The current (2015) streamgage network in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York was evaluated in order to design a network that would meet the hydrologic needs of many partners and serve a variety of purposes and interests, including estimation of streamflow statistics at ungaged sites. This study was done by the U.S. Geological Survey, in cooperation with the Pennsylvania Department of Environmental Protection and the Susquehanna River Basin Commission. The study area includes the Commonwealth of Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York. For this study, 229 streamgages were identified as reference streamgages that could be used to represent ungaged watersheds. Criteria for a reference streamgage are a minimum of 10 years of continuous record, minimally altered streamflow, and a drainage area less than 1,500 square miles. Some of the reference streamgages have been discontinued but provide historical hydrologic information valuable in the determination of streamflow characteristics of ungaged watersheds. Watersheds in the study area not adequately represented by a reference streamgage were identified by examining a range of basin characteristics, the extent of geographic coverage, and the strength of estimated streamflow correlations between gaged and ungaged sites.
Basin characteristics were determined for the reference streamgage watersheds and the 1,662 12-digit hydrologic unit code (HUC12) subwatersheds in Pennsylvania and the Susquehanna River Basin using a geographic information system (GIS) spatial analysis and nationally available GIS datasets. Basin characteristics selected for this study include drainage area, mean basin elevation, mean basin slope, percentage of urbanized area, percentage of forested area, percentage of carbonate bedrock, mean annual precipitation, and soil thickness. A GIS spatial analysis was used to identify HUC12 subwatersheds outside the range of basin characteristics of the reference streamgages. There were 320 HUC12 subwatersheds, or 19 percent of the study area, with basin characteristics outside the range represented by the reference streamgage watersheds.
A GIS spatial analysis was used to identify geographic gaps in the streamgage network. For each streamgage, a watershed area, called the gage statistical area (GSA), was delineated. The GSA shows the drainage area within a specific drainage-area ratio of the streamgage for transfer of streamflow statistics from that streamgage to ungaged sites on the valid statistical reach of the GSA for a streamgage. In Pennsylvania, a drainage-area ratio of 0.33–3 times the drainage area of the ungaged site was found to perform as well as, if not better than, more traditional ratios such as 0.5–1.5 (or 2) for transfer of selected streamflow statistics. A total of 1,102 HUC12 subwatersheds, or 66 percent of the study area, are outside the GSA for a reference streamgage.
The USGS Baseline Streamflow Estimator (BaSE) program was used to determine how well HUC12 subwatersheds outside the streamgage GSAs are represented by the reference streamgage network in Pennsylvania, based on estimated streamflow correlation. The centroid of each HUC12 subwatershed was run through the BaSE program to determine the reference streamgage with the highest estimated streamflow correlation. There were 929 HUC12 subwatersheds in Pennsylvania, or 56 percent of the State, with an estimated correlation coefficient less than 0.96.
The results from the basin characteristic, geographic, and streamflow correlation analyses were combined to identify 1,405 HUC12 subwatersheds in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York that lack a representative reference, based on at least one identified gap. Of the 1,405 HUC12 subwatersheds, 139 exhibited all three gaps, indicating a 8-percent gap in the reference streamgage network.
Streamgages in areas with similar hydrologic characteristics and in close proximity to one another can potentially provide similar information (termed streamgages with high substitution potential). Streamgages were considered to have a high substitution potential with a nearby streamgage(s) if (1) the streamflow correlation coefficient was equal to or greater than 0.96, (2) the streamgages had 10 years of concurrent record, and (3) the streamgages are in the same watershed within the GSA of the streamgage. Seventy-four current (2015) streamgages with high substitution potential with at least one other streamgage were identified in the study area. Although these identified streamgages have a high substitution potential, they provide valuable streamflow information to a stakeholder. Selected primary uses of these streamgages were identified to determine the overall need for an individual streamgage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165149","collaboration":"Prepared in cooperation with the Pennsylvania Department of Environmental Protection and the Susquehanna River Basin Commission ","usgsCitation":"Sloto, R.A., Stuckey, M.H., and Hoffman, S.A., 2017, Evaluation of the streamgage network for estimating streamflow statistics at ungaged sites in Pennsylvania and the Susquehanna River Basin in Pennsylvania and New York: U.S. Geological Survey Scientific Investigations Report 2016–5149, 102 p., https://doi.org/10.3133/sir20165149.","productDescription":"vi, 102 p.","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069147","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":340754,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5149//sir20165149.pdf","text":"Report","size":"85.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5149"},{"id":340753,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5149/coverthb.jpg"}],"country":"United States","state":"New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.77197265625,\n 39.70718665682654\n ],\n [\n -74.805908203125,\n 39.70718665682654\n ],\n [\n -74.805908203125,\n 42.97250158602597\n ],\n [\n -78.77197265625,\n 42.97250158602597\n ],\n [\n -78.77197265625,\n 39.70718665682654\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Monitoring natural resources - water, forests, and animal populations—is required to support effective management of natural resources. However, because monitoring activities are often specific to a discipline, issue, or agency, it is typically difficult to integrate data to answer questions that transcend geopolitical and jurisdictional boundaries. How do we reach the better data integration we need?
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017EO072689","usgsCitation":"Weltzin, J.F., Bayer, J.M., and Scully, R.A., 2017, Defining opportunities for collaboration across data life cycles: Eos, Transactions, American Geophysical Union, v. 98, HTML document, https://doi.org/10.1029/2017EO072689.","productDescription":"HTML document","ipdsId":"IP-082004","costCenters":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"links":[{"id":469858,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017eo072689","text":"Publisher Index Page"},{"id":341043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"98","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591426bae4b0e541a03e9600","contributors":{"authors":[{"text":"Weltzin, Jake F. 0000-0001-8641-6645 jweltzin@usgs.gov","orcid":"https://orcid.org/0000-0001-8641-6645","contributorId":189061,"corporation":false,"usgs":true,"family":"Weltzin","given":"Jake","email":"jweltzin@usgs.gov","middleInitial":"F.","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":false,"id":694610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bayer, Jennifer M. 0000-0001-9564-3110 jbayer@usgs.gov","orcid":"https://orcid.org/0000-0001-9564-3110","contributorId":3393,"corporation":false,"usgs":true,"family":"Bayer","given":"Jennifer","email":"jbayer@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":694612,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scully, Rebecca A. 0000-0003-0704-8907 rscully@usgs.gov","orcid":"https://orcid.org/0000-0003-0704-8907","contributorId":191891,"corporation":false,"usgs":true,"family":"Scully","given":"Rebecca","email":"rscully@usgs.gov","middleInitial":"A.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":694611,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187540,"text":"gip174 - 2017 - Eighty years of cooperative water science","interactions":[],"lastModifiedDate":"2019-10-17T06:41:25","indexId":"gip174","displayToPublicDate":"2017-05-09T11:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"174","displayTitle":"Eighty Years of Cooperative Water Science","title":"Eighty years of cooperative water science","docAbstract":"The Equus Beds aquifer in south-central Kansas is a primary water source for the city of Wichita. The Equus Beds aquifer storage and recovery (ASR) project was developed to help the city of Wichita meet increasing current and future demands. The Equus Beds ASR project is a recent part of an 80-year cooperative water science effort with the city of Wichita. The U.S. Geological Survey (USGS) Kansas Water Science Center characterizes river and aquifer water-quality and quantity and evaluates changes that may or may not be related to ASR. The USGS data are used by the city of Wichita to make informed management decisions, satisfy regulatory requirements, and serve as a baseline to detect any subsequent changes that may be related to ASR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip174","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., 2017, Eighty years of cooperative water science (ver. 1.1, October 2019): U.S. Geological Survey General Information Product 174, 2 p., https://doi.org/10.3133/gip174.