Dryland ecosystems are experiencing shifts in rainfall and plant community composition, which are expected to alter cycling and storage of soil carbon (C). Few experiments have been conducted to examine long‐term effects on (1) soil organic C (SOC) pools throughout the soil profile, and (2) soil inorganic C (SIC) pools as they relate to dynamic changes in C storage and climate change. We measured SOC and SIC from 0 to 1 m beneath plants and in adjacent interplant microsites following nearly 20 yr of experimental manipulations of plant community (native sagebrush steppe or monoculture of exotic crested wheatgrass) and the amount and timing of water availability (ambient, or doubling of annual rainfall in the dormant, DORM, or growing, GROW, season). Under sagebrush plants, GROW increased both SOC and SIC pools, resulting in total carbon (TC) pools 15% greater than plots receiving ambient precipitation, while DORM decreased SOC and SIC pools, decreasing TC pools 20% from ambient. Under crested wheatgrass plants, GROW increased SOC by 73% but decreased SIC by 11% relative to ambient, netting no change in TC pools, while DORM SIC pools were 5% greater than ambient, with no significant increase in either SOC or TC pools. GROW significantly increased TC pools for interplant microsites, regardless of vegetation treatment. At the community scale and summing C pools weighted by percent patch cover, patterns of TC pool were similar to plot measurements. Our findings suggest that sagebrush communities can become a net C source to the atmosphere with increases in dormant season rainfall rather than a C sink as previously predicted. We also provide evidence of SIC as an important and dynamic C sequestration mechanism in drylands. Consideration of vegetation type, all or most of the soil profile, and both organic and inorganic C pools are all important to accurately predict C sequestration with changing climate and disturbance in drylands.
","language":"English","publisher":"ESA","doi":"10.1002/ecs2.2655","usgsCitation":"Huber, D.P., Lohse, K.A., Commendador, A., Joy, S., Aho, K.A., Finney, B.P., and Germino, M., 2019, Vegetation and precipitation shifts interact to alter organic and inorganic carbon storage in cold desert soils: Ecosphere, v. 10, no. 3, e02655, 17 p., https://doi.org/10.1002/ecs2.2655.","productDescription":"e02655, 17 p.","ipdsId":"IP-098438","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":467806,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2655","text":"Publisher Index Page"},{"id":362174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Huber, David P.","contributorId":214275,"corporation":false,"usgs":false,"family":"Huber","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":759516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lohse, Kathleen A. 0000-0003-1779-6773","orcid":"https://orcid.org/0000-0003-1779-6773","contributorId":196995,"corporation":false,"usgs":false,"family":"Lohse","given":"Kathleen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":759517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Commendador, Amy","contributorId":214276,"corporation":false,"usgs":false,"family":"Commendador","given":"Amy","email":"","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":759518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joy, Stephen","contributorId":214277,"corporation":false,"usgs":false,"family":"Joy","given":"Stephen","email":"","affiliations":[{"id":38154,"text":"Idaho State University","active":true,"usgs":false}],"preferred":false,"id":759519,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aho, Ken A.","contributorId":196997,"corporation":false,"usgs":false,"family":"Aho","given":"Ken","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":759521,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Finney, Bruce P.","contributorId":199658,"corporation":false,"usgs":false,"family":"Finney","given":"Bruce","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":759520,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":759515,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70218844,"text":"70218844 - 2019 - Sediment monitoring during Elwha River dam removals: Lessons learned during the Nation’s largest dam removal project","interactions":[],"lastModifiedDate":"2022-01-12T15:31:53.765518","indexId":"70218844","displayToPublicDate":"2019-03-17T08:00:41","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Sediment monitoring during Elwha River dam removals: Lessons learned during the Nation’s largest dam removal project","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD 2019 Conference","conferenceDate":"June 24-28, 2019","conferenceLocation":"Reno, Nevada","language":"English","publisher":"Federal Interagency Sedimentation Conference (FISC) and Federal Interagency Hydrologic Modeling Conference (FIHMC)","usgsCitation":"Curran, C.A., Magirl, C.S., and Hilldale, R.C., 2019, Sediment monitoring during Elwha River dam removals: Lessons learned during the Nation’s largest dam removal project, in Proceedings of SEDHYD 2019, v. 1, Reno, Nevada, June 24-28, 2019, 4 p.","productDescription":"4 p.","ipdsId":"IP-108033","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":384451,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2019/#sedhyd-2019-proceedings"},{"id":384452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.60872268676759,\n 48.03620008671411\n ],\n [\n -123.56323242187499,\n 48.03620008671411\n ],\n [\n -123.56323242187499,\n 48.06729696036061\n ],\n [\n -123.60872268676759,\n 48.06729696036061\n ],\n [\n -123.60872268676759,\n 48.03620008671411\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Curran, Christopher A. 0000-0001-8933-416X ccurran@usgs.gov","orcid":"https://orcid.org/0000-0001-8933-416X","contributorId":1650,"corporation":false,"usgs":true,"family":"Curran","given":"Christopher","email":"ccurran@usgs.gov","middleInitial":"A.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magirl, Christopher S. 0000-0002-9922-6549 magirl@usgs.gov","orcid":"https://orcid.org/0000-0002-9922-6549","contributorId":1822,"corporation":false,"usgs":true,"family":"Magirl","given":"Christopher","email":"magirl@usgs.gov","middleInitial":"S.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812416,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hilldale, Robert C.","contributorId":139315,"corporation":false,"usgs":false,"family":"Hilldale","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":812417,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202298,"text":"sir20195003 - 2019 - Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","interactions":[],"lastModifiedDate":"2019-03-15T16:14:59","indexId":"sir20195003","displayToPublicDate":"2019-03-14T16:15:18","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5003","displayTitle":"Climate, Streamflow, and Lake-Level Trends in the Great Lakes Basin of the United States and Canada, Water Years 1960–2015","title":"Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015","docAbstract":"Water levels in the Great Lakes fluctuate substantially because of complex interactions among inputs (precipitation and streamflow), outputs (evaporation and outflow), and other factors. This report by the U.S. Geological Survey in cooperation with the Great Lakes Restoration Initiative was completed to describe trends in climate, streamflow, lake levels, and major water-budget components within the Great Lakes Basin for water years (WYs) 1960–2015 (study period). Resulting trends are applicable only to the study period and should not be considered indicative of longer-term trends.
