Grazing management recommendations often sacrifice the intrinsic heterogeneity of grasslands by prescribing uniform grazing distributions through smaller pastures, increased stocking densities, and reduced grazing periods. The lack of patch-burn grazing in semi-arid landscapes of the western Great Plains in North America requires alternative grazing management strategies to create and maintain heterogeneity of habitat structure (e.g., animal unit distribution, pasture configuration), but knowledge of their effects on grassland fauna is limited. The lesser prairie-chicken (Tympanuchus pallidicinctus), an imperiled, grassland-obligate, native to the southern Great Plains, is an excellent candidate for investigating effects of heterogeneity-based grazing management strategies because it requires diverse microhabitats among life-history stages in a semi-arid landscape. We evaluated influences of heterogeneity-based grazing management strategies on vegetation structure, habitat selection, and nest and adult survival of lesser prairie-chickens in western Kansas, USA. We captured and monitored 116 female lesser prairie-chickens marked with very high frequency (VHF) or global positioning system (GPS) transmitters and collected landscape-scale vegetation and grazing data during 2013–2015. Vegetation structure heterogeneity increased at stocking densities ≤0.26 animal units/ha, where use by nonbreeding female lesser prairie-chickens also increased. Probability of use for nonbreeding lesser prairie-chickens peaked at values of cattle forage use values near 37% and steadily decreased with use ≥40%. Probability of use was positively affected by increasing pasture area. A quadratic relationship existed between growing season deferment and probability of use. We found that 70% of nests were located in grazing units in which grazing pressure was <0.8 animal unit months/ha. Daily nest survival was negatively correlated with grazing pressure. We found no relationship between adult survival and grazing management strategies. Conservation in grasslands expressing flora community composition appropriate for lesser prairie-chickens can maintain appropriate habitat structure heterogeneity through the use of low to moderate stocking densities (<0.26 animal units/ha), greater pasture areas, and site-appropriate deferment periods. Alternative grazing management strategies (e.g., rest-rotation, season-long rest) may be appropriate in grasslands requiring greater heterogeneity or during intensive drought. Grazing management favoring habitat heterogeneity instead of uniform grazing distributions will likely be more conducive for preserving lesser prairie-chicken populations and grassland biodiversity.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.21984","usgsCitation":"Kraft, J.D., Haukos, D.A., Bain, M.R., Rice, M.B., Robinson, S., Sullins, D.S., Hagen, C., Pitman, J., Lautenbach, J., Plumb, R., and Lautenbach, J., 2021, Using grazing to manage herbaceous structure for a heterogeneity-dependent bird: Journal of Wildlife Management, v. 85, no. 2, p. 354-368, https://doi.org/10.1002/jwmg.21984.","productDescription":"15 p.","startPage":"354","endPage":"368","ipdsId":"IP-092108","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453841,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/jwmg.21984","text":"External Repository"},{"id":395138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.953125,\n 37.01132594307015\n ],\n [\n -98.382568359375,\n 37.01132594307015\n ],\n [\n -98.382568359375,\n 39.29179704377487\n ],\n [\n -101.953125,\n 39.29179704377487\n ],\n [\n -101.953125,\n 37.01132594307015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraft, John D.","contributorId":172789,"corporation":false,"usgs":false,"family":"Kraft","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":832156,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bain, Matthew R.","contributorId":272571,"corporation":false,"usgs":false,"family":"Bain","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":33811,"text":"TNC","active":true,"usgs":false}],"preferred":false,"id":832157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rice, Mindy B.","contributorId":214399,"corporation":false,"usgs":false,"family":"Rice","given":"Mindy","email":"","middleInitial":"B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":832158,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Samantha","contributorId":272573,"corporation":false,"usgs":false,"family":"Robinson","given":"Samantha","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832159,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sullins, Dan S.","contributorId":272574,"corporation":false,"usgs":false,"family":"Sullins","given":"Dan","email":"","middleInitial":"S.","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832160,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hagen, Christian A.","contributorId":272575,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":832161,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pitman, James","contributorId":176512,"corporation":false,"usgs":false,"family":"Pitman","given":"James","affiliations":[],"preferred":false,"id":832162,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lautenbach, Joseph","contributorId":272577,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Joseph","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832163,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Plumb, Reid","contributorId":272578,"corporation":false,"usgs":false,"family":"Plumb","given":"Reid","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832164,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lautenbach, Jonathan","contributorId":272579,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Jonathan","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832165,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70217305,"text":"70217305 - 2021 - Seed production patterns of surviving Sierra Nevada conifers show minimal change following drought","interactions":[],"lastModifiedDate":"2021-01-18T13:39:10.353799","indexId":"70217305","displayToPublicDate":"2021-01-15T07:37:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Seed production patterns of surviving Sierra Nevada conifers show minimal change following drought","docAbstract":"Reproduction is a key component of ecological resilience in forest ecosystems, so understanding how seed production is influenced by extreme drought is key to understanding forest recovery trajectories. If trees respond to mortality-inducing drought by preferentially allocating resources for reproduction, the recovery of the stand to pre-drought conditions may be enhanced accordingly. We used a 20-year annual seed capture data set to investigate whether seed production by three tree genera commonly found in the Sierra Nevada (Abies, Pinus, and Calocedrus) was correlated with variation in local weather, which included an extreme drought spanning multiple years. We tested whether average seed production differed during the drought years, and whether annual seed counts could be explained by three weather variables: spring temperature, annual precipitation, and summer climatic water deficit (CWD). We fit models testing for four separate effects: (1) a priming year model (weather 1 year prior to reproductive bud initiation), (2) a bud initiation model (weather in the year of reproductive bud initiation), (3) a pollination year model (weather in the year of pollination), and (4) maturation year model (weather in the year of seed maturation). For genera with two-year reproductive cycles, the pollination and maturation models were combined. We found support for the summer CWD Abies maturation year model, which suggested higher seed outputs immediately following dry summer conditions. The spring temperature pollination year model was selected for Pinus, which suggested that seed output is higher following warm spring weather during pollination. The annual precipitation priming year model was selected for Calocedrus, which showed a negative association between seed production and wetter conditions two years prior to seed production. More parent tree basal area resulted in higher seed output for all genera, though the confidence intervals overlapped 0 for Calocedrus. Permutation tests sugested there was no systematic difference in mean seed production during the drought after accounting for live tree basal area, regardless of genus. These results highlight the variability in response across genera, and suggest that the influence of seed production on forest recovery following drought-related mortality may depend on affected species and the timing of the mortality event within the masting cycle. A greater understanding of species-level masting to drought stress is needed to more precisely predict community-level recovery following drought.
