Changing climate conditions can make water management planning and drought preparedness decisions more complicated than ever before. Resource managers can no longer rely solely on historical data and trends to base their actions, and are in need of science that is relevant to their specific needs and can directly inform important planning decisions. Questions remain, however, regarding the most effective and efficient methods for extending scientific knowledge and products into management and decision-making.
This study analyzed two unique cases of water management to better understand how science can be translated into resource management actions and decision-making. In particular, this project sought to understand 1) the characteristics that make science actionable and useful for water resource management and drought preparedness, and 2) the ideal types of scientific knowledge or science products that facilitate the use of science in management and decision-making.
The first case study focused on beaver mimicry, an emerging nature-based solution that increases the presence of wood and woody debris in rivers and streams to mimic the actions of beavers. This technique has been rapidly adopted by natural resource managers as a way to restore riparian areas, increase groundwater infiltration, and slow surface water flow so that more water is available later in the year during hotter and dryer months. The second case study focused on an established research program, Colorado Dust on Snow, that provides water managers with scientific information explaining how the movement of dust particles from the Colorado Plateau influences hydrology and the timing and intensity of snow melt and water runoff into critical water sources. This program has support from and is being used by several water conservation districts in the state.
Understanding how scientific knowledge translates into action and decision-making in these cases is expected to strengthen our knowledge of actionable science in the context of drought and its impacts on ecosystems. The project team gathered qualitative data through stakeholder interviews and will conduct an extensive literature review. Findings from these efforts will also be incorporated into a broader Intermountain West synthesis effort to determine and assess commonalities and differences among socio-ecological aspects of drought adaptation and planning.
No abstract available.
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jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825997,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216826,"text":"70216826 - 2019 - Atlantic Salmon (Salmo salar) climate scenario planning pilot report","interactions":[],"lastModifiedDate":"2020-12-09T17:20:00.12116","indexId":"70216826","displayToPublicDate":"2019-12-31T10:42:02","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7459,"text":"Greater Atlantic Region Pollicy Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"19-05","displayTitle":"Atlantic Salmon (Salmo salar) climate scenario planning pilot report","title":"Atlantic Salmon (Salmo salar) climate scenario planning pilot report","docAbstract":"No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2019 Interim summary report: Asian carp monitoring and response plan","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Asian Carp Regional Coordinating Committee","usgsCitation":"Hlavacek, E., Harrison, T.J., Knights, B.C., and Brey, M.K., 2019, USGS Illinois River catch database and visualization: Interim Summary Report, 4 p.","productDescription":"4 p.","startPage":"51","endPage":"54","ipdsId":"IP-122169","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":398924,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391077,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html"}],"country":"United States","state":"illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.648193359375,\n 38.92522904714054\n ],\n [\n -88.099365234375,\n 38.92522904714054\n ],\n [\n -88.099365234375,\n 41.72213058512578\n ],\n [\n -90.648193359375,\n 41.72213058512578\n ],\n [\n -90.648193359375,\n 38.92522904714054\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":825998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826003,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826004,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70226988,"text":"70226988 - 2019 - Conceptual framework for assessing disturbance impacts on debris-flow initiation thresholds across hydroclimatic settings","interactions":[],"lastModifiedDate":"2021-12-23T16:25:36.085154","indexId":"70226988","displayToPublicDate":"2019-12-31T10:12:35","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Conceptual framework for assessing disturbance impacts on debris-flow initiation thresholds across hydroclimatic settings","docAbstract":"The destructive and deadly nature of debris flows has motivated research into empirical rainfall thresholds to provide situational awareness, inform early warning systems, and reduce loss of life and property. Disturbances such as wildfire and land-cover change can influence the hydrological processes of infiltration and runoff generation; in steep terrain this typically lowers empirical thresholds for debris-flow initiation. However, disturbance impacts, and the post-disturbance recovery may differ, depending on the severity, nature, extent, and duration of the disturbance, as well as on the prevailing hydroclimatic conditions. Thus, it can be difficult to predict impacts on debris-flows hazards in regions where historically such disturbances have been less frequent or severe. Given the increasing magnitude and incidence of wildfires, among other disturbances, we seek to develop a conceptual framework for assessing their impacts on debris-flow hazards across geographic regions. We characterize the severity of disturbances in terms of changes from undisturbed hydrologic functioning, including hillslope drainage and available unsaturated storage capacity, which can have contrasting influences on debris-flow initiation mechanisms in different hydroclimatic settings. We compare the timescale of disturbance-recovery cycles relative to the return period of threshold exceeding storms to describe vulnerability to post-disturbance debris flows. Similarly, we quantify resilience by comparing the timescales of disturbance-recovery cycles with those of disturbance-recurrence intervals. We illustrate the utility of these concepts using information from U.S. Geological Survey landslide monitoring sites in burned and unburned areas across the United States. Increasing severity of disturbance may influence both recovery timescales and lower the return period for debris-flow inducing storms, thus increasing the vulnerability to disturbance-related hazards while also decreasing system resilience. The proposed conceptual framework can inform future data acquisition and model development to improve debris-flow initiation thresholds in areas experiencing increasingly frequent, severe, and even overlapping landscape disturbances.