Information on low-flow characteristics of streams is essential for the management of water resources. This report provides equations for estimating the 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and the harmonic-mean flow at ungaged, unregulated stream sites in Indiana. These equations were developed using the low-flow statistics and basin characteristics for 108 continuous-record streamgages in Indiana with at least 10 years of daily mean streamflow data through the 2011 climate year (April 1 through March 31). The equations were developed in cooperation with the Indiana Department of Environmental Management.
Regression techniques were used to develop the equations for estimating low-flow frequency statistics and the harmonic-mean flows on the basis of drainage-basin characteristics. A geographic information system was used to measure basin characteristics for selected streamgages. A final set of 25 basin characteristics measured at all the streamgages were evaluated to choose the best predictors of the low-flow statistics.
Logistic-regression equations applicable statewide are presented for estimating the probability that selected low-flow frequency statistics equal zero. These equations use the explanatory variables total drainage area, average transmissivity of the full thickness of the unconsolidated deposits within 1,000 feet of the stream network, and latitude of the basin outlet. The percentage of the streamgage low-flow statistics correctly classified as zero or nonzero using the logistic-regression equations ranged from 86.1 to 88.9 percent.
Generalized-least-squares regression equations applicable statewide for estimating nonzero low-flow frequency statistics use total drainage area, the average hydraulic conductivity of the top 70 feet of unconsolidated deposits, the slope of the basin, and the index of permeability and thickness of the Quaternary surficial sediments as explanatory variables. The average standard error of prediction of these regression equations ranges from 55.7 to 61.5 percent.
Regional weighted-least-squares regression equations were developed for estimating the harmonic-mean flows by dividing the State into three low-flow regions. The Northern region uses total drainage area and the average transmissivity of the entire thickness of unconsolidated deposits as explanatory variables. The Central region uses total drainage area, the average hydraulic conductivity of the entire thickness of unconsolidated deposits, and the index of permeability and thickness of the Quaternary surficial sediments. The Southern region uses total drainage area and the percent of the basin covered by forest. The average standard error of prediction for these equations ranges from 39.3 to 66.7 percent.
The regional regression equations are applicable only to stream sites with low flows unaffected by regulation and to stream sites with drainage basin characteristic values within specified limits. Caution is advised when applying the equations for basins with characteristics near the applicable limits and for basins with karst drainage features and for urbanized basins. Extrapolations near and beyond the applicable basin characteristic limits will have unknown errors that may be large. Equations are presented for use in estimating the 90-percent prediction interval of the low-flow statistics estimated by use of the regression equations at a given stream site.
The regression equations are to be incorporated into the U.S. Geological Survey StreamStats Web-based application for Indiana. StreamStats allows users to select a stream site on a map and automatically measure the needed basin characteristics and compute the estimated low-flow statistics and associated prediction intervals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165102","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Martin, G.R., Fowler, K.K., and Arihood, L.D., 2016, Estimating selected low-flow frequency statistics and harmonic-mean flows for ungaged, unregulated streams in Indiana (ver 1.1, October 2016): U.S. Geological Survey Scientific Investigations Report 2016–5102, 45 p., https://dx.doi.org/10.3133/sir20165102.","productDescription":"Report: vii, 45 p.; Metadata; Read Me File; Spatial Data","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066895","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":327994,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2016/5102/readme.pdf","text":"Additional information about SIR 2016–5102 - read me 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\"}}]}","edition":"Version 1.0: Originally posted September 6, 2016; Version 1.1: October 24, 2016","contact":"Director, Indiana-Kentucky Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd
Indianapolis, IN 46278
In January 2014, a storage tank leaked, spilling a large quantity of 4-methylcyclohexane methanol into the Elk River in West Virginia and contaminating the water supply for more than 300,000 people. In response, the West Virginia Legislature passed Senate Bill 373, which requires the West Virginia Department of Health and Human Resources (WVDHHR) to assess the susceptibility and vulnerability of public surface-water-influenced groundwater supply sources (SWIGS) and surface-water intakes statewide. In response to this mandate for reassessing SWIGS statewide, the U.S. Geological Survey (USGS), in cooperation with the WVDHHR, Bureau of Public Health, Office of Environmental Health Services, compiled available data and summarized the results of previous groundwater studies to provide the WVDHHR with data that could be used as part of the process for assessing and determining SWIGS.
\nExisting geologic, hydrologic, well-construction, water-quality, and other related data and information from previous U.S. Geological Survey (USGS) hydrogeologic studies and the USGS National Water Information System (NWIS) database, in conjunction with data from the West Virginia Bureau for Public Health (WVBPH) Department of Health and Human Resources (WVDHHR) and the West Virginia Department of Environmental Protection database and files, were collected, compiled, and analyzed to help the WVDHHR to better assess public groundwater supply wells that may meet the definition of a surface-water-influenced- groundwater supply (SWIGS).
\nIn this study, measures of intrinsic susceptibility, which are characterized by the physical properties that affect the ease with which water moves through the unsaturated zone and, subsequently, into the saturated zone within an aquifer, showed that karst limestone aquifers are the aquifers most intrinsically susceptible to contamination within the State of West Virginia. Karst limestone aquifers are present within Cambrian- and Ordovician-age formations within West Virginia’s eastern panhandle and in Mississippian-age limestones within the Greenbrier River valley. Solution development within these limestone aquifers allows rapid recharge and flow of groundwater within the aquifer, both of which allow surface contaminants to easily enter the aquifer and travel long distances in a short period of time.
\nAlluvial aquifers bordering the Ohio River in western West Virginia are also potentially highly susceptible to contamination because these alluvial aquifers can receive significant recharge from the adjacent Ohio River. Any potential contaminants that may be present in the river have the potential to enter the aquifer and contaminate wells completed within the sand and gravel alluvial sediments within which the wells are completed. These same alluvial sediments, however, help to retard the movement of bacteria and other potentially pathogenic organisms, such as Cryptosporidia and Giardia lamblia, into the aquifer. As a result, samples from alluvial aquifers bordering the Ohio River and elsewhere within the State do not commonly test positive for indicator bacteria, such as total coliform, fecal coliform, or Escherichia coli (E. coli). The alluvial sediments do not, however, provide assimilative capacity with respect to water soluble compounds such as nitrate and certain volatile and semi-volatile organic compounds. Therefore, the Ohio River alluvial aquifers are highly susceptible to organic compounds present in the river or on the land surface near a well. These aquifers are also susceptible to nitrate contamination from fertilizers, pesticides, and manure, which are commonly used on the fertile agricultural soils present on terraces along the Ohio River.
\nAbandoned-coal-mine aquifers, which are typically used as a source of groundwater in southern West Virginia, are moderately susceptible to contamination. The vast network of voids from mine entries provide vast storage for groundwater in abandoned mine aquifers, and fracturing of overburden strata, which is common in areas of past or current mining, can allow rapid infiltration of contaminants to the aquifer. Where streams cross over below-drainage underground coal mines, there is an increased potential for contamination of coal-mine aquifers. As a result, above-drainage underground coal mines, those mines that are present at an elevation above local tributary drainage, are probably less susceptible to contamination than are below-drainage underground coal mines. Public groundwater supplies in abandoned coal mines need to be evaluated on a case-by-case basis to assess the potential for recharge of contaminated surface water to enter below-drainage underground coal-mine aquifers and to assess potential hydraulic conductivity to nearby surface-water bodies, such as lakes, ponds, rivers, or streams.
\nFractured-rock aquifers compose an additional major type of aquifer within the State of West Virginia. Owing to their low permeability and their typically small groundwater capture areas, fractured-rock aquifers within the State of West Virginia typically have low susceptibility to contamination. However, there are exceptions, and wells completed in fractured-rock aquifers that are in close proximity to streams may be adversely affected by induced recharge from the stream. Where such systems are present, frequent bacterial testing of the source water can be used to ascertain the potential for microbial contamination of the aquifer.
\nIntrinsic susceptibility alone does not fully predict whether or not a well is vulnerable to contamination, only that the hydrogeologic terrain is suitable for rapid transport of pathogenic organisms or chemical compounds to and within the aquifer. However, contaminants may or may not be present in the recharge water to an individual well or well field. Therefore, an assessment of potential contaminant sources, such as nearby gas wells, landfills, underground storage tanks, above ground storage tanks, major transportation corridors, surface or underground coal mines, and flood plains, is needed to assess vulnerability. The assessments need to be conducted on a case-by-case basis or, as has been done in this study, by collecting and compiling the number of potential contaminant sources that may be present in the source-water-protection area for an individual public groundwater supply source.
\nGroundwater public-supply systems in areas of high intrinsic susceptibility and with a large number of potential contaminant sources within the recharge or source-water-protection area of individual wells or well fields are potentially vulnerable to contamination and probably warrant further evaluation as potential SWIGS. However, measures can be taken to educate the local population and initiate safety protocols and protective strategies to appropriately manage contaminant sources to prevent release of contaminants to the aquifer, therefore, reducing vulnerability of these systems to contamination. However, each public groundwater supply source needs to be assessed on an individual basis. Data presented in this report can be used to categorize and prioritize wells and springs that have a high potential for intrinsic susceptibility or vulnerability to contamination.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165048","collaboration":"Prepared in cooperation with the West Virginia Department of Health and Human Resources, Bureau of Public Health, Office of Environmental Health Services","usgsCitation":"Kozar, M.D., and Paybins, K.S., 2016, Assessment of hydrogeologic terrains, well-construction characteristics, groundwater hydraulics, and water-quality and microbial data for determination of surface-water-influenced groundwater supplies in West Virginia (ver. 1.1, October 2016): U.S. Geological Survey Scientific Investigations Report 2016–5048, 55 p., https://dx.doi.org/10.3133/sir20165048.","productDescription":"Report: vii, 54 p.; 2 Figures; 3 Appendixes","numberOfPages":"67","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065870","costCenters":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":325448,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_figure3A.pdf","text":"Figure 3A -","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Major Geologic Formations in West Virginia"},{"id":325450,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix1.xlsx","text":"Appendix 1 - ","size":"168 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"Description of 324 wells in West Virginia sampled as part of the U.S. Geological Survey and West Virginia Department of Environmental Protection statewide Ambient Groundwater Quality Monitoring Network"},{"id":325449,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_figure3B.pdf","text":"Figure 3B -","size":"745 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"Major geologic formations in the study area of the Blue Ridge Physiographic Province USGS National Water Quality Assessment study in Virginia and North Carolina"},{"id":325446,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5048/coverthb2.jpg"},{"id":325452,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix3.xlsx","text":"Appendix 3 - ","size":"111 KB","linkHelpText":" Permit data for public groundwater supplies in West Virginia with accompanying counts of number of potential sources of contamination within the respective source-water-protection area for each public groundwater supply source"},{"id":325447,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048.pdf","text":"Report","size":"25.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5048"},{"id":325451,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5048/sir20165048_appendix2.xlsx","text":"Appendix 2 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Virginia\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: Originally posted August 30, 2016; Version 1.1: October 24, 2016","contact":"Director, West Virginia Water Science Center
U.S. Geological Survey
11 Dunbar Street
Charleston, WV 25301
http://wv.usgs.gov
Treated effluent discharged from municipal wastewater-treatment plants to the Assabet River in central Massachusetts includes phosphorus, which leads to increased growth of nuisance aquatic plants that decrease the river’s water quality and aesthetics in impounded reaches during the growing season. To improve the river’s water quality and aesthetics, the U.S. Environmental Protection Agency approved a total maximum daily load for phosphorus in 2004 that directed the wastewater-treatment plants to reduce the amount of total phosphorus discharged to the river by 2012. The permitted total phosphorus monthly average of 0.75 milligrams per liter during the aquatic plant growing season (April 1 through October 31) was reduced by the total maximum daily load to a target of 0.1 milligrams per liter by 2012, and the nongrowing-season limit was unchanged at 1.0 milligrams per liter.