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-086697","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":368327,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/gip/0174/versionHist.txt","text":"Version History","linkFileType":{"id":2,"text":"txt"},"description":"GIP 174 Version History"},{"id":368326,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0174/gip174.pdf","text":"Report","size":"1.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 174"},{"id":346650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0174/coverthb3.jpg"}],"country":"United States","state":"Kansas","county":"Wichita County","otherGeospatial":"Equus Beds Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-101.485,38.7002],[-101.1293,38.7001],[-101.1254,38.264],[-101.5405,38.2631],[-101.5669,38.2633],[-101.5694,38.7004],[-101.485,38.7002]]]},\"properties\":{\"name\":\"Wichita\",\"state\":\"KS\"}}]}","edition":"Version 1.0: May 9, 2017; Version 1.1: October 15, 2019","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Increases in anthropogenic emissions of sulfur (S) and nitrogen (N) have resulted in increases in the associated atmospheric deposition of acidic compounds. In sensitive watersheds, this deposition has initiated a cascade of negative environmental effects on aquatic ecosystems, resulting in a degradation or loss of valuable ecosystem goods and services. Here, we report the activities of an expert workgroup to synthesize information on acidic deposition-induced aquatic acidification from the published literature and to link critical load exceedances with ecosystem services and beneficiaries, using the Stressor–Ecological Production function–Final Ecosystem Services (STEPS) Framework and the Final Ecosystem Goods and Services Classification System (FEGS-CS). Experts identified and documented the sensitive aquatic ecosystem ecological endpoints valued by humans, and the environmental pathways through which these endpoints may experience degradation in response to acidification. Beneficiary groups were then identified for each sensitive ecological endpoint to clarify relationships between humans and the effects of aquatic acidification, and to lay the foundation for future research and analysis to value these FEGS.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1807","usgsCitation":"O’Dea, C., Anderson, S., Sullivan, T., Landers, D.H., and Casey, C.F., 2017, Impacts to ecosystem services from aquatic acidification: Using FEGS-CS to understand the impacts of air pollution: Ecosphere, v. 8, no. 5, e01807, 16 p., https://doi.org/10.1002/ecs2.1807.","productDescription":"e01807, 16 p.","ipdsId":"IP-079116","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":461605,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1807","text":"Publisher Index Page"},{"id":421128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"5","noUsgsAuthors":false,"publicationDate":"2017-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Dea, Claire","contributorId":330090,"corporation":false,"usgs":false,"family":"O’Dea","given":"Claire","email":"","affiliations":[{"id":78807,"text":"United States Department of Agriculture Forest Service, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":883962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Sarah M.","contributorId":245594,"corporation":false,"usgs":false,"family":"Anderson","given":"Sarah M.","affiliations":[{"id":49227,"text":"NSPIRE-IGERT Fellow, Washington State University, School of Biological Sciences","active":true,"usgs":false}],"preferred":false,"id":883963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Timothy","contributorId":223868,"corporation":false,"usgs":false,"family":"Sullivan","given":"Timothy","affiliations":[{"id":40780,"text":"E&S Environmental Chemistry","active":true,"usgs":false}],"preferred":false,"id":883964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landers, Dixon H.","contributorId":173699,"corporation":false,"usgs":false,"family":"Landers","given":"Dixon","email":"","middleInitial":"H.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":883965,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casey, C. Frank","contributorId":211865,"corporation":false,"usgs":true,"family":"Casey","given":"C.","email":"","middleInitial":"Frank","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":883966,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70187561,"text":"70187561 - 2017 - Development of a coastal drought index using salinity data","interactions":[],"lastModifiedDate":"2017-05-09T11:20:57","indexId":"70187561","displayToPublicDate":"2017-05-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1112,"text":"Bulletin of the American Meteorological Society","onlineIssn":"1520-0477","printIssn":"0003-0007","active":true,"publicationSubtype":{"id":10}},"title":"Development of a coastal drought index using salinity data","docAbstract":"A critical aspect of the uniqueness of coastal drought is the effects on the salinity dynamics of creeks, rivers, and estuaries. The location of the freshwater–saltwater interface along the coast is an important factor in the ecological and socioeconomic dynamics of coastal communities. Salinity is a critical response variable that integrates hydrologic and coastal dynamics including sea level, tides, winds, precipitation, streamflow, and tropical storms. The position of the interface determines the composition of freshwater and saltwater aquatic communities as well as the freshwater availability for water intakes. Many definitions of drought have been proposed, with most describing a decline in precipitation having negative impacts on the water supply. Indices have been developed incorporating data such as rainfall, streamflow, soil moisture, and groundwater levels. These water-availability drought indices were developed for upland areas and may not be ideal for characterizing coastal drought. The availability of real-time and historical salinity datasets provides an opportunity for the development of a salinity-based coastal drought index. An approach similar to the standardized precipitation index (SPI) was modified and applied to salinity data obtained from sites in South Carolina and Georgia. Using the SPI approach, the index becomes a coastal salinity index (CSI) that characterizes coastal salinity conditions with respect to drought periods of higher-saline conditions and wet periods of higher-freshwater conditions. Evaluation of the CSI indicates that it provides additional coastal response information as compared to the SPI and the Palmer hydrologic drought index, and the CSI can be used for different estuary types and for comparison of conditions along coastlines.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-15-00171.1","usgsCitation":"Conrads, P., and Darby, L.S., 2017, Development of a coastal drought index using salinity data: Bulletin of the American Meteorological Society, v. 98, no. 4, p. 753-766, https://doi.org/10.1175/BAMS-D-15-00171.1.","productDescription":"14 p.","startPage":"753","endPage":"766","ipdsId":"IP-067018","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":340993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-24","publicationStatus":"PW","scienceBaseUri":"5912d536e4b0e541a03d451b","contributors":{"authors":[{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":false,"id":694571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Darby, Lisa S.","contributorId":191873,"corporation":false,"usgs":false,"family":"Darby","given":"Lisa","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":694572,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187573,"text":"ofr20171050 - 2017 - Geophysical data collected during the 2014 minute 319 pulse flow on the Colorado River below Morelos Dam, United States and Mexico","interactions":[],"lastModifiedDate":"2017-05-09T18:05:56","indexId":"ofr20171050","displayToPublicDate":"2017-05-09T00:00:00","publicationYear":"2017","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":"2017-1050","title":"Geophysical data collected during the 2014 minute 319 pulse flow on the Colorado River below Morelos Dam, United States and Mexico","docAbstract":"Geophysical methods were used to monitor infiltration during a water release, referred to as a “pulse flow,” in the Colorado River delta in March and April 2014. The pulse flow was enabled by Minute 319 of the 1944 United States–Mexico Treaty concerning water of the Colorado River. Fieldwork was carried out by the U.S. Geological Survey and the Centro de Investigación Científica y de Educación Superior de Ensenada as part of a binational effort to monitor the hydrologic effects of the pulse flow along the limitrophe (border) reach of the Colorado River and into Mexico. Repeat microgravity measurements were made at 25 locations in the southern limitrophe reach to quantify aquifer storage change during the pulse flow. Observed increases in storage along the river were greater with distance to the south, and the amount of storage change decreased away from the river channel. Gravity data at four monitoring well sites indicate specific yield equal to 0.32±0.05. Electromagnetic induction methods were used at 12 transects in the limitrophe reach of the river along the United States– Mexico border, and farther south into Mexico. These data, which are sensitive to variation in soil texture and water content, suggest relatively homogeneous conditions. Repeat direct-current resistivity measurements were collected at two locations to monitor groundwater elevation. Results indicate rapid groundwater-level rise during the pulse flow in the limitrophe reach and smaller variation at a more southern transect. Together, these data are useful for hydrogeologic characterization and hydrologic model development. Electronic data files are provided in the accompanying data release (Kennedy and others, 2016a).