Analyses of climate trends used monthly data from the Parameter-elevation Regressions on Independent Slopes Model, which are available only for the United States. Trend tests were completed for annual and seasonal time series of monthly means for total precipitation, daily minimum air temperature (Tmin), and daily maximum air temperature (Tmax). Statistical significance for all time-trend tests (climate, streamflow, and lake levels) was determined using the Mann‑Kendall test for probability values less than or equal to 0.10. Trend analyses were completed without adjustments for serial correlation; however, a modified Mann-Kendall test was subsequently used to examine potential effects of short-term persistence in time-series data. Effects of short-term persistence were considered inconsequential for climate data and minor for streamflow data; however, the presence of short-term persistence in water-budget components had more substantial effects on trend analyses.
Spatial distributions of trends in climatic data for WYs 1960–2015 for the U.S. part of the Great Lakes Basin (land only) indicate (1) generally ubiquitous upward trends in Tmin and (2) a sharp transition from neutral or downward trends in precipitation northwest of Lake Michigan to generally upward trends east of Lake Michigan. Trends in Tmax were not statistically significant. Analyses of annual climatic data aggregated for the U.S. land part of the Great Lakes Basin indicated statistically significant upward trends for precipitation and Tmin, and similar statistically significant trends existed for all the individual lake subbasins except Lake Superior.
Of 103 U.S. Geological Survey streamgages analyzed for streamflow trends, 71 had significant annual trends (54 upward and 17 downward). Downward trends in annual streamflow are concentrated northwest of Lake Michigan (16 streamgages), and upward trends are concentrated east of Lake Michigan (53 streamgages). Of the 71 streamgages with significant annual trends, 70 had at least one season with a significant trend that matched the annual trend direction.
Of 35 Environment and Climate Change Canada streamgages analyzed, 22 had significant upward trends in annual streamflow, and all but 1 of these 22 had at least one season with a significant upward trend. None of the Environment and Climate Change Canada streamgages had significant downward annual trends, and only one had a significant downward seasonal trend.
Trends in lake levels and several major water-budget components affecting lake levels were analyzed for the study period. Significant downward trends in lake level and outflow for Lake Superior are driven primarily by low lake levels and outflows during WYs 1998–2014. A significant downward trend in runoff from the contributing drainage area also is indicated, which is consistent with numerous streamgages northwest of Lake Michigan with significant downward trends in annual streamflow. A significant upward trend in annual overlake evaporation also is indicated, which is consistent with the spatially distributed upward trends in annual Tmin.
The sum of overlake precipitation and runoff from the contributing drainage area for each of the Great Lakes, less overlake evaporation, composes a variable called net basin supply (NBS). A significant downward trend in NBS is indicated for Lake Superior, which is consistent with significant trends for individual components of runoff (downward) and evaporation (upward) that contributed to a significant downward trend for lake outflow. Statistically significant upward trends in NBS for Lake Saint Clair and Lake Ontario offset the downward trend for Lake Superior and combine with nonsignificant upward trends in NBS for Lakes Michigan and Huron and Lake Erie to produce a neutral trend in NBS for the basin.
A predictable pattern in monthly mean lake levels is noted for Lake Superior, with the minimum for each year usually during or near March and the maximum commonly during or near September or October. When an October lake level is in a period of substantial decline, potential for an ensuing short-term period of below-mean lake levels is enhanced. Downstream from Lake Superior, monthly lake levels have sawtooth patterns that somewhat resemble those for Lake Superior but with decreased predictability in timing.
Similar to Lake Superior, Lakes Michigan and Huron, Lake Saint Clair, and Lake Erie all have a prolonged period of low lake levels around WYs 1998–2014; however, a significant downward trend is indicated only for Lakes Michigan and Huron. All these lakes also have a period of low lake levels before about WY 1968, when minimum lake levels were lower than during WYs 1998–2014. The significant downward trend of outflow from Lake Superior is carried downstream into Lakes Michigan and Huron; however, trends in outflow from the next three lakes downstream (Lakes Saint Clair, Erie, and Ontario) are offset by increased precipitation and runoff and are not significant.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195003","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Norton, P.A., Driscoll, D.G., and Carter, J.M., 2019, Climate, streamflow, and lake-level trends in the Great Lakes Basin of the United States and Canada, water years 1960–2015: Scientific Investigations Report 2019–5003, 47 p., https://doi.org/10.3133/sir20195003.","productDescription":"Report: vi, 47 p.; Appendix Figures; Appendix Tables: 5","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-089551","costCenters":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":362031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5003/coverthb.jpg"},{"id":362032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003.pdf","text":"Report","size":"22.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362033,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.1_to_1.103.pdf","text":"Appendix figures 1.1–1.103","size":"940 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362034,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_figs_1.104_to_1.138.pdf","text":"Appendix figures 1.104–1.138","size":"333 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5003"},{"id":362035,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5003/sir20195003_appendix_tables_1.1_to_1.5.xlsx","text":"Appendix tables 1.1–1.5","size":"132 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5003"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.4716796875,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 41.44272637767212\n ],\n [\n -75.7177734375,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 50.035973672195496\n ],\n [\n -93.4716796875,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Measurements of the stable isotope ratios of nitrogen (15N/14N) and oxygen (18O/16O) in nitrate (NO3–) enable identification of sources, dispersal, and fate of natural and contaminant NO3– in aquatic environments. The 18O/16O of NO3– produced by nitrification is often assumed to reflect the proportional contribution of oxygen atom sources, water, and molecular oxygen, in a 2:1 ratio. Culture and seawater incubations, however, indicate oxygen isotopic equilibration between nitrite (NO2–) and water, and kinetic isotope effects for oxygen atom incorporation, which modulate the NO3– 18O/16O produced during nitrification. To investigate the influence of kinetic and equilibrium effects on the isotopic composition of NO3– produced from the nitrification of ammonia (NH3), we incubated streamwater supplemented with ammonium (NH4+) and increments of 18O-enriched water. Resulting NO3– 18O/16O ratios showed (1) a disproportionate sensitivity to the 18O/16O ratio of water, mediated by isotopic equilibration between water and NO2–, as well as (2) kinetic isotope discrimination during O atom incorporation from molecular oxygen and water. Empirically, the NO3– 18O/16O ratios thus produced fortuitously converge near the 18O/16O ratio of water. More elevated NO3– 18O/16O values commonly reported in soils and oxic groundwater may thus derive from processes additional to nitrification, including NO3– reduction.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b03386","usgsCitation":"Boshers, D.S., Granger, J., Tobias, C.R., Bohlke, J., and Smith, R.L., 2019, Constraining the oxygen isotopic composition of nitrate produced by nitrification: Environmental Science & Technology, v. 53, no. 3, p. 1206-1216, https://doi.org/10.1021/acs.est.8b03386.","productDescription":"11 p.","startPage":"1206","endPage":"1216","ipdsId":"IP-099166","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":362074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Boshers, Danielle S.","contributorId":214193,"corporation":false,"usgs":false,"family":"Boshers","given":"Danielle","email":"","middleInitial":"S.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":759321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Granger, Julie","contributorId":214194,"corporation":false,"usgs":false,"family":"Granger","given":"Julie","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":759322,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tobias, Craig R.","contributorId":191283,"corporation":false,"usgs":false,"family":"Tobias","given":"Craig","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":759323,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":759320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Richard L. 0000-0002-3829-0125 rlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3829-0125","contributorId":1592,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":759324,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201606,"text":"ofr20181187 - 2019 - Geomorphic survey of North Fork Eagle Creek, New Mexico, 2017","interactions":[],"lastModifiedDate":"2019-07-22T12:35:09","indexId":"ofr20181187","displayToPublicDate":"2019-03-14T13:05:15","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1187","displayTitle":"Geomorphic Survey of North Fork Eagle Creek, New Mexico, 2017","title":"Geomorphic survey of North Fork Eagle Creek, New Mexico, 2017","docAbstract":"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
Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Marbled murrelets (Brachyramphus marmoratus) have been listed as “endangered” by the State of California and “threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management, evaluation of conservation efficacy, and ultimately, to meet Federal- and State-mandated recovery efforts for this species. During June–August 2018, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1999–2018), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (San Francisco Bay to Point Sur in central California). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 137 detections of 227 individuals observed on 9 surveys. The abundance estimated for the entire study area using all surveys in 2018 was 370 birds (95-percent confidence interval, 250–546 birds). Estimated abundance from 2018 is comparable to most prior years of study, except for 2001–03, when greater abundances were estimated. In 2018, we estimated reproductive productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) using four detections of four HY individuals observed on six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.047 ± 0.024 standard error. We updated a synthesized database of all Zone 6 marbled murrelet survey data since 1999 with 2018 data to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1107","usgsCitation":"Felis, J.J., Kelsey, E.C., and Adams, J., 2019, Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2018 breeding season: U.S. Geological Survey Data Series 1107, 10 p., https://doi.org/10.3133/ds1107.","productDescription":"Report: v, 10 p.; Data release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103980","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":362066,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01RW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Annual marbled murrelet abundance and productivity surveys off central California (Zone 6), 1999–2018 (ver. 2.0, March 2019)"},{"id":362050,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1107/coverthb.jpg"},{"id":362051,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1107/ds1107.pdf","text":"Report","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1107"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.52090454101564,\n 36.923547681089296\n ],\n [\n -122.02102661132814,\n 36.923547681089296\n ],\n [\n -122.02102661132814,\n 37.52279705525959\n ],\n [\n -122.52090454101564,\n 37.52279705525959\n ],\n [\n -122.52090454101564,\n 36.923547681089296\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The enrichment factor (EF) is a widely used metric for determining how much the presence of an element in a sampling media has increased relative to average natural abundance because of human activity. Calculation of an EF requires the selection of both a background composition and a reference element, choices that can strongly influence the result of the calculation. Here, it is shown how carefully applied, classical principal component analysis (PCA) examined via biplots can guide the selections of background compositions and reference elements. Elemental data were treated using the centered log ratio (CLR) transformation, and multiple subsets of major and trace elements were examined to gain different perspectives. The methodology was applied to a dataset of elemental soil concentrations from around breccia pipe uranium mines in Arizona, U.S.A., with most samples collected via incremental sampling methodology. Storage of ore at the surface creates the potential for wind dispersal of ore-derived material. Uranium was found to be the best individual tracer of dispersal of ore-derived material to nearby soils, with EF values up to 75. Sulfur, As, Mo, and Cu were also enriched but to lesser degrees. The results demonstrate several practical benefits of a PCA in these situations: (1) the ability to identify one or more elements best suited to distinguish a specific source of enrichment from background composition; (2) understanding how background compositions vary within and between sites; (3) identification of samples containing enriched or anthropogenic materials based upon their integrated, multi-element composition. Calculating the most representative EF values is useful for numerical assessment of enrichment, whether anthropogenic or natural. As shown here, however, the PCA and biplot method provide a visual approach that integrates information from all elements for a given subset of data in a manner that yields geochemical insights beyond the power of the EF.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2019.01.122","usgsCitation":"Bern, C.R., Walton-Day, K., and Naftz, D.L., 2019, Improved enrichment factor calculations through principal component analysis: Examples from soils near breccia pipe uranium mines, Arizona, USA: Environmental Pollution, v. 248, p. 90-100, https://doi.org/10.1016/j.envpol.2019.01.122.","productDescription":"11 p.","startPage":"90","endPage":"100","ipdsId":"IP-102119","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":467818,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2019.01.122","text":"Publisher Index Page"},{"id":437543,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KTLXL8","text":"USGS data release","linkHelpText":"Surface Materials Data from Breccia-Pipe Uranium Mine and Reference Sites, Arizona, USA"},{"id":362042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.04907226562499,\n 35.5\n ],\n [\n -111,\n 35.5\n ],\n [\n -111,\n 37\n ],\n [\n -114.04907226562499,\n 37\n ],\n [\n -114.04907226562499,\n 35.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"248","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bern, Carleton R. 0000-0002-8980-1781 cbern@usgs.gov","orcid":"https://orcid.org/0000-0002-8980-1781","contributorId":201152,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton","email":"cbern@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":759222,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227949,"text":"70227949 - 2019 - Winter precipitation and summer temperature predict lake water quality at macroscales","interactions":[],"lastModifiedDate":"2022-02-02T16:04:07.07506","indexId":"70227949","displayToPublicDate":"2019-03-13T09:52:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Winter precipitation and summer temperature predict lake water quality at macroscales","docAbstract":"Climate change can have strong effects on aquatic ecosystems, including disrupting nutrient cycling and mediating processes that affect primary production. Past studies have been conducted mostly on individual or small groups of ecosystems, making it challenging to predict how future climate change will affect water quality at broad scales. We used a subcontinental-scale database to address three objectives: (1) identify which climate metrics best predict lake water quality, (2) examine whether climate influences different nutrient and productivity measures similarly, and (3) quantify the potential effects of a changing climate on lakes. We used climate data to predict lake water quality in ~11,000 north temperate lakes across 17 U.S. states. We developed a novel machine learning method that jointly models different measures of water quality using 48 climate metrics and accounts for properties inherent in macroscale data (e.g., spatial autocorrelation). Our results suggest that climate metrics related to winter precipitation and summer temperature were strong predictors of lake nutrients and productivity. However, we found variation in the magnitude and direction of the relationship between climate and water quality. We predict that a likely future climate change scenario of warmer summer temperatures will lead to increased nutrient concentrations and algal biomass across lakes (median ~3%–9% increase), whereas increased winter precipitation will have highly variable effects. Our results emphasize the importance of heterogeneity in the response of individual ecosystems to climate and are a caution to extrapolating relationships across space.