000 yr. The same corals that have dominated shallow-water reefs throughout the western Atlantic for hundreds of thousands of years, Acropora palmata, Orbicella spp., and other massive coral taxa, accounted for nearly 90% of Florida's Holocene reef framework. In contrast, the species that now have the highest relative abundances on the FKRT, primarily Porites astreoides and Siderastrea siderea, were rare in the reef framework, suggesting that recent shifts in species assemblages are unprecedented over millennial timescales. Although it may not be possible to return coral reefs to pre-Anthropocene states, our results suggest that coral-reef management focused on the conservation and restoration of the reef-building species of the past, will optimize efforts to preserve coral reefs, and the valuable ecosystem services they provide into the future.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2781","usgsCitation":"Toth, L., Stathakopoulos, A., Kuffner, I.B., Ruzicka, R.R., Colella, M.A., and Shinn, E.A., 2019, The unprecedented loss of Florida's reef-building corals and the emergence of a novel coral-reef assemblage: Ecology, v. 100, no. 9, e02781, 14 p., https://doi.org/10.1002/ecy.2781.","productDescription":"e02781, 14 p.","ipdsId":"IP-104540","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467556,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.2781","text":"Publisher Index Page"},{"id":437430,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93XXXA0","text":"USGS data release","linkHelpText":"The Absolute and Relative Composition of Holocene Reef Cores From the Florida Keys Reef Tract"},{"id":422972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys Reef Tract","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.52594320175321,\n 24.764514561822665\n ],\n [\n -83.03873267817458,\n 24.764514561822665\n ],\n [\n -83.08197998341461,\n 24.427458630027616\n ],\n [\n -81.47565150305881,\n 24.41058205242757\n ],\n [\n -80.33268700742039,\n 24.92709848702593\n ],\n [\n -80.06702498951552,\n 25.525116607289604\n ],\n [\n -80.25854876986541,\n 25.53069165144869\n ],\n [\n -80.598349025326,\n 25.03910062993201\n ],\n [\n -81.3644441467264,\n 24.826209468694913\n ],\n [\n -82.52594320175321,\n 24.764514561822665\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"100","issue":"9","noUsgsAuthors":false,"publicationDate":"2019-07-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888681,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888682,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888683,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruzicka, Robert R.","contributorId":204569,"corporation":false,"usgs":false,"family":"Ruzicka","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":888684,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Colella, Michael A.","contributorId":139979,"corporation":false,"usgs":false,"family":"Colella","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":13340,"text":"Fish & Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":888685,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shinn, Eugene A.","contributorId":210858,"corporation":false,"usgs":false,"family":"Shinn","given":"Eugene","email":"","middleInitial":"A.","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":888686,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}