Managing climate impacts to natural resources in protected areas can be hampered by lack of monitoring data, poor understanding of natural resource responses to climate, or lack of timely condition assessments that can inform management actions. Here we demonstrate the utility of water balance as a tool for understanding natural resource responses to climate by developing case studies focused on stream flow, vegetation production, and wildfire ignition at Great Sand Dunes National Park and Preserve (GSDNP), U.S.A. The efficacy of water balance to predict these responses stems from the explicit integration of climate with site conditions that modify the effects of climate. This in turn results in estimates of water availability, water use, and water need that are proximal drivers of aquatic and terrestrial natural resource conditions. The water balance model successfully forecasted stream flow (r2 = 0.69, P < 0.001); determined the critical water needs for maintaining annual vegetation production in different vegetation types spanning a large environmental gradient (r2 = 0.18–0.71); and predicted proportion of historic wildfire ignitions in forest (r2 = 0.96–0.99) and non-forest (r2 = 0.96–0.97) vegetation types. Collectively, these case studies demonstrate practical approaches to translate climate data into assessments of natural resource condition that inform long-term planning and near-term strategic actions needed for conservation of protected areas.
Landscapes evolve in response to prolonged and/or intense precipitation resulting from atmospheric processes at various spatial and temporal scales. Whereas synoptic (large‐scale) features (e.g., atmospheric rivers and hurricanes) govern regional‐scale hydrologic hazards such as widespread flooding, mesoscale features such as thunderstorms or squall lines are more likely to trigger localized geomorphic hazards such as landslides. Thus, to better understand relations between hydrometeorological drivers and landscape response, a knowledge of mesoscale meteorology and its impacts is needed. Here we investigate the extreme geomorphic response associated with one type of mesoscale meteorological feature, the narrow cold frontal rainband (NCFR). Resulting from low‐level convergence and shallow convection along a cold front, NCFRs are narrow bands of high‐intensity rainfall that occur in midlatitude areas of the world. Our study examines an NCFR impacting the Sierra Nevada foothills (California, USA) that initiated over 500 landslides, mobilized ~360,000 metric tons of sediment to the fluvial system (as much as 16 times the local annual sediment yield), and severely damaged local infrastructure and regional water transport facilities. Coupling geomorphological field investigations with meteorological analyses, we demonstrate that precipitation associated with the NCFR was both intense (maximum 15 min intensity of 70 mm/hr) and localized, resulting in a highly concentrated band of shallow landsliding. This meteorological phenomenon likely plays an important role in landscape evolution and hazard initiation. Other types of mesoscale meteorological features also occur globally and offer new avenues for understanding the effects of storms on landscapes.
Endocrine disrupting contaminants are of continuing concern for potentially contributing to reproductive dysfunction in largemouth and smallmouth bass in the Chesapeake Bay watershed (CBW) and elsewhere. Exposures to atrazine (ATR) have been hypothesized to have estrogenic effects on vertebrate endocrine systems. The incidence of intersex in male smallmouth bass from some regions of CBW has been correlated with ATR concentrations in water. Fish early life stages may be particularly vulnerable to ATR exposure in agricultural areas, as a spring influx of pesticides coincides with spawning and early development. Our objectives were to investigate the effects of early life stage exposure to ATR or the model estrogen 17α-ethinylestradiol (EE2) on sexual differentiation and gene expression in gonad tissue. We exposed newly hatched largemouth bass (LMB, Micropterus salmoides) from 7 to 80 days post-spawn to nominal concentrations of 1, 10, or 100 µg ATR/L or 1 or 10 ng EE2/L and monitored histological development and transcriptomic changes in gonad tissue. We observed a nearly 100% female sex ratio in LMB exposed to EE2 at 10 ng/L, presumably due to sex reversal of males. Many gonad genes were differentially expressed between sexes. Multidimensional scaling revealed clustering by gene expression of the 1 ng EE2/L and 100 µg ATR/L-treated male fish. Some pathways responsive to EE2 exposure were not sex-specific. We observed differential expression in male gonad in LMB exposed to EE2 at 1 ng/L of several genes involved in reproductive development and function, including star, cyp11a2, ddx4 (previously vasa), wnt5b, cyp1a and samhd1. Expression of star, cyp11a2 and cyp1a in males was also responsive to ATR exposure. Overall, our results confirm that early development is a sensitive window for estrogenic endocrine disruption in LMB and are consistent with the hypothesis that ATR exposure induces some estrogenic responses in the developing gonad. However, ATR-specific and EE2-specific responses were also observed.