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023088","usgsCitation":"Collins, S.M., Yuan, S., Tan, P.N., Oliver, S.K., Lapierre, J.F., Cheruvelil, K., Fergus, C., Skaff, N.K., Stachelek, J., Wagner, T., and Soranno, P., 2019, Winter precipitation and summer temperature predict lake water quality at macroscales: Water Resources Research, v. 55, no. 4, p. 2708-2721, https://doi.org/10.1029/2018WR023088.","productDescription":"14 p.","startPage":"2708","endPage":"2721","ipdsId":"IP-095436","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Collins, S. M.","contributorId":273184,"corporation":false,"usgs":false,"family":"Collins","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":832670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yuan, S.","contributorId":273185,"corporation":false,"usgs":false,"family":"Yuan","given":"S.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832671,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tan, P. N.","contributorId":273186,"corporation":false,"usgs":false,"family":"Tan","given":"P.","email":"","middleInitial":"N.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832672,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, S. K.","contributorId":273187,"corporation":false,"usgs":false,"family":"Oliver","given":"S.","email":"","middleInitial":"K.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":832673,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lapierre, J. F.","contributorId":273188,"corporation":false,"usgs":false,"family":"Lapierre","given":"J.","email":"","middleInitial":"F.","affiliations":[{"id":41192,"text":"Université de Montreal","active":true,"usgs":false}],"preferred":false,"id":832674,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheruvelil, K. S.","contributorId":273189,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"K. S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832675,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fergus, C. E.","contributorId":273190,"corporation":false,"usgs":false,"family":"Fergus","given":"C. E.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":832676,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Skaff, N. K.","contributorId":273191,"corporation":false,"usgs":false,"family":"Skaff","given":"N.","email":"","middleInitial":"K.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832677,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stachelek, J.","contributorId":273193,"corporation":false,"usgs":false,"family":"Stachelek","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832678,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832679,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Soranno, P. A.","contributorId":273195,"corporation":false,"usgs":false,"family":"Soranno","given":"P. A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":832680,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70210073,"text":"70210073 - 2019 - Recreational impacts to wildlife: Managing visitors and resources to protect wildlife","interactions":[],"lastModifiedDate":"2020-05-13T14:45:18.84393","indexId":"70210073","displayToPublicDate":"2019-03-13T09:44:24","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Recreational impacts to wildlife: Managing visitors and resources to protect wildlife","docAbstract":"Publication Abstract: Visitor use management is essential for maximizing benefits for visitors while achieving and maintaining desired resource conditions and visitor experiences on federally managed lands and waters. Visitor capacity, a component of visitor use management, is defined as the maximum amounts and types of visitor use that an area can accommodate while achieving and maintaining the desired resource conditions and visitor experiences that are consistent with the purposes for which the area was established. This visitor capacity guidebook, in combination with the “Visitor Use Management Framework” (the framework), provides managers with processes to collaboratively develop long-term strategies to manage the amounts and types of visitor use to protect resources, improve access, connect visitors to key experiences, and achieve desired conditions. The purpose of this guidebook is to provide cohesive guidance on identifying and implementing visitor capacity on federally managed lands and waters. Similar to the framework, the sliding scale of analysis is discussed throughout this guidebook to ensure the investment of time, money, and other resources for a project is commensurate with the complexity of the project and the consequences of the decision. Overall, this guidebook is meant to expand on the framework and guide a professional and consistent approach to identifying and implementing visitor capacity.\n\nPaper Abstract: Most protected natural areas, including parks, forests, and wildlife refuges, are managed under a dual mandate to preserve predominantly natural settings and processes while also accommodating recreational visitation. Visitor activities can have deleterious impacts to protected area vegetation, soil, water, wildlife, and cultural resources. The term impact denotes undesirable visitor-related effects to natural resources and/or wildlife. This paper reviews the management of recreation impacts to wildlife, including discussions of influential factors, impact indicators, and the range of management responses. This information is provided to assist recreation and land managers in avoiding or minimizing visitor impact to wildlife, particularly related to decision-making within the new Visitor Use Management (VUM) framework. Such decision-making requires a thorough understanding of the different types of wildlife impact and the use-related, environmental, and managerial factors that influence them.","language":"English","publisher":"National Park Service","collaboration":"Department of Interior, US National Park Service","usgsCitation":"Marion, J.L., 2019, Recreational impacts to wildlife: Managing visitors and resources to protect wildlife, 18 p.","productDescription":"18 p.","ipdsId":"IP-096635","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":374756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":374739,"type":{"id":15,"text":"Index Page"},"url":"https://visitorusemanagement.nps.gov/Content/documents/Contributing%20Paper_Impacts%20to%20Wildlife_Visitor%20Capacity_Edition%201.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marion, Jeffrey L. 0000-0003-2226-689X jeff_marion@usgs.gov","orcid":"https://orcid.org/0000-0003-2226-689X","contributorId":3614,"corporation":false,"usgs":true,"family":"Marion","given":"Jeffrey","email":"jeff_marion@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":788989,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70202411,"text":"sir20195009 - 2019 - Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","interactions":[],"lastModifiedDate":"2019-03-13T16:10:00","indexId":"sir20195009","displayToPublicDate":"2019-03-13T09:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5009","displayTitle":"Flood-Inundation Maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","title":"Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia","docAbstract":"Digital flood-inundation maps for a 16.4-mile reach of the Yellow River in Gwinnett County, Georgia, from 0.5 mile upstream from River Drive to Centerville Highway (Georgia State Route 124) were developed to depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at two U.S. Geological Survey (USGS) streamgages in the mapped area. The maps for the 9.0-mile reach from 0.5 mile upstream from River Drive to Stone Mountain Highway (U.S. Route 78) are referenced to the streamgage Yellow River near Snellville, Ga. (station 02206500), and the maps for the 7.4-mile reach from Stone Mountain Highway to Centerville Highway are referenced to the streamgage Yellow River at Ga. 124, near Lithonia, Ga. (02207120). Real-time stage information from these streamgages can be used with these maps to estimate near real-time areas of inundation. The forecasted peak-stage information for the USGS streamgages Yellow River near Snellville, Ga. (02206500), and Yellow River at Ga. 124, near Lithonia, Ga. (02207120), can be used in conjunction with the maps developed for this study to show predicted areas of flood inundation.