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Debris-flow hazards mitigation: Mechanics, monitoring, modeling, and assessment; proceedings of the Seventh International Conference on Debris-Flow Hazards Mitigation","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Seventh International Conference on Debris-Flow Hazards Mitigation","conferenceDate":"Jun 10-13, 2019","conferenceLocation":"Golden, CO","language":"English","publisher":"Association of Environmental and Engineering Geologists","doi":"10.25676/11124/173176","usgsCitation":"Mirus, B.B., Staley, D.M., Kean, J.W., Smith, J.B., Wooten, R., McGuire, L.A., and Ebel, B., 2019, Conceptual framework for assessing disturbance impacts on debris-flow initiation thresholds across hydroclimatic settings, in Debris-flow hazards mitigation: Mechanics, monitoring, modeling, and assessment; proceedings of the Seventh International Conference on Debris-Flow Hazards Mitigation, Golden, CO, Jun 10-13, 2019, 8 p., https://doi.org/10.25676/11124/173176.","productDescription":"8 p.","ipdsId":"IP-105027","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":393369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":829098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":829099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":829100,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":829101,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wooten, Rick","contributorId":217741,"corporation":false,"usgs":false,"family":"Wooten","given":"Rick","email":"","affiliations":[{"id":24614,"text":"North Carolina Geological Survey","active":true,"usgs":false}],"preferred":false,"id":829102,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":829103,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":829104,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211754,"text":"70211754 - 2019 - Recovery planning in a dynamic system: Integrating uncertainty into a decision support tool for an endangered songbird","interactions":[],"lastModifiedDate":"2020-08-07T14:59:38.820693","indexId":"70211754","displayToPublicDate":"2019-12-31T09:50:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Recovery planning in a dynamic system: Integrating uncertainty into a decision support tool for an endangered songbird","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Department of Energy, conducted aquifer tests during 2017–18 on 101 wells at and near the Idaho National Laboratory, Idaho, to define the hydraulic characteristics for individual wells. These were short-duration aquifer tests, conducted with a limited number of observations during routine sampling. Pumped intervals (water columns) for individual wells ranged from 12 to 790 feet (ft). Semi-constant discharge rates during aquifer testing ranged from 1 to 45 gallons per minute, water-level response to pumping ranged from no observed drawdown to 52.4 ft, and length of aquifer tests for individual wells ranged from 10 to 160 minutes. Individual well data were analyzed to estimate the capacity of the well to produce water (specific capacity) and to estimate values for transmissivity. Estimates of specific capacity for individual wells ranged from less than 1.0 to greater than (>) 3.0 × 103 gallons per minute per foot; estimates of transmissivity for individual wells ranged from 2.0 to >5.4 x 105 feet squared per day.
Geophysical log data, well construction information, and general geology for individual wells were presented and included in this report. Basic hydrogeologic features for individual wells were described, along with a composite of natural gamma, neutron, gamma-gamma dual density, and acoustic televiewer data (when available). The geophysical and geologic data were used to suggest the location and thickness of sediment layers along with fractured and dense basalt areas for individual wells. Geophysical data were used to describe the general geology where geologic descriptions and (or) driller notes were not available.
Director,
Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The Spatially Explicit Asian carp Population (SEAcarP) model was developed to inform management and research decisions with the goal of minimizing the abundance of Bighead Carp and Silver Carp (collectively referred to as “Asian carp” in this document) in the upper Illinois River waterway, thereby reducing risk of population expansion toward the Great Lakes and reducing potential impacts on native species. This model provides an objective, data-driven approach to maximize return on investment of management actions and facilitates defining research and monitoring priorities.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2019 Asian carp interim summary report","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Asian Carp Regional Coordinating Committee","usgsCitation":"Erickson, R.A., 2019, Asian carp population modeling to support an Adaptive Management framework, USGS Contribution, chap. of 2019 Asian carp interim summary report, p. 175-176.","productDescription":"2 p.","startPage":"175","endPage":"176","ipdsId":"IP-120441","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391078,"type":{"id":11,"text":"Document"},"url":"https://invasivecarp.us/Documents/Interim-Summary-Report-2019.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826005,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70223489,"text":"70223489 - 2019 - Diel feeding and movement activity of Northern Snakehead Channa argus","interactions":[],"lastModifiedDate":"2021-08-30T13:31:06.021598","indexId":"70223489","displayToPublicDate":"2019-12-31T08:30:48","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Diel feeding and movement activity of Northern Snakehead Channa argus","docAbstract":"Understanding the diel activity of a species can shed light on potential interactions with other species and inform management practices. To understand the diel activity of Northern Snakehead Channa argus, feeding habits and movement patterns were observed. Two hundred seventy-three Northern Snakehead were captured by boat electrofishing during May and June of 2007 and 2008. Their gut contents were extracted and preserved. The level of digestion of each prey item was estimated from fresh (1) to >50% digested (4) or empty (5). Random forest models were used to predict feeding activity based on time of day, tide level, date, water temperature, fish total length, and sex. Diel movement patterns were assessed by implanting Northern Snakehead with radio transmitters and monitoring them every 1.5 h for 24 h in both March and July 2007. Movement rates were compared between March and July and among four daily time periods. Independent variables accounted for only 6% of the variation in feeding activity; however, temporal feeding patterns were apparent. No fresh items were observed in guts between 12:30 and 7:30 am, and the proportion of empty stomachs increased at the end of May coinciding with the onset of spawning. Overall, fish moved greater distances during the July tracking period compared to March. Fish showed a greater propensity to move during daylight hours than at night during the March tracking period. A similar but nonsignificant (P > 0.05) pattern was observed in July. Movement and feeding data both indicated greater activity during daylight hours than at night, suggesting that Northern Snakehead is a diurnal species. Based on our preliminary findings, we hypothesize that a) diurnal species are more susceptible than nocturnal species to predation by Northern Snakehead and b) Northern Snakehead are more likely to compete for food with diurnal than nocturnal predators.