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171050","collaboration":"Prepared in Cooperation with Universidad Autónoma de Baja California and Centro de Investigación Científica y de Educación Superior de Ensenada","usgsCitation":"Kennedy, J.R., Callegary, J.B., Macy, J.P., Reyes-Lopez, J., Pérez-Flores, M., 2017, Geophysical data collected during the 2014 minute 319 pulse flow on the Colorado River below Morelos Dam, United States and Mexico: U.S. Geological Survey Open-File Report 2017–1050, 48 p., https://doi.org/10.3133/ofr20171050.","productDescription":"Report: vii, 48 Pp.; Data Release","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-067382","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":438349,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K935M8","text":"USGS data release","linkHelpText":"Geophysical Data Collected during the 2014 Minute 319 Pulse Flow"},{"id":341001,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1050/ofr20171050.pdf","text":"Report","size":"8.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1050"},{"id":341000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1050/coverthb.jpg"},{"id":341002,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7K935M8","text":"Data Release"}],"country":"Mexico, United States","state":"Arizona, Baja California, California","otherGeospatial":"Colorado River, Morelos Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 32.33333\n ],\n [\n -114.5,\n 32.33333\n ],\n [\n -114.5,\n 32.75\n ],\n [\n -115,\n 32.75\n ],\n [\n -115,\n 32.33333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Continued warming of the Arctic Ocean in coming decades is projected to trigger the release of teragrams (1 Tg = 106 tons) of methane from thawing subsea permafrost on shallow continental shelves and dissociation of methane hydrate on upper continental slopes. On the shallow shelves (<100 m water depth), methane released from the seafloor may reach the atmosphere and potentially amplify global warming. On the other hand, biological uptake of carbon dioxide (CO2) has the potential to offset the positive warming potential of emitted methane, a process that has not received detailed consideration for these settings. Continuous sea−air gas flux data collected over a shallow ebullitive methane seep field on the Svalbard margin reveal atmospheric CO2 uptake rates (−33,300 ± 7,900 μmol m−2⋅d−1) twice that of surrounding waters and ∼1,900 times greater than the diffusive sea−air methane efflux (17.3 ± 4.8 μmol m−2⋅d−1). The negative radiative forcing expected from this CO2 uptake is up to 231 times greater than the positive radiative forcing from the methane emissions. Surface water characteristics (e.g., high dissolved oxygen, high pH, and enrichment of 13C in CO2) indicate that upwelling of cold, nutrient-rich water from near the seafloor accompanies methane emissions and stimulates CO2 consumption by photosynthesizing phytoplankton. These findings challenge the widely held perception that areas characterized by shallow-water methane seeps and/or strongly elevated sea−air methane flux always increase the global atmospheric greenhouse gas burden.
Globally, Arctic and Subarctic regions have experienced the greatest temperature increases during the last 30 years. These extreme changes have amplified threats to the freshwater ecosystems that dominate the landscape in many areas by altering water budgets. Several studies in temperate environments have examined the adaptive capacity of organisms to enhance our understanding of the potential repercussions of warming and associated accelerated drying for freshwater ecosystems. However, few experiments have examined these impacts in Arctic or Subarctic freshwater ecosystems, where the climate is changing most rapidly. To evaluate the capacity of a widespread ectotherm to anticipated environmental changes, we conducted a mesocosm experiment with wood frogs (Rana sylvatica) in the Canadian Subarctic. Three warming treatments were fully crossed with three drying treatments to simulate a range of predicted changes in wetland environments. We predicted wetland warming and drying would act synergistically, with water temperature partially compensating for some of the negative effects of accelerated drying. Across all drying regimes, a 1 °C increase in water temperature increased the odds of survival by 1.79, and tadpoles in 52-day and 64-day hydroperiod mesocosms were 4.1–4.3 times more likely to survive to metamorphosis than tadpoles in 45-day mesocosms. For individuals who survived to metamorphosis, there was only a weak negative effect of temperature on size. As expected, increased temperatures accelerated tadpole growth through day 30 of the experiment. Our results reveal that one of the dominant herbivores in Subarctic wetlands, wood frog tadpoles, are capable of increasing their developmental rates in response to increased temperature and accelerated drying, but only in an additive manner. The strong negative effects of drying on survival, combined with lack of compensation between these two environmental drivers, suggest changes in the aquatic environment that are expected in this ecosystem will reduce mean fitness of populations across the landscape.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13543","usgsCitation":"Davenport, J., Hossack, B.R., and Fishback, L., 2017, Additive impacts of experimental climate change increase risk to an ectotherm at the Arctic's edge: Global Change Biology, v. 23, no. 6, p. 2262-2271, https://doi.org/10.1111/gcb.13543.","productDescription":"10 p.","startPage":"2262","endPage":"2271","ipdsId":"IP-074902","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":340967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-30","publicationStatus":"PW","scienceBaseUri":"591183ace4b0e541a03c1a40","contributors":{"authors":[{"text":"Davenport, Jon M.","contributorId":126727,"corporation":false,"usgs":false,"family":"Davenport","given":"Jon M.","affiliations":[{"id":6583,"text":"University of Montana, Division of Biological Sciences, Missoula, MT, USA 59812","active":true,"usgs":false}],"preferred":false,"id":694553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":694552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fishback, LeeAnn","contributorId":168514,"corporation":false,"usgs":false,"family":"Fishback","given":"LeeAnn","email":"","affiliations":[{"id":25316,"text":"Churchill Northern Studies Centre, P.O. Box 610, Churchill, Manitoba, R0B 0E0, Canada","active":true,"usgs":false}],"preferred":false,"id":694554,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187542,"text":"70187542 - 2017 - Unconventional oil and gas spills: Materials, volumes, and risks to surface waters in four states of the U.S.","interactions":[],"lastModifiedDate":"2018-04-02T16:48:30","indexId":"70187542","displayToPublicDate":"2017-05-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Unconventional oil and gas spills: Materials, volumes, and risks to surface waters in four states of the U.S.","docAbstract":"Extraction of oil and gas from unconventional sources, such as shale, has dramatically increased over the past ten years, raising the potential for spills or releases of chemicals, waste materials, and oil and gas. We analyzed spill data associated with unconventional wells from Colorado, New Mexico, North Dakota and Pennsylvania from 2005 to 2014, where we defined unconventional wells as horizontally drilled into an unconventional formation. We identified materials spilled by state and for each material we summarized frequency, volumes and spill rates. We evaluated the environmental risk of spills by calculating distance to the nearest stream and compared these distances to existing setback regulations. Finally, we summarized relative importance to drinking water in watersheds where spills occurred. Across all four states, we identified 21,300 unconventional wells and 6622 reported spills. The number of horizontal well bores increased sharply beginning in the late 2000s; spill rates also increased for all states except PA where the rate initially increased, reached a maximum in 2009 and then decreased. Wastewater, crude oil, drilling waste, and hydraulic fracturing fluid were the materials most often spilled; spilled volumes of these materials largely ranged from 100 to 10,000 L. Across all states, the average distance of spills to a stream was highest in New Mexico (1379 m), followed by Colorado (747 m), North Dakota (598 m) and then Pennsylvania (268 m), and 7.0, 13.3, and 20.4% of spills occurred within existing surface water setback regulations of 30.5, 61.0, and 91.4 m, respectively. Pennsylvania spills occurred in watersheds with a higher relative importance to drinking water than the other three states. Results from this study can inform risk assessments by providing improved input parameters on volume and rates of materials spilled, and guide regulations and the management policy of spills.