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.9614","usgsCitation":"Leet, J.K., Richter, C.A., Cornman, R.S., Berninger, J., Bhandari, R., Nicks, D., Zajicek, J., Blazer, V., and Tillitt, D.E., 2020, Effects of early life stage exposure of largemouth bass to atrazine or a model estrogen (17α-ethinylestradiol): PeerJ, v. 8, e9614, 26 p., https://doi.org/10.7717/peerj.9614.","productDescription":"e9614, 26 p.","ipdsId":"IP-113098","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":455149,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.9614","text":"Publisher Index Page"},{"id":436768,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93ZE9D6","text":"USGS data release","linkHelpText":"Effects of early life stage exposure of largemouth bass to atrazine or a model estrogen (17a-ethinylestradiol)"},{"id":379091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Pennsylvania, Maryland, West Virginia, Virginia, Delaware","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.00341796875,\n 42.84375132629021\n ],\n [\n -78.42041015625,\n 40.43022363450862\n ],\n [\n -79.82666015625,\n 39.2832938689385\n ],\n [\n -80.79345703125,\n 37.68382032669382\n ],\n [\n -79.69482421875,\n 37.31775185163688\n ],\n [\n -76.48681640625,\n 37.03763967977139\n ],\n [\n -75.38818359375,\n 38.77121637244273\n ],\n [\n -75.65185546874999,\n 39.605688178320804\n ],\n [\n -74.72900390625,\n 42.342305278572816\n ],\n [\n -76.00341796875,\n 42.84375132629021\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-10-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Leet, Jessica Kristin 0000-0001-8142-6043","orcid":"https://orcid.org/0000-0001-8142-6043","contributorId":225505,"corporation":false,"usgs":true,"family":"Leet","given":"Jessica","email":"","middleInitial":"Kristin","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":800531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":800532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":800533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berninger, Jason P.","contributorId":173602,"corporation":false,"usgs":false,"family":"Berninger","given":"Jason P.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":800534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bhandari, Ramji K.","contributorId":215751,"corporation":false,"usgs":false,"family":"Bhandari","given":"Ramji K.","affiliations":[{"id":39315,"text":"Department of Biology, University of North Carolina Greensboro, Greensboro, NC","active":true,"usgs":false}],"preferred":false,"id":800535,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nicks, Diane K.","contributorId":242624,"corporation":false,"usgs":false,"family":"Nicks","given":"Diane K.","affiliations":[{"id":27990,"text":"Deceased","active":true,"usgs":false}],"preferred":false,"id":800536,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zajicek, James L.","contributorId":211483,"corporation":false,"usgs":false,"family":"Zajicek","given":"James L.","affiliations":[{"id":38257,"text":"USGS-Columbia Environmental Research Center, Columbia, MO (Retired)","active":true,"usgs":false}],"preferred":false,"id":800537,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":800538,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tillitt, Donald E. 0000-0002-8278-3955 dtillitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8278-3955","contributorId":1875,"corporation":false,"usgs":true,"family":"Tillitt","given":"Donald","email":"dtillitt@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":800539,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70216810,"text":"70216810 - 2020 - Carbon storage and sediment trapping by Egeria densa Planch., a globally invasive, freshwater macrophyte","interactions":[],"lastModifiedDate":"2020-12-08T13:41:37.126308","indexId":"70216810","displayToPublicDate":"2020-10-02T07:40:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Carbon storage and sediment trapping by Egeria densa Planch., a globally invasive, freshwater macrophyte","docAbstract":"Invasive plants have long been recognized for altering ecosystem properties, but their long-term impacts on ecosystem processes remain largely unknown. In this study, we determined the impact of Egeria densa Planch, a globally invasive freshwater macrophyte, on sedimentation processes in a large tidal freshwater region. We measured carbon accumulation (CARs) and inorganic sedimentation rates in submerged aquatic vegetation SAV dominated by E. densa and compared these rates to those of adjacent tidal freshwater marshes. Study sites were chosen along a range of hydrodynamic conditions in the Sacramento-San Joaquin Delta of California, USA, where E. densa has been widespread since 1990. Cores were analyzed for bulk density, % inorganic matter, % organic carbon, 210Pb, and 137Cs. Our results show that E. densa patches constitute sinks for both “blue carbon” and inorganic sediment. Compared to marshes, E. densa patches have greater inorganic sedimentation rates (E. densa: 1103–5989 g m−2 yr−1, marsh: 393–1001 g m−2 yr−1, p < 0.01) and vertical accretion rates (E. densa: 0.4–1.3 cm yr−1, marsh: 0.3–0.5 cm yr−1, p < 0.05), but similar CARs (E. densa: 59–242 g C m−2 yr−1, marsh: 109–169 g C m−2 yr−1, p > 0.05). Sediment stored by E. densa likely reduces the resilience of adjacent marshes by depleting the sediment available for marsh-building. Because of its harmful traits, E. densa is not a suitable candidate for mitigating carbon pollution; however, currently invaded habitats may already contain a meaningful component of regional carbon budgets. Our results strongly suggest that E. densa patches are sinks for carbon and inorganic sediment throughout its global range, raising questions about how invasive SAV is altering biogeochemical cycling and sediment dynamics across freshwater ecosystems.