From October 2008 through April 2014, the U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, measured streamflow and collected weekly flow-proportional, composite samples of water from the Assabet River for analysis of concentrations of total phosphorus and orthophosphate. Streamflow and concentration data were used to estimate total phosphorus and orthophosphate loads in the river. The purpose of this monitoring effort was to evaluate phosphorus concentrations and loads in the river before, during, and after the wastewater-treatment-plant upgrades and to assess the effects of seasonal differences in permitted discharges. The locations of water-quality-monitoring stations, with respect to the Hudson and Ben Smith impoundments, enabled examination of effects of phosphorus entering and leaving the impoundments.
Annual median concentrations of total phosphorus in wastewater-treatment plants were reduced by more than 80 percent with the plant upgrades. Measured instream annual median concentrations of total phosphorus in the Assabet River decreased by about 38 to 50 percent at three of the four monitoring stations following the wastewater-treatment-plant upgrades. At the station farthest upstream, the median total phosphorus concentration remained unchanged throughout the study; this may be attributed to the site location and potential resuspension of particulate organic matter during periods of increased streamflow. Annual median loads from the wastewater-treatment plants were reduced by up to 91 percent following the upgrades, instream annual median total phosphorus loads at the three downstream stations decreased by 71 to 76 percent, and instream orthophosphate loads at the three downstream stations decreased by 79 to 87 percent.
Seasonal fluctuations (growing versus nongrowing) of total phosphorus and orthophosphate were observed instream before the upgrades. However, after the upgrades, fluctuations in phosphorus released from the treatment plants were slight and seasonal changes were typically not observed instream.
Annual loads entering and leaving the two impoundments were inconclusive in determining whether the impoundments were sources or sinks of total phosphorus during the study. Total phosphorus loads entering the Hudson impoundment were consistently greater than those leaving; however, there was uncertainty about the loads at the monitoring station upstream from this impoundment. At the Ben Smith impoundment, total phosphorus and orthophosphate loads downstream were slightly greater than those upstream from the impoundment, but the differences may reflect additions from tributaries and overland runoff.
Estimated instream total phosphorus concentrations and loads indicated that the decreases in total phosphorus in wastewater-treatment-plant discharges were accompanied by reductions measured in the Assabet River. A statistical analysis which incorporates the effect of varying flow conditions demonstrated significant reductions in total phosphorus concentrations after the wastewater-treatment-plant upgrades at three of the four instream monitoring stations. No significant change was observed at the most upstream location, the Assabet River at Port Street at Hudson, Massachusetts (station number 01096835), which may have been affected by flow-related resuspension of particulate phosphorus.
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10 Bearfoot Road
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Or visit our Web site at:
http://newengland.water.usgs.gov
We used Global Positioning System (GPS) radiotelemetry data from 7 breeding female wolves (Canis lupus; n = 14 dennings) in 3 regions across Alaska, USA, during 2008–2011 to develop and compare methods for estimating the onset of denning, and thus infer timing of parturition. We developed and tested 2 estimators based on a combination of GPS radiocollar location-fix failure and distance traveled between locations. We developed a quantitative method employing Generalized Additive Models to smooth time series of wolf data to estimate denning onset. In contrast, 3 study authors with first-hand experience with the study wolves implemented a subjective method of estimating denning onset by visual inspection of detection and distance traveled data. We then tested the visual method for repeatability by subjecting it to 10 wolf experts not associated with this study. Side-by-side comparison of estimators indicates that denning onset can be precisely measured using GPS detection success and distance traveled. Furthermore, the visual-inspection method was simple and rapid to implement and yielded more accurate (relative to assumed dates of denning onset) and precise results compared to the quantitative estimator. Although the Generalized Additive Model based approach had the advantage of estimating denning onset objectively following a set of prescribed rules in a statistical inferential framework, we found the method required significant technical capacity to implement and did not represent an improvement over simple visual-inspection-based estimates of denning onset.
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C.","contributorId":288778,"corporation":false,"usgs":false,"family":"Lake","given":"Bryce","email":"","middleInitial":"C.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":838276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangipane, Buck A.","contributorId":288781,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck","email":"","middleInitial":"A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":838277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielson, Ryan","contributorId":288785,"corporation":false,"usgs":false,"family":"Nielson","given":"Ryan","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":838278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lowe, Stacey","contributorId":288787,"corporation":false,"usgs":false,"family":"Lowe","given":"Stacey","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":838279,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177783,"text":"70177783 - 2016 - Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes","interactions":[],"lastModifiedDate":"2016-10-21T09:45:36","indexId":"70177783","displayToPublicDate":"2016-10-21T10:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes","docAbstract":"Airborne light detection and ranging (lidar) is a valuable tool for collecting large amounts of elevation data across large areas; however, the limited ability to penetrate dense vegetation with lidar hinders its usefulness for measuring tidal marsh platforms. Methods to correct lidar elevation data are available, but a reliable method that requires limited field work and maintains spatial resolution is lacking. We present a novel method, the Lidar Elevation Adjustment with NDVI (LEAN), to correct lidar digital elevation models (DEMs) with vegetation indices from readily available multispectral airborne imagery (NAIP) and RTK-GPS surveys. Using 17 study sites along the Pacific coast of the U.S., we achieved an average root mean squared error (RMSE) of 0.072 m, with a 40–75% improvement in accuracy from the lidar bare earth DEM. Results from our method compared favorably with results from three other methods (minimum-bin gridding, mean error correction, and vegetation correction factors), and a power analysis applying our extensive RTK-GPS dataset showed that on average 118 points were necessary to calibrate a site-specific correction model for tidal marshes along the Pacific coast. By using available imagery and with minimal field surveys, we showed that lidar-derived DEMs can be adjusted for greater accuracy while maintaining high (1 m) resolution.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.09.020","usgsCitation":"Buffington, K., Dugger, B.D., Thorne, K.M., and Takekawa, J.Y., 2016, Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes: Remote Sensing of Environment, v. 186, p. 616-625, https://doi.org/10.1016/j.rse.2016.09.020.","productDescription":"10 p.","startPage":"616","endPage":"625","ipdsId":"IP-079777","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470492,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.09.020","text":"Publisher Index Page"},{"id":438533,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GJFZHT","text":"USGS data release","linkHelpText":"LEAN-Corrected Collier County DEM for wetlands"},{"id":438532,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NQZXU3","text":"USGS data release","linkHelpText":"LEAN-Corrected Chesapeake Bay Digital Elevation Models, 2019"},{"id":438531,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97R4ES3","text":"USGS data release","linkHelpText":"LEAN-Corrected DEM for Suisun Marsh"},{"id":330288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"186","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5810c528e4b0f497e7972c1e","chorus":{"doi":"10.1016/j.rse.2016.09.020","url":"http://dx.doi.org/10.1016/j.rse.2016.09.020","publisher":"Elsevier BV","authors":"Buffington Kevin J., Dugger Bruce D., Thorne Karen M., Takekawa John Y.","journalName":"Remote Sensing of Environment","publicationDate":"12/2016"},"contributors":{"authors":[{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":651789,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Bruce D.","contributorId":176167,"corporation":false,"usgs":false,"family":"Dugger","given":"Bruce","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":651790,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, Karen M. 0000-0002-1381-0657 kthorne@usgs.gov","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":4191,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen","email":"kthorne@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":651788,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":176168,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":651791,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212562,"text":"70212562 - 2016 - Mapping annual forest cover in sub-humid and semi-arid regions through analysis of landsat and PALSAR imagery","interactions":[],"lastModifiedDate":"2020-08-21T13:59:59.041494","indexId":"70212562","displayToPublicDate":"2016-10-21T08:59:40","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping annual forest cover in sub-humid and semi-arid regions through analysis of landsat and PALSAR imagery","docAbstract":"Accurately mapping the spatial distribution of forests in sub-humid to semi-arid regions over years is a challenging task and causes difficulty to forest management. Relatively large uncertainties still exist in the spatial distribution of forests and deforestation in the sub-humid and semi-arid regions. Numerous publications have used either optical or synthetic aperture radar (SAR) remote sensing imagery, but the resultant forest cover maps often have large errors. In this study, we proposed a pixel- and rule-based algorithm to identify and map annual forests from 2007 to 2010 in Oklahoma, USA, a transition region with various climate and landscapes, using the integration of the L-band ALOS PALSAR Fine Beam Dual Polarization (FBD) mosaic dataset and Landsat images. The overall accuracy and Kappa coefficient of the PALSAR/Landsat forest map were about 88.2% and 0.75 in 2010, with the user and producer accuracy about 93.4% and 75.7%, based on the 3,270 random ground plots collected in 2012 and 2013. Compared with the forest products from JAXA, NLCD, OKESM and OKFRA, the PALSAR/Landsat forest map showed great improvement. The area of the PALSAR/Landsat forest was about 40,149 km2 in 2010, which was close to the area from OKFRA (40,468 km2), but much larger than those from JAXA (32,403 km2) and NLCD (37,628 km2). We analyzed annual forest cover dynamics, and the results show extensive deforestation (2,761 km2, 6.9% of the total forest area in 2010) and reforestation (3,630 km2, 9.0%) in the southeast and central Oklahoma, and the total area of forests increased by 684 km2 from 2007 to 2010. This study clearly demonstrates the potential of data fusion between PALSAR and Landsat images for mapping annual forest cover dynamics in sub-humid to semi-arid regions, and the resultant forest maps would be helpful to forest management.","language":"English","publisher":"MDPI","doi":"10.3390/rs8110933","usgsCitation":"Qin, Y., Xiao, X., Wang, J., Dong, J., Ewing, K., Hoagland, B., Hough, D.J., Fagin, T.D., Zou, Z., Geissler, G.L., Xian, G.Z., and Loveland, T., 2016, Mapping annual forest cover in sub-humid and semi-arid regions through analysis of landsat and PALSAR imagery: Remote Sensing, v. 8, no. 11, 933, 19 p., https://doi.org/10.3390/rs8110933.","productDescription":"933, 19 p.","ipdsId":"IP-080582","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110933","text":"Publisher Index 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(Geography)","active":false,"usgs":true}],"preferred":true,"id":797003,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70177110,"text":"ofr20161180 - 2016 - Selected techniques for monitoring water movement through unsaturated alluvium during managed aquifer recharge","interactions":[],"lastModifiedDate":"2019-12-27T11:30:40","indexId":"ofr20161180","displayToPublicDate":"2016-10-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1180","title":"Selected techniques for monitoring water movement through unsaturated alluvium during managed aquifer recharge","docAbstract":"Managed aquifer recharge is used to augment natural recharge to aquifers. It can be used to replenish aquifers depleted by pumping or to store water during wetter years for withdrawal during drier years. Infiltration from ponds is a commonly used, inexpensive approach for managed aquifer recharge.
