Thousands of screened water diversions throughout the Columbia River Basin of the Pacific Northwest are sources of entrainment (unintended diversion into an unsafe passage route), injury, and mortality for a range of fish species and screening criteria have been developed to reduce and mitigate these effects. Large knowledge gaps exist concerning the potential effects of these screens on juvenile and larval lampreys (Entosphenus and Lampetra spp.) that may be particularly vulnerable to screening effects owing to their small size, unique morphology, and poor swimming performance. The few studies that have evaluated screen impacts for lampreys have compared common screen materials in a laboratory setting using a large, recirculating flume, and have reported that screen size and material influence the risk of entrainment. We compared entrainment rate, impingement rate and duration, injury rate, and delayed (24-hour) mortality of larval lamprey (ammocoetes) exposed to two screen angles. A 20-degree screen was tested because it is a common configuration, and a 12-degree screen was selected to represent a screen more parallel to flow than the 20-degree screen. We included juvenile rainbow trout (Oncorhynchus mykiss) in tests for both screen angles so that any screen interaction improvements observed for lamprey could be considered in light of their effect on salmonids (Oncorhynchus spp.). Study fish were released singly into the flume at two locations: near-screen (SCR) releases at about 30 centimeters upstream of the screen and mid-channel (MID) releases at about 1.4 meters upstream of the screen. We completed 120 screen trials with ammocoetes and 117 screen trials with juvenile tout during April–May 2018. Ammocoetes had short mean trial durations (less than 30 seconds) and were most frequently bypassed. Over one-half of the ammocoetes briefly (for less than 1 second) contacted the screen. Rates of screen contact were similar between the two screen angles, but higher for SCR releases (about 83–90 percent) than for MID releases (about 28–44 percent). Screen impingements were not common (8.3 percent of ammocoetes) and did not result in significant injury or delayed mortality. The final logistic regression model showed that screen angle and release location did not significantly affect whether ammocoetes were bypassed. The size of the lamprey, expressed as mass or length, was the only significant predictor of fate. We noted an estimated 4.7-percent increase in the odds of being bypassed for every 1-milligram increase in ammocoete mass and a 49-percent increase in the odds of being bypassed for every 1-millimeter increase in length. Trout did not experience negative effects with the 12-degree screen; they had short mean trial durations (less than 40 seconds) and limited contact with the screens (11.1 percent of trout), were most commonly bypassed, and none were entrained. Screen angle and release location were not significant drivers of fate for trout. The 12-degree and 20-degree screens performed comparably for both lamprey and trout and effectively bypassed these fishes. Some metrics showed advantages for the 12-degree screen, suggesting that screens installed more parallel to flow might warrant further testing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191044","collaboration":"Prepared in cooperation with the Washington Department of Fish and Wildlife, McNary Fisheries Compensation Committee, and Yakama Nation Fisheries","usgsCitation":"Liedtke, T.L., Didricksen, D.J., Weiland, L.K., Rogala, J.A., and Lampman, R., 2019, Effectiveness of fish screens in protecting lamprey (Entosphenus and Lampetra spp.) ammocoetes—Pilot testing of variable screen angle: U.S. Geological Survey Open-File Report 2019-1044, 21 p., https://doi.org/10.3133/ofr20191044.","productDescription":"iv, 21 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-105978","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":363436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1044/coverthb2.jpg"},{"id":363437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1044/ofr20191044.pdf","text":"Report","size":"882 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1044"}],"country":"United States","otherGeospatial":"Columbia River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.57324218750001,\n 41.96765920367816\n ],\n [\n -112.763671875,\n 41.96765920367816\n ],\n [\n -112.763671875,\n 50.90303283111257\n ],\n [\n -127.57324218750001,\n 50.90303283111257\n ],\n [\n -127.57324218750001,\n 41.96765920367816\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. The protective services of these natural defenses are not assessed in the same rigorous economic terms as artificial defenses, such as seawalls, and therefore often are not considered in decision making. Here we combine engineering, ecologic, geospatial, social, and economic tools to provide a rigorous valuation of the coastal protection benefits of all U.S. coral reefs in the States of Hawaiʻi and Florida, the territories of Guam, American Samoa, Puerto Rico, and Virgin Islands, and the Commonwealth of the Northern Mariana Islands. We follow risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 3,100+ kilometers of U.S. reef-lined shorelines for different storm probabilities to account for the effect of coral reefs in reducing coastal flooding. We quantify the coastal flood risk reduction benefits provided by coral reefs across storm return intervals using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis to identify their annual expected benefits, a measure of the annual protection provided by coral reefs. Based on these results, the annual protection provided by U.S. coral reefs is estimated in:
Thus, the annual value of flood risk reduction provided by U.S. coral reefs is more than 18,000 lives and \\$1.805 billion in 2010 U.S. dollars. These data provide stakeholders and decision makers with spatially explicit, rigorous valuation of how, where, and when U.S. coral reefs provide critical coastal storm flood reduction benefits. The overall goal is to ultimately reduce the risk to, and increase the resiliency of, U.S. coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191027","collaboration":"Prepared in cooperation with the University of California Santa Cruz and The Nature Conservancy","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Cole, A.D., Lowe, E., Shope, J.B., Gibbs, A.E., Nickel, B.A., McCall, R.T., van Dongeren, A.R., and Beck, M.W., 2019, Rigorously valuing the role of U.S. coral reefs in coastal hazard risk reduction: U.S. Geological Survey Open-File Report 2019–1027, 42 p., https://doi.org/10.3133/ofr20191027.","productDescription":"Report: vi, 42 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103961","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":363119,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KMH2VX","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands"},{"id":363114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1027/ofr20191027.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1027"},{"id":363113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1027/coverthb.jpg"}],"country":"United States","state":"American Samoa, Commonwealth of the Northern Mariana Islands, Florida, Guam, Hawaii, Puerto Rico, U.S. Virgin Islands","contact":"Contact Information,
Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
California provides wintering habitat for most greater white-fronted geese (Anser albifrons [GWFG]) in the Pacific Flyway and this population has rapidly increased since the 1980s. Increased harvest of GWFG wintering in California may prevent agricultural depredation while providing increased hunting opportunities. However, changes in harvest levels are unlikely to be uniform across the species because of the presence of multiple subspecies of GWFG in the Pacific Flyway, each with their own population distribution and trends. White-fronted geese in the Cook Inlet Basin of south-central Alaska, a potentially vulnerable subspecies (Tule goose, A. a. elgasi), are among the geese that winter predominantly in the Sacramento Valley and Suisun and Napa marshes of north-central California. Efforts to limit sport harvest of Tule geese are complicated because although the subspecies is phenotypically larger and darker in color than other subspecies, they can be difficult to identify in the field and in hunter bag checks. To assist in an accurate assessment of Tule goose harvest, we used double-digest restriction site-associated deoxyribonucleic acid sequencing (ddRAD-seq) techniques to develop a genetic stock identification panel of single nucleotide polymorphisms (SNPs) to differentiate Tule geese from individuals belonging to other GWFG subspecies and populations that winter in California. Although the panel we developed was designed and tested for Fluidigm SNP-type technology, the ddRAD-seq sequences can be used to design SNP panels for use in other platforms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191040","collaboration":"Prepared in cooperation with the California Department of Fish and Wildlife","usgsCitation":"Wilson, R.E., Sonsthagen, S.A., DaCosta, J.M., Ely, C.R., Sorenson, M.D., and Talbot, S.L., 2019, Identification of single nucleotide polymorphisms for use in a genetic stock identification system for greater white-fronted goose (Anser albifrons) subspecies wintering in California: U.S. Geological Survey Open-File Report 2019-1040, 18 p., https://doi.org/10.3133/ofr20191040.","productDescription":"Report: iv, 18 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-106743","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":363313,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LYUFRH","text":"USGS data report","description":"USGS Data Report","linkHelpText":"Development of Single Nucleotide Polymorphisms (SNPs) in Greater-White Fronted Geese (Anser albifrons) for genetic stock identification on wintering grounds, 2019"},{"id":363317,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1040/coverthb.jpg"},{"id":363318,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1040/ofr20191040.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1040"}],"country":"United States","state":"Alaska, California","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Director, Eastern Mineral and Energy Resources Center