A one-dimensional step-backwater model was developed using the U.S. Army Corps of Engineers Hydrologic Engineering Center's River Analysis System (HEC–RAS) software for the Yellow River and was used to compute flood profiles for a 16.4-mile reach of the Yellow River. The hydraulic model was then used to simulate 16 water-surface profiles at 1.0-foot (ft) intervals at the Yellow River near Snellville streamgage and 17 water-surface profiles at 1.0-ft intervals at the Yellow River near Lithonia streamgage. At the Yellow River near Snellville streamgage, the profiles ranged from a stage of 18.0 ft, which is 819.1 ft above the North American Vertical Datum of 1988 (NAVD 88), to a stage of 33.0 ft, which is 834.1 ft above NAVD 88. At the Yellow River near Lithonia streamgage, the profiles ranged from the National Weather Service action stage of 13.0 ft, which is 732.5 ft above NAVD 88, to a stage of 29.0 ft, which is 748.5 ft above NAVD 88. The simulated water-surface profiles were then combined with a geographic information system digital elevation model—derived from light detection and ranging (lidar) data having a 5.0-ft horizontal resolution—to delineate the area flooded at each 1.0-ft interval of stream stage for both streamgages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195009","collaboration":"Prepared in cooperation with Gwinnett County, Georgia","usgsCitation":"Musser, J.W., 2019, Flood-inundation maps for the Yellow River from River Drive to Centerville Highway, Gwinnett County, Georgia: U.S. Geological Survey Scientific Investigations Report 2019–5009, 15 p., https://doi.org/10.3133/sir20195009.","productDescription":"Report: vi, 15 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-100804","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":361975,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKB3H2","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation and flood depth for the Yellow River in Gwinnett County, Georgia based on water-surface elevation at the U.S. Geological Survey streamgages Yellow River, near Snellville, Georgia (02206500) and Yellow River at Ga. 124, near Lithonia, Georgia (02207120)"},{"id":361973,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5009/coverthb.jpg"},{"id":361974,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5009/sir20195009.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5009"}],"country":"United States","state":"Georgia","county":"Gwinnett County","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.1667,\n 33.75\n ],\n [\n -84,\n 33.75\n ],\n [\n -84,\n 33.9167\n ],\n [\n -84.1667,\n 33.9167\n ],\n [\n -84.1667,\n 33.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
This postcard provides details about \"Cyanobacterial Harmful Algal Blooms and U.S. Geological Survey Science Capabilities, \"Open File Report 2016-1174, where you can find details about how U.S. Geological Survey (USGS) scientists use traditional methods and emerging technologies in collaboration with numerous partners to lead a diverse range of studies addressing harmful algal bloom (HAB) issues in water bodies throughout the United States. Cutting-edge USGS research in HABs has advanced scientific understanding and led to practical applications that help protect ecological and human health.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip188","collaboration":" ","usgsCitation":"U.S. Geological Survey, 2019, Harmful algal blooms: U.S. Geological Survey General Information Product 188, 2 p., https://doi.org/10.3133/gip188.","productDescription":"Postcard: 8.5 x 5.5 inches","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-104836","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":361806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0188/gip188.pdf","text":"Report","size":"344 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 188"},{"id":361805,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0188/coverthb3.jpg"},{"id":361807,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0188/gip188_highres.pdf","text":"Report","size":"1.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 188","linkHelpText":"- High Resolution"},{"id":361808,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161174","text":"Open-File Report 2016-1174","linkHelpText":"- Cyanobacterial Harmful Algal Blooms and U.S. Geological Survey Science Capabilities"},{"id":361811,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip185","text":"General Information Product 185","linkHelpText":"- Water Resources Science of the U.S. Geological Survey in New York"}],"contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
Glacial−interglacial variations in CO2 and methane in polar ice cores have been attributed, in part, to changes in global wetland extent, but the wetland distribution before the Last Glacial Maximum (LGM, 21 ka to 18 ka) remains virtually unknown. We present a study of global peatland extent and carbon (C) stocks through the last glacial cycle (130 ka to present) using a newly compiled database of 1,063 detailed stratigraphic records of peat deposits buried by mineral sediments, as well as a global peatland model. Quantitative agreement between modeling and observations shows extensive peat accumulation before the LGM in northern latitudes (>40°N), particularly during warmer periods including the last interglacial (130 ka to 116 ka, MIS 5e) and the interstadial (57 ka to 29 ka, MIS 3). During cooling periods of glacial advance and permafrost formation, the burial of northern peatlands by glaciers and mineral sediments decreased active peatland extent, thickness, and modeled C stocks by 70 to 90% from warmer times. Tropical peatland extent and C stocks show little temporal variation throughout the study period. While the increased burial of northern peats was correlated with cooling periods, the burial of tropical peat was predominately driven by changes in sea level and regional hydrology. Peat burial by mineral sediments represents a mechanism for long-term terrestrial C storage in the Earth system. These results show that northern peatlands accumulate significant C stocks during warmer times, indicating their potential for C sequestration during the warming Anthropocene.