Understanding the carbon transport within aquatic environments is crucial to quantifying global and local carbon budgets, yet limited empirical data currently (2021) exist. This report documents methodology and provides data for quantifying the aquatic export of carbon from a cypress swamp within Big Cypress National Preserve and is part of a larger carbon budget study. The U.S. Geological Survey operated two continuous monitoring stations, 022889001 and 022909471, that measured flow volume and water quality within the Big Cypress National Preserve in South Florida from September 2015 to October 2017. Station 022889001 represented the flow into the study area and station 022909471 represented the flow out of the study area. Site-specific regression models were developed by using continuously measured specific conductance and concomitant, discretely collected dissolved organic carbon, dissolved inorganic carbon, and particulate carbon samples to calculate total carbon (TC) concentrations at 15-minute intervals.
Calculated TC concentrations typically increased as flow was decreasing and decreased as flow was increasing. TC loads were calculated by multiplying concentrations and flow volume, and the difference between the load calculations for input/output locations of the swamp flow system was used to determine the aquatic carbon export from the study area.
Calculated monthly TC loads ranged from 0 metric tons in spring 2017 at both stations to 3,145 and 7,821 metric tons in September 2017 at 022889001 and 022909471, respectively. During 2016, the annual loads were 10,479 and 15,243 metric tons at 022889001 and 022909471, respectively. Calculated monthly aquatic TC exports from the study area ranged from −0.7 gram of carbon per square meter in May 2016 to 44.1 grams of carbon per square meter during September 2017. The carbon export from the study area varied monthly, increased as flow increased, and was greatly influenced by Hurricane Irma in September 2017. The aquatic TC export from the Sweetwater Strand study area was 42.0 grams of carbon per square meter per year in 2016, which is substantially (about 15 times) larger than the estimated overall mean riverine carbon export per square meter for the eastern United States; however, it was also less than the monthly export of carbon in September 2017. The monthly aquatic carbon export from the study area in September 2017 alone was greater than the aquatic carbon export from all of 2016, which is largely the result of the substantial increase in flow attributed to Hurricane Irma.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201136","collaboration":"Greater Everglades Priority Ecosystem Science Program","usgsCitation":"Booth, A.C., 2021, Development and application of surrogate models, calculated loads, and aquatic export of carbon based on specific conductance, Big Cypress National Preserve, South Florida, 2015–17: U.S. Geological Survey Open-File Report 2020–1136, 14 p., https://doi.org/10.3133/ofr20201136.","productDescription":"Report: v, 14 p.; Data Release; 2 Appendixes","onlineOnly":"Y","ipdsId":"IP-112929","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":382104,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix2.rtf","text":"Appendix 2","size":"960 kB","description":"OFR 2020-1136 Appendix 2 rtf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022909471: Loop Road Culverts Monroe Station to Florida Trail, Florida (rtf file)"},{"id":382062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1136/coverthb.jpg"},{"id":382063,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1136/ofr20201136.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136"},{"id":382064,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EXZLJT","text":"USGS data release","linkHelpText":"Calculated carbon concentrations, loads, and export in Big Cypress National Preserve, South Florida, 2015-2017"},{"id":382101,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix1.pdf","text":"Appendix 1","size":"424 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136 Appendix 1 pdf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022889001: Tamiami Canal 11 Mile Road to Monroe Station, Florida"},{"id":382102,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix2.pdf","text":"Appendix 2","size":"356 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136 Appendix 2 pdf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022909471: Loop Road Culverts Monroe Station to Florida Trail, Florida"},{"id":382103,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix1.rtf","text":"Appendix 1","size":"2.91 MB","description":"OFR 2020-1136 Appendix 1 rtf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022889001: Tamiami Canal 11 Mile Road to Monroe Station, Florida (rtf file)"}],"country":"United States","state":"Florida","otherGeospatial":"Big Cypress National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.22604370117186,\n 25.812254545273433\n ],\n [\n -80.8978271484375,\n 25.812254545273433\n ],\n [\n -80.8978271484375,\n 26.058016587844723\n ],\n [\n -81.22604370117186,\n 26.058016587844723\n ],\n [\n -81.22604370117186,\n 25.812254545273433\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Across the world, people increasingly choose to live in cities. By 2050, 70% of Earth's population will live in large urban areas. Upon considering a large city, questions arise such as, how much does that weigh? What are its effects on the landscape? Does it cause measurable subsidence? Here I calculate the weight of San Francisco Bay region urbanization, where 7.75 million people live at, or near the coast. It is difficult to account for everything that is in a city. I assume that most of the weight is buildings and their contents, which allows the use of base outline and height data to approximate their mass, which is cumulatively 1.6·1012 kg. I build a series of finite element models to study effects of pressure exerted by the weight distribution. Within the elastic realm, I look at compression, flexure, isostatic compensation, stress change, dilatation, and fluid flow changes. Within the nonlinear realm I show example calculations of primary and secondary settlement of soils under load. The combined modeled subsidence from building loads is at least 5–80 mm, with the largest contributions coming from nonlinear settlement and creep in soils. A general result is closing of pore space and redirection of pore fluids. While the calculated subsidence of the Bay Area is relatively small compared with other sources of elevation change such as pumping and recharge of aquifers, all sources of subsidence are concerning given an expected 200–300 mm sea level rise at San Francisco by the year 2050.
Water-quality effects after remediating abandoned draining mine tunnels using structural bulkheads were examined in two study areas in Colorado, USA. A bulkhead was installed in the Dinero mine tunnel in 2009 to improve water quality in Lake Fork Creek, a tributary to the upper Arkansas River. Although bulkhead installation improved pH, and manganese and zinc concentrations and loads at the Dinero mine tunnel, water-quality degradation was observed at the nearby Nelson tunnel. Only manganese concentrations improved in Lake Fork Creek downstream from the tunnel. To improve water quality in Cement Creek, a tributary of the Animas River, multiple bulkheads were installed in mine tunnels during 1996–2003 and a water treatment plant operated from 1989 to 2003 to treat drainage from several draining tunnels. After bulkhead installation and cessation of active water treatment (about 2003), water quality (pH and dissolved copper, manganese, and zinc concentrations) degraded at the mouth of Cement Creek. The patterns and timing were similar to post-bulkhead increased discharge and trace-metal loads at non-bulkheaded tunnels indicating the bulkheads might have been the cause. Pre-1989 water-quality data for Cement Creek are scarce, although limited historical data indicate possible, slight improvement in only manganese concentrations after bulkhead installation. Increased zinc loads in Lake Fork Creek and decreased pH through time in Cement Creek may indicate increased groundwater discharge to the streams after bulkhead installation. In these two study areas, bulkheads did not substantially improve downstream water quality.