U.S. Geological Survey
MS 954 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
A lagoon-wide, point-sampling survey of eelgrass (Zostera marina) abundance was conducted in Izembek Lagoon, Alaska, August 7–16, 2018, the ninth year of annual surveys (2007–11, 2015–18). Mean predicted aboveground biomass of eelgrass across 116 sampled points was 238 grams per square meter (g m-2) (95 percent confidence interval: 203–278 g m-2) in 2018, an increase of 240 percent from the previous year’s low estimate of 97 g m-2 (95 percent confidence interval: 78–120 g m-2). The increase marked the third year since 2015 where eelgrass biomass was above the long-term mean (158 g m-2). Eelgrass biomass was stable over the 9 years of this survey. A separate (transect) survey for eelgrass abundance at Grant Point-Old Boat Launch showed annual trends in eelgrass biomass similar to the lagoon-wide survey, but over a slightly longer time (2007–18). The estimates of above-average eelgrass biomass in Izembek Lagoon were likely influenced by relatively warm air temperatures and little or no ice in winter (air temperatures 2.7 degrees Celsius greater than the 12-year mean) and average (cool) air temperatures during the growing season (April–August) in 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191042","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., and Amundson, C.L., 2019, Monitoring annual trends in abundance of eelgrass (Zostera marina) at Izembek National Wildlife Refuge, Alaska, 2018: U.S. Geological Survey Open-File Report 2019-1042, 8 p., https://doi.org/10.3133/ofr20191042.","productDescription":"iv, 8 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-105984","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437489,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13EG9KS","text":"USGS data release","linkHelpText":"Eelgrass Biomass Model"},{"id":363193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1042/ofr20191042.pdf","text":"Report","size":"532 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1042"},{"id":363192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1042/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":" Izembek 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 -163.42987060546875,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.02802211299252\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Adaptive management has been applied to problems with multiple conflicting objectives in various natural resources settings to learn how management actions affect divergent values regarding system response. Hydropower applications have only recently begun to emerge in the field, yet in the specific example reported herein, stakeholders invested in determining the best management alternatives for attainment of a suite of objectives outlined in a long-term adaptive management program below R.L. Harris Dam, a large, privately owned dam in Alabama. Stakeholders convened an objective-setting workshop to engage a governance structure and developed a decision support model to determine appropriate actions that optimized stakeholder values. The process led to implemented change in dam operation inclusive of incorporating hypothetical responses in system parameters to management. To account for the iterative loop of adaptive management, yearly monitoring of state variables that approximated many stakeholder objectives was performed from 2005 to 2016 and data collected were incorporated into the decision model. Specific analysis of fish and macroinvertebrate population responses indicated a less than satisfactory response for some stakeholders to the flow-management changes at the dam. Uncertainty regarding the best management to provide adequate hydrologic and thermal habitats for fauna and boatable days for recreationists still exists. The project led to a Federal Energy Regulatory Commission process for renewing the license to operate the dam (beginning in 2018); adaptive management could be a viable path forward to ensure stakeholder satisfaction related to new management options.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191026","collaboration":"Prepared in cooperation with the Alabama Department of Conservation and Natural Resources, Alabama Power Company, U.S. Fish and Wildlife Service, and R.L. Harris Dam Adaptive Management Stakeholders","usgsCitation":"Irwin, E.R., ed., 2019, Adaptive management of flows from R.L. Harris Dam (Tallapoosa River, Alabama)—Stakeholder process and use of biological monitoring data for decision making: U.S. Geological Survey Open-File Report 2019–1026, 93 p., https://doi.org/10.3133/ofr20191026.","productDescription":"Report: x, 93 p.; 4 Appendixes; 1 Table","numberOfPages":"108","onlineOnly":"Y","ipdsId":"IP-096592","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":363058,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A2.pdf","text":"Appendix A2","size":"302 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A2","linkHelpText":"– Initial Bayesian Belief Network (2005), Training Cases and Learned Networks (2005–16)"},{"id":363057,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A1.pdf","text":"Appendix A1","size":"1.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A1","linkHelpText":"– Transcripts from the Adaptive Management Workshop, April 30–May 1, 2003"},{"id":363061,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_table_C2.1.pdf","text":"Table C2.1","size":"198 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Table C2.1","linkHelpText":"– Sum of total observations for each macroinvertebrate taxon at all sites, listed alphabetically by class, order, family and taxon"},{"id":363060,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_B.pdf","text":"Appendix B","size":"296 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix B","linkHelpText":"– R code used to conduct metapopulation analyses"},{"id":363056,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026.pdf","text":"Report","size":"5.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026"},{"id":363053,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1026/coverthb3.jpg"},{"id":363059,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A3.pdf","text":"Appendix A3","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A3","linkHelpText":"– Charter of the R.L. Harris Stakeholders Board"}],"country":"United States","state":"Alabama","otherGeospatial":"Tallapoosa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.7208251953125,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 32.93953889877841\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alabama Cooperative Fish and Wildlife Research Unit
School of Forestry and Wildlife Sciences
Auburn University
602 Duncan Dr.
Auburn, AL 36849–5418
The U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the European Association for Remote Sensing Companies, and the European Space Agency in coordination with the GEOValue Community hosted a side event to the Group on Earth Observations Plenary on October 23–24, 2017, in Washington, D.C. The workshop, entitled “Demonstrating the Value of Earth Observations: Methods, Practical Applications and Solutions,” brought together more than 60 international experts including economists, scientists, and engineers to consider the state of the science and applications of valuing Earth observations (EO).
This 2-day workshop built upon previous activities developed under the GEOValue initiative. This workshop brought together expert analysts from multiple disciplines and backgrounds who are developing methods to identify and measure the value of information generated from the use of satellite and in-situ data. The mix of government agencies, international financial institutions, and independent consultants who participated in the workshop blended to develop a rich mix of views, approaches, and outcomes.
During the first part of the workshop, the focus was on the latest science in valuing EO. A number of methodologies were described. Approaches generally assess the societal benefits of specific actions (for example, investments in EO). Some methods focus on broad measures of economic activity (for example, gross domestic product) or methods to assess total economic value such as contingent valuation surveys. Alternatively, use-case approaches (a use case is defined as an evaluation in which one or more decisions, applications, or other uses of data, information, and information products are specifically considered) start with the specific actions and how information is used to support decision making and affect outcomes.
The second part of the meeting was focused on the use and development of value chains and decision trees. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. In terms of EO, the value chain approach can be applied to consider societal benefits of the data and assess the value of data and data features. The EO value chain considers the geospatial data sources and the processing of the data into value added information to be incorporated into decision-support systems, leading to decision makers’ actions. To understand the value of EO, one would also need to recognize the demand side of the equation or how EO benefits users. Extending the value chain concept and incorporating tenets of Bayesian decision making, a decision tree would include one or more use cases. The value provided by the marginal increase in information could flow from one or several parts of the supply side of the value chain. The decision tree is based on the premise that information has no value if it is not used in at least one decision. By connecting the value chain and the decision tree, a framework is created that allows for conceptualizing the value of EO in its many uses. One can then apply economic techniques to monetize the marginal benefit of an outcome with information versus one without.