The Pleistocene- and Cretaceous-age sediments underlying Long Island, New York, compose an important sole-source aquifer system that is nearly 2,000 feet thick in some areas. Sediment characteristics of importance for water supply include water-transmitting properties—horizontal and vertical hydraulic conductivity—and the distribution of lignite, which provides an important control on oxygen-reduction (redox) conditions and water quality, in Cretaceous-age aquifers. Several decades of urbanization and the associated need to meet water demand have generated abundant data on the lithology of the aquifer sediments and the potential for an improved regional-scale understanding of this aquifer system. There is a range in the source and quality of the information, but large amounts of data, even of lesser quality, can yield insight into important aquifer characteristics.
The distribution of the horizontal and vertical hydraulic conductivity and the probability of occurrence of lignite and clay in the aquifer were developed for this study from a database of drilling records and geophysical logs. Lithologic descriptions were categorized into a set of standardized codes, which in turn, were aggregated into a set of general codes for the Pleistocene-age upper glacial and Cretaceous-age Magothy aquifers. General values of hydraulic conductivity were assigned to each code from published estimates on Long Island and analogous hydrogeologic environments on Cape Cod, Massachusetts. A binary value of 1 or 0 was assigned to each coded interval to indicate the presence or absence of lignite or based on keywords in the lithologic descriptions. This information was assembled into a geographic information system database that was queried sequentially and used to develop gridded values of each aquifer characteristic by use of ordinary kriging for a set of grids, each representing 10-foot-thick planar slices for the entire vertical thickness of each aquifer. These sets of grids, taken as a whole, represent a quasi-three-dimensional representation of each aquifer characteristic in both the upper glacial and Magothy aquifers.
The analysis of hydraulic conductivity shows patterns that generally reflect known depositional features of each unit and are consistent with the current understanding of the geology of the aquifers. Spatial patterns in the upper glacial aquifer show contrasts in estimated hydraulic conductivity: lower values occur in inland areas and likely are associated with glacial moraines; higher values generally occur to the south in association with glacial outwash. Higher values of hydraulic conductivity in the Magothy aquifer, which resulted from deltaic deposition, generally occur in the basal parts of the unit, are associated with channel-lag deposits and are found in parts of the aquifer known for large well yields. Lower values of hydraulic conductivity generally occur in middle parts of the aquifer associated with deposition in overbank and wetland environments. The probability of lignite occurrence is highest in this same vertical zone of the Magothy aquifer, consistent with deposition in wetland environments. The probability of lignite occurrence generally is highest along the southern shore of the island. Lignite occurrence generally is consistent with water-quality patterns; water quality in these same areas indicate chemically reducing conditions and redox-related iron biofouling commonly occurs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205023","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., and Finkelstein, J.S., 2020, Distribution of selected hydrogeologic characteristics of the upper glacial and Magothy aquifers, Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2020–5023, 21 p., https://doi.org/10.3133/sir20205023.","productDescription":"Report: iv, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-111547","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":377889,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":377890,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5023/coverthb.jpg"},{"id":377891,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5023/sir20205023.pdf","text":"Report","size":"8.