At some managed aquifer-recharge sites, the time when infiltrated water arrives at the water table is not always clearly shown by water-level data. As part of site characterization and operation, it can be desirable to track downward movement of infiltrated water through the unsaturated zone to identify when it arrives at the water table.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161180","collaboration":"Prepared in cooperation with the Antelope Valley-East Kern Water Agency","usgsCitation":"Nawikas, J.M, O’Leary, D.R., Izbicki, J.A., and Burgess, M.K., 2016, Selected techniques for monitoring water movement through unsaturated alluvium during managed aquifer recharge: U.S. Geological Survey Open-File Report 2016–5105, 8 p., https://dx.doi.org/10.3133/ofr20161180.","productDescription":"8 p.","numberOfPages":"8","onlineOnly":"Y","ipdsId":"IP-060596","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":329775,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1180/coverthb.jpg"},{"id":329776,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1180/ofr20161180.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1180"}],"country":"United States","state":"California","otherGeospatial":"Antelope Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.795166015625,\n 34.474863669009004\n ],\n [\n -117.20214843749999,\n 34.474863669009004\n ],\n [\n -117.20214843749999,\n 35.106428057364255\n ],\n [\n -118.795166015625,\n 35.106428057364255\n ],\n [\n -118.795166015625,\n 34.474863669009004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Joseph Nawikas
4165 Spruance Road, Suite 200
San Diego, CA 92101
(619) 225-6148
jnawika@usgs.gov
We used 22 yr of capture–mark–reencounter (CMR) data collected from 1988 to 2009 on about 12,500 birds at what went from three to five coastal colony sites in Massachusetts, New York, and Connecticut, United States, to examine spatial and temporal variation in breeding dispersal/fidelity rates of adult Roseate Terns (Sterna dougallii). At the start of our study, Roseate Terns nested at only one site (Bird Island) in Buzzards Bay, Massachusetts, but two more sites in this bay (Ram and Penikese Islands) were subsequently recolonized and became incorporated into our CMR metapopulation study. We examined four major hypotheses about factors we thought might influence colony-site fidelity and movement rates in the restructured system. We found some evidence that colony-site fidelity remained higher at long-established sites compared with newer ones and that breeding dispersal was more likely to occur among nearby sites than distant ones. Sustained predation at Falkner Island, Connecticut, did not result in a sustained drop in fidelity rates of breeders. Patterns of breeding dispersal differed substantially at the two restored sites. The fidelity of Roseate Terns at Bird dropped quickly after nearby Ram was recolonized in 1994, and fidelity rates for Ram soon approached those for Bird. After an oil spill in Buzzards Bay in April 2003, hazing (deliberate disturbance) of the terns at Ram prior to the start of egg-laying resulted in lowering of fidelity at this site, a decrease in immigration from Bird, and recolonization of Penikese by Roseate Terns. Annual fidelity rates at Penikese increased somewhat several years after the initial recolonization, but they remained much lower there than at all the other sites throughout the study period. The sustained high annual rates of emigration from Penikese resulted in the eventual failure of the restoration effort there, and in 2013, no Roseate Terns nested at this site.
","language":"English","publisher":"Ecological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1002/ecs2.1510","usgsCitation":"Spendelow, J.A., Monticelli, D., Nichols, J.D., Hines, J.E., Nisbet, I., Cormons, G., Hays, H., Hatch, J., and Mostello, C., 2016, Roseate Tern breeding dispersal and fidelity: Responses to two newly restored colony sites: Ecosphere, v. 7, no. 10, p. 1-16, https://doi.org/10.1002/ecs2.1510.","productDescription":"e01510; 16 p.","startPage":"1","endPage":"16","numberOfPages":"16","ipdsId":"IP-073068","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470495,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1510","text":"Publisher Index Page"},{"id":330240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Massachusetts, New York, Rhode 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Portugal","active":true,"usgs":false}],"preferred":false,"id":651637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":651638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":651639,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nisbet, 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Carolyn","contributorId":176117,"corporation":false,"usgs":false,"family":"Mostello","given":"Carolyn","email":"","affiliations":[],"preferred":false,"id":651644,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70177104,"text":"ofr20161104 - 2016 - Abstract volume for the 2016 biennial meeting of the Yellowstone Volcano Observatory","interactions":[],"lastModifiedDate":"2016-10-21T09:26:20","indexId":"ofr20161104","displayToPublicDate":"2016-10-20T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1104","title":"Abstract volume for the 2016 biennial meeting of the Yellowstone Volcano Observatory","docAbstract":"Every two years, scientists, natural resource managers, outreach specialists, and a variety of other interested parties get together for the biennial meeting of the Yellowstone Volcano Observatory (YVO). Each time, the theme varies. In past years, we have focused the meeting around topics including monitoring plans, emergency response, geodesy, and outreach. This year, we spent the first half-day devoted to recent research results, plans for upcoming studies, and geothermal monitoring. On the second day, our focus switched to eruption precursors, particularly as they apply to large caldera systems.
Very few large explosive eruptions from caldera systems have taken place in recorded history. Therefore, there are few empirical data with which to characterize the nature of volcanic unrest that might precede eruptions with volcano explosivity index (VEI) of six or greater. For this reason, we set up a series of talks that explore what we know and don’t know about large eruptions. We performed an informal expert elicitation (a frequently used method to characterize expert opinion) with a small number of our colleagues, which served as the basis for a productive discussion session.
This short volume of abstracts and extended abstracts provides a summary of the presentations made at the YVO meeting held in Mammoth Hot Springs, Wyoming, on May 10–11, 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161104","usgsCitation":"Lowenstern, J.B., ed., 2016, Abstract volume for the 2016 biennial meeting of the Yellowstone Volcano Observatory: U.S. Geological Survey Open-File Report 2016–1104, 46 p., https://www.dx.doi.org/10.3133/ofr20161104","productDescription":"iii, 46 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-076807","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":329774,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1104/ofr20161104.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1104"},{"id":329773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1104/coverthb.jpg"}],"contact":"Contact YVO
Volcano Science Center, Yellowstone Volcano Observatory
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
http://volcanoes.usgs.gov/yvo/
Late Cenozoic faulting and large-magnitude extension in the Great Basin of the western USA has created locally deep windows into the upper crust, permitting direct study of volcanic and plutonic rocks within individual calderas. The Caetano caldera in north–central Nevada, formed during the mid-Tertiary ignimbrite flare-up, offers one of the best exposed and most complete records of caldera magmatism. Integrating whole-rock geochemistry, mineral chemistry, isotope geochemistry and geochronology with field studies and geologic mapping, we define the petrologic evolution of the magmatic system that sourced the >1100 km3Caetano Tuff. The intra-caldera Caetano Tuff is up to ∼5 km thick, composed of crystal-rich (30–45 vol. %), high-silica rhyolite, overlain by a smaller volume of comparably crystal-rich, low-silica rhyolite. It defies classification as either a monotonous intermediate or crystal-poor zoned rhyolite, as commonly ascribed to ignimbrite eruptions. Crystallization modeling based on the observed mineralogy and major and trace element geochemistry demonstrates that the compositional zonation can be explained by liquid–cumulate evolution in the Caetano Tuff magma chamber, with the more evolved lower Caetano Tuff consisting of extracted liquids that continued to crystallize and mix in the upper part of the chamber following segregation from a cumulate-rich, and more heterogeneous, source mush. The latter is represented in the caldera stratigraphy by the less evolved upper Caetano Tuff. Whole-rock major, trace and rare earth element geochemistry, modal mineralogy and mineral chemistry, O, Sr, Nd and Pb isotope geochemistry, sanidine Ar–Ar geochronology, and zircon U–Pb geochronology and trace element geochemistry provide robust evidence that the voluminous caldera intrusions (Carico Lake pluton and Redrock Canyon porphyry) are genetically equivalent to the least evolved Caetano Tuff and formed from magma that remained in the lower chamber after ignimbrite eruption and caldera collapse. Thus, the Caetano Tuff contradicts models for the mutually exclusive origins of voluminous volcanic and plutonic magmas in the upper crust. Crystal-scale O isotope data indicate that the Caetano Tuff is one of the most 18O-enriched rhyolites in the Great Basin (δ18Omagma = 10·2 ± 0·2‰), supporting anatexis of local metasedimentary basement crust. Metapelite xenoliths in the Carico Lake pluton and ubiquitous xenocrystic zircons in the Caetano Tuff provide constraints for the anatexis process; these data point to shallow (<15 km) dehydration melting of a protolith similar to the Proterozoic McCoy Creek Group siliciclastic sediments in eastern Nevada, projected beneath Caetano in fault-stacked shelf sediments that were thickened during Mesozoic crustal shortening. Mean zircon U–Pb ages for different stratigraphic levels of the intra-caldera Caetano Tuff are 34·2–34·5 Ma, 0·2–0·5 Myr older than the caldera sanidine 40Ar/39Ar age of 34·00 ± 0·03 Ma, documenting protracted duration of assembly and homogenization of isotopically diverse upper crustal melts, followed by crystallization and zonation to generate the Caetano Tuff magma chamber. Sanidine rims in the least evolved Caetano Tuff and in the Carico Lake pluton and Redrock Canyon porphyry have sharply zoned Ba domains that point to crystal growth during magmatic recharge events. The recharge magma is inferred to have been compositionally similar to the Caetano Tuff magma, with increased Ba resulting from remelting of Ba-rich sanidine cumulates. Mush reactivation to generate the Caetano Tuff eruption was sufficiently rapid to preserve compositional gradients in the intracaldera ignimbrite, calling into question models that predict homogeneity as a prerequisite for remobilizing crystal-rich ignimbrite magmas.