A third part of the meeting applied the value chain and decision-tree frameworks to five specific thematic areas, each with the focus of using information for a decision point:
During the working session, five separate groups worked to define and delineate the value chains and decision trees associated with each topic, discussing the related challenges and data needs. The outcomes were reported back to the full group. Because of the complexity of the topics, most groups first identified a network of value chains and then narrowed the scope to develop a single value chain to address their group’s topic. Although they worked separately and on different topics, the groups came to similar conclusions, concurring that the value chain and decision-tree frameworks are very effective for informing quantitative impact assessments and developing a relatable narrative to assist the public in understanding the link between EO and citizens.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191033","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration, FourBridges, European Space Agency, and European Association of Remote Sensing Companies","usgsCitation":"Pearlman, F., Lawrence, C.B., Pindilli, E.J., Geppi, D., Shapiro, C.D., Grasso, M., Pearlman, J., Adkins, J., Sawyer, G., and Tassa, A., 2019, Demonstrating the value of Earth observations—Methods, practical applications, and solutions—Group on Earth Observations side event proceedings: U.S. Geological Survey Open-File Report 2019–1033, 33 p., https://doi.org/10.3133/ofr20191033.","productDescription":"vi, 33 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102614","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":363044,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1033/coverthb.jpg"},{"id":363045,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1033/ofr20191033.pdf","text":"Report","size":"1.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1033"}],"contact":"Science and Decisions Center
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: gs_emeh_sdc@usgs.gov
Hurricane Irma skirted the northern coasts of the U.S. Virgin Islands and Puerto Rico, with maximum sustained winds of 185 miles per hour (mi/h) on September 6, 2017. The hurricane first made landfall in Florida near Cudjoe Key, in the lower Florida Keys, with maximum sustained winds of 130 mi/h on September 10, 2017. The hurricane made a second Florida landfall on Marco Island, Florida, with maximum sustained winds of 115 mi/h on September 10, 2017. The U.S. Geological Survey (USGS), in cooperation with Federal Emergency Management Agency, deployed a temporary monitoring network of water-level and barometric pressure sensors at 249 locations along the Puerto Rico, Florida, Georgia, and South Carolina coasts to record the timing, areal extent, and magnitude of hurricane storm tide and coastal flooding generated by the hurricane. Immediately following the passage of Hurricane Irma, the sensors were retrieved, and the data were disseminated on the USGS Flood Event Viewer (https://stn.wim.usgs.gov/FEV/#IrmaSeptember2017). The storm-tide peak data values were verified by comparing data from hydrologic recorders and nearby high-water marks (HWMs). Following the hurricane, 508 independent HWM locations were flagged and surveyed relative to the North American Vertical Datum of 1988, National Geodetic Vertical Datum of 1929, or a local datum along the southeastern U.S. coast, and to Puerto Rico Vertical Datum of 2002 in Puerto Rico. Most HWMs were in Florida because of the path of the hurricane. The data from the Hurricane Irma storm-tide network are available on a provisional basis in tab-delimited, American Standard Code for Information Interchange (ASCII) format and Network Common Data Form (NetCDF) format by site for each sensor by using the USGS Flood Event Viewer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191013","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Byrne, M.J., Sr., and Dickman, M.R., 2019, Monitoring storm tide and flooding from Hurricane Irma along the U.S. Virgin Islands, Puerto Rico, and the Southeastern United States, September 2017 (ver. 1.1, July 2019): U.S. Geological Survey Open-File Report 2019–1013, 35 p., https://doi.org/10.3133/ofr20191013.","productDescription":"vi, 35 p.","numberOfPages":"46","onlineOnly":"N","ipdsId":"IP-095711","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":365693,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1013/ofr20191013.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1013"},{"id":365694,"rank":2,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1013/versionHist.txt","text":"Version History","size":"1.00 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2019–1013 Version History"},{"id":365697,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1013/coverthb2.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico, U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.9892578125,\n 24.00632619875113\n ],\n [\n -79.4970703125,\n 24.00632619875113\n ],\n [\n -79.4970703125,\n 32.0639555946604\n ],\n [\n -88.9892578125,\n 32.0639555946604\n ],\n [\n -88.9892578125,\n 24.00632619875113\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.02734375,\n 16.04581345375217\n ],\n [\n -63.45703124999999,\n 16.04581345375217\n ],\n [\n -63.45703124999999,\n 20.96143961409684\n ],\n [\n -68.02734375,\n 20.96143961409684\n ],\n [\n -68.02734375,\n 16.04581345375217\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 16, 2019; Version 1.1: July 25, 2019 ","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The U.S. Geological Survey, in cooperation with the Idaho Department of Environmental Quality, sampled groundwater from 15 wells during spring 2018 near the city of Marsing in rural northwestern Owyhee County, southwestern Idaho. Samples were analyzed for field parameters, nutrients, trace elements, major inorganics, and dissolved gas, including methane. To examine trends in individual wells and in the region, ammonia and nitrate results from the spring 2018 sampling were compared with data collected from 1996 to 2015 by the Idaho Department of Environmental Quality and the Idaho State Department of Agriculture.
Fourteen of the 15 samples collected in 2018 contained arsenic (0.13–33.8 micrograms per liter [μg/L]), with 7 arsenic concentrations greater than the U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL) of 10 μg/L. Iron (465–4,180 μg/L), manganese (54–693 μg/L), sulfate (300–624 milligrams per liter [mg/L]), and total dissolved solids (511–1,350 mg/L) were detected at concentrations greater than EPA secondary maximum contaminant levels (SMCL) in water-quality samples from 6, 10, 4, and 14 of the 15 wells, respectively. Fourteen of the 15 samples contained ammonia concentrations from 0.12 to 7.34 milligrams per liter (mg/L). Six samples contained nitrate concentrations from 0.08 to 24.6 mg/L, with one sample greater than the EPA MCL of 10 mg/L for drinking water. The presence of both ammonia and nitrate in four samples indicated multiple nutrient and groundwater sources and varying redox states. Ammonia concentrations tended to increase downgradient throughout the study area.
Nutrient trend analysis identified water-quality samples from 2 of the 15 wells with increasing nitrate concentrations from 1999–2018 and 2005–2018. The well with increasing nitrate concentrations from 2005–2018 showed a decreasing trend in ammonia concentrations during the same time period. Groundwater-quality samples from the 13 remaining wells showed no temporal trends. A Regional Kendall test, which evaluates trends at numerous wells across the study area to determine if a consistent trend exists for the area, was done to analyze 539 ammonia concentrations from 91 wells over 20 years (1999–2018) and 591 nitrate concentrations from 107 wells over 23 years (1996–2018). The Regional Kendall Test for ammonia had a tau correlation coefficient of -0.073 with a p-value of 0.072, and nitrate had a tau correlation coefficient of -0.041 with a p-value of 0.198, both indicating no statistically significant trends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191032","collaboration":"Prepared in cooperation with the Idaho Department of Environmental Quality","usgsCitation":"Skinner, K.D., 2019, Groundwater quality and nutrient trends near Marsing, southwestern Idaho, 2018: U.S. Geological Survey Open-File Report 2019-1032, 23 p., https://doi.org/10.3133/ofr20191032.","productDescription":" iv, 24 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-095202","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":362894,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1032/ofr20191032.pdf","text":"Report","size":"27.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1032"},{"id":362893,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1032/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Marsing","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.94345474243164,\n 43.523410314985455\n ],\n [\n -116.7856979370117,\n 43.523410314985455\n ],\n [\n -116.7856979370117,\n 43.60687218565255\n ],\n [\n -116.94345474243164,\n 43.60687218565255\n ],\n [\n -116.94345474243164,\n 43.523410314985455\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
The goal of this research was to evaluate the impacts of Hurricane Sandy on surface elevation trends in estuarine marshes located across the northeast region of the United States from Virginia to Maine using data from an opportunistic (in other words, not strategic) and collaborative network (from here on, an opportunistic network) of surface elevation table-marker horizon (SET-MH) stations. First, we built a data-base of metadata for 965 individual stations from 96 unique geographical locations that included the location, geomorphic setting, and wetland type for each SET-MH station. The dominant estuarine settings included in the analyses were back-barrier lagoonal marshes and emergent marshes along embayments and tidal tributaries. We then calculated prestorm elevation trends to compare to poststorm elevation measurements to determine the storm impact on each station trend. We hypothesized that the effect of Hurricane Sandy on marsh elevation trends would differ by position relative to landfall (right or left) and distance from landfall in southern New Jersey, as both of these variables influence the presence or absence of storm surge as a result of the physical characteristics of tropical cyclones (in other words, strongest winds typically occur to the right of landfall). Storm surge was spatially less extensive and less deep (~1 meter [m]) in marshes located to the left (in other words, south) of landfall compared to marshes located to the right (in other words, north) of landfall where storm surge covered a larger area and was deeper (3–4 m). About 63 percent of 223 eligible stations had a poststorm trend that was similar to the prestorm trend (in other words, less than ±5 millimeters [mm]), indicating little storm impact on elevation trends at those sites. The remaining 37 percent of stations exhibited significant poststorm deviations from the prestorm trend (in other words, greater than ±5 mm). Of these, stations located to the left of landfall had a significant and greater deviation in their elevation trend, and the deviation was more likely to be positive (elevation gain) compared to marshes located to the right of landfall, which had a significant deviation in their elevation trend that was more likely to be negative (elevation loss). This finding is directly related to storm surge impacts on marsh sediment deposition, where deep storm surge (3–4 m) results in sediment deposition in habitats inland of coastal marshes but less so in the marshes themselves. Substrate compaction by the storm surge over-burden may have contributed to elevation loss, but this was not measured because sufficient marker horizon data were not available for analysis. In contrast, to the left of landfall the wind-driven flooding of sediment laden water pushed into the headwaters of rivers and small bays with an ~1 m surge, and resulted in more prevalent sediment deposition on the marsh surfaces and elevation gain. In general, the findings support previous research showing that the physical characteristics of the storm (for example, wind speed, storm surge height, impact angle of landfall) combined with the local wetland conditions (for example, marsh productivity, groundwater level, tide height) are important factors determining a storm’s impact on soil elevation, and that the soil elevation response can vary widely among multiple wetland sites impacted by the same storm and among different storms for the same wetland site.