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5023"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.11926269531249,\n 40.49291502689579\n ],\n [\n -71.85058593749999,\n 40.49291502689579\n ],\n [\n -71.7681884765625,\n 41.269549502842565\n ],\n [\n -74.11926269531249,\n 41.10832999732831\n ],\n [\n -74.11926269531249,\n 40.49291502689579\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Eastern oysters have been acknowledged for their important contribution to human well-being by providing goods and services including nitrogen removal from water bodies. In this study, we integrated daily environmental data (2008–2016) and filtration rate model parameter uncertainty to estimate nitrogen removal from denitrification and nitrogen burial services provided by the current extent of oyster (Crassostrea virginica) reefs in Mobile Bay, Alabama. Oyster landing data (2008–2016) in the Bay were also used to estimate nitrogen removal through oyster harvest. A replacement cost method using an engineering solution from wastewater treatment plants was implemented to quantify the economic benefit of the nitrogen removal. The estimated total nitrogen removal services provided by oyster reefs in Mobile Bay was 34,911 ± 5,032 kg N yr−1 (mean ± 1sd), in which 22,095 ± 3,305 kg N yr−1 from denitrification, 11,047 ± 1,652 kg N yr−1 from burial of nitrogen into sediments and 1,769 ± 876 kg N yr−1 by oyster harvest. The mean economic benefit was $76,455 ± 11,020 yr−1 which was estimated as $73.2 ± 11.5 ha−1 yr−1. This method could be used for any time period to estimate the nitrogen removal service in Mobile Bay. With proper modification of model parameters, this method could also be used elsewhere to estimate nitrogen removal services provided by oysters.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2020.106541","usgsCitation":"Lai, Q., Irwin, E.R., and Zhang, Y., 2020, Estimating nitrogen removal services of eastern oyster (Crassostrea virginica) in Mobile Bay, Alabama: Ecological Indicators, v. 117, p. 1-9, https://doi.org/10.1016/j.ecolind.2020.106541.","productDescription":"106541, 9 p.","startPage":"1","endPage":"9","ipdsId":"IP-109626","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":455156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2020.106541","text":"Publisher Index Page"},{"id":395631,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Gulf of Mexico, Mobile Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.25042724609374,\n 30.23652704486517\n ],\n [\n -88.06228637695312,\n 30.203300547277813\n ],\n [\n -87.69012451171875,\n 30.22466172703242\n ],\n [\n -87.88375854492186,\n 30.456960567387625\n ],\n [\n -87.85491943359375,\n 30.822063696500948\n ],\n [\n -88.05130004882812,\n 30.86686781614027\n ],\n [\n -88.13232421875,\n 30.657996912582398\n ],\n [\n -88.25042724609374,\n 30.23652704486517\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lai, Quan","contributorId":204521,"corporation":false,"usgs":false,"family":"Lai","given":"Quan","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":833537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irwin, Elise R. 0000-0002-6866-4976 eirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-6866-4976","contributorId":2588,"corporation":false,"usgs":true,"family":"Irwin","given":"Elise","email":"eirwin@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":833538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Yaoqi","contributorId":275164,"corporation":false,"usgs":false,"family":"Zhang","given":"Yaoqi","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":833750,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216486,"text":"70216486 - 2020 - Twelve-year dynamics and rainfall thresholds for alternating creep and rapid movement of the Hooskanaden landslide from integrating InSAR, pixel offset tracking, and borehole and hydrological measurements","interactions":[],"lastModifiedDate":"2020-11-23T14:24:51.327379","indexId":"70216486","displayToPublicDate":"2020-10-01T08:13:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Twelve-year dynamics and rainfall thresholds for alternating creep and rapid movement of the Hooskanaden landslide from integrating InSAR, pixel offset tracking, and borehole and hydrological measurements","docAbstract":"The Hooskanaden landslide is a large (~600 m wide × 1,300 m long), deep (~30 – 45 m) slide located in southwestern Oregon. Since 1958, it has had five moderate/major movements that catastrophically damaged the intersecting U.S. Highway 101, along with persistent slow wet‐season movements and a long‐term accelerating trend due to coastal erosion. Multiple remote sensing approaches, borehole measurements, and hydrological observations have been integrated to interpret the motion behaviors of the slide. Pixel offset tracking of both Sentinel‐1 and Sentinel‐2 images was carried out to reconstruct the 3‐D displacement field of the 2019 major event, and the results agree well with field measurements. A 12‐year displacement history of the landslide from 2007 to 2019 has been retrieved by incorporating offsets from Light Detection and Ranging (LiDAR) digital elevation model (DEM) gradients and Interferometric Synthetic Aperture Radar (InSAR) processing of ALOS and Sentinel‐1 images. Comparisons with daily/hourly ground precipitation reveal that the motion dynamics are predominantly controlled by intensity and temporal pattern of rainfall. A new empirical threefold rainfall threshold was therefore proposed to forecast the dates for the moderate/major movements. This threshold relies upon antecedent water‐year and previous 3‐day and daily precipitation and was able to represent observed movement periods well. Adaptation of our threshold methodology could prove useful for other large, deep landslides for which temporal forecasting has long been generally intractable. The averaged characteristic hydraulic conductivity and diffusivity were estimated as 6.6 × 10−6 m/s and 6.6 × 10−4 m2/s, respectively, based on the time lags between rainfall pulses and slide accelerations. Hydrologic modeling using these parameters helps to explain the ability of the new rainfall threshold.