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/petrology/egw051","usgsCitation":"Watts, K.E., John, D.A., Colgan, J.P., Henry, C., Bindeman, I.N., and Schmitt, A.K., 2016, Probing the volcanic-plutonic connection and the genesis of crystal-rich rhyolite in a deeply dissected supervolcano in the Nevada Great Basin: Source of the late Eocene Caetano Tuff: Journal of Petrology, v. 57, no. 8, p. 1599-1644, https://doi.org/10.1093/petrology/egw051.","productDescription":"46 p.","startPage":"1599","endPage":"1644","ipdsId":"IP-064750","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":470499,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egw051","text":"Publisher Index Page"},{"id":329728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-07","publicationStatus":"PW","scienceBaseUri":"58088686e4b0f497e78e24b9","contributors":{"authors":[{"text":"Watts, Kathryn E. 0000-0002-6110-7499 kwatts@usgs.gov","orcid":"https://orcid.org/0000-0002-6110-7499","contributorId":5081,"corporation":false,"usgs":true,"family":"Watts","given":"Kathryn","email":"kwatts@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":651310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"John, David A. 0000-0001-7977-9106 djohn@usgs.gov","orcid":"https://orcid.org/0000-0001-7977-9106","contributorId":1748,"corporation":false,"usgs":true,"family":"John","given":"David","email":"djohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":651311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Colgan, Joseph P. 0000-0001-6671-1436 jcolgan@usgs.gov","orcid":"https://orcid.org/0000-0001-6671-1436","contributorId":1649,"corporation":false,"usgs":true,"family":"Colgan","given":"Joseph","email":"jcolgan@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":651312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henry, Christopher D.","contributorId":175501,"corporation":false,"usgs":false,"family":"Henry","given":"Christopher D.","affiliations":[{"id":6689,"text":"Nevada Bureau of Mines and Geology","active":true,"usgs":false}],"preferred":false,"id":651314,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bindeman, Ilya N.","contributorId":175500,"corporation":false,"usgs":false,"family":"Bindeman","given":"Ilya","email":"","middleInitial":"N.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":651313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmitt, Axel K.","contributorId":127614,"corporation":false,"usgs":false,"family":"Schmitt","given":"Axel","email":"","middleInitial":"K.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":651315,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70176894,"text":"sir20165105 - 2016 - Flood-inundation maps for the Peckman River in the Townships of Verona, Cedar Grove, and Little Falls, and the Borough of Woodland Park, New Jersey, 2014","interactions":[],"lastModifiedDate":"2017-07-17T13:36:38","indexId":"sir20165105","displayToPublicDate":"2016-10-19T10:00:00","publicationYear":"2016","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":"2016-5105","title":"Flood-inundation maps for the Peckman River in the Townships of Verona, Cedar Grove, and Little Falls, and the Borough of Woodland Park, New Jersey, 2014","docAbstract":"Digital flood-inundation maps for an approximate 7.5-mile reach of the Peckman River in New Jersey, which extends from Verona Lake Dam in the Township of Verona downstream through the Township of Cedar Grove and the Township of Little Falls to the confluence with the Passaic River in the Borough of Woodland Park, were created by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ depict estimates of the probable areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Peckman River at Ozone Avenue at Verona, New Jersey (station number 01389534). Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/.
Flood profiles were simulated for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the most current stage-discharge relations at USGS streamgages on the Peckman River at Ozone Avenue at Verona, New Jersey (station number 01389534) and the Peckman River at Little Falls, New Jersey (station number 01389550). The hydraulic model was then used to compute eight water-surface profiles for flood stages at 0.5-foot (ft) intervals ranging from 3.0 ft or near bankfull to 6.5 ft, which is approximately the highest recorded water level during the period of record (1979–2014) at USGS streamgage 01389534, Peckman River at Ozone Avenue at Verona, New Jersey. The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging (lidar) data to delineate the area flooded at each water level.
The availability of these maps along with Internet information regarding current stage from the USGS streamgage provides emergency management personnel and residents with information, such as estimates of inundation extents, based on water stage, that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165105","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Niemoczynski, M.J., and Watson, K.M., 2016, Flood-inundation maps for the Peckman River in the Townships of Verona, Cedar Grove, and Little Falls, and the Borough of Woodland Park, New Jersey, 2014: U.S. Geological Survey Scientific Investigations Report 2016-5105, 13 p. https://dx.doi.org/10.3133/sir20165105","productDescription":"vii, 13 p.","onlineOnly":"Y","ipdsId":"IP-053115","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":329482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5105/sir20165105.pdf","text":"Report","size":"7.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5105"},{"id":329481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5105/coverthb.jpg"},{"id":343948,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7C53J0H","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation Mapping Data for the Peckman River in the Townships of Verona, Cedar Grove, and Little Falls, and the Borough of Woodland Park, New Jersey, 2014"}],"country":"United States","state":"New Jersey","city":" Cedar Grove, Little Falls, Verona, Woodland Park","otherGeospatial":"Peckman River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.4,\n 41\n ],\n [\n -74.4,\n 40.8\n ],\n [\n -74.1,\n 40.8\n ],\n [\n -74.1,\n 41\n ],\n [\n -74.4,\n 41\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ, 08648
http://nj.usgs.gov/
Water levels declined from 2003 to 2011 in many lakes in Ramsey and Washington Counties in the northeast Twin Cities Metropolitan Area, Minnesota; however, water levels in other northeast Twin Cities Metropolitan Area lakes increased during the same period. Groundwater and surface-water exchanges can be important in determining lake levels where these exchanges are an important component of the water budget of a lake. An understanding of groundwater and surface-water exchanges in the northeast Twin Cities Metropolitan Area has been limited by the lack of hydrologic data. The U.S. Geological Survey, in cooperation with the Metropolitan Council and Minnesota Department of Health, completed a field and statistical study assessing lake-water levels and regional and local groundwater and surface-water exchanges near northeast Twin Cities Metropolitan Area lakes. This report documents the analysis of collected hydrologic, water-quality, and geophysical data; and existing hydrologic and geologic data to (1) assess the effect of physical setting and climate on lake-level fluctuations of selected lakes, (2) estimate potential percentages of surface-water contributions to well water across the northeast Twin Cities Metropolitan Area, (3) estimate general ages for waters extracted from the wells, and (4) assess groundwater inflow to lakes and lake-water outflow to aquifers downgradient from White Bear Lake.
Statistical analyses of lake levels during short-term (2002–10) and long-term (1925–2014) periods were completed to help understand lake-level changes across the northeast Twin Cities Metropolitan Area. Comparison of 2002–10 lake levels to several landscape and geologic characteristics explained variability in lake-level changes for 96 northeast Twin Cities Metropolitan Area lakes. Application of several statistical methods determined that (1) closed-basin lakes (without an active outlet) had larger lake-level declines than flow-through lakes with an outlet; (2) closed-basin lake-level changes reflected groundwater-level changes in the Quaternary, Prairie du Chien, and Jordan aquifers; (3) the installation of outlet-control structures, such as culverts and weirs, resulted in smaller multiyear lake-level changes than lakes without outlet-control structures; (4) water levels in lakes primarily overlying Superior Lobe deposits were significantly more variable than lakes primarily overlying Des Moines Lobe deposits; (5) lake-level declines were larger with increasing mean lake-level elevation; and (6) the frequency of some of these characteristics varies by landscape position. Flow-through lakes and lakes with outlet-control structures were more common in watersheds with more than 50 percent urban development compared to watersheds with less than 50 percent urban development. A comparison of two 35-year periods during 1925–2014 revealed that variability of annual mean lake levels in flow-through lakes increased when annual precipitation totals were more variable, whereas variability of annual mean lake levels in closed-basin lakes had the opposite pattern, being more variable when annual precipitation totals were less variable.
Oxygen-18/oxygen-16 and hydrogen-2/hydrogen-1 ratios for water samples from 40 wells indicated the well water was a mixture of surface water and groundwater in 31 wells, whereas ratios from water sampled from 9 other wells indicated that water from these wells receive no surface-water contribution. Of the 31 wells with a mixture of surface water and groundwater, 11 were downgradient from White Bear Lake, likely receiving water from deeper parts of the lake.
Age dating of water samples from wells indicated that the age of water in the Prairie du Chien and Jordan aquifers can vary widely across the northeast Twin Cities Metropolitan Area. Estimated ages of recharge for 9 of the 40 wells sampled for chlorofluorocarbon concentrations ranged widely from the early 1940s to mid-1970s. The wide range in estimated ages of recharge may have resulted from the wide range in the open-interval lengths and depths for the wells.