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.12.142","usgsCitation":"Maloney, K.O., Baruch-Mordo, S., Patterson, L.A., Nicot, J., Entrekin, S., Fargione, J.E., Kiesecker, J.M., Konschnik, K., Ryan, J.N., Trainor, A.M., Saiers, J.E., and Wiseman, H.J., 2017, Unconventional oil and gas spills: Materials, volumes, and risks to surface waters in four states of the U.S.: Science of the Total Environment, v. 581-582, p. 369-377, https://doi.org/10.1016/j.scitotenv.2016.12.142.","productDescription":"9 p.","startPage":"369","endPage":"377","ipdsId":"IP-081040","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":469866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.12.142","text":"Publisher Index Page"},{"id":340940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Geological Survey Washington Water Science Center","interactions":[],"lastModifiedDate":"2017-05-09T10:19:44","indexId":"ofr20171044","displayToPublicDate":"2017-05-08T00:00:00","publicationYear":"2017","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":"2017-1044","title":"Quality-assurance plan for water-quality activities in the U.S. Geological Survey Washington Water Science Center","docAbstract":"In accordance with guidelines set forth by the Office of Water Quality in the Water Mission Area of the U.S. Geological Survey, a quality-assurance plan has been created for use by the Washington Water Science Center (WAWSC) in conducting water-quality activities. This qualityassurance plan documents the standards, policies, and procedures used by the WAWSC for activities related to the collection, processing, storage, analysis, and publication of water-quality data. The policies and procedures documented in this quality-assurance plan for water-quality activities complement the quality-assurance plans for surface-water and groundwater activities at the WAWSC.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171044","usgsCitation":"Conn, K.E., Huffman, R.L., and Barton, Cynthia, 2017, Quality-assurance plan for water-quality activities in the U.S. Geological Survey Washington Water Science Center: U.S. Geological Survey Open-File Report 2017–1044, 66 p., https://doi.org/10.3133/ofr20171044.","productDescription":"vi, 66 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-083785","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":340969,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1044/ofr20171044.pdf","text":"Report","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1044"},{"id":340968,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1044/coverthb.jpg"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
In order to address the 2008/10 and Supplemental 2014 NOAA Fisheries Biological Opinion for operation of the Federal Columbia River Power System, the U.S. Army Corps of Engineers (USACE) and the Bureau of Reclamation (Reclamation) developed and have begun implementation of Caspian tern (Hydroprogne caspia) management plans. This implementation includes redistribution of the Caspian terns in the Columbia River estuary and the mid-Columbia River region to reduce predation on salmonids listed under the Endangered Species Act. Key elements of the plans include (1) reducing nesting habitat for Caspian terns in the Columbia River estuary and the mid-Columbia River region, and (2) creating or modifying nesting habitat at alternative sites within the Caspian tern breeding range. USACE and Reclamation developed Caspian tern nesting habitat at the U.S. Fish and Wildlife Service Don Edwards San Francisco Bay National Wildlife Refuge (DENWR), California, prior to the 2015 nesting season. Furthermore, to reduce or eliminate potential conflicts between nesting Caspian terns and threatened western snowy plovers (Charadrius alexandrinus nivosus), nesting habitat for snowy plovers also was developed. Seven recently constructed islands within two managed ponds (Ponds A16 and SF2) of DENWR were modified to provide habitat attractive to nesting Caspian terns (5 islands) and snowy plovers (2 islands). These 7 islands were a subset of 46 islands recently constructed in Ponds A16 and SF2 to provide waterbird nesting habitat as part of the South Bay Salt Pond (SBSP) Restoration Project.
We used social attraction methods (decoys and electronic call systems) to attract Caspian terns and snowy plovers to these seven modified islands, and conducted surveys between March and September of 2015 and 2016 to evaluate nest numbers, nest density, and productivity. Results from the 2015 nesting season, the first year of the study, indicated that island modifications and social attraction measures were successful in establishing Caspian tern breeding colonies at Ponds A16 and SF2 of DENWR. The success of 2015 continued in 2016, the second year of the study. In 2016, Caspian terns nested on two of the five islands modified for Caspian terns (one island in Pond A16 and one island in Pond SF2). Caspian terns initiated at least 317 nests, fledged at least 158 chicks, and had a breeding success rate of 0.50 fledged chicks per breeding pair. This represents a 42 percent increase in nests initiated, a 9 percent decrease in the number of fledged chicks, and a 36 percent decrease in the number of chicks fledged per breeding pair in 2016 compared to 2015. Although overall productivity decreased from 2015, these results indicate that the Caspian tern breeding population on modified islands of the DENWR is increasing relative to 2015, the first year of the effort, and relative to years prior to 2015 when no breeding colonies of Caspian terns existed in Ponds A16 or SF2. These results indicate the effectiveness of social attraction measures in helping to establish tern nesting colonies in San Francisco Bay. Conversely, for the second year in a row, snowy plovers did not attempt to nest on any island in Ponds A16 and SF2. Social attraction measures similar to those used in this study, but targeting other colonial species such as Forster’s terns (Sterna forsteri) and American avocets (Recurvirostra americana), may help to establish waterbird breeding colonies at wetlands enhanced as part of the SBSP Restoration Project.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171055","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and the Bureau of Reclamation","usgsCitation":"Hartman, C.A., Ackerman, J.T., Herzog, M.P., Strong, Cheryl, Trachtenbarg, David, and Shore, C.A., 2017, Evaluation of Caspian tern (Hydroprogne caspia) and snowy plover (Charadrius alexandrinus nivosus) nesting on modified islands at the Don Edwards San Francisco Bay National Wildlife Refuge, California—2016 Annual Report: U.S. Geological Survey Open-File Report 2017-1055, 37 p., https://doi.org/10.3133/ofr20171055.","productDescription":"vi, 37 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-083253","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":340952,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1055/ofr20171055.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1055"},{"id":340951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1055/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Don Edwards San Francisco Bay National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.26409912109375,\n 37.41052799460727\n ],\n [\n -121.90292358398438,\n 37.41052799460727\n ],\n [\n -121.90292358398438,\n 37.6289157524452\n ],\n [\n -122.26409912109375,\n 37.6289157524452\n ],\n [\n -122.26409912109375,\n 37.41052799460727\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
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Riparian ecosystems are valuable to the ecological and human communities that depend on them. Over the past century, they have been subject to shifting management practices to maximize human use and ecosystem services, creating a complex relationship between water policy, management, and the natural ecosystem. This has necessitated research on the spatial and temporal dynamics of riparian vegetation change. The San Acacia Reach of the Middle Rio Grande has experienced multiple management and river flow fluctuations, resulting in threats to its riparian and aquatic ecosystems. This research uses remote sensing data, GIS, a review of management decisions, and an assessment of climate to both quantify how riparian vegetation has been altered over time and provide interpretations of the relationships between riparian change and shifting climate and management objectives. This research focused on four management phases from 1935 to 2014, each highlighting different management practices and climate-driven river patterns, providing unique opportunities to observe a direct relationship between river management, climate, and riparian response. Overall, we believe that management practices coupled with reduced surface river-flows with limited overbank flooding influenced the compositional and spatial patterns of vegetation, including possibly increasing non-native vegetation coverage. However, recent restoration efforts have begun to reduce non-native vegetation coverage.