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.1813305116","usgsCitation":"Treat, C.C., Kleinen, T., Broothaerts , N., Dalton, A.S., Dommain, R., Douglas, T.A., Drexler, J.Z., Finkelstein, S., Grosse, G., Hope, G., Hutchings, J., Jones, M.C., Kuhry, P., Lacourse, T., Lahteenoja, O., Loisel, J., Notebaert, B., Payne, R., Peteet, D.M., Sannel, A.B., Stelling, J.M., Strauss, J., Swindles, G.T., Talbot, J., Tarnocai, C., Verstraeten, G., Williams , C., Xia, Z., Yu, Z., Valiranta, M., Hattestrand, M., Alexanderson, H., and Brovkin, V., 2019, Widespread global peatland establishment and persistence over the last 130,000 y: PNAS, v. 116, no. 11, p. 4822-4827, https://doi.org/10.1073/pnas.1813305116.","productDescription":"6 p.","startPage":"4822","endPage":"4827","ipdsId":"IP-091584","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":467822,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1813305116","text":"Publisher Index Page"},{"id":373582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Treat, Claire C.","contributorId":96606,"corporation":false,"usgs":true,"family":"Treat","given":"Claire","email":"","middleInitial":"C.","affiliations":[{"id":25501,"text":"University of Eastern Finland","active":true,"usgs":false}],"preferred":false,"id":785865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleinen, Thomas 0000-0001-9550-5164","orcid":"https://orcid.org/0000-0001-9550-5164","contributorId":50427,"corporation":false,"usgs":true,"family":"Kleinen","given":"Thomas","email":"","affiliations":[{"id":32387,"text":"Max Planck Institute for Meteorology, Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":785866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broothaerts , Nils ","contributorId":223665,"corporation":false,"usgs":true,"family":"Broothaerts ","given":"Nils ","affiliations":[],"preferred":false,"id":785867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalton, April S.","contributorId":223666,"corporation":false,"usgs":true,"family":"Dalton","given":"April","email":"","middleInitial":"S.","affiliations":[{"id":13300,"text":"3Department of Earth Sciences, University of Toronto, Toronto, Ontario M5S 3B1, Canada.","active":true,"usgs":false}],"preferred":false,"id":785868,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dommain, Rene","contributorId":220666,"corporation":false,"usgs":false,"family":"Dommain","given":"Rene","email":"","affiliations":[{"id":40220,"text":"University of Potsdam, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":785869,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas, Thomas A. 0000-0003-1314-1905","orcid":"https://orcid.org/0000-0003-1314-1905","contributorId":64553,"corporation":false,"usgs":false,"family":"Douglas","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":33087,"text":"Cold Regions Research and Engineering Laboratory","active":true,"usgs":false}],"preferred":true,"id":785870,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - 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However, these zones are prone to anthropogenic disturbances that include shoreline development, urbanization, nutrient inputs, agricultural and(or) recreational use. Among recreational uses, public access sites are often developed to accommodate boaters and facilitate lake access via boat ramps. Although the ‘foot print’ associated with boat ramp structures can be relatively small compared to total shoreline coverage, little is known about the relative effects of boating activity on littoral zones and macroinvertebrate communities. In this study, we assess the relative impact of boating-related activities on aquatic macroinvertebrate communities at high-use (primary) and low-use (secondary) boat ramps on five glacial lakes of eastern South Dakota. Macroinvertebrate assemblages were dominated by few taxa that included Chironomidae, Corixidae, Caenidae, and Amphipoda. Moreover, boat ramp use did not influence abundance or diversity of aquatic macroinvertebrates. Habitat, specifically substrate composition, was also similar between primary and secondary boat ramps despite more intense use associated with primary boat ramps. Our results support related findings that aquatic invertebrate assemblages in the Prairie Pothole Region are structured by regional environmental variability (climate, habitat, and water quality) resulting in communities with characteristically low diversity and tolerant taxa. This may explain why small-scale disturbance associated with boating activity has no discernable effect on macroinvertebrate assemblages in glacial lakes of the Prairie Pothole Region.
In the High Plains, U.S., native prairie conversion to cropland agriculture has resulted in a loss of service delivery capabilities from most depressional wetlands as a result of sedimentation. Restoring historic hydrological conditions to affected wetlands may rejuvenate some services, however, there may be tradeoffs due to emissions of CH4 and N2O. We evaluated the influence of two predominant conservation programs (Wetlands Reserve Program, WRP and Conservation Reserve Program, CRP) on gas emissions (CO2, CH4, N2O) from 42 playas and uplands in the High Plains of Nebraska. Because playa restoration through the WRP is most prevalent in the Rainwater Basin (RWB), we studied 27 playas/uplands among reference condition, cropland, and WRP land uses. We studied 15 playas/uplands within native grassland, cropland, and CRP land uses in the Western High Plains (WHP) of Nebraska. Emissions were collected bi-weekly from April-October of 2012 and 2013 from four landscape positions extending outward from the wetland center into upland. In RWB playas, CH4 and N2O emissions were similar among land uses but CO2 was 28% higher in cropland than WRP wetlands. Cropland uplands emitted 648% more N2O than reference and WRP uplands. Overall, net CO2-equiv emissions were lower in playas/uplands in WRP, suggesting that benefits of playa restoration may include climate mitigation services as well as increased water storage capacity and biodiversity provisioning. In the WHP, cropland and grassland playas emitted 46 and 23 times more CH4, respectively, than CRP in 2013. Playas in CRP emitted 43% less N2O than cropland playas. In 2013, net emissions for cropland and native grassland playas were 75% and 39% greater, respectively, than CRP playas. In the WHP, the benefits of lower gas emissions must be appropriately weighted against tradeoffs of ecosystem services related to shorter hydroperiods as a result of reduced runoff into playas in CRP.