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JF005640","usgsCitation":"Xu, Y., Lu, Z., Schulz, W.H., and Kim, J., 2020, Twelve-year dynamics and rainfall thresholds for alternating creep and rapid movement of the Hooskanaden landslide from integrating InSAR, pixel offset tracking, and borehole and hydrological measurements: JGR Earth Surface, e2020JF005640, 17 p., https://doi.org/10.1029/2020JF005640.","productDescription":"e2020JF005640, 17 p.","ipdsId":"IP-122021","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455164,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jf005640","text":"Publisher Index Page"},{"id":380683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Hooskanaden Landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.4146728515625,\n 42.09822241118974\n ],\n [\n -124.11392211914062,\n 42.09822241118974\n ],\n [\n -124.11392211914062,\n 42.2752765520868\n ],\n [\n -124.4146728515625,\n 42.2752765520868\n ],\n [\n -124.4146728515625,\n 42.09822241118974\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-10-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Y.","contributorId":245125,"corporation":false,"usgs":false,"family":"Xu","given":"Y.","affiliations":[{"id":49088,"text":"Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA","active":true,"usgs":false}],"preferred":false,"id":805387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Z.","contributorId":199276,"corporation":false,"usgs":false,"family":"Lu","given":"Z.","affiliations":[],"preferred":false,"id":805388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":805389,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, J.","contributorId":245126,"corporation":false,"usgs":false,"family":"Kim","given":"J.","affiliations":[{"id":49088,"text":"Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75275, USA","active":true,"usgs":false}],"preferred":false,"id":805390,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216471,"text":"70216471 - 2020 - Improved prediction of management-relevant groundwater discharge characteristics throughout river networks","interactions":[],"lastModifiedDate":"2020-11-20T13:56:50.942422","indexId":"70216471","displayToPublicDate":"2020-10-01T07:54:15","publicationYear":"2020","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":"Improved prediction of management-relevant groundwater discharge characteristics throughout river networks","docAbstract":"Groundwater discharge zones connect aquifers to surface water, generating baseflow and serving as ecosystem control points across aquatic ecosystems. The influence of groundwater discharge on surface flow connectivity, fate and transport of contaminants and nutrients, and thermal habitat depends strongly on hydrologic characteristics such as the spatial distribution, age, and depth of source groundwater flow paths. Groundwater models have the potential to predict spatial discharge characteristics within river networks, but models are often not evaluated against these critical characteristics and model equifinality with respect to discharge processes is a known challenge. We quantify discharge characteristics across a suite of groundwater models with commonly used frameworks and calibration data. We developed a base model (MODFLOW‐NWT) for a 1,570‐km2 watershed in the northeastern United States and varied the calibration data, control of river‐aquifer exchange directionality, and resolution. Most models (n = 11 of 12) fit similarly to calibration metrics, but patterns in discharge location, flow path depth, and subsurface travel time varied substantially. We found (1) a 15% difference in the percent of discharge going to first‐order streams, (2) threefold variations in flow path depth, and (3) sevenfold variations in the subsurface travel times among the models. We recalibrated three models using a synthetic discharge location data set. Calibration with discharge location data reduced differences in simulated discharge characteristics, suggesting an approach to improved equifinality based on widespread field‐based mapping of discharge zones. Our work quantifying variation across common modeling approaches is an important step toward characterizing and improving predictions of groundwater discharge characteristics.