The final objective of this project was to create a framework using metadata from the opportunistic network of SET-MH stations that could be used to develop a strategic monitoring network designed to address specific climate change impacts and related phenomena identified by land managers and stakeholders. We evaluated the spatial distribution and density of SET-MH stations in relation to geographic coverage, marsh setting, availability of public land, and historical storm surge footprints and hurricane return intervals in order to identify gaps in our understanding of risk and our ability to assess it. Analyses revealed that the general geographic coverage of SET-MH stations is limited given the low percentage of marsh patches with stations, low density of stations, the clumped distribution of stations, and the often limited and uneven distribution of stations in wetlands with a high historical frequency of hurricane strikes and storm surge impacts. These findings can be used by managers and planners to inform the creation of a strategic monitoring network that can, in turn, inform management and adaptation plans for coastal resources in the region. Final plan designs will need to consider financial and infrastructural support required for station maintenance, as well as data collection and management over the long term.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181142","usgsCitation":"Cahoon, D.R., Olker, J.H., Yeates, A.G., Guntenspergen, G.R., Grace, J.B., Adamowicz, S.C., Anisfeld, S., Baldwin, A.H., Barrett, N., Beckett, L., Benzecry, A., Blum, L.K., Burdick, D.M., Crouch, W., Ekberg, M.C., Fernald, S., Grimes, K.W., Grzyb, J., Hartig, E.K., Kreeger, D.A., Larson, M., Lerberg, S., Lynch, J.C., Maher, N., Maxwell-Doyle, M., Mitchell, L.R., Mora, J., O’Neill, V., Padeletti, A., Prosser, D., Quirk, T., Raposa, K.B., Reay, W.G., Siok, D., Snow, C., Starke, A., Staver, L., Stevenson, J.C., and Turner, V., 2019, Hurricane Sandy impacts on coastal wetland resilience: U.S. Geological Survey Open-File Report 2018–1142, 117 p., https://doi.org/10.3133/ofr20181142.","productDescription":"xii, 117 p.","ipdsId":"IP-089853","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":362852,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1142/ofr20181142.pdf","text":"Report","size":"30.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1142"},{"id":362851,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1142/coverthb1.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.251953125,\n 17.811456088564483\n ],\n [\n -70.9716796875,\n 17.811456088564483\n ],\n [\n -70.9716796875,\n 41.07935114946899\n ],\n [\n -93.251953125,\n 41.07935114946899\n ],\n [\n -93.251953125,\n 17.811456088564483\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12311 Beech Forest Road
Laurel, MD 20708
Adapting cultural resources to climate-change effects challenges traditional cultural resource decision making because some adaptation strategies can negatively affect the integrity of cultural resources. Yet, the inevitability of climate-change effects—even given the uncertain timing of those effects—necessitates that managers begin prioritizing resources for climate-change adaptation. Prioritization imposes an additional management challenge: managers must make difficult tradeoffs to achieve desired management outcomes related to maximizing the resource values. This report provides an overview of a pilot effort to integrate vulnerability (exposure and sensitivity), significance, and use potential metrics in a decision framework—the Optimal Preservation (OptiPres) Model—to inform climate adaptation planning of a subset of buildings in historic districts (listed on the National Register of Historic Places) at Cape Lookout National Seashore. The OptiPres Model uses a numerical optimization algorithm to assess the timing and application of a portfolio of adaptation actions that could most effectively preserve an assortment of buildings associated with different histories, intended uses, and construction design and materials over a 30-year planning horizon. The outputs from the different budget scenarios, though not prescriptive, provide visualizations of and insights to the sequence and type of optimal actions and the changes to individual building resource values and accumulated resource values. Study findings suggest the OptiPres Model has planning utility related to fiscal efficiency by identifying a budget threshold necessary to maintain the historical significance and use potential of historical buildings while reducing vulnerability (collectively, the accumulated resource value). Specifically, findings identify that a minimum of the industry standard ($222,000 annually for the 17 buildings) is needed to maintain the current accumulated resource value. Additionally, results suggest that additional appropriations provided on regular intervals when annual appropriations are at the industry standard are nearly as efficient as annual appropriations at twice the rate of industry standards and increase the amount of accumulated resource values to nearly the same level. However, periodic increases in funding may increase the risks posed to buildings from the probability of a natural hazard (that is, damage or loss from a hurricane). Suggestions for model refinements include developing standardized cost estimations for adaptation actions based on square footage and building materials, developing metrics to quantify the historical integrity of buildings, integrating social values data, including additional objectives (such as public safety) in the model, refining vulnerability data and transforming the data to include risk assessment, and incorporating stochastic events (that is, hurricane and wind effects) into the model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181180","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Seekamp, E., Post van der Burg, M., Fatorić, S., Eaton, M.J., Xiao, X., and McCreary, A., 2019, Optimizing historical preservation under climate change—An overview of the optimal preservation model and pilot testing at Cape Lookout National Seashore: U.S. Geological Survey Open-File Report 2018–1180, 46 p., https://doi.org/10.3133/ofr20181180.","productDescription":"vii, 46 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-096582","costCenters":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":362669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1180/ofr20181180.pdf","text":"Report","size":"4.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1180"},{"id":362668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1180/coverthb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Cape Lookout National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.66397094726562,\n 34.699848377328934\n ],\n [\n -76.67770385742188,\n 34.67274685882317\n ],\n [\n -76.53213500976562,\n 34.557466483188996\n ],\n [\n -76.02264404296875,\n 35.06147690849717\n ],\n [\n -76.0638427734375,\n 35.09519259251624\n ],\n [\n -76.53076171875,\n 34.66597009307397\n ],\n [\n -76.66397094726562,\n 34.699848377328934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Climate Adaptation Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 516
Reston, VA 20192
Email: casc@usgs.gov
The Point-Source Load Estimation Tool (PSLoadEsT) provides a user-friendly interface for generating reproducible load calculations for point source dischargers while managing common data challenges including duplicates, incompatible input tables, and incomplete or missing nutrient concentration or effluent flow data. Maintaining a consistent method across an entire study area is important when estimating loads to be used as calibration data for regional water-quality models. PSLoadEsT is written using the open-source programming language R and has an easy-to-use interface written in Visual Basic for Applications® within a Microsoft Access® database file that guides the user through the necessary steps to estimate point source loads. The purpose of this report is to provide a detailed user guide for PSLoadEsT.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191025","collaboration":"National Water Quality Assessment Program","usgsCitation":"Gorman Sanisaca, L.E., Skinner, K.D., and Maupin, M.A., 2019, Annual wastewater nutrient data preparation and load estimation using the Point Source Load Estimation Tool (PSLoadEsT): U.S. Geological Survey Open-File Report 2019-1025, 48 p., https://doi.org/10.3133/ofr20191025.","productDescription":"Report: vi, 48 p.; Additional Report Piece","onlineOnly":"Y","ipdsId":"IP-099356","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":437510,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QWVZ4L","text":"USGS data release","linkHelpText":"Point-Source Load Estimation Tool (PSLoadEsT)"},{"id":362728,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1025/coverthb.jpg"},{"id":362733,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9QWVZ4L","text":"PSLoadEsT Software release","description":"PSLoadEsT Software release"},{"id":362729,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1025/ofr20191025.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1025"},{"id":362732,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1101","text":"Data Series 1101","description":"Data Series 1101","linkHelpText":"Point-Source Nutrient Loads to Streams of the Conterminous United States, 2012"}],"contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Situated in the Mississippi Alluvial Plain of the Gulf Coast Prairie Landscape Conservation Cooperative (GCP LCC), the Chitimacha Tribe is one of four federally recognized tribes in Louisiana. The Tribal seat, trust lands/ reservation, and adjacent Tribal owned lands are located near Charenton, Louisiana, totaling nearly 1,000 acres. The Chitimacha, with a population of approximately 1,400 people, are currently impacted by storm surge, which is expected to increase with climate change. The additional stress from storms will likewise increase the vulnerability to catastrophic impact in the event of a breach in the Atchafalaya Basin Spillway levee. A collaborative effort between the U.S. Geological Survey (USGS) and the Chitimacha Tribe has been initiated to provide resources and expertise to increase the Tribe’s ability to prevent, plan, and prepare for these environmental challenges. By enhancing technical skills, providing access to environmental data, and increasing awareness of environmental issues, the Chitimacha will be better prepared to plan and adapt to the environmental impacts facing their lands related to land use and climate change.