","language":"English","publisher":"Scientific Research","doi":"10.4236/as.2019.102016","usgsCitation":"Daniel, D.W., Smith, L.M., McMurry, S.T., Tangen, B., Dahl, C.F., Euliss, N., and LaGrange, T., 2019, Effects of land use on greenhouse gas flux in playa wetlands and associated watersheds in the High Plains, USA: Agricultural Sciences, v. 10, no. 2, p. 181-201, https://doi.org/10.4236/as.2019.102016.","productDescription":"21 p.","startPage":"181","endPage":"201","ipdsId":"IP-070774","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467824,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4236/as.2019.102016","text":"Publisher Index Page"},{"id":361981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daniel, Dale W.","contributorId":191880,"corporation":false,"usgs":false,"family":"Daniel","given":"Dale","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":759081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Loren M.","contributorId":196856,"corporation":false,"usgs":false,"family":"Smith","given":"Loren","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":759082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McMurry, Scott T.","contributorId":191876,"corporation":false,"usgs":false,"family":"McMurry","given":"Scott","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":759083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759080,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dahl, Charles F. cdahl@usgs.gov","contributorId":4052,"corporation":false,"usgs":true,"family":"Dahl","given":"Charles","email":"cdahl@usgs.gov","middleInitial":"F.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Euliss, Ned ceuliss@usgs.gov","contributorId":192021,"corporation":false,"usgs":true,"family":"Euliss","given":"Ned","email":"ceuliss@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":759085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"LaGrange, Ted","contributorId":207141,"corporation":false,"usgs":false,"family":"LaGrange","given":"Ted","email":"","affiliations":[{"id":17640,"text":"Nebraska Game and Parks Commission","active":true,"usgs":false}],"preferred":false,"id":759086,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202202,"text":"sir20195004 - 2019 - Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-10-23T09:27:27","indexId":"sir20195004","displayToPublicDate":"2019-03-11T13:38:08","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5004","displayTitle":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018","title":"Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018","docAbstract":"Libraries of digital flood-inundation maps that spanned a combined 37.2-mile reach of the Meramec River that extended upstream from Eureka, Missouri, to downstream near the confluence of the Meramec and Mississippi Rivers were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, and the cities of Pacific, Eureka, Wildwood, and Arnold. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the cooperative USGS streamgages for the Meramec River near Eureka, Mo. (USGS station 07019000), the Meramec River at Valley Park, Mo. (USGS station 07019130), the Meramec River at Fenton, Mo. (USGS station 07019210), and the Meramec River at Arnold, Mo. (USGS station 07019300). Near-real-time stage data at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service (NWS) Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites (listed as NWS sites erkm7, vllm7, fnnm7, and arnm7, respectively).
Flood profiles were computed for the stream reach by means of a calibrated one-dimensional step-backwater hydraulic model. The model was calibrated using a stage-discharge relation at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000) and documented high-water marks from the flood of December 2015 through January 2016.
The calibrated hydraulic model was used to compute water-surface profiles: 1 set for the Meramec River near Eureka, Mo., streamgage (USGS station 07019000); 1 set for the Meramec River at Valley Park, Mo., streamgage (USGS station 07019130); 7 sets for the Meramec River at Fenton, Mo., streamgage (USGS station 07019210) for a range of Mississippi River conditions; and 8 sets for the Meramec River at Arnold, Mo., streamgage (USGS station 07019300) for a range of Mississippi River conditions. The water-surface profiles were produced for stages at 1-foot (ft) intervals referenced to the datum from each streamgage and ranging from the NWS action stage, or near bankfull discharge, to the stage corresponding to the estimated 0.2-percent annual exceedance probability (500-year recurrence interval) flood, as determined at the Meramec River near Eureka, Mo., streamgage (USGS station 07019000). The simulated water-surface profiles then were combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.28-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each simulated 1-ft stage increment. Previously published flood-inundation maps were updated and incorporated in the flood map libraries for USGS stations 07019130 and 07019210 to complete the map sets corresponding to eight Mississippi River conditions.
The availability of these maps, along with internet information regarding current stage from the USGS streamgages and forecasted high-flow stages from the NWS, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures and for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195004","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Metropolitan St. Louis Sewer District, Missouri Department of Transportation, Missouri American Water, Federal Emergency Management Agency Region 7, the city of Pacific, the city of Eureka, the city of Wildwood, and the city of Arnold","usgsCitation":"Dietsch, B.J., and Strauch, K.R., 2019, Flood-inundation maps of the Meramec River from Eureka to Arnold, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5004, 12 p., https://doi.org/10.3133/sir20195004.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-096951","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361924,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5004/sir20195004.pdf","text":"Report","size":"1.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5004"},{"id":361925,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B0XLJL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-Inundation Maps of the Meramec River from Eureka to Arnold, Missouri, 2018"},{"id":361923,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5004/coverthb.jpg"}],"country":"United States","state":"Missouri","city":"Arnold, Eureka","otherGeospatial":"Meramec River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.66398620605469,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.38795699631396\n ],\n [\n -90.33405303955078,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.565347844885466\n ],\n [\n -90.66398620605469,\n 38.38795699631396\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Water levels in lakes and wetlands in the central North Dakota Missouri Coteau region that were either dry or only sporadically held water since before the 1930s have been rising since the early 1990s in response to an extended wet period. The lakes have remained full since the mid-1990s, which has provided benefits to migratory waterfowl, fisheries, and wildlife. A small shift in climate conditions, either to drier or wetter conditions, can have a large effect on the lake levels of these water bodies. The North Dakota Game and Fish Department identified five lakes as candidates for sustaining long-term fisheries. The lakes are in Kidder, Stutsman, and Logan Counties, and some lakes might receive inflow from mostly freshwater aquifers, such as the Central Dakota and Streeter aquifers, and were mostly dry during the early 1990s. After about 1995, the lakes had filled up and were deep enough to sustain populations of game fish such as walleye, perch, and northern pike. Before investing in development of permanent fisheries and associated infrastructure, such as campgrounds and boat ramps, fisheries biologists needed to know if the lake levels are likely to remain high in coming decades.
The U.S. Geological Survey, in cooperation with the North Dakota Game and Fish Department, developed a water-balance model to determine the effects of precipitation, evapotranspiration, and groundwater interaction on lake volumes. The model was developed using climate input data and lake volumes for the calibration period 1992 through 2016, during which historical lake volumes could be estimated using land surface elevation data and Landsat images. Long-term (1940–2018) climate input data were used with the water-balance model to reconstruct historical lake volumes prior to the calibration period, and block-bootstrapping was used to simulate potential future climate input data and lake volumes for 2017 through 2067. The simulated future lake volumes were used to estimate the likelihood of annual lake volumes remaining consistent, increasing, or decreasing through the year 2067.