The United States Atlantic Outer Continental Shelf (OCS) has considerable offshore wind energy potential. Capturing that resource is part of a broader effort to reduce CO2 emissions. While few turbines have been constructed in U.S. waters, over a dozen currently planned offshore wind projects have the potential to displace marine birds, potentially leading to effective habitat loss. We focused on three diving birds identified in Europe to be vulnerable to displacement. Our research aimed to determine their potential exposure to areas designated or proposed for offshore wind development along the Atlantic OCS.
Satellite tracking technology was used to determine the spatial and temporal use and movement patterns of Surf Scoters (Melanitta perspicillata), Red‐throated Loons (Gavia stellata) and Northern Gannets (Morus bassanus), and calculate their exposure to each offshore wind area. We tagged 236 adults in 2012–2015 on the Atlantic OCS from New Jersey to North Carolina; an additional 147 birds tagged in previous tracking studies were integrated into our analyses. Tracking data were analysed in two‐week intervals using dynamic Brownian bridge movement models to develop composite spatial utilization distributions. For each species, these distributions were then used to calculate the spatio‐temporal exposure to each offshore wind area.
Surf Scoters and Red‐throated Loons were exposed to offshore wind areas almost exclusively during migration because these species were distributed among coastal and inshore waters during winter months. In contrast, Northern Gannets ranged over a much larger area, reaching farther offshore and south in winter, thus exhibited the greatest exposure to extant offshore wind areas.
Results of this study provide better understanding of how diving birds use current and potential future offshore wind areas on the Atlantic OCS, and can inform permitting, risk assessment and pre‐ and post‐construction impact assessments of offshore energy infrastructure.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13168","usgsCitation":"Stenhouse, I.J., Berlin, A., Gilbert, A.T., Goodale, M., Gray, C.O., Montevecchi, W.A., Savoy, L., and Spiegel, C.S., 2020, Assessing the exposure of three diving bird species to offshore wind areas on the U.S. Atlantic Outer Continental Shelf using satellite telemetry: Diversity and Distributions, v. 26, no. 12, p. 1703-1714, https://doi.org/10.1111/ddi.13168.","productDescription":"12 p.","startPage":"1703","endPage":"1714","ipdsId":"IP-115177","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13168","text":"Publisher Index Page"},{"id":378982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Atlantic Outer Continental Shelf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.84912109375,\n 34.90395296559004\n ],\n [\n -78.46435546874999,\n 33.815666308702774\n ],\n [\n -78.02490234374999,\n 33.43144133557529\n ],\n [\n -77.40966796874999,\n 32.99023555965106\n ],\n [\n -76.28906249999999,\n 32.76880048488168\n ],\n [\n -74.81689453125,\n 33.24787594792436\n ],\n [\n -73.41064453125001,\n 33.68778175843934\n ],\n [\n -71.96044921875001,\n 35.60371874069731\n ],\n [\n -70.97167968749999,\n 38.30718056188316\n ],\n [\n -70.048828125,\n 41.11246878918088\n ],\n [\n -71.03759765625,\n 41.60722821271717\n ],\n [\n -72.48779296874999,\n 41.52502957323799\n ],\n [\n -73.23486328124999,\n 40.896905775860006\n ],\n [\n -76.04736328125,\n 38.58252615935333\n ],\n [\n -76.552734375,\n 37.56199695314352\n ],\n [\n -76.83837890624999,\n 36.42128244364947\n ],\n [\n -77.84912109375,\n 34.90395296559004\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stenhouse, Iain J","contributorId":242039,"corporation":false,"usgs":false,"family":"Stenhouse","given":"Iain","email":"","middleInitial":"J","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":800421,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berlin, Alicia 0000-0002-5275-3077 aberlin@usgs.gov","orcid":"https://orcid.org/0000-0002-5275-3077","contributorId":168416,"corporation":false,"usgs":true,"family":"Berlin","given":"Alicia","email":"aberlin@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":800422,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilbert, Andrew T","contributorId":242040,"corporation":false,"usgs":false,"family":"Gilbert","given":"Andrew","email":"","middleInitial":"T","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":800423,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goodale, M Wing","contributorId":242041,"corporation":false,"usgs":false,"family":"Goodale","given":"M Wing","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":800424,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, Carrie O","contributorId":242042,"corporation":false,"usgs":false,"family":"Gray","given":"Carrie","email":"","middleInitial":"O","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":800425,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Montevecchi, William A","contributorId":242043,"corporation":false,"usgs":false,"family":"Montevecchi","given":"William","email":"","middleInitial":"A","affiliations":[{"id":26965,"text":"Memorial University of Newfoundland","active":true,"usgs":false}],"preferred":false,"id":800426,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Savoy, Lucas","contributorId":171896,"corporation":false,"usgs":false,"family":"Savoy","given":"Lucas","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":800427,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Spiegel, Caleb S.","contributorId":216938,"corporation":false,"usgs":false,"family":"Spiegel","given":"Caleb","email":"","middleInitial":"S.