","language":"English","publisher":"MDPI","doi":"10.3390/land6020029","usgsCitation":"Petrakis, R., van Leeuwen, W., Villarreal, M.L., Tashjian, P., Dello Russo, R., and Scott, C.A., 2017, Historical analysis of riparian vegetation change in response to shifting management objectives on the Middle Rio Grande: Land, v. 6, no. 2, p. 1-23, https://doi.org/10.3390/land6020029.","productDescription":"Article 29; 23 p.","startPage":"1","endPage":"23","ipdsId":"IP-079082","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469864,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land6020029","text":"Publisher Index Page"},{"id":438351,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7154F84","text":"USGS data release","linkHelpText":"Middle Rio Grande Multitemporal Land Cover Classifications - 1935, 1962, 1987, 1999, and 2014"},{"id":340957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Middle Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.03979492187499,\n 33.61919376817004\n ],\n [\n -106.75140380859374,\n 33.61919376817004\n ],\n [\n -106.75140380859374,\n 34.27197081112463\n ],\n [\n -107.03979492187499,\n 34.27197081112463\n ],\n [\n -107.03979492187499,\n 33.61919376817004\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-22","publicationStatus":"PW","scienceBaseUri":"591183afe4b0e541a03c1a44","contributors":{"authors":[{"text":"Petrakis, Roy E. 0000-0001-8932-077X rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":694506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Leeuwen, Willem","contributorId":148978,"corporation":false,"usgs":false,"family":"van Leeuwen","given":"Willem","email":"","affiliations":[],"preferred":false,"id":694507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":694505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tashjian, Paul","contributorId":191857,"corporation":false,"usgs":false,"family":"Tashjian","given":"Paul","email":"","affiliations":[],"preferred":false,"id":694508,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dello Russo, Regina","contributorId":191858,"corporation":false,"usgs":false,"family":"Dello Russo","given":"Regina","email":"","affiliations":[],"preferred":false,"id":694509,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Christopher A.","contributorId":31664,"corporation":false,"usgs":true,"family":"Scott","given":"Christopher","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":694510,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70187326,"text":"ofr20171047 - 2017 - Characterization of peak streamflows and flood inundation at selected areas in North Carolina following Hurricane Matthew, October 2016","interactions":[],"lastModifiedDate":"2017-08-29T15:36:32","indexId":"ofr20171047","displayToPublicDate":"2017-05-05T12:00:00","publicationYear":"2017","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":"2017-1047","title":"Characterization of peak streamflows and flood inundation at selected areas in North Carolina following Hurricane Matthew, October 2016","docAbstract":"The passage of Hurricane Matthew through central and eastern North Carolina during October 7–9, 2016, brought heavy rainfall, which resulted in major flooding. More than 15 inches of rain was recorded in some areas. More than 600 roads were closed, including Interstates 95 and 40, and nearly 99,000 structures were affected by floodwaters. Immediately following the flooding, the U.S. Geological Survey documented 267 high-water marks, of which 254 were surveyed. North Carolina Emergency Management documented and surveyed 353 high-water marks. Using a subset of these highwater marks, six flood-inundation maps were created for hard-hit communities. Digital datasets of the inundation areas, study reach boundary, and water-depth rasters are available for download. In addition, peak gage-height data, peak streamflow data, and annual exceedance probabilities (in percent) were determined for 24 U.S. Geological Survey streamgages located near the heavily flooded communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171047","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Musser, J.W., Watson, K.M., and Gotvald, A.J., 2017, Characterization of peak streamflows and flood inundation at selected areas in North Carolina following Hurricane Matthew, October 2016 (ver. 2.0, August 2017): U.S. Geological Survey Open-File Report 2017–1047, 23 p., https://doi.org/10.3133/ofr20171047.","productDescription":"Report: v, 23 p.; Data Release, Version History","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-085645","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":340658,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75X276T","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation, flood depth, and high-water marks for selected areas in North Carolina from the October 2016 flood"},{"id":340659,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1047/ofr20171047.pdf","text":"Report","size":"4.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1047"},{"id":340657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1047/coverthb3.jpg"},{"id":342197,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1047/versionHist.txt","size":"2.31 MB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.75,\n 34\n ],\n [\n -76.75,\n 34\n ],\n [\n -76.75,\n 36.116667\n ],\n [\n -79.75,\n 36.116667\n ],\n [\n -79.75,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 2016; Version 1.1: June 2017; Version 2.0: August 2017","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Congress directed the Federal Government to work with States to restore the Nation’s largest estuary.
Chesapeake Bay restoration provides important economic and ecological benefits:
Chesapeake Bay Coordinator
MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
No abstract available.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.3801","usgsCitation":"Brooks, B.W., Lazorchak, J.M., Howard, M., Johnson, M.V., Morton, S.L., Perkins, D.A., Reavie, E.D., Scott, G.L., Smith, S.A., and Steevens, J., 2017, In some places, in some cases, and at some times, harmful algal blooms are the greatest threat to inland water quality: Environmental Toxicology and Chemistry, v. 36, no. 5, p. 1125-1127, https://doi.org/10.1002/etc.3801.","productDescription":"3 p.","startPage":"1125","endPage":"1127","ipdsId":"IP-080953","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":461609,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.3801","text":"Publisher Index Page"},{"id":349592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"5","noUsgsAuthors":false,"publicationDate":"2017-05-01","publicationStatus":"PW","scienceBaseUri":"5a60fbd5e4b06e28e9c236c8","contributors":{"authors":[{"text":"Brooks, Bryan W. 0000-0002-6277-9852","orcid":"https://orcid.org/0000-0002-6277-9852","contributorId":198868,"corporation":false,"usgs":false,"family":"Brooks","given":"Bryan","email":"","middleInitial":"W.","affiliations":[{"id":35352,"text":"Department of Environmental Science, Baylor University, Waco, TX, USA","active":true,"usgs":false}],"preferred":false,"id":724144,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lazorchak, James M.","contributorId":14750,"corporation":false,"usgs":true,"family":"Lazorchak","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":724145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Meredith D.A.","contributorId":201037,"corporation":false,"usgs":false,"family":"Howard","given":"Meredith D.A.","affiliations":[],"preferred":false,"id":724149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Mari-Vaughn V. 0000-0002-2944-2529","orcid":"https://orcid.org/0000-0002-2944-2529","contributorId":189195,"corporation":false,"usgs":false,"family":"Johnson","given":"Mari-Vaughn","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":724146,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morton, Steve L.","contributorId":201038,"corporation":false,"usgs":false,"family":"Morton","given":"Steve","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":724150,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perkins, Dawn A.K.","contributorId":201039,"corporation":false,"usgs":false,"family":"Perkins","given":"Dawn","email":"","middleInitial":"A.K.","affiliations":[],"preferred":false,"id":724151,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reavie, Euan D.","contributorId":41755,"corporation":false,"usgs":true,"family":"Reavie","given":"Euan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":724147,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Scott, Geoffrey L.","contributorId":201040,"corporation":false,"usgs":false,"family":"Scott","given":"Geoffrey","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":724152,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, Stephanie A.","contributorId":201041,"corporation":false,"usgs":false,"family":"Smith","given":"Stephanie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724153,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":65415,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":724148,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70182142,"text":"sir20175010 - 2017 - Simulation of groundwater flow in the glacial aquifer system of northeastern Wisconsin with variable model complexity","interactions":[],"lastModifiedDate":"2017-05-04T15:33:55","indexId":"sir20175010","displayToPublicDate":"2017-05-04T12:30:00","publicationYear":"2017","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":"2017-5010","title":"Simulation of groundwater flow in the glacial aquifer system of northeastern Wisconsin with variable model complexity","docAbstract":"The U.S. Geological Survey, National Water-Quality Assessment seeks to map estimated intrinsic susceptibility of the glacial aquifer system of the conterminous United States. Improved understanding of the hydrogeologic characteristics that explain spatial patterns of intrinsic susceptibility, commonly inferred from estimates of groundwater age distributions, is sought so that methods used for the estimation process are properly equipped. An important step beyond identifying relevant hydrogeologic datasets, such as glacial geology maps, is to evaluate how incorporation of these resources into process-based models using differing levels of detail could affect resulting simulations of groundwater age distributions and, thus, estimates of intrinsic susceptibility.