Of the five lakes, Sibley Lake was the most likely to sustain a long-term fishery for a period longer than 50 years. The simulated lake volumes for Alkaline Lake, Big Mallard Marsh, and Remmick Lake indicated the lakes have a 50-percent chance to fall below 75 percent of their 2016 volume by about 2030, 2067, and 2025, respectively. Simulation results for Marvin Miller Lake were substantially different compared to the other four lakes and indicated the lake has a 50-percent chance to fall below 75 percent of its 2016 volume prior to 2025.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195007","collaboration":"Prepared in cooperation with the North Dakota Game and Fish Department","usgsCitation":"Lundgren, R.F., York, B.C., Stroh, N.A., and Vecchia, A.V., 2019, Water-balance modeling of selected lakes for evaluating viability as long-term fisheries in Kidder, Logan, and Stutsman Counties, North Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5007, 22 p., https://doi.org/10.3133/sir20195007.","productDescription":"Report: v, 22 p.; Downloads","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102504","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":361913,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5007/coverthb.jpg"},{"id":361914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5007/sir20195007.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5007"},{"id":361915,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2019/5007/downloads/","text":"Water-Balance Model R code scripts","linkFileType":{"id":6,"text":"zip"},"description":"Water-Balance Model R code scripts"}],"country":"United States","state":"North Dakota","county":"Kidder County, Logan County, Stutsman County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.1667,\n 46.5\n ],\n [\n -99.1667,\n 46.5\n ],\n [\n -99.1667,\n 47.5\n ],\n [\n -100.1667,\n 47.5\n ],\n [\n -100.1667,\n 46.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
In 2013, the U.S. Geological Survey, in cooperation with the Keweenaw Bay Indian Community, began monitoring the water quality of springs and seeps within the Yellow Dog and Salmon Trout watersheds in Marquette County, Michigan. The objectives of this study were to (1) monitor streamflow and analyze the hydrology of the watersheds and (2) characterize the water quality in the watersheds prior to development of mineral resources within the watershed. Three continuous-record streamgages (U.S. Geological Survey stations 04043238, 04043244, and 04043275) were examined to identify runoff and baseflow components of streamflow and the relative magnitudes of those components. Streamflow at each station was dominated by groundwater discharge with about 70 to 80 percent of the annual streamflow being groundwater-derived baseflow.
From May 2013 to October 2016, 239 water-quality samples were collected at 15 stations within the Yellow Dog and Salmon Trout watersheds. Of the 15 stations sampled, 8 of the stations were springs and 7 of the stations were streams. Samples were analyzed for nutrient, trace metal, and major-ion species at all stations with additional suspended-sediment samples collected at the 7 stream stations. Where applicable, water-quality results were compared to aquatic health guidelines used by the Michigan Department of Environmental Quality. Copper concentrations exceeded the final chronic value five times and the aquatic maximum value once, whereas silver concentrations exceeded the final chronic value twice and the aquatic maximum value once. Results indicate that chloride concentrations may be increasing at some stations, but values are generally low with a median concentration of 0.25 milligram per liter.
Bed-sediment chemistry was evaluated twice for each stream sampling station. Samples were collected in the first and last year of the study and analyzed for trace metals. Sediment chemistry results were compared to consensus-based sediment quality guidelines. None of the metal constituents analyzed exceeded the threshold effect concentration or probable effect concentration thresholds, indicating a healthy aquatic environment in relation to bed-sediment quality.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185152","collaboration":"Prepared in cooperation with the Keweenaw Bay Indian Community","usgsCitation":"Hoard, C.J., and Weaver, T.L., 2019, Water quality and hydrology of the Yellow Dog and Salmon Trout watersheds, Marquette County, Michigan, 2013–16: U.S. Geological Survey Scientific Investigations Report 2018–5152, 24 p., https://doi.org/10.3133/sir20185152.","productDescription":"viii, 24 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098434","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5152/coverthb.jpg"},{"id":361779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5152/sir20185152.pdf","text":"Report","size":"9.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5152"}],"country":"United States","state":"Michigan","county":"Marquette County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.07464599609375,\n 46.64283679198892\n ],\n [\n -87.60017395019531,\n 46.64283679198892\n ],\n [\n -87.60017395019531,\n 46.91884832811514\n ],\n [\n -88.07464599609375,\n 46.91884832811514\n ],\n [\n -88.07464599609375,\n 46.64283679198892\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911
Context
Movement of prey on hydrologically pulsed, spatially heterogeneous wetlands can result in transient, high prey concentrations, when changes in landscape features such as connectivity between flooded areas alternately facilitate and impede prey movement. Predators track and exploit these concentrations, depleting them as they arise.
Objectives
We sought to describe how prey pulses of fish rapidly form and persist on wetland landscapes, while enduring constant consumption by wading birds, without being fully depleted. Specifically, we questioned how is the predator–prey relationship mediated by interactions between animal movement and dynamic landscape connectivity?
Methods
Two models were developed of the predator–prey-landscape system with qualitatively different representations of space, to identify and quantify prey pulsing dynamics that were robust across modeled assumptions. The first included a homogeneous landscape described by simple geometry, and implicit fish movement as wetland volume contracts. The second modeled transverse movement across a heterogeneous landscape, with isolated drying patches.
Results
Both models produced rapid fish prey concentrations as the wetland dried to shallow water depths. These conditions are critical for making prey available to wading birds. Fish were also rapidly depleted by birds, representing daily caloric intake supporting birds. Model 1 provided average estimates across the modeled domain. Model 2 mapped locations of emerging prey hotspots on the landscape through time.
Conclusions
Our models tracked predator, prey, and landscape dynamics in parallel, inducing systems dynamics from empirical observations. Explicit inclusion of dynamic wetland hydrologic connectivity, a key landscape mechanism, allowed for a comprehensive picture of links between landscape dynamics and the adapted predator–prey system.
Within isolated and fragmented populations, species interactions such as predation can cause shifts in community structure and demographics in tidal marsh ecosystems. It is critical to incorporate species interactions into our understanding when evaluating the effects of sea‐level rise and storm surges on tidal marshes. In this study, we hypothesize that avian predators will increase their presence and hunting activities during high tides when increased inundation makes their prey more vulnerable. We present evidence that there is a relationship between tidal inundation depth and time of day on the presence, abundance, and behavior of avian predators. We introduce predation pressure as a combined probability of predator presence related to water level. Focal surveys were conducted at four tidal marshes in the San Francisco Bay, California where tidal inundation patterns were monitored across 6 months of the winter. Sixteen avian predator species were observed. During high tide at Tolay Slough marsh, ardeids had a 29‐fold increase in capture attempts and 4 times greater apparent success rate compared with low tide. Significantly fewer raptors and ardeids were found on low tides than on high tides across all sites. There were more raptors in December and January and more ardeids in January than in other months. Ardeids were more prevalent in the morning, while raptors did not exhibit a significant response to time of day. Modeling results showed that raptors had a unimodal response to water level with a peak at 0.5 m over the marsh platform, while ardeids had an increasing response with water level. We found that predation pressure is related to flooding of the marsh surface, and short‐term increases in sea levels from high astronomical tides, sea‐level rise, and storm surges increase vulnerability of tidal marsh wildlife.