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800428,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70214569,"text":"sir20205096 - 2020 - Trends in concentration, loads, and sources of trace metals and nutrients in the Spokane River Watershed, northern Idaho, water years 1990–2018","interactions":[],"lastModifiedDate":"2020-10-01T16:51:47.8491","indexId":"sir20205096","displayToPublicDate":"2020-09-30T12:48:23","publicationYear":"2020","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":"2020-5096","displayTitle":"Trends in Concentrations, Loads, and Sources of Trace Metals and Nutrients in the Spokane River Watershed, Northern Idaho, Water Years 1990–2018","title":"Trends in concentration, loads, and sources of trace metals and nutrients in the Spokane River Watershed, northern Idaho, water years 1990–2018","docAbstract":"A long history of mining and widespread metals contamination in the Coeur d’Alene River watershed and downstream into the Spokane River has led to the area’s designation as a Superfund site and to extensive, ongoing (as of 2020) remedial actions. Long-term water-quality and streamflow data, collected by the U.S. Geological Survey for up to 29 years at 20 sampling sites in the Coeur d’Alene, Spokane and St. Joe River watersheds, were analyzed to evaluate the impact of remedial actions on metals in surface water. Analyses focused on total and dissolved cadmium, zinc and lead. Trends in total phosphorus, total nitrogen and dissolved orthophosphate were also evaluated; although these nutrients are not constituents of concern for the Superfund site, they are important to the health of Coeur d’Alene Lake.
Dissolved cadmium, zinc and lead concentrations were compared to ambient water-quality criteria at 20 sample sites. For the 12 sites with the most extensive data records, Weighted Regressions on Time, Discharge and Season (WRTDS) models were developed to estimate flow-normalized annual mean concentrations and flow-normalized annual total loads; these results were used to evaluate trends because flow-normalization dampens the impact of interannual streamflow variability on concentrations and loads. WRTDS models with Kalman filtering (WRTDS_K) were developed to estimate annual mean concentrations and annual total loads; these results were used to evaluate spatial patterns in constituent sources. Models were developed for total and dissolved cadmium, lead, and zinc; total phosphorus and nitrogen; and dissolved orthophosphate, although not all constituents were modeled for all sites due to limited sample sizes. Bootstrapped confidence intervals were constructed to determine the statistical likelihood of trends and the slope of trends in flow-normalized concentrations and loads during the period of record (13–29 years, depending on the site), water years 1999–2009, and water years 2009–18.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205096","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Zinsser, L.M., 2020, Trends in concentration, loads, and sources of trace metals and nutrients in the Spokane River Watershed, northern Idaho, water years 1990-2018: U.S. Geological Survey Scientific Investigations Report 2020–5096, 58 p., https://doi.org/10.3133/sir20205096.","productDescription":"Report: vii, 58 p.; Appendix 1-2; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-116912","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":378922,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5096/coverthb.jpg"},{"id":378923,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096"},{"id":378924,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096_appendix1.pdf","text":"Appendix 1","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096 Appendix 1"},{"id":378925,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5096/sir20205096_appendix2.pdf","text":"Appendix 2","size":"35.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5096 Appendix 2"},{"id":378926,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91LNE8J","text":"USGS data release","description":"USGS Data Release","linkHelpText":"WRTDS annual concentrations, loads and statistical trend likelihoods for sites in the Spokane River watershed, water years 1990-2018"}],"country":"United States","state":"Idaho","otherGeospatial":"Spokane River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.18017578125,\n 46.98025235521883\n ],\n [\n -114.80712890625,\n 46.98025235521883\n ],\n [\n -114.80712890625,\n 48.29781249243716\n ],\n [\n -117.18017578125,\n 48.29781249243716\n ],\n [\n -117.18017578125,\n 46.98025235521883\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