This report describes the construction and calibration of three groundwater-flow models of northeastern Wisconsin that were developed with differing levels of complexity to provide a framework for subsequent evaluations of the effects of process-based model complexity on estimations of groundwater age distributions for withdrawal wells and streams. Preliminary assessments, which focused on the effects of model complexity on simulated water levels and base flows in the glacial aquifer system, illustrate that simulation of vertical gradients using multiple model layers improves simulated heads more in low-permeability units than in high-permeability units. Moreover, simulation of heterogeneous hydraulic conductivity fields in coarse-grained and some fine-grained glacial materials produced a larger improvement in simulated water levels in the glacial aquifer system compared with simulation of uniform hydraulic conductivity within zones. The relation between base flows and model complexity was less clear; however, the relation generally seemed to follow a similar pattern as water levels. Although increased model complexity resulted in improved calibrations, future application of the models using simulated particle tracking is anticipated to evaluate if these model design considerations are similarly important for understanding the primary modeling objective - to simulate reasonable groundwater age distributions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175010","usgsCitation":"Juckem, P.F., Clark, B.R., and Feinstein, D.T., 2017, Simulation of groundwater flow in the glacial aquifer system of northeastern Wisconsin with variable model complexity: U.S. Geological Survey Scientific Investigations Report 2017–5010, 52 p., https://doi.org/10.3133/sir20175010.","productDescription":"viii, 52 p.","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077195","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":336160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5010/coverthb.jpg"},{"id":336162,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/sir2017-5010.xml","text":"USGS data release","description":"USGS data 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\"}}]}","publicComments":"National Water-Quality Assessment","contact":"Director, Wisconsin Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The U.S. Geological Survey Monthly Water Balance Model Futures Portal (https://my.usgs.gov/mows/) is a user-friendly interface that summarizes monthly historical and simulated future conditions for seven hydrologic and meteorological variables (actual evapotranspiration, potential evapotranspiration, precipitation, runoff, snow water equivalent, atmospheric temperature, and streamflow) at locations across the conterminous United States (CONUS).
The estimates of these hydrologic and meteorological variables were derived using a Monthly Water Balance Model (MWBM), a modular system that simulates monthly estimates of components of the hydrologic cycle using monthly precipitation and atmospheric temperature inputs. Precipitation and atmospheric temperature from 222 climate datasets spanning historical conditions (1952 through 2005) and simulated future conditions (2020 through 2099) were summarized for hydrographic features and used to drive the MWBM for the CONUS. The MWBM input and output variables were organized into an open-access database. An Open Geospatial Consortium, Inc., Web Feature Service allows the querying and identification of hydrographic features across the CONUS. To connect the Web Feature Service to the open-access database, a user interface—the Monthly Water Balance Model Futures Portal—was developed to allow the dynamic generation of summary files and plots based on plot type, geographic location, specific climate datasets, period of record, MWBM variable, and other options. Both the plots and the data files are made available to the user for download
Director, USGS Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
The occurrence of arsenic and uranium in groundwater at concentrations that exceed drinking-water standards is a concern because of the potential adverse effects on human health. Some early studies of arsenic occurrence in groundwater considered anthropogenic causes, but more recent studies have focused on sources of naturally occurring arsenic to groundwater, such as minerals within aquifer materials that are in contact with groundwater. Arsenic and uranium in groundwater in New England have been shown to have a strong association to the geologic setting and nearby streambed sediment concentrations. In New Hampshire and Massachusetts, arsenic and uranium concentrations greater than human-health benchmarks have shown distinct spatial patterns when related to the bedrock units mapped at the local scale.
The Connecticut Department of Public Health (DPH) reported that there are about 322,600 private wells in Connecticut serving approximately 823,000 people, or 23 percent of the State’s population. The State does not require that existing private wells be routinely tested for arsenic, uranium, or other contaminants; consequently, private wells are only sampled at the well owner’s discretion or when they are newly constructed. The U.S. Geological Survey (USGS), in cooperation with the DPH, completed an assessment in 2016 on the distribution of concentrations of arsenic and uranium in groundwater from bedrock in Connecticut. This report presents the major findings for arsenic and uranium concentrations from water samples collected from 2013 to 2015 from private wells.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
When nature and humanity change Earth’s landscapes - through flood or fire, public policy, natural resources management, or economic development - the results are often dramatic and lasting.
Wildfires can reshape ecosystems. Hurricanes with names like Sandy or Katrina will howl for days while altering the landscape for years. One growing season in the evolution of drought-resistant genetics can transform semiarid landscapes into farm fields.
In the past, valuable land cover maps created for understanding the effects of those events - whether changes in wildlife habitat, water-quality impacts, or the role land use and land cover play in affecting weather and climate - came out at best every 5 to 7 years. Those high quality, high resolution maps were good, but users always craved more: even higher quality data, additional land cover and land change variables, more detailed legends, and most importantly, more frequent land change information.
Now a bold new initiative called Land Change Monitoring, Assessment, and Projection (LCMAP) promises to fulfill that demand.
Developed at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, LCMAP provides definitive, timely information on how, why, and where the planet is changing. LCMAP’s continuous monitoring process can detect changes as they happen every day that Landsat satellites acquire clear observations. The result will be to place near real-time information in the hands of land and resource managers who need to understand the effects these changes have on landscapes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip172","usgsCitation":"Young, S.M., 2017, Land Change Monitoring, Assessment, and Projection (LCMAP) revolutionizes land cover and land change research: U.S. Geological Survey General Information Product 172, 4 p., https://doi.org/10.3133/gip172.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-083103","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0172/coverthb.jpg"},{"id":340453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0172/gip172.pdf","size":"7.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 172"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental-scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental-extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1-km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed-scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed-scale modeling unit over the conterminous United States (n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national-scale categorization of snowmelt processes.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12520","usgsCitation":"Driscoll, J.M., Hay, L.E., and Bock, A.R., 2017, Spatiotemporal variability of snow depletion curves derived from SNODAS for the conterminous United States, 2004-2013: Journal of the American Water Resources Association, v. 53, no. 3, p. 655-666, https://doi.org/10.1111/1752-1688.12520.","productDescription":"12 p.","startPage":"655","endPage":"666","ipdsId":"IP-079682","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":340646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}\n","volume":"53","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-13","publicationStatus":"PW","scienceBaseUri":"59084922e4b0fc4e448ffd3e","contributors":{"authors":[{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":693604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":693605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":693606,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188091,"text":"70188091 - 2017 - Stable isotope analyses of oxygen (18O:17O:16O) and chlorine (37Cl:35Cl) in perchlorate: reference materials, calibrations, methods, and interferences","interactions":[],"lastModifiedDate":"2017-05-31T12:36:37","indexId":"70188091","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3233,"text":"Rapid Communications in Mass Spectrometry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Stable isotope analyses of oxygen (18O:17O:16O) and chlorine (37Cl:35Cl) in perchlorate: reference materials, calibrations, methods, and interferences","title":"Stable isotope analyses of oxygen (18O:17O:16O) and chlorine (37Cl:35Cl) in perchlorate: reference materials, calibrations, methods, and interferences","docAbstract":"Rationale
Perchlorate (ClO4−) is a common trace constituent of water, soils, and plants; it has both natural and synthetic sources and is subject to biodegradation. The stable isotope ratios of Cl and O provide three independent quantities for ClO4− source attribution and natural attenuation studies: δ37Cl, δ18O, and δ17O (or Δ17O or 17Δ) values. Documented reference materials, calibration schemes, methods, and interferences will improve the reliability of such studies.