Results from stable isotope analyses of water samples, lake-sediment coring, continuous seismic-reflection profiling, and water-level and flow monitoring indicated that there is groundwater inflow from nearshore sites and lake-water outflow from deep-water sites in White Bear Lake. Continuous seismic-reflection profiling indicated that deep sections of White Bear, Pleasant, Turtle, and Big Marine Lakes have few trapped gases and little organic material, which indicates where groundwater and lake-water exchanges are more likely. Water-level differences between White Bear Lake and piezometer and seepage measurements in deep waters of the lake indicate that groundwater and lake-water exchange is happening in deep waters, predominantly downgradient from the lake and into the lake sediment. Seepage fluxes measured in the nearshore sites of White Bear Lake generally were higher than seepage fluxes measured in the deep-water sites, which indicates that groundwater-inflow rates at most of the nearshore sites are higher than lake-water outflow from the deep-water sites.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Water levels and groundwater and surface-water exchanges in lakes of the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015 (Scientific Investigations Report 2016–5139)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165139A","collaboration":"Prepared in cooperation with the Metropolitan Council and Minnesota Department of Health","usgsCitation":"Jones, P.M., Trost, J.J., Diekoff, A.L., Rosenberry, D.O., White, E.A., Erickson, M.L., Morel, D.L., and Heck, J.M., 2016, Statistical analysis of lake levels and field study of groundwater and surface-water exchanges in the northeast Twin Cities Metropolitan Area, Minnesota, 2002 through 2015: U.S. Geological Survey Scientific Investigations Report 2016–5139–A, 86 p., https://dx.doi.org/10.3133/sir20165139A.","productDescription":"Report: 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U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
Very low frequency (VLF, 0.001–0.005 Hz) waves are important drivers of flooding of low-lying coral reef-islands. In particular, VLF wave resonance is known to drive large wave runup and subsequent overwash. Using a 5 month data set of water levels and waves collected along a cross-reef transect on Roi-Namur Island in the Republic of the Marshall Islands, the observed VLF motions were categorized into four different classes: (1) resonant, (2) (nonresonant) standing, (3) progressive-growing, and (4) progressive-dissipative waves. Each VLF class is set by the reef flat water depth and, in the case of resonance, the incident-band offshore wave period. Using an improved method to identify VLF wave resonance, we find that VLF wave resonance caused prolonged (∼0.5–6.0 h), large-amplitude water surface oscillations at the inner reef flat ranging in wave height from 0.14 to 0.83 m. It was induced by relatively long-period, grouped, incident-band waves, and occurred under both storm and nonstorm conditions. Moreover, observed resonant VLF waves had nonlinear, bore-like wave shapes, which likely have a larger impact on the shoreline than regular, sinusoidal waveforms. As an alternative technique to the commonly used Fast Fourier Transformation, we propose the Hilbert-Huang Transformation that is more computationally expensive but can capture the wave shape more accurately. This research demonstrates that understanding VLF waves on reef flats is important for evaluating coastal flooding hazards.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JC011834","usgsCitation":"Gawehn, M., van Dongeran, A., van Rooijen, A., Storlazzi, C.D., Cheriton, O., and Reniers, A., 2016, Identification and classification of very low frequency waves on a coral reef flat: Journal of Geophysical Research C: Oceans, v. 121, no. 10, p. 7560-7574, https://doi.org/10.1002/2016JC011834.","productDescription":"15 p.","startPage":"7560","endPage":"7574","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074557","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470502,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jc011834","text":"Publisher Index Page"},{"id":330435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Republic of the Marshall Islands","otherGeospatial":"Roi-Namur Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 167.47086524963376,\n 9.403254406542626\n ],\n [\n 167.47395515441895,\n 9.403000374334932\n ],\n [\n 167.47610092163083,\n 9.402831019426165\n ],\n [\n 167.4767017364502,\n 9.401391499354965\n ],\n [\n 167.4788475036621,\n 9.400883432017688\n ],\n [\n 167.48090744018555,\n 9.401052787879665\n ],\n [\n 167.48313903808594,\n 9.400883432017688\n ],\n [\n 167.48485565185547,\n 9.399359225530548\n ],\n [\n 167.48665809631345,\n 9.3976656548948\n ],\n [\n 167.48743057250977,\n 9.395125283406898\n ],\n [\n 167.48614311218262,\n 9.391314691232486\n ],\n [\n 167.48236656188965,\n 9.388520230330098\n ],\n [\n 167.4777317047119,\n 9.389959803912719\n ],\n [\n 167.47532844543457,\n 9.390721928682085\n ],\n [\n 167.47163772583008,\n 9.390467887278579\n ],\n [\n 167.4700927734375,\n 9.391822772611278\n ],\n [\n 167.46700286865234,\n 9.390467887278579\n ],\n [\n 167.46374130249023,\n 9.391145330607298\n ],\n [\n 167.46305465698242,\n 9.393855090673826\n ],\n [\n 167.4638271331787,\n 9.396818866469888\n ],\n [\n 167.4623680114746,\n 9.399782616894754\n ],\n [\n 167.46331214904785,\n 9.402322954202532\n ],\n [\n 167.46580123901367,\n 9.403423761244147\n ],\n [\n 167.46906280517578,\n 9.403762470398455\n ],\n [\n 167.47086524963376,\n 9.403254406542626\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-18","publicationStatus":"PW","scienceBaseUri":"5811c0efe4b0f497e79a5a67","contributors":{"authors":[{"text":"Gawehn, Matthijs","contributorId":176243,"corporation":false,"usgs":false,"family":"Gawehn","given":"Matthijs","email":"","affiliations":[],"preferred":false,"id":651982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Dongeran, Ap","contributorId":176244,"corporation":false,"usgs":false,"family":"van Dongeran","given":"Ap","email":"","affiliations":[],"preferred":false,"id":651983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Rooijen, Arnold","contributorId":148987,"corporation":false,"usgs":false,"family":"van Rooijen","given":"Arnold","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":651984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":651981,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cheriton, Olivia 0000-0003-3011-9136 ocheriton@usgs.gov","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":149003,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","email":"ocheriton@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":651985,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reniers, Ad","contributorId":176245,"corporation":false,"usgs":false,"family":"Reniers","given":"Ad","affiliations":[],"preferred":false,"id":651986,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177061,"text":"70177061 - 2016 - Biomarkers reveal sea turtles remained in oiled areas following the Deepwater Horizon oil spill","interactions":[],"lastModifiedDate":"2016-10-18T10:37:08","indexId":"70177061","displayToPublicDate":"2016-10-18T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Biomarkers reveal sea turtles remained in oiled areas following the Deepwater Horizon oil spill","docAbstract":"Assessments of large-scale disasters, such as the Deepwater Horizon oil spill, are problematic because while measurements of post-disturbance conditions are common, measurements of pre-disturbance baselines are only rarely available. Without adequate observations of pre-disaster organismal and environmental conditions, it is impossible to assess the impact of such catastrophes on animal populations and ecological communities. Here, we use long-term biological tissue records to provide pre-disaster data for a vulnerable marine organism. Keratin samples from the carapace of loggerhead sea turtles record the foraging history for up to 18 years, allowing us to evaluate the effect of the oil spill on sea turtle foraging patterns. Samples were collected from 76 satellite-tracked adult loggerheads in 2011 and 2012, approximately one to two years after the spill. Of the 10 individuals that foraged in areas exposed to surface oil, none demonstrated significant changes in foraging patterns post spill. The observed long-term fidelity to foraging sites indicates that loggerheads in the northern Gulf of Mexico likely remained in established foraging sites, regardless of the introduction of oil and chemical dispersants. More research is needed to address potential long-term health consequences to turtles in this region. Mobile marine organisms present challenges for researchers to monitor effects of environmental disasters, both spatially and temporally. We demonstrate that biological tissues can reveal long-term histories of animal behavior and provide critical pre-disaster baselines following an anthropogenic disturbance or natural disaster.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1366","usgsCitation":"Vander Zanden, H.B., Bolten, A.B., Tucker, A.D., Hart, K.M., Lamont, M.M., Fujisaki, I., Reich, K.J., Addison, D.S., Mansfield, K.L., Phillips, K.F., Pajuelo, M., and Bjorndal, K.A., 2016, Biomarkers reveal sea turtles remained in oiled areas following the Deepwater Horizon oil spill: Ecological Applications, v. 26, no. 7, p. 2145-2155, https://doi.org/10.1002/eap.1366.","productDescription":"11 p.","startPage":"2145","endPage":"2155","ipdsId":"IP-067050","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":501616,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://stars.library.ucf.edu/scopus2015/2884","text":"External Repository"},{"id":329660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"7","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-23","publicationStatus":"PW","scienceBaseUri":"5807351be4b0841e59e288a5","contributors":{"authors":[{"text":"Vander Zanden, Hannah B.","contributorId":138885,"corporation":false,"usgs":false,"family":"Vander Zanden","given":"Hannah","email":"","middleInitial":"B.","affiliations":[{"id":12562,"text":"Department of Geology and Geophysics, University of Utah; Archie Carr Center for Sea Turtle Research, University of Florida","active":true,"usgs":false}],"preferred":false,"id":651175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bolten, Alan B.","contributorId":20247,"corporation":false,"usgs":false,"family":"Bolten","given":"Alan","email":"","middleInitial":"B.","affiliations":[{"id":12567,"text":"Archie Carr Center for Sea Turtle Research, Department of Biology, University of Florida","active":true,"usgs":false}],"preferred":false,"id":651176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, Anton D.","contributorId":79232,"corporation":false,"usgs":false,"family":"Tucker","given":"Anton","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":651177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":651178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lamont, Margaret M. 0000-0001-7520-6669 mlamont@usgs.gov","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":4525,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"mlamont@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":651179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":651180,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reich, Kimberly J.","contributorId":175452,"corporation":false,"usgs":false,"family":"Reich","given":"Kimberly","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":651181,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Addison, David S.","contributorId":138886,"corporation":false,"usgs":false,"family":"Addison","given":"David","email":"","middleInitial":"S.","affiliations":[{"id":12563,"text":"Conservancy of Southwest Florida","active":true,"usgs":false}],"preferred":false,"id":651182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mansfield, Katherine L.","contributorId":138887,"corporation":false,"usgs":false,"family":"Mansfield","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":12564,"text":"Department of Biology, University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":651183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Phillips, Katrina F.","contributorId":138888,"corporation":false,"usgs":false,"family":"Phillips","given":"Katrina","email":"","middleInitial":"F.","affiliations":[{"id":12565,"text":"Rosenstiel School of Atomospheric Science, University of Miami","active":true,"usgs":false}],"preferred":false,"id":651184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pajuelo, Mariela","contributorId":138890,"corporation":false,"usgs":false,"family":"Pajuelo","given":"Mariela","email":"","affiliations":[{"id":12567,"text":"Archie Carr Center for Sea Turtle Research, Department of Biology, University of Florida","active":true,"usgs":false}],"preferred":false,"id":651185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bjorndal, Karen A.","contributorId":96997,"corporation":false,"usgs":false,"family":"Bjorndal","given":"Karen","email":"","middleInitial":"A.","affiliations":[{"id":12567,"text":"Archie Carr Center for Sea Turtle Research, Department of Biology, University of Florida","active":true,"usgs":false}],"preferred":false,"id":651186,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70168542,"text":"70168542 - 2016 - Daniel Goodman’s empirical approach to Bayesian statistics","interactions":[],"lastModifiedDate":"2017-04-24T10:43:22","indexId":"70168542","displayToPublicDate":"2016-10-18T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Daniel Goodman’s empirical approach to Bayesian statistics","docAbstract":"Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.