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"American Fisheries Society symposium 89","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"The First International Snakehead Symposium","conferenceDate":"Jul 17-19, 2019","conferenceLocation":"Alexandria, VA","language":"English","publisher":"American Fisheries Society","doi":"10.47886/9781934874585.ch6","usgsCitation":"Lapointe, N., Saylor, R., and Angermeier, P.L., 2019, Diel feeding and movement activity of Northern Snakehead Channa argus, in American Fisheries Society symposium 89, Alexandria, VA, Jul 17-19, 2019, 13 p., https://doi.org/10.47886/9781934874585.ch6.","productDescription":"13 p.","ipdsId":"IP-103396","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388658,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"lower Potomac River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.33551025390624,\n 38.46864331036051\n ],\n [\n -76.87408447265625,\n 38.46864331036051\n ],\n [\n -76.87408447265625,\n 38.9914373369788\n ],\n [\n -77.33551025390624,\n 38.9914373369788\n ],\n [\n -77.33551025390624,\n 38.46864331036051\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lapointe, Nicolas W. R.","contributorId":264893,"corporation":false,"usgs":false,"family":"Lapointe","given":"Nicolas W. R.","affiliations":[{"id":54575,"text":"Canadian Wildlife Federation","active":true,"usgs":false}],"preferred":false,"id":822151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saylor, Ryan K.","contributorId":264894,"corporation":false,"usgs":false,"family":"Saylor","given":"Ryan K.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":822152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angermeier, Paul L. 0000-0003-2864-170X biota@usgs.gov","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":166679,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul","email":"biota@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822150,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236885,"text":"70236885 - 2019 - Significant seismic behavior features of two tall buildings inferred from response records","interactions":[],"lastModifiedDate":"2022-09-21T13:21:38.68656","indexId":"70236885","displayToPublicDate":"2019-12-31T08:10:53","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Significant seismic behavior features of two tall buildings inferred from response records","docAbstract":"In this paper, recent studies of recorded responses of behavior and performances of two instrumented tall buildings subjected to long-period motions from events that originate at far distances (e.g. 100-800km) are presented. Significant results indicate that (a) computed average drift ratios are substantial (~0.5%), and (b) there is permanent shift of fundamental frequencies for a tall building which was hundreds of km away from the epicenter of a large (M9.0) earthquake. In addition, (c) there are significant local site effects and basin effects, some causing resonance of buildings, (d) beating effect are observed particularly in elongated responses whereby elongated responses can contribute to low-cycle fatigue, and significantly, and (e) identified critical viscous damping percentages are low (<3%). This is consistent with recent recommendations of the Los Angeles Tall Buildings Design Council (LATBDC) 1 and the Tall Buildings Initiative (TBI) of Pacific Earthquake Engineering Center (PEER)2, and (f) beating effects are observed particularly in elongated responses whereby elongated responses can contribute to low-cycle fatigue.
Analyses of one tall building from Japan affected during the 11 March 2011 M9.0 Tohoku earthquake, and one in Los Angeles, California during the 17 January 1994 M6.7 Northridge earthquake are presented. A variety of methods including spectral analyses, system identification, and time-frequency functions are used to extract dynamic response characteristics (modal frequencies and damping), drift ratios, and effect of site conditions including basin effects.
In general, data-driven analyses show that, the two tall buildings (as well as many others not reported herein) exhibit (a) lower damping than those used in current design process analyses (<3%) and (b) a beating effect and significant basin effect.
These are significant: (1) Additional damping generating elements can be considered during design processes to decrease the prolonged and amplified responses. (2) Basin effects are not considered during design, it is important to at least consider looking into such effects as these can result in resonance and amplified responses as shown in recent studies.
","conferenceTitle":"12th Canadian Conference on Earthquake Engineering","conferenceDate":"Jun 17-20, 2019","conferenceLocation":"Quebec City, Canada","language":"English","publisher":"Canadian Association for Earthquake Engineering (CAEE)","usgsCitation":"Celebi, M., 2019, Significant seismic behavior features of two tall buildings inferred from response records, 12th Canadian Conference on Earthquake Engineering, Quebec City, Canada, Jun 17-20, 2019, 8 p.","productDescription":"8 p.","ipdsId":"IP-104455","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407114,"type":{"id":15,"text":"Index Page"},"url":"https://www.caee.ca/12cceeproceedings/"}],"country":"Japan, United States","city":"Los Angeles, Osaka","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 135.43533325195312,\n 34.615126683462194\n ],\n [\n 135.57815551757812,\n 34.615126683462194\n ],\n [\n 135.57815551757812,\n 34.73709847578162\n ],\n [\n 135.43533325195312,\n 34.73709847578162\n ],\n [\n 135.43533325195312,\n 34.615126683462194\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.2843017578125,\n 34.020794936018724\n ],\n [\n -118.19915771484374,\n 34.020794936018724\n ],\n [\n -118.19915771484374,\n 34.07143110146331\n ],\n [\n -118.2843017578125,\n 34.07143110146331\n ],\n [\n -118.2843017578125,\n 34.020794936018724\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852464,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70215577,"text":"70215577 - 2019 - Sixty years of White-tailed Deer (Odocoileus virginianus) yarding in a Gray Wolf (Canis lupus)–deer system","interactions":[],"lastModifiedDate":"2020-10-23T12:51:40.450304","indexId":"70215577","displayToPublicDate":"2019-12-31T07:48:16","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7173,"text":"Canadian Field Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Sixty years of White-tailed Deer (Odocoileus virginianus) yarding in a Gray Wolf (Canis lupus)–deer system","docAbstract":"This article synthesizes information from over a six-decade period of studies of White-tailed Deer (Odocoileus virginianus) use of a winter yard and subject to Gray Wolf (Canis lupus) predation in northeastern Minnesota. It also adds spring migration data from 35 adult female deer and fawns studied there during 1998, 1999, 2001, 2014, and 2017. Twenty-nine of these deer migrated in spring a mean distance of 29 km (SE = 4), a maximum distance of 78 km, and at a mean bearing of 83° (SE = 12; range 21–348). These findings are similar to those from 49 deer (both sexes) from the same yard studied during 1974–1984, that migrated a mean distance of 25 km (SE = 1.8) and a mean bearing of 77° ± 4 SE. Between the two periods, the wolf population fluctuated considerably, the winter range of deer in the area where these deer spent summer greatly diminished, and both derechos and fires disturbed the habitat. This study attests to the selective advantage of the migratory tradition of deer in this yard.