The Bureau of Land Management (BLM) requested assistance from the U.S. Geological Survey (USGS) to conduct an assessment study to identify areas that may have economic potential for the future extraction of lignite and leonardite resources in the Williston Basin in North Dakota. The study will be used by the BLM to assist with the preparation of a revised resource management plan for the Williston Basin, in accordance with BLM planning policies.
The assessment of the economic potential of lignite resources required the establishment of criteria defining an economic lignite deposit. In consultation with the BLM, criteria were established to delineate drill holes that contained economic lignite beds. The criteria established are a minimum lignite bed thickness, a minimum cumulative lignite thickness, a maximum cumulative stripping ratio, and a maximum overburden. Likewise, an assessment of the economic potential of leonardite deposits required the establishment of criteria delineating drill holes that contained economic leonardite deposits. The criteria established are a minimum leonardite bed thickness, a minimum cumulative leonardite thickness, and a maximum overburden.
The drill hole data utilized in this study were obtained from the National Coal Resources Data System database and from several coal companies. Data from more than 20,000 drill holes, both proprietary and nonproprietary, were used to compile areas of economic potential for lignite or leonardite.
Areas delineated as having lignite or leonardite resources with economic potential, based on the established criteria, were present in 24 counties in the western portion of North Dakota. Areas of economic potential were delineated using a visual best-fit method without croplines. Areas defined as having economic potential for certain lignite beds or leonardite deposits may extend beyond known croplines in this study.
Stratigraphically, the lignite and leonardite deposits in the Williston Basin in North Dakota are mostly found in the Paleocene Fort Union Formation. Thick (greater than 20 feet) and laterally extensive (greater than 5 square miles) lignite beds are present in the Fort Union Formation throughout the Sentinel Butte and Tongue River Members. Lignite beds are also present in the Ludlow Member of the Fort Union Formation, although they are not as numerous or thick as they are in the overlying Sentinel Butte and Tongue River Members. As a result of lateral facies changes and migrating fluvial channel complexes in the Fort Union Formation, lignite beds of varying thickness occupy different stratigraphic horizons vertically throughout the Williston Basin.
The calculation of volumes for lignite and leonardite resources was not part of the scope of this study requested by the BLM, but a future study by the USGS may involve a comprehensive assessment of lignite resources and reserves in the Williston Basin. This future study could combine geologic data compiled in this study with geologic data from a previously unpublished 2019 assessment study by the USGS in the Williston Basin in eastern Montana. This future USGS study could also include the calculation of volumes for lignite resources and reserves, based on economic models derived using analogs from active mining operations in the Williston Basin and available spot market or contract coal prices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201135","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Shaffer, B.N., 2021, An assessment of the economic potential of lignite and leonardite resources in the Williston Basin, North Dakota: U.S. Geological Survey Open-File Report 2020–1135, 14 p., https://doi.org/10.3133/ofr20201135.","productDescription":"vi, 14 p.","onlineOnly":"Y","ipdsId":"IP-120360","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":436582,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93GGU6P","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Mercer and Oliver Counties, North Dakota"},{"id":436581,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93GGU6P","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Mercer and Oliver Counties, North Dakota"},{"id":436580,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NWIHEE","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in McLean County, North Dakota"},{"id":436579,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NWIHEE","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in McLean County, North Dakota"},{"id":436578,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94V9WV8","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Billings County, North Dakota"},{"id":436577,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94V9WV8","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Billings County, North Dakota"},{"id":436576,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90636SP","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Golden Valley County, North Dakota"},{"id":436575,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90636SP","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Golden Valley County, North Dakota"},{"id":436574,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FHHH4T","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Dunn County, North Dakota"},{"id":382138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1135/coverthb.jpg"},{"id":382139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1135/ofr20201135.pdf","text":"Report","size":"6.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1135"}],"country":"United States","state":"North Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0625,\n 45.89000815866184\n ],\n [\n -99.931640625,\n 45.89000815866184\n ],\n [\n -99.931640625,\n 49.009050809382046\n ],\n [\n -104.0625,\n 49.009050809382046\n ],\n [\n -104.0625,\n 45.89000815866184\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Quantitative reconstructions of vegetation abundance from sediment-derived pollen systems provide unique insights into past ecological conditions. Recently, the use of pollen accumulation rates (PAR, grains cm−2 year−1) has shown promise as a bioproxy for plant abundance. However, successfully reconstructing region-specific vegetation dynamics using PAR requires that accurate assessments of pollen deposition processes be quantitatively linked to spatially-explicit measures of plant abundance. Our study addressed these methodological challenges. Modern PAR and vegetation data were obtained from seven lakes in the western Klamath Mountains, California. To determine how to best calibrate our PAR-biomass model, we first calculated the spatial area of vegetation where vegetation composition and patterning is recorded by changes in the pollen signal using two metrics. These metrics were an assemblage-level relevant source area of pollen (aRSAP) derived from extended R-value analysis (sensu Sugita, 1993) and a taxon-specific relevant source area of pollen (tRSAP) derived from PAR regression (sensu Jackson, 1990). To the best of our knowledge, aRSAP and tRSAP have not been directly compared. We found that the tRSAP estimated a smaller area for some taxa (e.g. a circular area with a 225 m radius for Pinus) than the aRSAP (a circular area with a 625 m radius). We fit linear models to relate PAR values from modern lake sediments with empirical, distance-weighted estimates of aboveground live biomass (AGLdw) for both the aRSAP and tRSAP distances. In both cases, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus, and TCT (Taxodiaceae, Cupressaceae, and Taxaceae families) – were statistically significant and reasonably precise estimators of contemporary AGLdw. However, predictions weighted by the distance defined by aRSAP tended to be more precise. The relative root-mean squared error for the aRSAP biomass estimates was 9% compared to 12% for tRSAP. Our results demonstrate that calibrated PAR-biomass relationships provide a robust method to infer changes in past plant biomass.