","language":"English","doi":"10.7287/peerj.preprints.1755v1","usgsCitation":"Gerrodette, T., Ward, E., Taylor, R.L., Schwarz, L.K., Eguchi, T., Wade, P., and Himes Boor, G., 2016, Daniel Goodman’s empirical approach to Bayesian statistics: PeerJ, 18 p., https://doi.org/10.7287/peerj.preprints.1755v1.","productDescription":"18 p.","ipdsId":"IP-055681","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":462061,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7287/peerj.preprints.1755v1","text":"External Repository"},{"id":340166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ff0e9ce4b006455f2d61b8","contributors":{"authors":[{"text":"Gerrodette, Tim","contributorId":167034,"corporation":false,"usgs":false,"family":"Gerrodette","given":"Tim","email":"","affiliations":[{"id":7054,"text":"NOAA/NMFS, Silver Spring, MD","active":true,"usgs":false}],"preferred":false,"id":692524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":167035,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":620821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":620819,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwarz, Lisa K.","contributorId":167036,"corporation":false,"usgs":false,"family":"Schwarz","given":"Lisa","email":"","middleInitial":"K.","affiliations":[{"id":6641,"text":"University of California at Merced","active":true,"usgs":false}],"preferred":false,"id":692525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eguchi, Tomoharu","contributorId":167037,"corporation":false,"usgs":false,"family":"Eguchi","given":"Tomoharu","email":"","affiliations":[{"id":7054,"text":"NOAA/NMFS, Silver Spring, MD","active":true,"usgs":false}],"preferred":false,"id":620823,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wade, Paul","contributorId":167038,"corporation":false,"usgs":false,"family":"Wade","given":"Paul","email":"","affiliations":[{"id":7054,"text":"NOAA/NMFS, Silver Spring, MD","active":true,"usgs":false}],"preferred":false,"id":620824,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Himes Boor, Gina","contributorId":167039,"corporation":false,"usgs":false,"family":"Himes Boor","given":"Gina","affiliations":[{"id":5120,"text":"Montana State University, Department of Mathematical Sciences, Bozeman, MT 59717","active":true,"usgs":false}],"preferred":false,"id":620825,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70171470,"text":"sir20165068 - 2016 - Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California","interactions":[],"lastModifiedDate":"2018-07-05T12:42:08","indexId":"sir20165068","displayToPublicDate":"2016-10-17T13:00:00","publicationYear":"2016","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":"2016-5068","title":"Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California","docAbstract":"A daily precipitation-runoff model, referred to as the Los Angeles Basin watershed model (LABWM), was used to estimate recharge and runoff for a 5,047 square kilometer study area that included the greater Los Angeles area and all surface-water drainages potentially contributing recharge to a 1,450 square kilometer groundwater-study area underlying the greater Los Angeles area, referred to as the Los Angeles groundwater-study area. The recharge estimates for the Los Angeles groundwater-study area included spatially distributed recharge in response to the infiltration of precipitation, runoff, and urban irrigation, as well as mountain-front recharge from surface-water drainages bordering the groundwater-study area. The recharge and runoff estimates incorporated a new method for estimating urban irrigation, consisting of residential and commercial landscape watering, based on land use and the percentage of pervious land area.
The LABWM used a 201.17-meter gridded discretization of the study area to represent spatially distributed climate and watershed characteristics affecting the surface and shallow sub-surface hydrology for the Los Angeles groundwater study area. Climate data from a local network of 201 monitoring sites and published maps of 30-year-average monthly precipitation and maximum and minimum air temperature were used to develop the climate inputs for the LABWM. Published maps of land use, land cover, soils, vegetation, and surficial geology were used to represent the physical characteristics of the LABWM area. The LABWM was calibrated to available streamflow records at six streamflow-gaging stations.
Model results for a 100-year target-simulation period, from water years 1915 through 2014, were used to quantify and evaluate the spatial and temporal variability of water-budget components, including evapotranspiration (ET), recharge, and runoff. The largest outflow of water from the LABWM was ET; the 100-year average ET rate of 362 millimeters per year (mm/yr) accounted for 66 percent of the combined water inflow of 551 mm/yr, including 488 mm/yr from precipitation and 63 mm/yr from urban irrigation. The simulated ET rate varied from a minimum of 0 mm/yr for impervious areas to high values of more than 1,000 mm/yr for many areas, including the south-facing slopes of the San Gabriel Mountains, stream channels underlain by permeable soils and thick root zones, and pervious locations receiving inflows both from urban irrigation and surface water. Runoff was the next largest outflow, averaging 145 mm/yr for the 100-year period, or 26 percent of the combined precipitation and urban-irrigation inflow. Recharge averaged 45 mm/yr, or about 8 percent of the combined inflow from precipitation and urban irrigation.
Simulation results indicated that recharge in response to urban irrigation was an important component of spatially distributed recharge, contributing an average of 56 percent of the total recharge to the eight LABWM subdomains containing the Los Angeles groundwater study area. The 100‑year average recharge rate for the eight subdomains was 41 mm/yr, or 8,473 hectare-meters per year (ha-m/yr), with urban irrigation included in the simulation compared to a recharge rate of 18 mm/yr, or 3,741 ha-m/yr, with urban irrigation excluded. In contrast to recharge, the effect of urban irrigation on runoff was slight; runoff was 72,667 ha-m/yr with urban irrigation included compared to 72,618 ha-m/yr with urban irrigation excluded, an increase of only 48 ha-m/yr (about 0.1 percent).
Simulation results also indicated that potential recharge from hilly drainages outside of, but bordering and tributary to, the lower-lying area of the Los Angeles groundwater study area, in this study referred to as mountain-front recharge, could provide an important contribution to the total recharge for the groundwater basins. The time-averaged recharge rate was similar to the combined direct and mountain-front recharge components estimated in a previous study and used as input for a calibrated groundwater model. The annual (water year) recharge estimates simulated in this study, however, indicated much greater year-to-year variability, which was dependent on year-to-year variability in the magnitude and distribution of daily precipitation, compared to the previous estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165068","collaboration":"Prepared in cooperation with the Water Replenishment District of Southern California","usgsCitation":"Hevesi, J.A., and Johnson, T.D., 2016, Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California: U.S. Geological Survey Scientific Investigations Report 2016–5068, 192 p., https://dx.doi.org/10.3133/sir20165068.","productDescription":"x, 192 p.","numberOfPages":"208","onlineOnly":"Y","ipdsId":"IP-053146","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":328887,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5068/coverthb.jpg"},{"id":328888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5068/sir20165068_.pdf","text":"Report","size":"32.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5068"}],"country":"United States","state":"California","otherGeospatial":"Los Angeles Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.59078979492186,\n 34.29353023058858\n ],\n [\n -117.97531127929688,\n 33.631772324639655\n ],\n [\n -118.15246582031249,\n 33.75288969455201\n ],\n [\n -118.29666137695312,\n 33.70035029271861\n ],\n [\n -118.41339111328125,\n 33.74032885072381\n ],\n [\n -118.43673706054688,\n 33.775722878425604\n ],\n [\n -118.39828491210936,\n 33.82023008524739\n ],\n [\n -118.44223022460938,\n 33.9285481685662\n ],\n [\n -118.50952148437499,\n 34.016241889667015\n ],\n [\n -118.60565185546874,\n 34.03672867489511\n ],\n [\n -118.67706298828125,\n 34.34230217446123\n ],\n [\n -118.40377807617189,\n 34.426168904360736\n ],\n [\n -117.87368774414064,\n 34.38197934098774\n ],\n [\n -117.59078979492186,\n 34.29353023058858\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov
Annual stream loads of mercury (Hg) and inputs of wet and dry atmospheric Hg deposition to the landscape were investigated in watersheds of the Western United States and the Canadian-Alaskan Arctic. Mercury concentration and discharge data from flow gauging stations were used to compute annual mass loads with regression models. Measured wet and modeled dry deposition were compared to annual stream loads to compute ratios of Hg stream load to total Hg atmospheric deposition. Watershed land uses or cover included mining, undeveloped, urbanized, and mixed. Of 27 watersheds that were investigated, 15 had some degree of mining, either of Hg or precious metals (gold or silver), where Hg was used in the amalgamation process. Stream loads in excess of annual Hg atmospheric deposition (ratio > 1) were observed in watersheds containing Hg mines and in relatively small and medium-sized watersheds with gold or silver mines, however, larger watersheds containing gold or silver mines, some of which also contain large dams that trap sediment, were sometimes associated with lower load ratios (< 0.2). In the non-Arctic regions, watersheds with natural vegetation tended to have low ratios of stream load to Hg deposition (< 0.1), whereas urbanized areas had higher ratios (0.34–1.0) because of impervious surfaces. This indicated that, in ecosystems with natural vegetation, Hg is retained in the soil and may be transported subsequently to streams as a result of erosion or in association with dissolved organic carbon. Arctic watersheds (Mackenzie and Yukon Rivers) had a relatively elevated ratio of stream load to atmospheric deposition (0.27 and 0.74), possibly because of melting glaciers or permafrost releasing previously stored Hg to the streams. Overall, our research highlights the important role of watershed characteristics in determining whether a landscape is a net source of Hg or a net sink of atmospheric Hg.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.02.112","usgsCitation":"Domagalski, J.L., Majewski, M.S., Alpers, C.N., Eckley, C.S., Eagles-Smith, C.A., Schenk, L.N., and Wherry, S., 2016, Comparison of mercury mass loading in streams to atmospheric deposition in watersheds of Western North America: Evidence for non-atmospheric mercury sources: Science of the Total Environment, v. 568, p. 638-650, https://doi.org/10.1016/j.scitotenv.2016.02.112.","productDescription":"13 p.","startPage":"638","endPage":"650","ipdsId":"IP-069584","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":332020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"568","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"585116bbe4b08138bf1abd56","contributors":{"authors":[{"text":"Domagalski, Joseph L. 0000-0002-6032-757X joed@usgs.gov","orcid":"https://orcid.org/0000-0002-6032-757X","contributorId":1330,"corporation":false,"usgs":true,"family":"Domagalski","given":"Joseph","email":"joed@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Majewski, Michael S. majewski@usgs.gov","contributorId":440,"corporation":false,"usgs":true,"family":"Majewski","given":"Michael","email":"majewski@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eckley, Chris S.","contributorId":167256,"corporation":false,"usgs":false,"family":"Eckley","given":"Chris","email":"","middleInitial":"S.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":655593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":655594,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655595,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":655707,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70176306,"text":"sir20165124 - 2016 - FishVis, A regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change in the Great Lakes Region","interactions":[],"lastModifiedDate":"2019-12-30T14:43:18","indexId":"sir20165124","displayToPublicDate":"2016-10-13T00:00:00","publicationYear":"2016","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":"2016-5124","title":"FishVis, A regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change in the Great Lakes Region","docAbstract":"Climate change is expected to alter the distributions and community composition of stream fishes in the Great Lakes region in the 21st century, in part as a result of altered hydrological systems (stream temperature, streamflow, and habitat). Resource managers need information and tools to understand where fish species and stream habitats are expected to change under future conditions. Fish sample collections and environmental variables from multiple sources across the United States Great Lakes Basin were integrated and used to develop empirical models to predict fish species occurrence under present-day climate conditions. Random Forests models were used to predict the probability of occurrence of 13 lotic fish species within each stream reach in the study area. Downscaled climate data from general circulation models were integrated with the fish species occurrence models to project fish species occurrence under future climate conditions. The 13 fish species represented three ecological guilds associated with water temperature (cold, cool, and warm), and the species were distributed in streams across the Great Lakes region. Vulnerability (loss of species) and opportunity (gain of species) scores were calculated for all stream reaches by evaluating changes in fish species occurrence from present-day to future climate conditions. The 13 fish species included 4 cold-water species, 5 cool-water species, and 4 warm-water species. Presently, the 4 cold-water species occupy from 15 percent (55,000 kilometers [km]) to 35 percent (130,000 km) of the total stream length (369,215 km) across the study area; the 5 cool-water species, from 9 percent (33,000 km) to 58 percent (215,000 km); and the 4 warm-water species, from 9 percent (33,000 km) to 38 percent (141,000 km).