The U.S. Energy Independence and Security Act of 2007 authorized the U.S. Geological Survey (USGS) to conduct a national assessment of the potential volume of hydrocarbons recoverable by injection of carbon dioxide (CO2) into known oil reservoirs with historical production. The implementation of CO2 enhanced oil recovery (CO2-EOR) techniques could increase the U.S. recoverable hydrocarbon resource base. Use of anthropogenic CO2 in the CO2-EOR process could reduce the amount of CO2 released to the atmosphere by allowing a percentage of the injected CO2 to remain in reservoir pore space once occupied by produced oil and water or by CO2 dissolution in oil and water in the reservoir.
The USGS has developed a new methodology for the national assessment of technically recoverable oil resources that may be produced by using current CO2-EOR technologies. The methodology relies on a proprietary reservoir-level database, the comprehensive resource database (CRD). The CRD incorporates commercially available geologic and engineering data, and USGS-defined play averages or province averages of reservoir data were used to populate incomplete records. Values from the CRD are used to estimate the original oil in place (OOIP) for each reservoir. The inputs are reviewed by USGS geologists, particularly when play or province averages have been used. Monte Carlo simulation is used to produce a numerical probability distribution for the OOIP for each reservoir, with the mean defined as the value of the OOIP in the CRD. A reservoir model (CO2 Prophet, developed for the U.S. Department of Energy by Texaco, Inc.) is used to determine the incremental recovery factors for oil during the CO2-EOR process, on an individual reservoir basis. The model is also used to estimate the volume of CO2 remaining in the reservoir after the CO2-EOR process is complete. Empirical decline curve analysis and comparison with data from published papers and reports on CO2-EOR projects are utilized to substantiate the simulation results. Numerical distributions of recovery factors are prepared for variations in the reservoir lithology (clastic or carbonate). The distribution of incremental oil is computed by multiplying the appropriate probability distribution of recovery factors by the individual reservoir distribution of the OOIP. A way to estimate the CO2 remaining in the reservoir after the completion of the CO2-EOR process is also included in the methodology.
Assessment results will be aggregated to play, petroleum province, regional, and national scales. This assessment methodology has been tested on the Horseshoe Atoll, Upper Pennsylvanian-Wolfcampian play in the Permian Basin Province in Texas; the play consists of 27 reservoirs having at least 2 billion barrels of OOIP that are amenable to the CO2-EOR process. The play was selected as a test case because CO2-EOR production data and published reports are available for several reservoirs within the play. Preliminary estimates of oil recoverable by implementation of miscible CO2-EOR are comparable to those reported in the literature and obtained by reservoir decline curve analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195115","usgsCitation":"Warwick, P.D., Attanasi, E.D., Olea, R.A., Blondes, M.S., Freeman, P.A., Brennan, S.T., Merrill, M.D., Verma, M.K., Karacan, C.Ö., Shelton, J.L., Lohr, C.D., Jahediesfanjani, H., and Roueché, J.N., 2019, A probabilistic assessment methodology for carbon dioxide enhanced oil recovery and associated carbon dioxide retention: U.S. Geological Survey Scientific Investigations Report 2019–5115, 51 p., https://doi.org/10.3133/sir20195115.","productDescription":"x, 51 p.","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069832","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":399600,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109570.htm"},{"id":370863,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5115/sir20195115.pdf","text":"Report","size":"8.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5115"},{"id":370862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5115/coverthb.jpg"}],"contact":"Eastern Energy Resources Science Center
12201 Sunrise Valley Drive
956 National Center
Reston, VA 20192
Beach erosion is a persistent problem along most open-ocean shores of the United States. Along the Arctic coast of Alaska, coastal erosion is widespread and threatens communities, defense and energy-related infrastructure, and coastal habitat. As coastal populations continue to expand and infrastructure and habitat are increasingly threatened by erosion, there is increased demand for accurate information regarding past and present trends and rates of shoreline movement.
Shoreline change was evaluated by comparing three to four historical shoreline positions derived from 1950s-era topographic surveys and black and white aerial photography, 1980s-era color-infrared Alaska High-Altitude Aerial Photography, 2003 natural color aerial photography, and 2010s-era natural color aerial photography. Long-term (1950s–2010s) and short-term (1980s–2010s) shoreline change rates were calculated using linear-regression and end-point methods, respectively, at transects spaced approximately every 50 meters along both the mainland and barrier island coasts.