Methods
Three large batches of KClO4 with contrasting isotopic compositions were synthesized and analyzed against VSMOW-SLAP, atmospheric O2, and international nitrate and chloride reference materials. Three analytical methods were tested for O isotopes: conversion of ClO4− to CO for continuous-flow IRMS (CO-CFIRMS), decomposition to O2 for dual-inlet IRMS (O2-DIIRMS), and decomposition to O2 with molecular-sieve trap (O2-DIIRMS+T). For Cl isotopes, KCl produced by thermal decomposition of KClO4 was reprecipitated as AgCl and converted into CH3Cl for DIIRMS.
Results
KClO4 isotopic reference materials (USGS37, USGS38, USGS39) represent a wide range of Cl and O isotopic compositions, including non-mass-dependent O isotopic variation. Isotopic fractionation and exchange can affect O isotope analyses of ClO4− depending on the decomposition method. Routine analyses can be adjusted for such effects by normalization, using reference materials prepared and analyzed as samples. Analytical errors caused by SO42−, NO3−, ReO42−, and C-bearing contaminants include isotope mixing and fractionation effects on CO and O2, plus direct interference from CO2 in the mass spectrometer. The results highlight the importance of effective purification of ClO4− from environmental samples.
Conclusions
KClO4 reference materials are available for testing methods and calibrating isotopic data for ClO4− and other substances with widely varying Cl or O isotopic compositions. Current ClO4−extraction, purification, and analysis techniques provide relative isotope-ratio measurements with uncertainties much smaller than the range of values in environmental ClO4−, permitting isotopic evaluation of environmental ClO4− sources and natural attenuation.
","language":"English","publisher":"Wiley","doi":"10.1002/rcm.7751","usgsCitation":"Bohlke, J., Mroczkowski, S.J., Sturchio, N.C., Heraty, L.J., Richman, K.W., Sullivan, D.B., Griffith, K.N., Gu, B., and Hatzinger, P., 2017, Stable isotope analyses of oxygen (18O:17O:16O) and chlorine (37Cl:35Cl) in perchlorate: reference materials, calibrations, methods, and interferences: Rapid Communications in Mass Spectrometry, v. 31, no. 1, p. 85-110, https://doi.org/10.1002/rcm.7751.","productDescription":"26 p.","startPage":"85","endPage":"110","ipdsId":"IP-079870","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":341925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-12","publicationStatus":"PW","scienceBaseUri":"592fd63de4b0e9bd0ea896e9","contributors":{"authors":[{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":696640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mroczkowski, Stanley J. 0000-0001-8026-6025 smroczko@usgs.gov","orcid":"https://orcid.org/0000-0001-8026-6025","contributorId":2628,"corporation":false,"usgs":true,"family":"Mroczkowski","given":"Stanley","email":"smroczko@usgs.gov","middleInitial":"J.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":696641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sturchio, Neil C.","contributorId":149375,"corporation":false,"usgs":false,"family":"Sturchio","given":"Neil","email":"","middleInitial":"C.","affiliations":[{"id":15289,"text":"University of Illinois, Ven Te Chow Hydrosystems Laboratory","active":true,"usgs":false}],"preferred":false,"id":696642,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heraty, Linnea J.","contributorId":192520,"corporation":false,"usgs":false,"family":"Heraty","given":"Linnea","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":696643,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richman, Kent W.","contributorId":192519,"corporation":false,"usgs":false,"family":"Richman","given":"Kent","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":696644,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sullivan, Donald B.","contributorId":192517,"corporation":false,"usgs":false,"family":"Sullivan","given":"Donald","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":696645,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Griffith, Kris N.","contributorId":192518,"corporation":false,"usgs":false,"family":"Griffith","given":"Kris","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":696646,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gu, Baohua","contributorId":15504,"corporation":false,"usgs":true,"family":"Gu","given":"Baohua","affiliations":[],"preferred":false,"id":696648,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hatzinger, Paul B.","contributorId":43204,"corporation":false,"usgs":true,"family":"Hatzinger","given":"Paul B.","affiliations":[],"preferred":false,"id":696647,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70188109,"text":"70188109 - 2017 - Climate legacy and lag effects on dryland plant communities in the southwestern U.S.","interactions":[],"lastModifiedDate":"2017-05-31T13:23:34","indexId":"70188109","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Climate legacy and lag effects on dryland plant communities in the southwestern U.S.","docAbstract":"Climate change effects on vegetation will likely be strong in the southwestern U.S., which is projected to experience large increases in temperature and changes in precipitation. Plant communities in the southwestern U.S. may be particularly vulnerable to climate change as the productivity of many plant species is strongly water-limited. This study examines the relationship between climate and vegetation condition using a time-series of Landsat imagery across grassland, shrubland, and woodland communities on the Colorado Plateau, USA. We improve on poorly understood inter-annual climate-vegetation relationships by exploring how the responses of different plant communities depend on climate legacies (>12 months) and lag behind shorter-term (3–12 month) changes in water availability. Our results show a prolonged drying trend on the Colorado Plateau since the early 1990s that was punctuated in several years by intense droughts. In areas that experienced sustained dry conditions or a drying trend, vegetation greenness (a proxy for production) increased linearly when conditions were interrupted by wetting events. In contrast, in areas that experienced sustained wet conditions or a wetting trend, vegetation greenness was weakly or not related to wetting events, indicating that production may saturate if vegetation experiences sufficient water availability. Shrubland and woodland communities had stronger relationships with climate at long lags (6–12 months) and many maintained greenness under sustained water deficit, whereas grassland communities had stronger relationships at short lags (3–6 months) and lost greenness even in periods of short-term drought. The results of our study show the importance of identifying climate legacies and lags when assessing indicators of ecological drought, which can be used to improve forecasts of which plant communities will be vulnerable under future climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2016.10.024","usgsCitation":"Bunting, E., Munson, S.M., and Villarreal, M.L., 2017, Climate legacy and lag effects on dryland plant communities in the southwestern U.S.: Ecological Indicators, v. 74, p. 216-229, https://doi.org/10.1016/j.ecolind.2016.10.024.","productDescription":"14 p.","startPage":"216","endPage":"229","ipdsId":"IP-080256","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438355,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90CRK5N","text":"USGS data release","linkHelpText":"Dataset for climate legacy and lag effects on dryland plant communities in the southwestern U.S."},{"id":341940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.115234375,\n 35.8356283888737\n ],\n [\n -106.94091796875,\n 35.8356283888737\n ],\n [\n -106.94091796875,\n 40.96330795307353\n ],\n [\n -113.115234375,\n 40.96330795307353\n ],\n [\n -113.115234375,\n 35.8356283888737\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"74","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592fd63ce4b0e9bd0ea896e6","contributors":{"authors":[{"text":"Bunting, Erin 0000-0001-9103-6065 ebunting@usgs.gov","orcid":"https://orcid.org/0000-0001-9103-6065","contributorId":168488,"corporation":false,"usgs":true,"family":"Bunting","given":"Erin","email":"ebunting@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":696775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":696776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":696777,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191541,"text":"70191541 - 2017 - Coal-tar-based pavement sealants—a potent source of PAHs","interactions":[],"lastModifiedDate":"2017-10-17T11:03:27","indexId":"70191541","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2593,"text":"Lakeline","active":true,"publicationSubtype":{"id":10}},"title":"Coal-tar-based pavement sealants—a potent source of PAHs","docAbstract":"P avement sealants are applied to the asphalt pavement of many parking lots, driveways, and even playgrounds in North America (Figure 1), where, when first applied, they render the pavement glossy black and looking like new. Sealant products used commercially in the central, eastern, and northern United States typically are coal-tarbased, whereas those used in the western United States typically are asphalt-based. Although the products look similar, they are chemically different. Coal-tarbased pavement sealants typically are 25-35 percent (by weight) coal tar or coal-tar pitch, materials that are known human carcinogens and that contain high concentrations of polycyclic aromatic hydrocarbons (PAHs) and related chemicals (unless otherwise noted, all Figure 1. Pavement sealant is commonly used to seal parking lots, playgrounds, and driveways throughout the United States. Sealants used in the central, northern, eastern, and southern United States typically contain coal tar or coal-tar pitch, both of which are known human carcinogens. Photos by the U.S. Geological Survey. data in this article are from Mahler et al. 2012 and references therein).