","language":"English","publisher":"SAGE Publishing","doi":"10.1177/0959683620988038","usgsCitation":"Knight, C.A., Baskaran, M., Bunting, M.J., Champagne, M.R., Potts, M.D., Wahl, D., Wanket, J., and Battles, J.J., 2021, Linking modern pollen accumulation rates to biomass: Quantitative vegetation reconstruction in the western Klamath Mountains, NW California, USA: The Holocene, v. 31, no. 5, p. 814-829, https://doi.org/10.1177/0959683620988038.","productDescription":"16 p.","startPage":"814","endPage":"829","ipdsId":"IP-122720","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":453850,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hull-repository.worktribe.com/output/3679635","text":"External Repository"},{"id":382454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Western Klamath Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.365234375,\n 40.052847601823984\n ],\n [\n -121.4208984375,\n 40.052847601823984\n ],\n [\n -121.4208984375,\n 42.220381783720605\n ],\n [\n -124.365234375,\n 42.220381783720605\n ],\n [\n -124.365234375,\n 40.052847601823984\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Knight, Clarke A. 0000-0003-0002-6959","orcid":"https://orcid.org/0000-0003-0002-6959","contributorId":248212,"corporation":false,"usgs":false,"family":"Knight","given":"Clarke","email":"","middleInitial":"A.","affiliations":[{"id":49825,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California 94720 USA","active":true,"usgs":false}],"preferred":false,"id":808617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baskaran, Mark","contributorId":87867,"corporation":false,"usgs":false,"family":"Baskaran","given":"Mark","email":"","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":808618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunting, M. Jane 0000-0002-3152-5745","orcid":"https://orcid.org/0000-0002-3152-5745","contributorId":248213,"corporation":false,"usgs":false,"family":"Bunting","given":"M.","email":"","middleInitial":"Jane","affiliations":[{"id":49826,"text":"Department of Geography, Geology and Environment, University of Hull, Cottingham Road, Hull, HU6 7RX UK","active":true,"usgs":false}],"preferred":false,"id":808619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Champagne, Marie Rhondelle 0000-0001-8236-3910","orcid":"https://orcid.org/0000-0001-8236-3910","contributorId":248214,"corporation":false,"usgs":true,"family":"Champagne","given":"Marie","email":"","middleInitial":"Rhondelle","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":808620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Potts, Matthew D. 0000-0001-7442-3944","orcid":"https://orcid.org/0000-0001-7442-3944","contributorId":248215,"corporation":false,"usgs":false,"family":"Potts","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":49825,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California 94720 USA","active":true,"usgs":false}],"preferred":false,"id":808621,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wahl, David 0000-0002-0451-3554","orcid":"https://orcid.org/0000-0002-0451-3554","contributorId":206113,"corporation":false,"usgs":true,"family":"Wahl","given":"David","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":808622,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wanket, James","contributorId":248216,"corporation":false,"usgs":false,"family":"Wanket","given":"James","email":"","affiliations":[{"id":49829,"text":"Department of Geography, California State University, Sacramento, Sacramento, California 95819 USA","active":true,"usgs":false}],"preferred":false,"id":808623,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Battles, John J.","contributorId":102006,"corporation":false,"usgs":false,"family":"Battles","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":808624,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70222946,"text":"70222946 - 2021 - B-positive: A robust estimator of aftershock magnitude distribution in transiently incomplete catalogs","interactions":[],"lastModifiedDate":"2021-08-10T13:59:41.764808","indexId":"70222946","displayToPublicDate":"2021-01-13T08:57:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"displayTitle":"B-positive: A robust estimator of aftershock magnitude distribution in transiently incomplete catalogs","title":"B-positive: A robust estimator of aftershock magnitude distribution in transiently incomplete catalogs","docAbstract":"The earthquake magnitude-frequency distribution is characterized by the b-value, which describes the relative frequency of large versus small earthquakes. It has been suggested that changes in b-value after an earthquake can be used to discriminate whether that earthquake is part of a foreshock sequence or a more typical mainshock-aftershock sequence, with a decrease in b-value heralding a larger earthquake to come. However, the measurement of b-value during an active aftershock sequence is strongly biased by short-term incompleteness of the earthquake catalog and by data-windowing, and these biases have the same direction as the proposed signal. Here I develop a new estimator of the b-value that is insensitive to transient changes in catalog completeness and that does not require data windowing. The new estimator “b-positive” is based on the positive-only subset of the differences in magnitude between successive earthquakes, which are described by a double-exponential (Laplace) distribution with the same b-value as the magnitude distribution itself. The b-positive estimator greatly improves the robustness of continuous b-value measurements during active earthquake sequences, as well as in historical catalogs with unknown or variable completeness. The new estimator confirms some of the observations of Gulia and Wiemer (2019), although at a reduced level, showing a decrease and recovery of the b-value during several recent foreshock sequences that cannot be attributed simply to measurement bias. However, the unbiased b-value changes may be too subtle to use in a real-time earthquake alarm system.
Land use alteration such as livestock grazing can affect water quality in habitats of at-risk wildlife species. Data from managed wetlands are needed to understand levels of exposure for aquatic life stages and monitor grazing-related changes afield. We quantified spatial and temporal variation in water quality in wetlands occupied by threatened Oregon spotted frog (Rana pretiosa) at Klamath Marsh National Wildlife Refuge in Oregon, United States (US). We used analyses for censored data to evaluate the importance of habitat type and grazing history in predicting concentrations of nutrients, turbidity, fecal indicator bacteria (FIB; total coliforms, Escherichia coli (E. coli), and enterococci), and estrogenicity, an indicator of estrogenic activity. Nutrients (orthophosphate and ammonia) and enterococci varied over time and space, while E. coli, total coliforms, turbidity, and estrogenicity were more strongly associated with local livestock grazing metrics. Turbidity was correlated with several grazing-related constituents and may be particularly useful for monitoring water quality in landscapes with livestock use. Concentrations of orthophosphate and estrogenicity were elevated at several sites relative to published health benchmarks, and their potential effects on Rana pretiosa warrant further investigation. Our data provided an initial assessment of potential exposure of amphibians to grazing-related constituents in western US wetlands. Increased monitoring of surface water quality and amphibian population status in combination with controlled laboratory toxicity studies could help inform future research and targeted management strategies for wetlands with both grazing and amphibians of conservation concern.