Shoreline change rates calculated on more than 24,000 individual transects indicate that between 1948 and 2016 the northern coast of Alaska between Icy Cape and Cape Prince of Wales was slightly erosional, with 68 percent of the total transects showing shoreline retreat over the long term and 63 percent over the short term. However, only 9 percent of the total transects showed shoreline retreat greater than 1 meter per year (m/yr) over the long and short term, respectively. Mean rates of shoreline change of −0.2±0.1 and −0.2±0.3 m/yr, were calculated for the long and short term, respectively. Many rates measured were near the limit of our shoreline change uncertainty estimates. Erosion and accretion rates on individual transects ranged from −8.3 to +9.6 m/yr over the long term and −16.0 to +20.0 m/yr over the short-term analysis periods. The highest rates of erosion and accretion were associated with the formation and migration of inlets along barrier island coasts. The highest erosional rates of change were measured in the southern part of the study area between Sullivan Lake and Cape Prince of Wales. The highest accretional rates of change were measured in the northern part of the study area on the open-ocean coast of barrier islands fronting Kasegaluk Lagoon.
Open-ocean exposed shorelines compose 85 percent of all transects and 70 percent were erosional over the long term. Sheltered mainland-lagoon shorelines compose 15 percent of all transects in the study area and 58 percent were erosional over the long term. Although mean shoreline change rates were quite low along all coasts, exposed shorelines retreated at twice the rate (−0.2±0.1 m/yr) of sheltered shorelines (−0.1±0.1 m/yr). Barrier shoreline transects (includes barrier islands, spits, and beaches) compose 49 percent of the total transects and 56 percent of all exposed shoreline transects. Mean shoreline change rates on exposed barrier shorelines were only slightly greater than exposed mainland shorelines (−0.3±0.1 and −0.2±0.1 m/yr, respectively). Mean shoreline change rates on sheltered barrier shorelines were similar to sheltered mainland shorelines (−0.1±0.3 m/yr).
In contrast to the majority of the Nation’s shorelines, for all but three months of the year (July–September), the north coast of Alaska has historically been protected by landfast sea ice from processes such as waves, winds, and currents that typically drive coastal change on beaches in more temperate regions of the world. Projected and observed increases in periods of sea-ice-free conditions, as sea ice melts earlier and forms later in the year, particularly in the autumn, when large storms are more common in the Arctic, suggest that Arctic coasts will be more vulnerable to storm surge and wave energy, potentially resulting in accelerated shoreline erosion and terrestrial habitat loss in the future. Increases in air and sea water temperatures may also increase erosion of the ice-rich, coastal permafrost bluffs present along much of Alaska’s Arctic coast. More frequent shoreline change data collection and analysis in this rapidly changing environment should be considered in order to evaluate shoreline change trends in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191146","usgsCitation":"Gibbs, A.E., Snyder, A.G., and Richmond, B.M., 2019, National assessment of shoreline change — Historical shoreline change along the north coast of Alaska, Icy Cape to Cape Prince of Wales: U.S. Geological Survey Open-File Report 2019–1146, 52 p., https://doi.org/10.3133/ofr20191146.","productDescription":"Report: vi, 52 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-111408","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399433,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109572.htm"},{"id":370888,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1146/coverthb.jpg"},{"id":370889,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1146/ofr20191146.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1146"},{"id":370890,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H1S1PV","linkHelpText":"National assessment of shoreline change—A GIS compilation of updated vector shorelines and associated shoreline change data for the north coast of Alaska, Icy Cape to Cape Prince of Wales"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.1194,\n 65.5739\n ],\n [\n -160.9839,\n 65.5739\n ],\n [\n -160.9839,\n 70.3322\n ],\n [\n -168.1194,\n 70.3322\n ],\n [\n -168.1194,\n 65.5739\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
Stream stability, flood frequency, and streambed scour potential were evaluated at 20 Alaskan river- and stream-spanning bridges lacking a quantitative scour analysis or having unknown foundation details. Three of the bridges had been assessed shortly before the study described in this report but were re-assessed using different methods or data. Channel instability related to mining may affect scour at one site, while channel instability related to flow distribution changes can be seen at one site. One bridge was closed because of abutment scour prior to the study. Otherwise, channels generally showed stable bed elevations.
Contraction and abutment scour were calculated for all 20 bridges, and pier scour was calculated for the 2 bridges that had piers. Vertical contraction (pressure flow) scour was calculated for one site at which the modeled water surface was higher than the superstructure of the bridge. Hydraulic variables for the scour calculations were derived from one-dimensional and two-dimensional hydraulic models of the 1- and 0.2-percent annual exceedance probability floods (also known as the 100- and 500-year floods, respectively). Scour also was calculated for large recorded floods at two sites.