For this project, USGS researchers asked how Chitimacha Tribal Lands might be impacted by future sea level rise scenario projections. These models illustrate some flooding within the northernmost boundary of Chitimacha Tribal Lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191030","collaboration":"Prepared in cooperation with The Chitimacha Tribe of Louisiana; Gulf Coast Prairie Landscape Conservation Cooperative","usgsCitation":"Spear, K.A., Jones, W., Griffith, K., Tirpak, B.E., and Walden, K., 2019, Potential sea level rise on Chitimacha Tribal Lands in Louisiana: U.S. Geological Survey Open-File Report 2019–1030, 1 sheet, https://doi.org/10.3133/ofr20191030.","productDescription":"1 Sheet: 24.0 x 36.0 inches","onlineOnly":"Y","ipdsId":"IP-090045","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":502765,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108491.htm","linkFileType":{"id":5,"text":"html"}},{"id":362387,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1030/ofr20191030.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1030"},{"id":362386,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1030/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chitimacha Tribal Lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.5833,\n 29.8167\n ],\n [\n -91.5,\n 29.8167\n ],\n [\n -91.5,\n 29.9167\n ],\n [\n -91.5833,\n 29.9167\n ],\n [\n -91.5833,\n 29.8167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
Salmon populations spawning in the Lake Ozette watershed of northwestern Washington were once sufficiently abundant to support traditional Tribal fisheries, and were later harvested by settlers. However, in 1974 and 1975, the sockeye salmon (Oncorhynchus nerka) harvest decreased to 0 from a high of more than 17,500 in 1949, thus stimulating research into the causes of decrease, which resulted in eventual listing of the population as threatened under the Endangered Species Act in 1999. The listing status was upheld in 2005 and 2014 following 5-year reviews. Meanwhile, research results were compiled in a limiting factors analysis (LFA) and a recovery plan was developed. Although there has been some improvement in sockeye abundance since listing, the numbers remain too low to allow harvest and it is not yet clear which of the many potential limiting factors are most consequential.
As part of the LFA process, a population model was developed to determine values of life-history parameters that would enable the population to survive for 100 years. The model was based on the best available data, but data are limited for the Lake Ozette system. Results informed the qualitative assessment of the importance of limiting factors used to develop the recovery plan for Lake Ozette sockeye. The model was built in Microsoft Excel® and is difficult to use. The purpose of the model described herein is to synthesize the results of the LFA in a form that can be manipulated by resource managers and the public to create scenarios, test hypotheses, and observe sensitivities of results to changes in parameters. The goal is to provide a tool that enables research, monitoring and management to be focused on the most impactful elements and processes, including identifying the information gaps that are most critical to fill.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191031","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Woodward, A., Haggerty, M., and Crain, P., 2019, Life-history model for sockeye salmon (Oncorhynchus nerka) at Lake Ozette, northwestern Washington—Users' guide: U.S. Geological Survey Open-File Report 2019-1031, 79 p., https://doi.org/10.3133/ofr20191031.","productDescription":"viii, 79 p.","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-101934","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":362633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1031/coverthb.jpg"},{"id":362634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1031/ofr20191031.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1031"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Ozette","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.68074798583986,\n 48.033560004128255\n ],\n [\n -124.59320068359374,\n 48.033560004128255\n ],\n [\n -124.59320068359374,\n 48.15509285476017\n ],\n [\n -124.68074798583986,\n 48.15509285476017\n ],\n [\n -124.68074798583986,\n 48.033560004128255\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Understanding the diet of invasive species helps researchers to more accurately assess the health, survivorship, growth, and stability of an invasive fish species, as well as their effects on native populations. Techniques capable of identifying multiple prey species from fish stomach contents have been developed. In this study, a multi-locus metabarcoding approach was used to identify fish and invertebrate prey in stomach samples of Ictalurus furcatus (blue catfish), which were collected from two sites on the Mattawomen Creek and Nanjemoy Creek in Maryland.
The mitochondrial 12S (mt12S) and mitochondrial 16S (mt16S) gene regions were sequenced and compared. First, a mock sample for each gene region was created with the pooled polymerase chain reaction product of known fish species, and quantities of the sample were used to determine efficacy of the amplicon. Results varied between gene regions analyzed. Then, when using the mt12S primers, next-generation sequencing determined that nine fish species were found at levels greater than 1 percent of the diet of blue catfish. The most common species were Perca flavescens (yellow perch) and Cyprinus carpio (common carp). The mt16S gene region analyses found 10 fish species at greater than 1 percent of the diet, which primarily included Orconectes limosus (spinycheek crayfish), Alosa pseudoharengus (alewife), and yellow perch. Partially digested eggs were identified using next-generation sequencing of yellow perch in two of the stomach samples, and a TaqMan® quantitative polymerase chain reaction (qPCR) assay was developed to more economically identify egg species in the future.
The yellow-perch-specific TaqMan® qPCR assay was tested using primers that were developed to detect a 154-base-pair amplicon in the mitochondrial control region. Consumption of yellow perch eggs indicates that blue catfish could potentially negatively affect young-of-year recruitment of this native sportfish. Analyses of two gene regions helped confirm the major prey of the fish sampled and allowed identification of fish species as prey that were not included in a database for the two gene regions. We concluded that the mitochondrial ribosomal-marker-based next-generation sequencing method is useful in determining the prey of fish species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191021","usgsCitation":"Iwanowicz, D.D., Schill, W.B., Sanders, L.R., Groves, T., and Groves, M.C., 2019, Establishing molecular methods to quantitatively profile gastric diet items of fish—Application to the invasive blue catfish (Ictalurus furcatus): U.S. Geological Survey Open-File Report 2019–1021, 15 p., https://doi.org/10.3133/ofr20191021.","productDescription":"Report: vii, 15 p.; Appendix","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103768","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":362344,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1021/ofr20191021.pdf","text":"Report","size":"1.89 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1021"},{"id":362345,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1021/ofr20191021_appendix.pdf","size":"660 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":362343,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1021/coverthb2.jpg"}],"country":"United States","otherGeospatial":"Potomac River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.26272583007812,\n 38.396029684120315\n ],\n [\n -77.12059020996094,\n 38.396029684120315\n ],\n [\n -77.12059020996094,\n 38.634036452919226\n ],\n [\n -77.26272583007812,\n 38.634036452919226\n ],\n [\n -77.26272583007812,\n 38.396029684120315\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The U.S. Geological Survey (USGS) has been working with Alaska Department of Transportation and Public Facilities (ADOT&PF) since 1993 to provide hydraulic assessments of scour for bridges throughout Alaska. The purpose of the program is to evaluate, monitor, and study streambed scour at bridges in Alaska; this includes surveying streambed elevations at regular intervals and monitoring real-time bed elevation changes. Over the duration of the scour program (1994–2017), repeated cross sections have been surveyed along the lengths of 76 bridges. Channel soundings are depth-from-bridge measurements on either the upstream or downstream side of a bridge. Flow, depth, and velocity dictated whether streambed elevations were measured using either USGS sounding weights on cable reels, weighted measuring tapes, or acoustic Doppler current profilers. The soundings were done on an annual basis at most sites. In addition to annual soundings, channel soundings were made during floods or periods of scour. Results show that general scour can be uniform or non-uniform across the channel. The magnitude and distribution of scour across the channel are influenced by several factors that include streambed sediment type, degree of channel contraction at the bridge crossing, influence of instream structures, and bridge pier location and alignment. The data collected from the repeat soundings can be used to identify long-term aggradation or degradation of the streambed, as well as seasonal changes in streambed elevations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191028","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities","usgsCitation":"Dworsky, K.L., and Conaway, J.S., 2019, Measurement of long-term channel change through repeated cross-section surveys at bridge crossings in Alaska: U.S. Geological Survey Open-File Report 2019-1028, 118 p., https://doi.org/10.3133/ofr20191028.","productDescription":"Report: vii, 118 p.; 2 Appendices","numberOfPages":"130","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101816","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":437525,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G663NX","text":"USGS data release","linkHelpText":"Sounding Cross Section Surveys at Alaska Bridge Crossings"},{"id":362475,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1028/coverthb.jpg"},{"id":362477,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1028/ofr20191028_appendix01.xlsx","text":"Appendix 1","size":"2.6 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1028 Appendix 1"},{"id":362476,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1028/ofr20191028.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1028"},{"id":362478,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1028/ofr20191028_appendix02.pdf","text":"Appendix 2","size":"4.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1028 Appendix 2"}],"country":"United States","state":"Alaska","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