Fish models linked to projections from 13 downscaled climate models projected that in the mid to late 21st century (2046–65 and 2081–2100, respectively) habitats suitable for all 4 cold-water species and 4 of 5 cool-water species under present-day conditions will decline as much as 86 percent and as little as 33 percent, and habitats suitable for all 4 warm-water species will increase as much as 33 percent and as little as 7 percent. This report documents the approach and data used to predict and project fish species occurrence under present-day and future climate conditions for 13 lotic fish species in the United States Great Lakes Basin. A Web-based decision support mapping application termed “FishVis” was developed to provide a means to integrate, visualize, query, and download the results of these projected climate-driven responses and help inform conservation planning efforts within the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165124","collaboration":"Prepared in cooperation with Michigan State University, Michigan Department of Natural Resources Institute of Fisheries Research, and the Wisconsin Department of Natural Resources","usgsCitation":"Stewart, J.S., Covert, S.A., Estes, N.J., Westenbroek, S.M., Krueger, Damon, Wieferich, D.J., Slattery, M.T., Lyons, J.D., McKenna, J.E., Jr., Infante, D.M., and Bruce, J.L., 2016, FishVis, A regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change in the Great Lakes Region: U.S. Geological Survey Scientific Investigations Report 2016–5124, 15 p., with appendixes, https://dx.doi.org/10.3133/sir20165124.","productDescription":"Report: viii, 15 p.; Appendixes 1-4","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071837","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":438537,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74T6GGG","text":"USGS data release","linkHelpText":"FishVis, predicted occurrence and vulnerability for 13 fish species for current (1961 - 1990) and future (2046 - 2100) climate conditions in Great Lakes streams."},{"id":329488,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5124/coverthb.jpg"},{"id":329489,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5124/sir20165124.pdf","text":"Report","size":"2.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5124"},{"id":329490,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5124/sir20165124_appendixes1to4.xlsx","text":"Appendixes 1–4","size":"34.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5124 Appendixes"}],"country":"United States","otherGeospatial":"Great Lakes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.94433593749999,\n 46.5286346952717\n ],\n [\n -86.66015624999999,\n 46.164614496897094\n ],\n [\n -88.24218749999999,\n 44.715513732021336\n ],\n [\n -87.978515625,\n 43.004647127794435\n ],\n [\n -87.5390625,\n 41.47566020027821\n ],\n [\n -86.484375,\n 41.44272637767212\n ],\n [\n -85.869140625,\n 44.465151013519616\n ],\n [\n -84.55078125,\n 45.30580259943578\n ],\n [\n -83.671875,\n 44.715513732021336\n ],\n [\n -83.935546875,\n 43.51668853502906\n ],\n [\n -83.14453125,\n 42.90816007196054\n ],\n [\n -83.6279296875,\n 41.705728515237524\n ],\n [\n -81.34277343749999,\n 41.44272637767212\n ],\n [\n -78.6181640625,\n 42.779275360241904\n ],\n [\n -75.76171875,\n 43.67581809328341\n ],\n [\n -76.37695312499999,\n 44.465151013519616\n ],\n [\n -78.837890625,\n 43.866218006556394\n ],\n [\n -81.03515625,\n 42.87596410238256\n ],\n [\n -82.177734375,\n 42.74701217318067\n ],\n [\n -81.2109375,\n 44.5278427984555\n ],\n [\n -79.62890625,\n 44.402391829093915\n ],\n [\n -81.73828125,\n 46.255846818480315\n ],\n [\n -83.8916015625,\n 46.6795944656402\n ],\n [\n -84.5947265625,\n 47.754097979680026\n ],\n [\n -86.3525390625,\n 48.66194284607006\n ],\n [\n -87.8466796875,\n 49.26780455063753\n ],\n [\n -89.912109375,\n 48.42920055556841\n ],\n [\n -92.021484375,\n 47.15984001304432\n ],\n [\n -92.94433593749999,\n 46.5286346952717\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
This report updates and revises the 2010 “ Status of Fish Habitats in the United States” that summarized initial results of a comprehensive national assessment of aquatic habitats at an unprecedented scale and level of detail. This 2015 report provides even greater detail and improves our knowledge of the condition of fish habitat in the United States. The 2010 inland streams assessment characterized fish habitat condition using stream fish data from more than 26,000 stream reaches, while the 2015 assessment was based on fish data from more than 39,000 stream reaches nationally. To increase accuracy, the 2015 inland stream assessment incorporated 12 additional human disturbance variables into the fish analysis when compared to the 2010 assessment. Associations between all human disturbance variables summarized in both catchments as well as stream buffers were tested against stream fish metrics to develop assessment scores. Additional variables incorporated into the 2015 assessment and their summary within catchments and buffers allowed for more explicit characterization of the diverse set of disturbances to stream fish habitats occurring across the Nation than what occurred in 2010, and this was made possible due in part to advances in available GIS layers. With the incorporation of these additional disturbances, managers and decision makers can use assessment results to more explicitly identify limits to stream fish habitats. Even with the additional disturbances incorporated into 2015 assessment, results may overestimate fish habitat condition, as localized and regionally-specific disturbances are still not available in some cases.
","language":"English","publisher":"National Fish Habitat Partnership","usgsCitation":"Crawford, S., Whelan, G., Infante, D.M., Blackhart, K., Daniel, W.M., Fuller, P., Birdsong, T.W., Wieferich, D.J., McClees-Funinan, R., Stedman, S., Herreman, K., and Ruhl, P.M., 2016, Through a fish's eye: The status of fish habitats in the United States 2015, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-077455 ","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"links":[{"id":358334,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://assessment.fishhabitat.org/ "},{"id":358335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10ada0e4b034bf6a7e78db","contributors":{"authors":[{"text":"Crawford, Steve","contributorId":209632,"corporation":false,"usgs":false,"family":"Crawford","given":"Steve","email":"","affiliations":[],"preferred":false,"id":748439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whelan, Gary","contributorId":146115,"corporation":false,"usgs":false,"family":"Whelan","given":"Gary","email":"","affiliations":[{"id":16584,"text":"Fisheries Division, Michigan Department of Natural Resources, P.O. Box 30446, Lansing, MI 48909","active":true,"usgs":false}],"preferred":false,"id":748440,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Infante, Dana M. 0000-0003-1385-1587","orcid":"https://orcid.org/0000-0003-1385-1587","contributorId":150821,"corporation":false,"usgs":false,"family":"Infante","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":18112,"text":"Dept. of Fisheries and Wildlife,","active":true,"usgs":false}],"preferred":false,"id":748441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blackhart, Kristan","contributorId":209633,"corporation":false,"usgs":false,"family":"Blackhart","given":"Kristan","email":"","affiliations":[],"preferred":false,"id":748448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Daniel, Wesley M. 0000-0002-7656-8474 wdaniel@usgs.gov","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":194723,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley","email":"wdaniel@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":748442,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Pam 0000-0002-9389-9144 pfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9389-9144","contributorId":167676,"corporation":false,"usgs":true,"family":"Fuller","given":"Pam","email":"pfuller@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":748443,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Birdsong, Timothy W.","contributorId":172473,"corporation":false,"usgs":false,"family":"Birdsong","given":"Timothy","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":748444,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wieferich, Daniel J. 0000-0003-1554-7992 dwieferich@usgs.gov","orcid":"https://orcid.org/0000-0003-1554-7992","contributorId":176205,"corporation":false,"usgs":true,"family":"Wieferich","given":"Daniel","email":"dwieferich@usgs.gov","middleInitial":"J.","affiliations":[{"id":5069,"text":"Office of the AD Core Science Systems","active":true,"usgs":true},{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":748438,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McClees-Funinan, Ricardo 0000-0002-3254-1843 rmcclees-funinan@usgs.gov","orcid":"https://orcid.org/0000-0002-3254-1843","contributorId":5988,"corporation":false,"usgs":true,"family":"McClees-Funinan","given":"Ricardo","email":"rmcclees-funinan@usgs.gov","affiliations":[{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"preferred":true,"id":748445,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stedman, Susan","contributorId":209634,"corporation":false,"usgs":false,"family":"Stedman","given":"Susan","email":"","affiliations":[],"preferred":false,"id":748449,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Herreman, Kyle","contributorId":187650,"corporation":false,"usgs":false,"family":"Herreman","given":"Kyle","email":"","affiliations":[],"preferred":false,"id":748450,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ruhl, Peter M. 0000-0002-5032-6266 pmruhl@usgs.gov","orcid":"https://orcid.org/0000-0002-5032-6266","contributorId":4300,"corporation":false,"usgs":true,"family":"Ruhl","given":"Peter","email":"pmruhl@usgs.gov","middleInitial":"M.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":748451,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70215549,"text":"70215549 - 2016 - Detecting and inferring cause of change in an Alaska nearshore marine ecosystem","interactions":[],"lastModifiedDate":"2020-10-22T13:57:43.359142","indexId":"70215549","displayToPublicDate":"2016-10-12T08:51:47","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Detecting and inferring cause of change in an Alaska nearshore marine ecosystem","docAbstract":"Community composition, species abundance, and species distribution are expected to change while monitoring ecosystems over time, and effective management of natural resources requires understanding mechanisms contributing to change. Marine ecosystems in particular can be difficult to monitor, in part due to large, multidimensional spatial scales and complex dynamics. However, within the temperate marine ecosystems, the nearshore food web is reasonably well described. This food web is ecologically and socially important, spatially constrained, and has been the focus of extensive experimental research that describes the underlying mechanisms important to system dynamics. Here, we describe a monitoring program initiated in 2006 that focuses on the nearshore benthic food web in the Gulf of Alaska, whose design anticipates potential causes of ecosystem change to improve rigor, resolution, and confidence in understanding the mechanisms underlying change. We established 15 long‐term monitoring sites across more than 1000 km of coastline, including 10 within two national parks and 5 within Prince William Sound, area of the 1989 Exxon Valdez oil spill. The program evaluates six ecological indicators and more than 200 species that range from primary producers to top‐level consumers, and is designed to examine both bottom‐up and top‐down dynamics. Employing a design that allows broad spatial inference and selecting species with direct food‐web linkages, we demonstrate the ability of our monitoring program to simultaneously detect change and assess potential mechanisms underlying that change. Detecting change and understanding mechanisms can help guide management and conservation policy. Specifically, we provide an example focusing on the sea otter (Enhydra lutris) that illustrates how (1) analytical methods are used to evaluate changes on various scales and infer potential mechanisms of change, (2) food‐web linkages can enhance the understanding of changes and their effects, and (3) data can be used to inform management.