At many sites, overflow of road approaches relieves the bridge during floods and lessens the potential for scour. Two-dimensional hydraulic models are superior to one-dimensional hydraulic models at distributing flow between bridges, road approaches, and floodplains, and therefore likely produce more reasonable scour values at sites with substantial floodplain flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195110","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities","usgsCitation":"Beebee, R.A., Dworsky, K.L., and Knopp, S.J., 2019, Streambed scour evaluations and conditions at selected bridge Sites in Alaska, 2016–17 (version 1.1, April 2023): U.S. Geological Survey Scientific Investigations Report 2019-5110, 32 p., https://doi.org/10.3133/sir20195110.","productDescription":"Report: vi, 32 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099321","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":399597,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109571.htm"},{"id":370872,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5110/coverthb2.jpg"},{"id":370873,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5110/sir20195110.pdf","text":"Report","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5110"},{"id":415671,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5110/sir20195110_RevisionHistory.txt","description":"SIR 2019-5110 Version History"},{"id":370874,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LUTFHZ","linkHelpText":"Tabular input/output data and model files for 19 hydraulic models for streambed scour evaluations at selected bridge sites, Alaska, 2016–17"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.41259765625,\n 59.01794033995248\n ],\n [\n -144.77783203125,\n 59.01794033995248\n ],\n [\n -144.77783203125,\n 64.97006438589436\n ],\n [\n -155.41259765625,\n 64.97006438589436\n ],\n [\n -155.41259765625,\n 59.01794033995248\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: April 2023; Version 1.0: December 2019","contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Throughout the western United States, efforts are underway to better understand and preserve migration and movement corridors for mule deer and other big game and to minimize the impacts of development and other land-use change on populations. San Diego County is home to a unique non-migratory subspecies of mule deer, the Southern mule deer (Odocoileus hemionus fuliginatus; herein referred to as “mule deer”). Because it is the only large herbivorous mammal in San Diego, connectivity among mule deer groups is an important indicator of functional connectivity throughout San Diego County urban preserves and has therefore been monitored within central and eastern San Diego County using DNA fingerprinting since 2005. To continue this effort and to assess genetic connectivity in north San Diego County (herein “North County”), we genotyped scat samples from preserves in the area and tissue samples from Marine Corps Base Camp Pendleton (MCBCP). We used non-invasive capture/recapture analyses and pedigree analyses for assessing short-term movement and population clustering analyses to assess gene flow in North County. Additionally, we performed similar analyses on the combined San Diego County dataset, which was composed of the North County dataset collected for this study and a previously collected dataset from central and eastern San Diego County. Using recapture data, we found multiple instances of mule deer crossing roads in urban North County preserves, with several of these events occurring in areas where there are underpasses and culverts known to be used by mule deer. Corroborating previous studies in the region and statewide, pedigree and population structure analyses support the presence of two genetic clusters for mule deer in San Diego County—the “Coastal” and “Inland/Mountain” clusters. Low estimates of effective population size, especially in the Coastal cluster, suggest that to further understand potential vulnerabilities of mule deer in this region, it is important to continue to monitor connectivity, in particular, at the boundary between these two clusters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191138","usgsCitation":"Mitelberg, A., Smith, J.G., and Vandergast, A.G., 2019, DNA Fingerprinting of Southern mule deer (Odocoileus hemionus fuliginatus) in north San Diego County, California (2018–19): U.S. Geological Survey Open-File Report 2019–1138, 25 p., https://doi.org/10.3133/ofr20191138.","productDescription":"vi, 25 p.","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-112707","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":437245,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXWXA9","text":"USGS data release","linkHelpText":"Microsatellite Genetic Marker Genotypes from Southern Mule Deer (Odocoileus hemionus fuliginatus) Sampled in San Diego County, California"},{"id":370869,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1138/ofr20191138.pdf","text":"Report","size":"31 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":370868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1138/coverthb.jpg"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.31201171875001,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 32.713355353177555\n ],\n [\n -116.05957031249999,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 33.25706340236547\n ],\n [\n -117.31201171875001,\n 32.713355353177555\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This report summarizes a national assessment of flowing waters conducted by the U.S. Geological Survey’s (USGS) National Water-Quality Assessment (NAWQA) Project and addresses several pressing questions about the modification of natural flows in streams and rivers. The assessment is based on the integration, modeling, and synthesis of monitoring data collected by the USGS and the U.S. Environmental Protection Agency at more than 7,000 streams and rivers across the conterminous United States from 1980 to 2014. Key findings include the following. First, flow in many of the Nation’s streams and rivers is different from what it would be under natural conditions. In particular, low flows are more frequent, are of shorter duration, and vary less from one year to the next than they would naturally. In addition, high flows have been reduced in magnitude, are of shorter duration, are less frequent, and vary less from one year to the next than they would naturally. Other characteristics of natural flows also have been modified. Second, over the last 60 years (1955–2014), climatic trends have caused a change of 50 percent or more in one or more streamflow attributes at two-thirds of climate-sensitive streamgaging sites. However, these climate-induced changes have been less influential on streamflow modification than have land and water-management practices. Third, in every region assessed, streamflow modification was associated with reduced ecological health, as indicated by two biological communities—invertebrates and fish. Biological communities were increasingly likely to be impaired (defined as having lost a statistically significant number of species) in streams with flows most different from natural conditions. Finally, several case studies are presented that illustrate viable management strategies for balancing the water needs of people and ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1461","collaboration":"National Water-Quality ProgramNational Water-Quality Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Digital flood-inundation maps for an 8.8-mile reach of the North Platte River, from 1.5 miles upstream from the Highway 92 bridge to 3 miles downstream from the Highway 71 bridge in Scottsbluff County, were created by the U.S. Geological Survey (USGS) in cooperation with the Cities of Scottsbluff and Gering, Nebraska. The flood-inundation maps, which can be accessed through the Flood Inundation Mapping (FIM) Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program?qt-science_center_objects=0#qt-science_center_objects, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the North Platte River at Scottsbluff, Nebr. (station number 06680500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service (site SBRN1) at https://water.weather.gov/ahps2/hydrograph.php?wfo=cys&gage=sbrn1.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated by using the current (2018) stage-discharge relation at the North Platte River at Scottsbluff, Nebr., streamgage.