The range of the southern sea otter (Enhydra lutris nereis) spans most of the central California coast from Half Moon Bay to Gaviota. Some coastal areas within this range are heavily developed and highly impacted by humans, while other areas are wild and largely pristine. Determining the relative importance of food resource abundance, environmental conditions, and anthropogenic increases in pathogens and pollutants to population change in sea otters is critical to understanding limitations to population growth. To investigate the causal links between the sluggish population growth of sea otters in central California and factors that could be driving variation in survival and reproduction, we designed a study to compare two distinct subpopulations—one in an area of low human impact (Big Sur) and one in an area of high human impact (Monterey). Between 2008 and 2011, the U.S. Geological Survey and collaborators conducted a telemetry-based study of sea otters at these two locations. The results of this study were not consistent with the hypothesis that sea otters adjacent to human population centers (Monterey) experience higher exposure to pollutants and pathogens than those in lower impacted areas (Big Sur). In fact, based on serological analysis, female sea otters from Big Sur showed higher exposure rates to Toxoplasma gondii than did female otters from Monterey, while domoic acid exposure appeared to be similar at both sites. Gene expression (specifically transcription) analysis did not indicate any consistent differences between the two populations that would have suggested a response to pathogen or toxin exposure, although there were temporal changes in gene transcription for sea otters at Big Sur following potential exposure to run-off from wildfires that occurred during the study. Together, these metrics suggest that variation in exposure to environmental stressors occurred, but patterns were not clearly attributable to differences in human population densities or land-use patterns. When compared to Monterey, sea otters in Big Sur spent more time feeding, had a higher degree of dietary specialization, were in poorer body condition, and had lower survival rates (both pups and adults). Together, these metrics suggest that otters at Big Sur had greater nutritional stress, consistent with lower per-capita resource abundance. Overall, study results indicate that density-dependent population regulation, mediated by per-capita resource abundance, is the most significant factor currently limiting population growth in the center part of the range. Additionally, spatial and temporal variation in environmental and anthropogenic stressors also can affect sea otter health, although patterns of variation are complex and are not simply a function of proximity to human populations. We also found that exposure to environmental stressors (either natural or anthropogenic in origin) often is associated with resource limitation. Finally, our results indicate that sea otter populations are structured at relatively small spatial scales, and the processes that regulate population abundance (including density-dependent resource abundance) also occur at these smaller, more local scales.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191022","usgsCitation":"Tinker, M.T., Tomoleoni, J.A., Weitzman, B.P., Staedler, M., Jessup, D., Murray, M.J., Miller, M., Burgess, T., Bowen, L., Miles, A.K., Thometz, N., Tarjan, L., Golson, E., Batac, F., Dodd, E., Berberich, E., Kunz, J., Bentall, G., Fujii, J., Nicholson, T., Newsome, S., Melli, A., LaRoche, N., MacCormick, H., Johnson, A., Henkel, L., Kreuder-Johnson, C., and Conrad, P., 2019, Southern sea otter (Enhydra lutris nereis) population biology at Big Sur and Monterey, California --Investigating the consequences of resource abundance and anthropogenic stressors for sea otter recovery: U.S. Geological Survey Open-File Report 2019 -1022, 225 p., https://doi.org/10.3133/ofr20191022.","productDescription":"xiv, 225 p.","numberOfPages":"244","onlineOnly":"Y","ipdsId":"IP-066698","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437531,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98B08RO","text":"USGS data release","linkHelpText":"Sea Otter Capture Data from the Big Sur-Monterey Study (2008-2011)"},{"id":362213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1022/coverthb.jpg"},{"id":362214,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1022/ofr20191022.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1022"}],"country":"United States","state":"California","otherGeospatial":"Monterey, Big Sur","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.02651977539062,\n 36.488661268293136\n ],\n [\n -121.74774169921875,\n 36.488661268293136\n ],\n [\n -121.74774169921875,\n 36.677230602346214\n ],\n [\n -122.02651977539062,\n 36.677230602346214\n ],\n [\n -122.02651977539062,\n 36.488661268293136\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.95098876953125,\n 36.2165791734887\n ],\n [\n -121.74636840820312,\n 36.2165791734887\n ],\n [\n -121.74636840820312,\n 36.3693276982337\n ],\n [\n -121.95098876953125,\n 36.3693276982337\n ],\n [\n -121.95098876953125,\n 36.2165791734887\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive
Modoc Hall, Room 4004
Sacramento, California 95819
Mercury monitoring results from about 300 Morone saxatilis (striped bass) muscle tissue samples collected by the State of Utah from Lake Powell resulted in a Utah/Arizona fish consumption advisory issued in 2012 for approximately the lower 100 kilometers of the reservoir. Chemical, physical, and biological data were collected during two synoptic sampling cruises on Lake Powell during May/June 2014 and August 2015 to test three hypotheses associated with a conceptual model developed to explain the observed geographic concentration gradient of Hg in fish tissue samples. This model proposes that in the transition from a primarily riverine system to a reservoir, there is a change in the concentration and composition of water-column particulate material, increasing in the proportion of organic content moving downstream, as the larger size fractions of the inorganic particulate load are deposited in the upper reservoir. This change alleviates light limitation of phytoplankton production and leads to a higher proportion of autochthonous primary production in the downstream direction. This, in turn, drives increased microbial methylmercury (MeHg) production in the benthos and potentially the water column, in the downstream direction, and results in the observed elevated fish Hg levels in the lower part of the reservoir. The model also proposes that there are differences between the main stem of Lake Powell and side canyons, embayments, or secondary rivers entering the reservoir, in terms of Hg cycling dynamics and bioaccumulations, driven mainly by differences in hydrology. Finally, seasonal differences in Hg dynamics within the reservoir are proposed, based on seasonal dynamics associated with primary production and the physical process of seasonal stratification.
A total of three statistically testable hypotheses were proposed and postulated that measurable differences in key Hg and non-Hg metrics exist between: (1) the upper and lower reservoir; (2) main stem and river arm/side canyon/embayment sites; and (3) early-season (May/June 2014, less stratified) and late-season (August 2015, stratified) conditions. Statistically modeled least square means in combination with the graphical analysis of Hg and non-Hg parameters were used to examine the data collected during the study and test these hypotheses. Data collected during the study are included in a U.S. Geological Survey data release and are available online at https://doi.org/10.5066/F74X560J.
In general, water-column, plankton, and surface sediment samples collected during the synoptic sampling cruises are supportive of the three hypotheses associated with the conceptual model. In support of hypothesis 1 (comparing upper and lower reservoir sites), the least square mean for turbidity was higher in the upper reservoir. In contrast, surface water particulate organic carbon (as a percentage of total particulate mass), particulate MeHg (by mass [in nanograms per gram] and as a percentage of total mercury [THg]), and particulate-dissolved partitioning coefficients for THg and MeHg were higher in the lower reservoir. Plankton THg concentrations also were significantly (probability [p] less than (<) 0.05) higher in the lower reservoir. Surface sediment metrics in support of hypothesis 1 include higher MeHg production potential rates in the lower reservoir. In contrast, there were no statistically significant differences between the upper and lower reservoir for surface sediment percent of MeHg and MeHg concentration, percent MeHg, or methylation rate constants. These spatial trends associated with hypothesis 1 indicate a pathway for enhanced Hg bioavailability in the lower reservoir.
Hypothesis 2, which tested for differences between main stem and river arm/side canyon/embayment sites, was supported by a number of water-column parameters, including particulate THg and MeHg concentrations by mass (in nanograms per gram) and percent particulate MeHg being significantly (p<0.05) higher in the river arms, side canyons, and embayments relative to the main stem channel. Plankton MeHg concentrations (by mass [in nanograms per gram] and volume [in nanograms per liter] and as a percentage of THg) were elevated in river arm/side canyon/embayment sites compared to main stem sites, indicating an enhanced potential for MeHg bioaccumulation at the base of the pelagic food web in river arms, side canyons, and embayments. In contrast, few of the sediment metrics differed between main stem and river arm/side canyon/embayment sampling sites; however, the potential for MeHg degradation in surface sediment was significantly higher in the main stem. The data indicate that river arm/side canyon/embayment sites may experience enhanced Hg bioaccumulation, compared to the main stem, because of higher MeHg levels at the base of the pelagic food web. This conclusion is supported by the elevated Hg detected in striped bass muscle tissue samples collected in the San Juan Arm during this study (2014). Fish collected from the lower reservoir exhibited a distinct Hg isotopic signature that was enriched in delta (δ)202Hg and capital delta (Δ)199Hg relative to fish samples collected from either Good Hope Bay or the San Juan Arm.