Morphometric data from fish are typically generated using one of two methods: direct measurements made on a specimen or extraction of distances from a digital picture. We compared data on 12 morphometrics collected with these two methods on the same collection of Cisco Coregonus artedi from Lake Ontario, North America, to assess the degree of bias in measurements made directly on a specimen- vs. an image-based method. We also assessed the degree of reproducibility within the image-based method by evaluating the amount of variation between different analysts for each morphometric method. Our results indicate specific morphometrics may be more prone to bias across the two methods and between analysts. Four of 12 morphometrics evaluated showed significant deviation from a 1:1 relationship that would be expected if the imaged-based method produced accurate specimen-based measurements. Pelvic fin length and pelvic–anal fin distance had the highest between-analyst variation for image-based landmarks, indicating low reproducibility for these metrics, compared with pectoral fin or total length, which had lower between-analyst variation. Although some morphometric measurements can be accurately obtained with either method, and therefore potentially used interchangeably in studies on Cisco morphology, our findings highlight the importance of considering method bias in morphometric studies that use data collected by different methods.
Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as < 65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated U.S. is about 6,000 km3, or about 0.5% of globally stored young groundwater.
This study reports historic capture-mark-recapture survival and abundance estimates of common bottlenose dolphins (Tursiops truncatus) based on photo-identification surveys of coastal Venezuela (along the Aragua coast between Turiamo Bay and Puerto Colombia). We used the most recent data available: dolphins identified by unique dorsal fin marks during wet and dry season surveys conducted from 2004 to 2008. Dolphin encounter histories were analyzed in the Closed Capture Robust Design framework, with the top model including random movement, constant survival, and capture-recapture probabilities that varied by secondary periods. Survival of marked adults was estimated at 0.99 (95% CI = 0.97 to 1.00). Population estimates for all adults (marked and unmarked) averaged 31 animals (SD = 13.8), and for all dolphins (all adults and calves), 41 animals (SD = 17.2). Coastal bottlenose dolphins face numerous threats, including ship strikes, oil spills, conflict with recreational and industrial fisheries, other negative human interactions, biotoxins, chemicals, noise, freshwater discharge, and coastal development. Further, small populations are, in general, at increased risk due to reduced resiliency and recovery potential when exposed to such threats and to expected environmental and demographic stochasticity. These historic estimates of abundance and survival are critical for establishing a reference state and indicate a need for ongoing monitoring of the small dolphin population while the Aragua coast is still, as of yet, relatively little impacted by humans. Should coastal development increase (as is the global trend) and/or environmental catastrophes (e.g., harmful algal blooms, hurricanes, and oil spills) occur, these historic estimates will be essential for assessing impacts and guiding management and conservation interventions. Our results show year-round dolphin presence and highlight the Venezuelan coastal–oceanic landscape as an area of both future research and conservation importance.
Harmful algal blooms produce toxins that bioaccumulate in the food web and adversely affect humans, animals, and entire marine ecosystems. Blooms of the diatom Pseudo-nitzschia can produce domoic acid (DA), a toxin that most commonly causes neurological disease in endothermic animals, with cardiovascular effects that were first recognized in southern sea otters. Over the last 20 years, DA toxicosis has caused significant morbidity and mortality in marine mammals and seabirds along the west coast of the USA. Identifying DA exposure has been limited to toxin detection in biological fluids using biochemical assays, yet measurement of systemic toxin levels is an unreliable indicator of exposure dose or timing. Furthermore, there is little information regarding repeated DA exposure in marine wildlife. Here, the association between long-term environmental DA exposure and fatal cardiac disease was investigated in a longitudinal study of 186 free-ranging sea otters in California from 2001 – 2017, highlighting the chronic health effects of a marine toxin. A novel Bayesian spatiotemporal approach was used to characterize environmental DA exposure by combining several DA surveillance datasets and integrating this with life history data from radio-tagged otters in a time-dependent survival model. In this study, a sea otter with high DA exposure had a 1.7-fold increased hazard of fatal cardiomyopathy compared to an otter with low exposure. Otters that consumed a high proportion of crab and clam had a 2.5- and 1.2-times greater hazard of death due to cardiomyopathy than otters that consumed low proportions. Increasing age is a well-established predictor of cardiac disease, but this study is the first to identify that DA exposure affects the risk of cardiomyopathy more substantially in prime-age adults than aged adults. A 4-year-old otter with high DA exposure had 2.3 times greater risk of fatal cardiomyopathy than an otter with low exposure, while a 10-year old otter with high DA exposure had just 1.2 times greater risk. High Toxoplasma gondii titers also increased the hazard of death due to heart disease 2.4-fold. Domoic acid exposure was most detrimental for prime-age adults, whose survival and reproduction are vital for population growth, suggesting that persistent DA exposure will likely impact long-term viability of this threatened species. These results offer insight into the pervasiveness of DA in the food web and raise awareness of under-recognized chronic health effects of DA for wildlife at a time when toxic blooms are on the rise.
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network (AWQMN). During water year 2019 (October 1, 2018, through September 30, 2019), water-quality data were collected at 73 stations: 71 AWQMN and alternate AWQMN stations, and 2 U.S. Geological Survey National Water Quality Monitoring Program stations. Among the stations in this report, four stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticides are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed monitored by the station. Some stations have been classified based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) they monitor. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented for representative streamflow-gaging stations in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ds1132","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kay, R.T., 2021, Quality of surface water in Missouri, water year 2019: U.S. Geological Survey Data Series 1132, 26 p., https://doi.org/10.3133/ds1132.","productDescription":"Report: v, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119904","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381906,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1132/ds1132.pdf","text":"Report","size":"1.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1132"},{"id":381905,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1132/coverthb.jpg"},{"id":382023,"rank":3,"type":{"id":30,"text":"Data 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Accelerating erosion of the Alaska Beaufort Sea coast is increasing inputs of organic matter from land to the Arctic Ocean, and improved estimates of organic matter stocks in eroding coastal permafrost are needed to assess their mobilization rates under contemporary conditions. We collected three permafrost cores (4.5–7.5 m long) along a geomorphic gradient near Drew Point, Alaska, where recent erosion rates average 17.2 m year−1. Down-core patterns indicate that organic-rich soils and lacustrine sediments (12–45% total organic carbon; TOC) in the active layer and upper permafrost accumulated during the Holocene. Deeper permafrost (below 3 m elevation) mainly consists of Late Pleistocene marine sediments with lower organic matter content (∼1% TOC), lower C:N ratios, and higher δ13C values. Radiocarbon-based estimates of organic carbon accumulation rates were 11.3 ± 3.6 g TOC m−2 year−1 during the Holocene and 0.5 ± 0.1 g TOC m−2 year−1 during the Late Pleistocene (12–38 kyr BP). Within relict marine sediments, porewater salinities increased with depth. Elevated salinity near sea level (∼20–37 in thawed samples) inhibited freezing despite year-round temperatures below 0°C. We used organic matter stock estimates from the cores in combination with remote sensing time-series data to estimate carbon fluxes for a 9 km stretch of coastline near Drew Point. Erosional fluxes of TOC averaged 1,369 kg C m−1 year−1 during the 21st century (2002–2018), nearly doubling the average flux of the previous half-century (1955–2002). Our estimate of the 21st century erosional TOC flux year−1 from this 9 km coastline (12,318 metric tons C year−1) is similar to the annual TOC flux from the Kuparuk River, which drains a 8,107 km2 area east of Drew Point and ranks as the third largest river on the North Slope of Alaska. Total nitrogen fluxes via coastal erosion at Drew Point were also quantified, and were similar to those from the Kuparuk River. This study emphasizes that coastal erosion represents a significant pathway for carbon and nitrogen trapped in permafrost to enter modern biogeochemical cycles, where it may fuel food webs and greenhouse gas emissions in the marine environment.