The hydraulic model was then used to compute 10 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 9 ft, or near bankfull, to 18 ft, which exceeds the stage that corresponds to the estimated 1-percent annual exceedance probability flood (100-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging data having a 0.6-ft root mean square error and 2-ft horizontal resolution resampled to a 6-ft grid to delineate the area flooded at each water level. The availability of these maps, along with internet information regarding current stage from the USGS streamgage, may provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195099","collaboration":"Prepared in cooperation with the City of Scottsbluff and the City of Gering","usgsCitation":"Strauch, K.R., 2019, Flood-inundation maps for the North Platte River at Scottsbluff and Gering, Nebraska, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5099, 9 p., https://doi.org/10.3133/sir20195099.","productDescription":"Report: vi, 9 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-102434","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":399544,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109564.htm"},{"id":370451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5099/sir20195099.pdf","text":"Report","size":"25.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5099"},{"id":370452,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NCAIKN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-inundation geospatial datasets for the North Platte River at Scottsbluff and Gering, Nebraska"},{"id":370450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5099/coverthb.jpg"}],"country":"United States","state":"Nebraska","city":"Scottsbluff, Gering","otherGeospatial":"North Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.05426025390625,\n 41.74467659677642\n ],\n [\n -103.33740234375,\n 41.74467659677642\n ],\n [\n -103.33740234375,\n 42.05948945192712\n ],\n [\n -104.05426025390625,\n 42.05948945192712\n ],\n [\n -104.05426025390625,\n 41.74467659677642\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Beaver Lake was constructed in 1966 on the White River in the northwest corner of Arkansas for flood control, hydroelectric power, public water supply, and recreation. The surface area of Beaver Lake is about 27,900 acres and approximately 449 miles of shoreline are at the conservation pool level (1,120 feet above the North American Vertical Datum of 1988). Sedimentation in reservoirs can result in reduced water storage capacity and a reduction in usable aquatic habitat. Therefore, accurate and up-to-date estimates of reservoir water capacity are important for managing pool levels, power generation, recreation, and downstream aquatic habitat. Many of the lakes operated by the U.S. Army Corps of Engineers are periodically surveyed to monitor bathymetric changes that affect water capacity. In October 2018, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed one such survey of Beaver Lake using a multibeam echosounder. The echosounder data were combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3445","collaboration":"Prepared in cooperation with the U.S. Army Corp of Engineers, Southwestern Division, Little Rock District","usgsCitation":"Huizinga, R.J., Ellis, J.T., Sharpe, J.B., LeRoy, J.Z., and Richards, J.M., 2019, Bathymetric map and surface area and capacity table for Beaver Lake near Rogers, Arkansas, 2018: U.S. Geological Survey Scientific Investigations Map 3445,\n2 sheets, https://doi.org/10.3133/sim3445.","productDescription":"2 Sheets: 44 x 36 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-113370","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":370609,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3445/coverthb.jpg"},{"id":399518,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109565.htm"},{"id":370612,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91PLLGV","text":"USGS data release","linkHelpText":"Bathymetric and supporting data for Beaver Lake near Rogers, Arkansas, 2018"},{"id":370610,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3445/sim3445_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3445 Sheet 1"},{"id":370611,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3445/sim3445_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3445 Sheet 2"}],"scale":"24000","country":"United States","state":"Arkansas","city":"Rogers","otherGeospatial":"Beaver Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.11575317382812,\n 36.17446549576358\n ],\n [\n -93.79440307617188,\n 36.17446549576358\n ],\n [\n -93.79440307617188,\n 36.45829281489\n ],\n [\n -94.11575317382812,\n 36.45829281489\n ],\n [\n -94.11575317382812,\n 36.17446549576358\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The U.S. Geological Survey (USGS), in cooperation with the Oklahoma Department of Transportation, updated peak-streamflow regression equations for estimating flows with annual exceedance probabilities from 50 to 0.2 percent for the State of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the State by use of a generalized least-squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for stream basins with drainage areas less than 2,510 square miles that are not affected by regulation. The standard model error ranged from 31.28 to 49.32 percent for the different annual exceedance probabilities that were computed.
Annual-maximum peak flows observed at 212 USGS streamgages through water year 2017 were used for the regression analysis, excluding the Oklahoma Panhandle region. The USGS StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 212 streamgages in and near Oklahoma are provided in this report.
This report contains descriptions of the methods that can be used to estimate peak streamflows at ungaged sites by using estimates from streamgages on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater-retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195143","collaboration":"Prepared in cooperation with the Oklahoma Department of Transportation","usgsCitation":"Lewis, J.M., Hunter, S.L., and Labriola, L.G., 2019, Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma developed by using streamflow data through 2017 (ver. 1.1, March 2020): U.S. Geological Survey Scientific Investigations Report 2019–5143, 39 p., https://doi.org/10.3133/sir20195143.","productDescription":"Report: v, 39 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-111975","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":373219,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5143/sir20195143_v1.1.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5143"},{"id":370619,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B99TQZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data release of basin characteristics, generalized skew map and peak-streamflow frequency estimates in Oklahoma, 2017"},{"id":373218,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5143/coverthb2.jpg"},{"id":373266,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5143/versionHist.txt","text":"Version History","description":"Version History"},{"id":399618,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109563.htm"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.919921875,\n 36.87962060502676\n ],\n [\n -102.83203125,\n 34.415973384481866\n ],\n [\n -97.91015624999999,\n 33.97980872872457\n ],\n [\n -94.5703125,\n 33.17434155100208\n ],\n [\n -93.515625,\n 33.97980872872457\n ],\n [\n -93.251953125,\n 37.125286284966805\n ],\n [\n -93.7353515625,\n 38.09998264736481\n ],\n [\n -99.8876953125,\n 38.09998264736481\n ],\n [\n -101.953125,\n 37.71859032558816\n ],\n [\n -102.919921875,\n 36.87962060502676\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: March 2020; Version 1.0: December 2019","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
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River connectivity is defined as the water-mediated exchange of matter, energy, and biota between different elements of the riverine landscape. Connectivity is an especially important concept in large-river corridors (channel plus floodplain ) because large rivers integrate fluxes of water, sediment, nutrients, contaminants, and other transported constituents emanating from large contributing drainage basins, and thereby contribute to the complexity of large-river ecosystems. Large rivers are also highly valued for socioeconomic goods and services, which has led to historical fragmentation, lack of connectivity, and contentiousness about best policies for managing large-river corridors. The classification is intended to serve as a template for understanding geographic variation in large rivers within the Midwest, to aid in designing scientific studies of large river ecological processes, and to match specific river-management and restoration objectives to specific river reaches. The focus of the classification is on measuring river connectivity from available hydrological and geomorphic data.