Hypothesis 3 tested for differences between early (May/June) high-flow and late (August) low-flow seasons. This test was supported by a range of non-Hg metrics (nitrate, phosphate, chlorophyll a, dissolved oxygen, fluorescent dissolved organic matter, temperature, and pH) that reflect the increase in chlorophyll a, decrease in nutrients, and buildup of stratified conditions in the transition from early- to late-season sampling periods. Significant seasonal differences also were noted for multiple Hg metrics, including (a) water-column filtered and particulate (by mass) MeHg and THg concentrations; (b) plankton MeHg and THg concentration (by mass); and (c) sediment percent MeHg, Hg(II)-methylation rate constant, and microbial ribosomal ribonucleic acid, small subunit 16 (16S rRNA) abundance, all of which were higher during the late-season synoptic sampling. Overall, the surface sediment metrics are consistent with a seasonal shift from the early-season synoptic results, when the availability of Hg(II) exerts a primary control on MeHg production, to the late-season synoptic sampling, when microbial activity is a dominant driver of MeHg production.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181159","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Naftz, D.L., Marvin-DiPasquale, M., Krabbenhoft, D.P., Aiken, G., Boyd, E.S., Conaway, C.H., Ogorek, J., and Anderson, G.M., 2019, Biogeochemical and physical processes controlling mercury methylation and bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014 and 2015: U.S. Geological Survey Open-File Report 2018–1159, 81 p., https://doi.org/10.3133/ofr20181159.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-095917","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1159/coverthb.jpg"},{"id":359577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1159/ofr20181159.pdf","text":"Report","size":"9.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1159"},{"id":359578,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74X560J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for Biogeochemical and Physical Processes Controlling Mercury Methylation and Bioaccumulation in Lake Powell, Glen Canyon National Recreation Area, Utah and Arizona, 2014–2015"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Glen Canyon, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63551330566406,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 36.75594019674357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle West
Valley City, UT 84119
About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is derived from groundwater pumping along North Fork Eagle Creek in the Eagle Creek Basin near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial losses of vegetation in the basin, the monitoring of North Fork Eagle Creek for short-term geomorphic change has been required by the U.S. Department of Agriculture Forest Service, Lincoln National Forest, as part of the permitting decision that allows for the continued pumping of the production wells. The monitoring of short-term geomorphic change in North Fork Eagle Creek began in June 2017 with a geomorphic survey of the stream reach located between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (USGS site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The 2017 geomorphic survey was conducted by the U.S. Geological Survey (USGS), in cooperation with the Village of Ruidoso, and was the first in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey are summarized and interpreted in this report and are provided in their entirety in its companion data release.
The study reach is 1.86 miles long, and large sections of the reach are characterized by intermittent streamflow. Where water is normally present (including at the upper and lower portions of the reach near the streamflow-gaging stations), the discharge typically remains below 2 cubic feet per second throughout the year. Therefore, if geomorphic change is to occur, it will likely be driven by seasonal high-flow events. Discharge records from streamflow-gaging stations in the Eagle Creek Basin indicated that high-flow events in the basin (with peaks above 50 cubic feet per second) typically occurred during the North American monsoon months of July, August, and September. Additionally, the records appear to indicate that, as expected, overland runoff and “flashy” responses to rainfall have increased in the 5 years since the 2012 Little Bear Fire.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach. Cross-section survey results indicated that channel characteristics (including channel width and area) varied widely along the study reach. Also, as part of the survey, woody debris accumulations and pools in the channel of the study reach were identified, cataloged, photographed, and surveyed for location. There were 58 woody debris accumulations and 14 pools found in the study reach. On the basis that debris jams could be a driver of geomorphic change in North Fork Eagle Creek, woody debris accumulations were classified according to their debris jam potential. The burn marks found on some woody debris indicated that the 2012 Little Bear Fire may be a contributing factor to the volume of debris in North Fork Eagle Creek. However, the woody debris present at the time of the survey did not appear to have substantially affected the geomorphic state of the study reach. Further, the structure and composition of the woody debris accumulations indicated that, under high-flow conditions, most woody debris would likely be transported downstream and out of the study reach without causing substantial geomorphic change through further jamming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181187","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2019, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2017: U.S. Geological Survey Open-File Report 2018–1187, 28 p., https://doi.org/10.3133/ofr20181187.","productDescription":"Report: v., 28 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","ipdsId":"IP-093851","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":362041,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PR7TX3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2017 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":362039,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1187/coverthb.jpg"},{"id":362040,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1187/ofr20181187.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1187"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.98236083984375,\n 33.02939031998959\n ],\n [\n -104.98260498046875,\n 33.02939031998959\n ],\n [\n -104.98260498046875,\n 33.68549637289138\n ],\n [\n -105.98236083984375,\n 33.68549637289138\n ],\n [\n -105.98236083984375,\n 33.02939031998959\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, NM 87113
Geochemical compositions of fine-grained stream sediment from four drainages on the north shore of the island of Kauai, Hawaii, west of Hanalei and two back-beach sites were explored to increase understanding about land-based runoff and ecological risk from runoff to nearshore coral communities. Stream and beach sediment were collected between July 30 and August 2, 2016, and major, minor, and trace elements in the less than 63 micrometer-diameter fraction were analyzed by inductively coupled plasma optical emission spectroscopy and mass spectroscopy. The potentially toxic metals Cr, Cu, Ni, and Zn exceeded levels at which adverse biological effects could be observed; however, these metals seemed to be largely mineral-bound and thus were unlikely to harm organisms. Cd and Pb were below levels of ecological concern. Only a small amount of fine-grained sediment was retained on beaches west of Hanalei sampled in summer 2016 (mean=8.8 percent, median=0.4 percent, range=0–92.8 percent, n=41). Although the scarcity of fine-grained sediment precluded land-based runoff sourcing to the nearshore region, it did indicate that fine-grained sediment and associated contaminants did not accumulate over the long term in the sampled intertidal, subtidal, and reef-flat environments, which would reduce sediment-related pressures on coral communities there.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191007","usgsCitation":"Takesue, R.K., and Storlazzi, C.D., 2018, Stream sediment geochemistry of four small drainages on the north shore of Kauai west of Hanalei: U.S. Geological Survey Open-File Report 2019–1007, 11 p., https://doi.org/10.3133/ofr20191007.","productDescription":"iv, 11 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-101369","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":362068,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1007/ofr20191007.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1007"},{"id":362067,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1007/coverthb.jpg"}],"country":"United States","state":"Hawaii","city":"Hanalei","otherGeospatial":"Kauai","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.6086883544922,\n 22.19233236966165\n ],\n [\n -159.49607849121094,\n 22.19233236966165\n ],\n [\n -159.49607849121094,\n 22.226978081564294\n ],\n [\n -159.6086883544922,\n 22.226978081564294\n ],\n [\n -159.6086883544922,\n 22.19233236966165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
Two hundred adult white sucker (Catostomus commersonii), age 3 years and older, were collected from the lower Sheboygan River Area of Concern in 2017, during the spring spawning run. Fish were euthanized, weighed, and measured, and any visible abnormalities were documented. Pieces of raised skin lesions as well as five to eight pieces of liver were removed and preserved for histopathological analyses. Skin and liver neoplasm prevalence was determined for assessment of the Fish Tumors or Other Deformities Beneficial Use Impairment. Although 45.5 percent of the suckers had raised skin lesions, the prevalence of skin neoplasms, either papilloma or squamous cell carcinoma, was 29.5 percent. This observation was similar to the prevalence (32.6 percent) of skin neoplasms in 2012; however, the percentage of squamous cell carcinoma was higher in 2017 (9.5 percent) than in 2012 (2.1 percent). The prevalence of liver neoplasms in 2017 (8.5 percent) was similar to that in 2012 (8.3 percent).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191014","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources","usgsCitation":"Blazer, V.S., Walsh, H.L., Braham, R.P., and Mazik, P.M., 2019, Assessment of skin and liver neoplasms in white sucker (Catostomus commersonii) collected in the Sheboygan River Area of Concern, Wisconsin, in 2017: U.S. Geological Survey Open-File Report 2019–1014, 18 p., https://doi.org/10.3133/ofr20191014.","productDescription":"vi, 18 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103639","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":361922,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1014/ofr20191014.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1014"},{"id":361921,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1014/coverthb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Lower Sheboygan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.82230377197266,\n 43.71801614233635\n ],\n [\n -87.69132614135742,\n 43.71801614233635\n ],\n [\n -87.69132614135742,\n 43.7596885685863\n ],\n [\n -87.82230377197266,\n 43.7596885685863\n ],\n [\n -87.82230377197266,\n 43.71801614233635\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The Mw 6.0 South Napa earthquake of August 24, 2014, produced complex and extensive surface faulting and other ground deformation features. Following the event, geologists made more than 1,200 field observations at locations where tectonic faulting and ground failure produced visible deformation that fractured and disturbed the ground surface. At a few locations, large-scale, detailed, field-based maps of fault rupture and ground deformation were produced. The South Napa earthquake response was one of the first times when post-earthquake reconnaissance data were mostly collected and disseminated electronically. The advantages and opportunities these new methods bring to our research also pose new challenges to large-scale compilation efforts and demonstrate the value of developing guidelines and better standardization across the community to more optimally utilize developing technology in future post-earthquake investigations. Some suggestions for standardizing the collection and dissemination of post-earthquake field reconnaissance data are provided herein.