Debris flows evolve in both time and space in complex ways, commonly starting as coherent failures but then quickly developing structures such as roll waves and surges. These processes are readily observed but difficult to study or quantify because of the speed at which they evolve. Many methods for studying debris flows consist of point measurements (e.g., flow height or basal stresses), which are inherently limited in spatial coverage and cannot fully characterize the spatiotemporal evolution of a flow. In this study, we use terrestrial lidar to measure debris-flow profiles at high sampling rates to examine debris-flow movement with high temporal and spatial precision and accuracy. We acquired measurements during gate-release experiments at the U.S. Geological Survey debris-flow flume, a unique experimental facility where debris flows can be artificially generated at a large scale. A lidar scanner was used to record repeat topographic profiles of the moving debris flows along the length of the flume with a narrow swath width (∼1 mm) at a rate of 60 Hz. The high-resolution lidar profiles enabled us to quantify flow front velocity of the debris flows and provided an unprecedented record of the development and evolution of the flow structure with a sub-second time resolution. The findings of this study demonstrate how to obtain quantitative measurements of debris-flow movement. In addition, the data help us to quantitatively define the development of a saltating debris-flow front and roll waves behind the debris-flow front. Such measurements may help constrain future modeling efforts.
Groundwater-derived solute fluxes to the ocean have long been assumed static and subordinate to riverine fluxes, if not neglected entirely, in marine isotope budgets. Here we present concentration and isotope data for Li, Mg, Ca, Sr, and Ba in coastal groundwaters to constrain the importance of groundwater discharge in mediating the magnitude and isotopic composition of terrestrially derived solute fluxes to the ocean. Data were extrapolated globally using three independent volumetric estimates of groundwater discharge to coastal waters, from which we estimate that groundwater-derived solute fluxes represent, at a minimum, 5% of riverine fluxes for Li, Mg, Ca, Sr, and Ba. The isotopic compositions of the groundwater-derived Mg, Ca, and Sr fluxes are distinct from global riverine averages, while Li and Ba fluxes are isotopically indistinguishable from rivers. These differences reflect a strong dependence on coastal lithology that should be considered a priority for parameterization in Earth-system models.
Recreational fisheries are social-ecological systems (SES), and knowledge of human dimensions coupled with ecology are critically needed to understand their system dynamics. Creel surveys, which typically occur in-person and on-site, serve as an important tool for informing fisheries management. Recreational fisheries creel data have the potential to inform large-scale understanding of social and ecological dynamics, but applications are currently limited by a disconnect between the questions posed by social-ecological researchers and the methods in which surveys are conducted. Although innovative use of existing data can increase understanding of recreational fisheries as SES, creel surveys should also adapt to changing information needs. These opportunities include using the specific temporal and spatial scope of creel survey data, integrating these data with alternative data sources, and increasing human dimensions understanding. This review provides recommendations for adapting survey design, implementation, and analysis for SES-focused fisheries management. These recommendations are: (1) increasing human dimensions knowledge; (2) standardization of surveys and data; (3) increasing tools and training available to fisheries scientists; and (4) increasing accessibility and availability of data. Incorporation of human dimensions information into creel surveys will increase the ability of fisheries management to regulate these important systems from an integrated SES standpoint.
In support of nutrient reduction efforts, total phosphorus loads and yields were computed for the Turkey River at Garber, Iowa (U.S. Geological Survey station 05412500), for January 1, 2018, to April 30, 2020, based on continuously monitored turbidity sensor data. Sample data were used to create a total phosphorus turbidity-surrogate model. Streamflow-based total phosphorus models were used during periods of missing sensor data to obtain a more complete annual total phosphorus load. This report presents methods needed to accurately compute site-specific loads and track annual progress toward nutrient reduction goals within the State.
Annual total phosphorus loads for the Turkey River at Garber, Iowa, were 1,740 and 1,490 U.S. short tons for 2018 and 2019, respectively, with annual yields ranging from 3.01 to 3.53 pounds per acre per year, compared to a mean statewide yield of 0.73 pound per acre per year needed to achieve the total phosphorus-reduction goal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205131","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Garrett, J.D., 2021, The use of continuous water-quality time-series data to compute total phosphorus loadings for the Turkey River at Garber, Iowa, 2018–20: U.S. Geological Survey Scientific Investigations Report 2020–5131, 13 p., https://doi.org/10.3133/sir20205131.","productDescription":"Report: vi, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119794","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5131/sir20205131.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5131"},{"id":381970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5131/coverthb.jpg"},{"id":382022,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"National Water Information System"}],"country":"United States","state":"Iowa","city":"Garber","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.27939224243163,\n 42.73276565598371\n ],\n [\n -91.24471664428711,\n 42.73276565598371\n ],\n [\n -91.24471664428711,\n 42.74953333969568\n ],\n [\n -91.27939224243163,\n 42.74953333969568\n ],\n [\n -91.27939224243163,\n 42.73276565598371\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The U.S. Geological Survey (USGS), in cooperation with the Rosebud Sioux Tribe, completed a study to characterize water-level fluctuations in observation wells to examine driving factors that affect water levels in and near the Rosebud Indian Reservation, which comprises all of Todd County. The study investigates concerns regarding potential effects of groundwater withdrawals and climate conditions on groundwater levels within an area that includes Todd County and a surrounding area that extends 10 miles north, east, and west of the county border. Characterization of water-level fluctuations in observation wells and relative driving factors was accomplished by statistical trend analysis.