We provide a multiscale assessment and classification for segments of 15 rivers that meet various criteria for largeness. All rivers are tributaries to the Mississippi River system. The 11,600 kilometers (km) that qualified as large were classified by major alterations (unimpounded, navigation pools, storage reservoir) and additionally assessed for their network continuity as a function of numbers and heights of dams. Among the 15 rivers, 55 percent of segment length was unimpounded, 30 percent was in navigation pools, and 15 percent was under storage reservoirs. Assessment of network longitudinal connectivity among river segments documented the contrast between river segments with low-head navigation dams (Upper Mississippi, Illinois, Ohio, Green, and Cumberland Rivers) and those segments with high-head dams (mostly in the Upper Missouri River). The longest unimpounded river pathways exist in the Lower Missouri River and connected tributaries where nearly 1,300 km of the Missouri River connect to an additional 1,800 km of the Middle and Lower Mississippi Rivers.
At our finest scale, we present a statistically based, component classification based on 10-km segments. Cluster analysis of hydrologic variables from 66 streamflow-gaging stations yielded 5 clusters calculated from 5 ecohydrological metrics related to lateral connectivity with the floodplain. A separate cluster analysis of 5 geomorphologic variables associated with each of the 1,172 river segments also yielded 5 clusters. When the hydrologic variables were associated with corresponding segments, the cluster analysis yielded 8 hydrogeomorphic clusters that could be explained in terms of their contribution to floodplain connectivity. Although the clusters overlap considerably in principal component space, the resulting hydrogeomorphic classification leads to a physically reasonable distribution of classes. The resulting classification is intended to increase geographic awareness of the range of variation of connectivity potential among large rivers of the Upper Midwest, to increase understanding of the extent of alteration of these rivers, and potentially to serve as a template for stratifying study designs of large-river corridor ecological processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195132","usgsCitation":"Jacobson, R.B., Rohweder, J.J., and DeJager, N.R., 2019, A hydrogeomorphic classification of connectivity of large rivers of the Upper Midwest, United States: U.S. Geological Survey Scientific Investigations Report 2019–5132, 55 p., https://doi.org/10.3133/sir20195132.","productDescription":"Report: vi, 55 p.; 2 Data Releases","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-104678","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399611,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109562.htm"},{"id":370623,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5132/sir20195132.pdf","text":"Report","size":"9.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5132"},{"id":370625,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HFYOLO","text":"USGS data release","linkHelpText":"River valley boundaries and transects generated for select large rivers of the Upper Midwest, United States"},{"id":370624,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YGOKWZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Segment-scale classification, large rivers of the Upper Midwest United States"},{"id":370622,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5132/coverthb.jpg"}],"country":"United States","state":"Colorado, Illinois, Indiana, Iowa, Kansas, Kentucky, Minnesota, Missouri, Montana, Nebraska, New York, North Carolilna,North Dakota, Ohio, Pennsylvania, South Dakota, Tennessee, Virginia, West Virginia, Wisconsin, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.8251953125,\n 35.460669951495305\n ],\n [\n -80.5078125,\n 37.020098201368114\n ],\n [\n -80.15625,\n 35.60371874069731\n ],\n [\n -79.541015625,\n 37.43997405227057\n ],\n [\n -79.365234375,\n 39.87601941962116\n ],\n [\n -77.7392578125,\n 42.74701217318067\n ],\n [\n -82.265625,\n 40.91351257612758\n ],\n [\n -86.8359375,\n 41.73852846935917\n ],\n [\n -87.5830078125,\n 41.60722821271717\n ],\n [\n -88.9453125,\n 43.61221676817573\n ],\n [\n -89.4287109375,\n 43.45291889355465\n ],\n [\n -89.7802734375,\n 46.10370875598026\n ],\n [\n -90.439453125,\n 46.37725420510028\n ],\n [\n -93.42773437499999,\n 46.86019101567027\n ],\n [\n -94.9658203125,\n 47.57652571374621\n ],\n [\n -96.85546875,\n 45.55252525134013\n ],\n [\n -100.94238281249999,\n 48.42920055556841\n ],\n [\n -104.150390625,\n 49.210420445650286\n ],\n [\n -112.0166015625,\n 49.03786794532644\n ],\n [\n -113.99414062499999,\n 45.79816953017265\n ],\n [\n -112.9833984375,\n 44.5278427984555\n ],\n [\n -111.09374999999999,\n 44.77793589631623\n ],\n [\n -106.3916015625,\n 41.47566020027821\n ],\n [\n -105.6884765625,\n 39.67337039176558\n ],\n [\n -104.150390625,\n 38.75408327579141\n ],\n [\n -94.921875,\n 38.16911413556086\n ],\n [\n -91.93359375,\n 36.98500309285596\n ],\n [\n -89.736328125,\n 36.80928470205937\n ],\n [\n -85.8251953125,\n 35.460669951495305\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201