Field observations and maps were integrated with airborne imagery, lidar, and InSAR to produce a comprehensive, large-scale digital map of fault rupture and zones of ground deformation. The map, observations, and photo database are summarized here in appendixes and figures and are also available as a series of digital data products within a companion U.S. Geological Survey data release (Ponti and others, 2019); the characteristics of fault rupture and ground deformation features are summarized in detail in the body of this report.
The results of this compilation reveal that faulting occurred within a 2-km-wide zone on six, roughly parallel traces within the West Napa Fault System. Most of the fault slip, and all the afterslip, occurred on the 21-km-long westernmost trace (Trace A). Maximum coseismic slip was greater than 40 cm and possibly as great as 60 cm, with the slip maximum located about 10 km north of the epicenter. Extensive ground deformation also occurred off the principal fault traces. Deformation characteristics of these features were not consistent with either primary faulting or shaking-induced ground failure and remain enigmatic, although this report includes speculation about possible origins.
The use of InSAR was invaluable for identifying and mapping secondary traces with small displacements, and for delineating the overall details of the extensive rupture. InSAR data also highlighted other areas with possible ground deformation—some of which are found coincident with previously mapped fault traces, whereas others are in areas where no faults were previously mapped. Several of these regions had no visible ground deformation, whereas others did produce features that were inconsistent with tectonic faulting, so care must be taken not to over interpret the InSAR data without careful, corroborating field investigations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191018","usgsCitation":"Ponti, D.J, Rosa, C.M, and Blair, J.L., 2019, The Mw 6.0 South Napa earthquake of August 24, 2014—Observations of surface faulting and ground deformation, with recommendations for improving post-earthquake field investigations: U.S. Geological Survey Open-File Report 2019–1018, 50 p., 15 appendixes, https://doi.org/10.3133/ofr20191018.","productDescription":"Report: v, 57 p.; Appendixes 1-15","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081675","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":361845,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix10.pdf","text":"Appendix 10","size":"5.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 10","linkHelpText":"— Shaking-related features resulting from lateral spreads and bank failures"},{"id":361841,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix06.pdf","text":"Appendix 6","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 6","linkHelpText":"— Surface faulting along Trace E"},{"id":361846,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix12.pdf","text":"Appendix 12","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 12","linkHelpText":"— Isolated cracking on slopes"},{"id":361838,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix03.pdf","text":"Appendix 3","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 3","linkHelpText":"— Surface faulting along Trace 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MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 11","linkHelpText":"— Ridge-top fractures"},{"id":361848,"rank":14,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix13.pdf","text":"Appendix 13","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 13","linkHelpText":"— Fractures associated with UAVSAR lineaments"},{"id":361849,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix14.pdf","text":"Appendix 14","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 14","linkHelpText":"— Areas of extensive curb and sidewalk damage"},{"id":361850,"rank":16,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix15.pdf","text":"Appendix 15","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 15","linkHelpText":"— Pavement cracks south of the Soda Creek Fault"},{"id":361851,"rank":17,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendixes1_15.zip","text":"Appendixes 1–15","size":"81.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2019-1018 Appendixes 2–15"},{"id":361852,"rank":18,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P26W84","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Digital datasets documenting fault rupture and ground deformation features produced by the Mw 6.0 South Napa Earthquake of August 24, 2014"},{"id":361842,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix07.pdf","text":"Appendix 7","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 7","linkHelpText":"— Surface faulting along Trace F"},{"id":361835,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1018/coverthb.jpg"},{"id":361844,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix09.pdf","text":"Appendix 9","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 9","linkHelpText":"— Shaking-induced deformation owing to landslide reactivation or fill settlement"},{"id":361837,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix02.pdf","text":"Appendix 2","size":"41.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 2","linkHelpText":"— Surface faulting along Trace A"},{"id":361839,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1018/ofr20191018_appendix04.pdf","text":"Appendix 4","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1018 Appendix 4","linkHelpText":"— Surface faulting along Trace C"}],"country":"United States","state":"California","county":"Napa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.46871948242186,\n 38.1237539824224\n ],\n [\n -122.34100341796875,\n 38.1237539824224\n ],\n [\n -122.21603393554688,\n 38.1237539824224\n ],\n [\n -122.21878051757811,\n 38.3868805698475\n ],\n [\n -122.47009277343749,\n 38.3868805698475\n ],\n [\n -122.46871948242186,\n 38.1237539824224\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Earthquake Science Center—Menlo Park
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345 Middlefield Road, MS 977
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Enhanced geothermal systems (EGS) technology aims to engineer a productive geothermal reservoir in regions of hot, but low permeability, rocks. In any EGS operation, the rock mass requires stimulation by high pressure injection of fluids, which has the potential to induce seismicity. To address the seismic hazard specifically, a probabilistic seismic hazard assessment (PSHA) is often required and is generally part of an induced seismicity mitigation plan (ISMP). A specific PSHA for the proposed Fallon, Nev., FORGE site is outlined below that relies solely on hypothetical stimulation scenarios and analog sites to assess the hazard of induced seismicity in the absence of local microseismicity. Partially due to the lack of existing seismicity at the site and partially to arrive at conservative estimates of the hazard, the PSHA is calculated for a range of b-values. Results indicate that the conservative estimates of seismic hazard at locations having significant, sensitive infrastructure near the proposed site are very low.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191020","usgsCitation":"Kaven, J.O., Majer, E.L., Foxall, W., Sonnenthal, E.L., Pettitt, W., 2019, Seismic hazard assessment at the Fallon, Nevada, Frontier Observatory for Research in Geothermal Energy site: U.S. Geological Survey Open-File Report 2019–1020, 16 p., https://doi.org/10.3133/ofr20191020.","productDescription":"iv, 16 p.","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-100995","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":361881,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1020/coverthb2.jpg"},{"id":361882,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1020/ofr20191020.pdf","text":"Report","size":"7.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1020"}],"country":"United States","state":"Nevada","city":"Fallon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.85765075683594,\n 39.40967202224426\n ],\n [\n -118.38661193847655,\n 39.40967202224426\n ],\n [\n -118.38661193847655,\n 39.66755655314589\n ],\n [\n -118.85765075683594,\n 39.66755655314589\n ],\n [\n -118.85765075683594,\n 39.40967202224426\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center — Menlo Park
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The Florida Coastal Mapping Program is a nascent but highly relevant program that has the potential to greatly enhance the “Blue Economy” of Florida by coordinating and facilitating sea-floor mapping efforts and aligning partner and stakeholder activities for increased efficiency and cost reduction. Sustained acquisition of modern coastal mapping information for Florida may improve management of resources and reduce costs by eliminating redundancy. Economic growth could be aided by improved data to support emerging sectors such as aquaculture and renewable energy.
The present focus of the Florida Coastal Mapping Program is on modern, high-resolution bathymetric and coastal topobathymetric data, which can be immediately used to update navigational charts and identify navigation hazards, provide fundamental baseline data for scientific research, and provide information for use by emergency managers and responders. Derivative products include identifying sand resources for beach nourishment, creating vastly improved models for coastal erosion and flooding, identifying coastal springs, and creating benthic habitat maps. The uses and applications of the data generated could grow over time. The process of creating a steering committee and technical team, conducting an inventory and gaps analysis, soliciting feedback from the stakeholder and partner communities, and developing a prioritization process has provided a framework on which a successful program can develop a sustainable funding strategy that may be an investment the citizens of Florida could benefit from for decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191017","collaboration":"Prepared in cooperation with the Florida Institute of Oceanography, Florida Fish and Wildlife Research Institute, and Florida Department of Environmental Protection","usgsCitation":"Hapke, C.J., Kramer, P.A., Fetherston-Resch, E.H., Baumstark, R.D., Druyor, R., Fredericks, X., and Fitos, E., 2019, Florida Coastal Mapping Program—Overview and 2018 workshop report: U.S. Geological Survey Open-File Report 2019–1017, 19 p., https://doi.org/10.3133/ofr20191017.","productDescription":"vii, 19 p.","numberOfPages":"28","ipdsId":"IP-099357","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":361829,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1017/ofr20191017.pdf","text":"Report","size":"5.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1017"},{"id":361828,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1017/coverthb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.00,\n 24.5\n ],\n [\n -80,\n 24.5\n ],\n [\n -80,\n 30.75\n ],\n [\n -88.00,\n 30.75\n ],\n [\n -88.00,\n 24.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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600 Fourth Street South
St. Petersburg, FL 33701