Water samples were collected and analyzed for arsenic and radon from 36 private, mostly domestic wells that tap the Rancocas aquifer in southern New Castle and northern Kent Counties, Delaware, during the summer of 2015. Both arsenic and radon are from natural mineral sources, in particular glauconitic and other marine-derived sediments, which are important components of the geologic formations comprising the Rancocas aquifer. Routine testing of domestic wells is not required in Delaware; as a result, many homeowners are not aware of potential water-quality problems with these chemicals in their well water. Arsenic has previously been detected at levels of potential concern for human health in this aquifer in adjacent parts of Maryland where it is referred to as the Aquia aquifer. Arsenic and radon also have previously been detected in several Rancocas aquifer wells in Delaware. The Delaware Department of Natural Resources and Environmental Control intends to use the data from this project to better identify areas with potential for levels of concern for domestic well owners. This report includes chemical results and maps showing the distribution of sampled wells and concentrations of arsenic and radon. All data collected for this study also are available in the U.S. Geological Survey’s National Water Information System database.
Arsenic was detected above the minimum reporting limit of 0.1 micrograms per liter (µg/L) in 34 of the 36 wells sampled with concentrations ranging from about 0.11 to 27 µg/L. In 15 of the samples, arsenic concentrations were at or above the U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level (MCL) of 10 µg/L for public wells. Most of the higher concentrations are clustered along a band running from the southwest to northeast in the southern part of the study area.
Radon, which is an inert gas derived from radium, was detected in all water samples with concentrations ranging from 85 to 1,870 picocuries per liter (pCi/L). Currently, the EPA has not set a MCL for radon in public water systems. There were no samples where radon was detected at a concentration exceeding the proposed alternative MCL of 4,000 pCi/L. Samples from 16 of 36 wells were above the lower proposed MCL of 300 pCi/L. Most of these samples were from wells greater than 200 feet deep located in a similar part of the aquifer as the higher concentrations of arsenic along an east-northeasterly line in the southern part of the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161143","isbn":"978-1-4113 4086-2","collaboration":"Prepared in cooperation with the Delaware Department of Natural Resources and Environmental Control (DNREC) Water Supply Section, Groundwater Protection Branch","usgsCitation":"Denver, J.M., 2016, Occurrence and distribution of arsenic and radon in water from private wells in the Rancocas aquifer, southern New Castle and northern Kent Counties, Delaware, 2015: U.S. Geological Survey Open-File Report 2016–1143, 15 p., https://dx.doi.org/10.3133/ofr20161143. ","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"N","ipdsId":"IP-076094","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":329414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1143/ofr20161143.pdf","text":"Report","size":"1.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1143"},{"id":329413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1143/coverthb.jpg"}],"country":"United States","state":"Delaware","county":"Kent County, New Castle County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.43624877929688,\n 39.310925412127155\n ],\n [\n -75.76034545898438,\n 39.298705113102244\n ],\n [\n -75.77888488769531,\n 39.50827899034114\n ],\n [\n -75.57220458984375,\n 39.51834388059882\n ],\n [\n -75.43624877929688,\n 39.310925412127155\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
Or visit our Web site at:
http://md.water.usgs.gov
Digital flood-inundation maps for a 3.68-mile reach of the Wabash River extending 1.77 miles upstream and 1.91 miles downstream from streamgage 03378500 at New Harmony, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage at Wabash River at New Harmony, Ind. (station 03378500). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site (NHRI3).
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relations at the Wabash River at New Harmony, Ind., streamgage and the documented high-water marks from the flood of April 27–28, 2013. The calibrated hydraulic model was then used to compute 17 water-surface profiles for flood stages at approximately 1-foot intervals referenced to the streamgage datum and ranging from 10.0 feet, or near bankfull, to 25.4 feet, the highest stage of the stage-discharge rating curve used in the model. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging (lidar) data having a 0.98-ft vertical accuracy and 4.9-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps along with Internet information regarding current stage from the USGS streamgage at Wabash River at New Harmony, Ind., and forecasted stream stages from the NWS will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165119","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Fowler, K.K., 2016, Flood-inundation maps for the Wabash River at New Harmony, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5119, 14 p., https://dx.doi.org/10.3133/sir20165119.","productDescription":"Report: vii, 14 p.; Metadata; Read Me; Spatial Data","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066894","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":329430,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5119/downloads/metadata_depth_grids.pdf","text":"Metadata Depth Grids","size":"94.3 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5119"},{"id":329431,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5119/downloads/metadata_shapefile.pdf","text":"Metadata Shapefiles","size":"94.9 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5119"},{"id":329432,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2016/5119/downloads/00Readme.pdf","text":"Readme","size":"82.6 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5119"},{"id":329433,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5119/downloads/depth_grids.zip","text":"Depth Grids","size":"144 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5119"},{"id":329434,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5119/downloads/shapefiles.zip","text":"Shape File","size":"2.40 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5119"},{"id":329410,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5119/coverthb.jpg"},{"id":329411,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5119/sir20165119.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5119"}],"country":"United States","state":"Indiana","city":"New Harmony","otherGeospatial":"Wabash River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.03173065185545,\n 38.10700680156137\n ],\n [\n -88.03173065185545,\n 38.171003529816126\n ],\n [\n -87.8580093383789,\n 38.171003529816126\n ],\n [\n -87.8580093383789,\n 38.10700680156137\n ],\n [\n -88.03173065185545,\n 38.10700680156137\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana-Kentucky Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
http://in.water.usgs.gov/
The Alaskan breeding population of Steller’s eiders (Polysticta stelleri) was listed as threatened under the Endangered Species Act in 1997 in response to perceived declines in abundance throughout their breeding and nesting range. Aerial surveys suggest the breeding population is small and highly variable in number, with zero birds counted in 5 of the last 25 years. Research was conducted to evaluate competing population process models of Alaskan-breeding Steller’s eiders through comparison of model projections to aerial survey data. To evaluate model efficacy and estimate demographic parameters, a Bayesian state-space modeling framework was used and each model was fit to counts from the annual aerial surveys, using sequential importance sampling and resampling. The results strongly support that the Alaskan breeding population experiences population level nonbreeding events and is open to exchange with the larger Russian-Pacific breeding population. Current recovery criteria for the Alaskan breeding population rely heavily on the ability to estimate population viability. The results of this investigation provide an informative model of the population process that can be used to examine future population states and assess the population in terms of the current recovery and reclassification criteria.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161062","usgsCitation":"Dunham, Kylee, and Grand, J.B., 2016, Evaluating models of population process in a threatened population of Steller’s eiders—A retrospective approach: U.S. Geological Survey Open-File Report 2016–1062, 14 p., https://dx.doi.org/10.3133/ofr20161062.","productDescription":"vi, 14 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074896","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":329250,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/preview/ofr20161084","text":"Open-File Report 2016–1084","description":"Open-File Report 2016–1084","linkHelpText":"- Viability of the Alaskan Breeding Population of Steller’s Eiders"},{"id":329247,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1062/ofr20161062.pdf","text":"Report","size":"355 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1062"},{"id":329246,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1062/coverthb.jpg"}],"contact":"Chief, Cooperative Research Units
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192-0002
https://www.usgs.gov/science/mission-areas/ecosystems
Tsunamis represent significant threats to human life and development in coastal communities. This quantitative study examines the influence of household characteristics on evacuation actions taken by 211 respondents in American Samoa who were at their homes during the 29 September 2009 Mw 8.1 Samoa Islands earthquake and tsunami disaster. Multiple logistic regression analysis of survey data was used to examine the association between evacuation and various household factors. Findings show that increases in distance to shoreline were associated with a slightly decreased likelihood of evacuation, whereas households reporting higher income had an increased probability of evacuation. The response in American Samoa was an effective one, with only 34 fatalities in a tsunami that reached shore in as little as 15 minutes. Consequently, future research should implement more qualitative study designs to identify event and cultural specific determinants of household evacuation behaviour to local tsunamis.
","language":"English","publisher":"Wiley","doi":"10.1111/disa.12170","usgsCitation":"Apatu, E.J., Gregg, C.E., Wood, N.J., and Wang, L., 2016, Household evacuation characteristics in American Samoa during the 2009 Samoa Islands tsunami: Disasters, v. 40, no. 4, p. 779-798, https://doi.org/10.1111/disa.12170.","productDescription":"10 p.","startPage":"779","endPage":"798","ipdsId":"IP-052408","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":329464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-05","publicationStatus":"PW","scienceBaseUri":"57fe679ce4b0824b2d143707","contributors":{"authors":[{"text":"Apatu, Emma J. I.","contributorId":175197,"corporation":false,"usgs":false,"family":"Apatu","given":"Emma","email":"","middleInitial":"J. I.","affiliations":[{"id":24762,"text":"University of North Florida","active":true,"usgs":false}],"preferred":false,"id":650398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gregg, Chris E.","contributorId":175198,"corporation":false,"usgs":false,"family":"Gregg","given":"Chris","email":"","middleInitial":"E.","affiliations":[{"id":27535,"text":"East Tennessee State University","active":true,"usgs":false}],"preferred":false,"id":650399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":650397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Liang","contributorId":175199,"corporation":false,"usgs":false,"family":"Wang","given":"Liang","email":"","affiliations":[{"id":27535,"text":"East Tennessee State University","active":true,"usgs":false}],"preferred":false,"id":650400,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}