Two statistical methods were used for analysis of temporal trends for climatic and hydrologic data. To determine which trend analysis to use, applicable datasets were tested for statistically significant short-term persistence (STP). In the absence of significant STP, existence of statistical trends was determined using the standard Mann-Kendall test for probability values less than or equal to 0.10 (90-percent confidence level); however, a modified Mann-Kendall test was used for datasets where statistically significant STP was detected. Trend magnitudes were computed using the Sen’s slope estimator.
Monthly data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) were aggregated to obtain annual and seasonal datasets for total precipitation, minimum air temperature (Tmin), and maximum air temperature (Tmax) for the study area and a surrounding buffer area. Trend tests for total precipitation, Tmin, and Tmax were completed for annual and seasonal time series for water years 1956–2017, which is about 2 years before the earliest available water-level measurements. A 2-year offset was arbitrarily selected because scrutiny of water-level and precipitation data indicated that responses of groundwater levels for many of the observation wells lagged major changes in precipitation patterns by about 2 years. Statistically significant upward trends were detected for annual precipitation and annual Tmin for almost all of the study area and the surrounding buffer area. Statistically significant downward trends in Tmax were detected for a very small part of the study area; however, the sparse spatial coverage reduces confidence that these are true trends. Spatial distributions of statistically significant trends in seasonal climate data were generally similar to the annual trends, but with substantial differences in the spatial density of the trends.
Groundwater trends for 58 observation wells were analyzed for three separate water-level parameters (minimum, median, and maximum) because wells are measured sporadically and data are biased towards more frequent measurements during periods of heaviest irrigation demand. Trends in the time series of annual precipitation (from PRISM) starting 2 years earlier than for the associated water-level trend also were analyzed for the location of each individual observation well. Sen’s slope and Mann-Kendall probability values (p-values) were computed for the three water-level parameters and for the annual precipitation time series. Graphs showing results of trend analyses for each observation well also showed changes over time in the sum of licensed groundwater withdrawals within six specified radii (0.5, 1, 2, 3, 4, and 5 miles) of each well as a qualitative indicator of proximal groundwater demand.
Of all 58 observation wells considered, 28 wells had significant upward trends for at least one of the three water-level parameters, 11 wells had significant downward trends for at least one water-level parameter, and 19 wells did not have any significant trends. Significant upward trends in annual precipitation were detected for 48 of the 58 wells.
Results of trend analyses likely show the effects of groundwater withdrawals on water levels in the Ogallala aquifer in areas of substantial demand. Precipitation trends are significantly upward for 43 of the 48 wells completed in the Ogallala aquifer that were analyzed. Of the 48 Ogallala aquifer wells, 24 had significant upward trends for at least one water-level parameter (17 with all 3); however, 10 wells had statistically significant downward trends for at least one water-level parameter (8 with all 3 parameters). All but one of the wells with significant downward trends are located in the south-central part of the study area where licensed irrigation withdrawals are concentrated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205119","collaboration":"Prepared in cooperation with the Rosebud Sioux Tribe","usgsCitation":"Valseth, K.J., and Driscoll, D.G., 2021, Trends in groundwater levels in and near the Rosebud Indian Reservation, South Dakota, water years 1956–2017: U.S. Geological Survey Scientific Investigations Report 2020–5119, 46 p., https://doi.org/10.3133/sir20205119.","productDescription":"Report: v, 46 p.; 2 Appendixes; Data Release","onlineOnly":"Y","ipdsId":"IP-111377","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":382008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"National Water Information System"},{"id":381910,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119_appendix2.pdf","text":"Appendix 2","size":"132 kB","description":"SIR 2020-5119 Appendix 2"},{"id":381909,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119_appendix1.pdf","text":"Appendix 1","size":"404 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5119 Appendix 1"},{"id":381908,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5119/sir20205119.pdf","text":"Report","size":"4.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5119"},{"id":381907,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5119/coverthb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Rosebud Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.612548828125,\n 43.01268088642034\n ],\n [\n -99.8492431640625,\n 43.01268088642034\n ],\n [\n -99.8492431640625,\n 43.600284023536325\n ],\n [\n -101.612548828125,\n 43.600284023536325\n ],\n [\n -101.612548828125,\n 43.01268088642034\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
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This report documents statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM). The U.S. Geological Survey developed SELDM and the statistics documented in this report in cooperation with the Federal Highway Administration to indicate the risk for stormwater flows, concentrations, and loads to exceed user-selected water-quality goals and the potential effectiveness of mitigation measures to reduce such risks. In SELDM, three treatment variables—hydrograph extension, volume reduction, and water-quality treatment—are simulated by using the trapezoidal distribution and the rank correlation with the associated runoff variables. This report describes methods for calculating the trapezoidal distribution statistics and rank correlation coefficients for these treatment variables and methods for estimating the minimum irreducible concentration (MIC), which is the lowest expected effluent concentration from a BMP site or a category of BMPs. These statistics are different from the statistics commonly used to characterize or compare BMPs; they are designed to provide a stochastic transfer function to approximate the quantity, duration, and quality of BMP effluent given the associated inflow values for a population of storm events.
Analyses for this study were done with data extracted from a modified copy of the December 2019 version of the International Stormwater Best Management Practices Database. Statistics for volume reduction, hydrograph extension, and water-quality treatment were developed with selected data. The medians of the best-fit statistics for selected constituents were used to construct generalized cumulative distribution functions for the three treatment variables. For volume reduction and hydrograph extension, selection of a Spearman’s rank correlation coefficient (rho) value that is the average of the median and maximum values for the BMP category may help generate realistic simulation results in SELDM. The median rho value may be selected to help generate realistic simulation results for water-quality treatment variables.
Water-quality treatment statistics, including trapezoidal ratios and MIC values, were developed for 51 runoff-quality constituents commonly measured in highway and urban runoff studies. Statistics were calculated for water-quality properties, sediment and solids, nutrients, major and trace inorganic elements, organic compounds, and biologic constituents.