","language":"English","publisher":"North American Lake Management Society","usgsCitation":"Mahler, B., and Van Metre, P., 2017, Coal-tar-based pavement sealants—a potent source of PAHs: Lakeline, v. 37, no. 1, p. 13-18.","productDescription":"6 p.","startPage":"13","endPage":"18","ipdsId":"IP-082495","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":346679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346647,"type":{"id":15,"text":"Index Page"},"url":"https://www.nalms.org/lakeline-magazine/"}],"volume":"37","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e71691e4b05fe04cd331a9","contributors":{"authors":[{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":712708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Metre, Peter C. 0000-0001-7564-9814 pcvanmet@usgs.gov","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":172246,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter C.","email":"pcvanmet@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":712709,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70187141,"text":"ofr20171029 - 2017 - Guidelines for preparation of State water-use estimates for 2015","interactions":[],"lastModifiedDate":"2017-05-02T08:59:14","indexId":"ofr20171029","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","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":"2017-1029","title":"Guidelines for preparation of State water-use estimates for 2015","docAbstract":"The U.S. Geological Survey (USGS) has estimated the use of water in the United States at 5-year intervals since 1950. This report describes the water-use categories and data elements used for the national water-use compilation conducted as part of the USGS National Water-Use Science Project. The report identifies sources of water-use information, provides standard methods and techniques for estimating water use at the county level, and outlines steps for preparing documentation for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands.
As part of this USGS program to document water use on a national scale, estimates of water withdrawals for the categories of public supply, self-supplied domestic, industrial, irrigation, and thermoelectric power are prepared for each county in each State, District, or territory by using the guidelines in this report. County estimates of water withdrawals for aquaculture, livestock, and mining are prepared for each State by using a county-based national model, although water-use programs in each State or Water Science Center have the option of producing independent county estimates of water withdrawals for these categories. Estimates of water withdrawals and consumptive use for thermoelectric power will be aggregated to the county level for each State by the national project; additionally, irrigation consumptive use at the county level will also be provided, although study chiefs in each State have the option of producing independent county estimates of water withdrawals and consumptive use for these categories.
Estimates of deliveries of water from public supplies for domestic use by county also will be prepared for each State. As a result, total domestic water use can be determined for each State by combining self-supplied domestic withdrawals and public-supplied domestic deliveries. Fresh groundwater and surface-water estimates will be prepared for all categories of use, and saline groundwater and surface-water estimates by county will be prepared for the categories of public supply, industrial, mining, and thermoelectric power. Power production for thermoelectric power and irrigated acres by irrigation system type will be compiled. If data are available, reclaimed-wastewater use will be compiled for the public-supply, industrial, mining, thermoelectric-power, and irrigation categories.
Optional water-use categories are commercial, hydroelectric power, and wastewater treatment. Optional data elements are public-supply deliveries to commercial, industrial, and thermoelectric-power users; consumptive use (for categories other than thermoelectric power and irrigation); irrigation conveyance loss; and number of facilities. Aggregation of water-use data by stream basin (eight-digit hydrologic unit code) and principal aquifers also is optional.
Water-use data compiled by the States will be stored in the USGS Aggregate Water-Use Data System (AWUDS). This database is a comprehensive aggregated database designed to store mandatory and optional data elements. AWUDS contains several routines that can be used for quality assurance and quality control of the data, and AWUDS produces tables of water-use data from the previous compilations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171029","collaboration":"National Water-Use Science Project","usgsCitation":"Bradley, M.W., comp., 2017, Guidelines for preparation of State water-use estimates for 2015: U.S. Geological Survey Open-File Report 2017–1029, 54 p., https://doi.org/10.3133/ofr20171029.","productDescription":"viii, 54 p.","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-078880","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":340450,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1029/coverthb2.jpg"},{"id":340259,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1029/ofr20171029.pdf","text":"Report","size":"719 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1029"},{"id":340258,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1029/coverthb.jpg"}],"contact":"Director, Lower Mississippi-Gulf Water Science Center—Tennessee
640 Grassmere Park
Suite 100
Nashville, TN 37211
The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program classifies, describes, and maps existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VMI Program is managed by the NPS I&M Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, NPS Northeast Temperate Network, and NPS Appalachian National Scenic Trail (APPA) have completed vegetation classification and mapping of APPA for the NPS VMI Program.
Mappers, ecologists, and botanists collaborated to affirm vegetation types within the U.S. National Vegetation Classification (USNVC) of APPA and to determine how best to map the vegetation types by using aerial imagery. Analyses of data from 1,618 vegetation plots were used to describe USNVC associations of APPA. Data from 289 verification sites were collected to test the field key to vegetation associations and the application of vegetation associations to a sample set of map polygons. Data from 269 validation sites were collected to assess vegetation mapping prior to submitting the vegetation map for accuracy assessment (AA). Data from 3,265 AA sites were collected, of which 3,204 were used to test accuracy of the vegetation map layer. The collective of these datasets affirmed 280 USNVC associations for the APPA vegetation mapping project.
To map the vegetation and land cover of APPA, 169 map classes were developed. The 169 map classes consist of 150 that represent natural (including ruderal) vegetation types in the USNVC, 11 that represent cultural (agricultural and developed) vegetation types in the USNVC, 5 that represent natural landscapes with catastrophic disturbance or some other modification to natural vegetation preventing accurate classification in the USNVC, and 3 that represent nonvegetated water (non-USNVC). Features were interpreted from viewing 4-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems (GIS). (Digital aerial imagery was collected each fall during 2009–11 to capture leaf-phenology change of hardwood trees across the latitudinal range of APPA.) The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in GIS. Polygon units were mapped to either a 0.5-hectare (ha) or 0.25-ha minimum mapping unit, depending on vegetation type or scenario; however, polygon units were mapped to 0.1 ha for alpine vegetation.
A geodatabase containing various feature-class layers and tables provide locations and support data to USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, validation sites, AA sites, project boundary extent and zones, and aerial image centers and flight lines. The feature-class layer and related tables of the vegetation map layer provide 30,395 polygons of detailed attribute data covering 110,919.7 ha, with an average polygon size of 3.6 ha; the vegetation map coincides closely with the administrative boundary for APPA.
Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 28,242 polygons (92.9% of polygons) and cover 106,413.0 ha (95.9%) of the map extent for APPA. The map layer indicates APPA to be 92.4% forest and woodland (102,480.8 ha), 1.7% shrubland (1866.3 ha), and 1.8% herbaceous cover (2,065.9 ha). Map classes representing park-special vegetation (undefined in the USNVC) apply to 58 polygons (0.2% of polygons) and cover 404.3 ha (0.4%) of the map extent. Map classes representing USNVC cultural types apply to 1,777 polygons (5.8% of polygons) and cover 2,516.3 ha (2.3%) of the map extent. Map classes representing nonvegetated water (non-USNVC) apply to 332 polygons (1.1% of polygons) and cover 1,586.2 ha (1.4%) of the map extent.