Analysis of MIC values provides information to guide professional judgement for selecting values for simulating water quality at sites of interest. The MIC is a lower bound for BMP discharge concentrations and will therefore replace simulated BMP discharge concentrations below the selected value. A new method for estimating MIC values, the lognormal variate of inflow concentrations, was developed in this report and these statistics were calculated for individual constituents and constituent categories. Inflow quality is correlated to MIC values for some constituents, but regional soil concentrations were not strongly correlated to MIC values.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205136","collaboration":"Prepared in cooperation with the Federal Highway Administration","usgsCitation":"Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136.","productDescription":"Report: 41 p.; 4 Tables; Data Release; Software Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119618","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":381933,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.04.txt","text":"Table 1.4","size":"89.4 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of correlations between the geometric mean concentration of inflows and selected minimum irreducible concentration estimates"},{"id":381930,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.01.txt","text":"Table 1.1","size":"91.2 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Median of selected treatment statistics for individual constituents"},{"id":381932,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.03.txt","text":"Table 1.3","size":"89.2 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of the lognormal variate values of selected minimum irreducible concentrations"},{"id":381929,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X3ECTD","text":"USGS data release","linkHelpText":"Statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM)"},{"id":381927,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5136"},{"id":381928,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9XBPIOB","text":"USGS software release","linkHelpText":"- Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0"},{"id":381931,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5136/sir20205136_table01.02.txt","text":"Table 1.2","size":"87.5 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Estimates of the minimum irreducible concentration"},{"id":381926,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5136/coverthb.jpg"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Director, Science Analytics and Synthesis
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225
Landsat Collections ensure that all Landsat Level-1 data are consistently calibrated and processed and retain traceability of data quality provenance. Landsat Collection 2 introduces improvements that harness recent advancements in data processing, algorithm development, data access, and distribution capabilities. Collection 2 includes Landsat Level-1 data for all sensors since 1972 and global Level-2 surface reflectance and surface temperature scene-based products for data acquired since 1982 starting with the Landsat Thematic Mapper sensor era.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213002","usgsCitation":"U.S. Geological Survey, 2021, Landsat Collection 2 (ver. 1.1, January 15, 2021): U.S. Geological Survey Fact Sheet 2021–3002, 4 p., https://doi.org/10.3133/fs20213002.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-123860","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":382187,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2021/3002/versionHist.txt","text":"Version History","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"FS 2021-3002 Version History"},{"id":381947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3002/coverthb2.jpg"},{"id":381948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3002/fs20213002.pdf","text":"Report","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3002 Version 1.1"}],"edition":"Version 1.0: January 7, 2021; Version 1.1: January 15, 2021","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
In cooperation with the State of Hawai‘i Commission on Water Resource Management and in collaboration with the University of Hawaiʻi Water Resources Research Center, the U.S. Geological Survey developed a water-resource monitoring program—a rainfall, surface-water, and groundwater data-collection program—that is required to meet State needs for water-resource assessment, management, and protection in Hawai‘i. Current and foreseeable issues related to water-resource management and climate-change effects guided the evaluation of data-collection sites within the monitoring program. Data-collection sites currently (2018) being operated in Hawai‘i were evaluated, and additional data-collection sites were selected on the basis of their usefulness for characterizing anthropogenic effects on water resources or representing natural conditions. Data-collection strategies consist of a combination of continuous long-term monitoring to evaluate trends and climate-change effects and occasional and periodic intensive monitoring to enhance spatial understanding of hydrologic conditions and to address current issues in priority areas—areas that currently have water-availability issues or are expected to have the greatest socioeconomic or ecological effects because of climate change.
Priority areas for rainfall monitoring consist of urban and agricultural lands, areas with high rainfall and high-rainfall gradient, and areas within the trade-wind inversion band. Surface-water priority areas consist of streams with major surface-water diversions, with established interim instream-flow standards, in a surface-water management area, that support water leases, and with uncertainties in hydrogeologic characteristics. Priority areas for groundwater monitoring consist of areas with high withdrawal, declining water levels, reduced recharge, limited alternative sources, and uncertainties in hydrogeologic characteristics.
Data-quality objectives for the rainfall, surface-water, and groundwater monitoring programs that describe anticipated uses of the data were established with the goal of producing useful, reliable, and accurate water-resource information of sufficient precision to support decision making. The data-quality objectives also consider quality-assurance and quality-control programs that ensure defensible data. Establishment of common data-quality objectives not only assures comparability of data collected by multiple agencies but also allows data from academic, private, and public organizations to be useful for meeting State monitoring needs, provided the data meet appropriate data-quality objectives and data-accessibility requirements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205115","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management and in collaboration with the University of Hawai‘i Water Resources Research Center","usgsCitation":"Cheng, C.L., Izuka, S.K., Kennedy, J.J., Frazier, A.G., and Giambelluca, T.W., 2021, Water-resource management monitoring needs, State of Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2020-5115, 114 p., https://doi.org/10.3133/sir20205115.","productDescription":"xviii, 114 p.","numberOfPages":"114","onlineOnly":"Y","ipdsId":"IP-106432","costCenters":[{"id":525,"text":"Pacific Islands Water Science 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\"}}]}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
The 2008–2018 lava lake at the summit of Kīlauea marked the longest sustained period of lava lake activity at the summit in decades and provided a new opportunity for observing and understanding lava lake behavior. The individual chapters of this Professional Paper volume cover the basic chronology of the eruption, rich historical background, observations and measurements of lake activity, hydrological setting, as well as geophysical and other monitoring data that tracked the activity.
The primary focus of this Professional Paper is the 2008–2018 lava lake activity, ending with the draining of the lake in May 2018. The 2018 summit collapse events that followed the lake draining, and which dramatically altered the topography of the summit region, are published elsewhere.
As this volume was published online in January 2021, a new lava lake had just formed in Halemaʻumaʻu. This new activity is further testament to the dynamic nature of Halemaʻumaʻu and the proclivity for lava lake activity at the summit of Kīlauea.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1867","usgsCitation":"Patrick, M., Orr, T., Swanson, D., and Houghton, B., eds., The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i: U.S. Geological Survey Professional Paper 1867, variously paged, https://doi.org/10.3133/pp1867.","productDescription":"Multi-Chaptered report","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"links":[{"id":382006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1867/a/covrthb.jpg"}],"contact":"Contact HVO
Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue
Suite A-8
Hilo, HI 96720