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During water years (WY) 2013–2017, the U.S. Geological Survey, National Water-Quality Assessment (NAWQA) Project, sampled the National Water Quality Network – Rivers and Streams (NWQN) year-round and reported on 221 pesticides at 72 sites across the United States in agricultural, developed, and mixed land use watersheds. The Pesticide Toxicity Index (PTI) was used to estimate the potential chronic and acute toxicity to three taxonomic groups – fish, cladocerans, and benthic invertebrates. For invertebrates (either cladocerans, benthic invertebrates, or both), the maximum PTI score exceeded the predicted acute toxicity screening level at 18 of the 72 sites (25%) at some point during WY 2013–2017. The predicted toxicity of a single pesticide compound was found to overwhelm the toxicity of other pesticides in the mixtures after concentrations were toxicity weighted. For this study, about 71%, 72%, and 92% of the Fish-, Cladoceran-, and Benthic Invertebrate-PTI scores, respectively, had one pesticide compound primarily contributing to sample potential toxicity (>50%).
There were 17 (13 insecticides, 2 herbicides, 1 fungicide, and 1 synergist) of the 221 pesticide compounds analyzed that were the primary drivers of potential toxicity in each water sample in which the PTI and TUmax (toxic unit score for the pesticide that makes the single largest contribution to the PTI) scores were above predicted chronic (>0.1) or acute (>1) toxicity levels for one of the three taxa. For cladocerans and benthic invertebrates, the drivers of predicted chronic (>0.1) and acute (>1) PTIs were mostly insecticides. For cladocerans, the pesticide compounds driving the PTI scores were bifenthrin, carbaryl, chlorpyrifos, diazinon, dichlorvos, dicrotophos, diflubenzuron, flubendiamide, and tebupirimfos. For benthic invertebrates, atrazine (an herbicide), as well as the insecticides – bifenthrin, carbaryl, carbofuran, chlorpyrifos, diazinon, dichlorvos, fipronil, imidacloprid, and methamidophos – were the drivers of predicted toxicity. For fish, there were three pesticide types that contributed the most to predicted chronic (>0.1) PTIs – acetochlor, an herbicide; carbendazim, a fungicide degradate; and piperonylbutoxide, a synergist.
","language":"English","doi":"10.1016/j.scitotenv.2020.141285","usgsCitation":"Covert, S.A., Shoda, M.E., Stackpoole, S.M., and Stone, W.W., 2020, Pesticide mixtures show potential toxicity to aquatic life in U.S. streams, water years 2013-2017: Science of the Total Environment, v. 745, 141285, 12 p., https://doi.org/10.1016/j.scitotenv.2020.141285.","productDescription":"141285, 12 p.","ipdsId":"IP-117042","costCenters":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":455178,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.141285","text":"Publisher Index Page"},{"id":436772,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T86C5U","text":"USGS data 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The increased availability of high-resolution elevation data from light detection and ranging (lidar) has helped improve methods for extracting these metrics at a greater accuracy across regional scales. The Floodplain and Channel Evaluation Tool (FACET) was developed as an open source tool to calculate a suite of geomorphic metrics describing channel and floodplain geometry from high-resolution digital elevation models (DEMs), providing estimates of channel width, bank height, cross-sectional area, and floodplain extent. Field data from sites in the Chesapeake Bay and Delaware River watersheds were used to calibrate and validate FACET within five physiographic provinces in the Mid-Atlantic region of the United States. Stream banks were identified using either a slope-threshold method at cross sections which are automatically generated at a user-defined interval along the delineated stream network, or by applying a curvature-threshold method for grid cells within a buffered distance from the stream network. The floodplain extent was mapped using a height above nearest drainage (HAND) grid and empirical regression models built for each physiographic province relating the HAND threshold to drainage area. Other user-defined input parameters within FACET control the sensitivity of calculations to DEM resolution, relief, and stream order, allowing for the ability to optimize FACET at multiple scales and/or regions if field survey data are available for calibration. 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Expedition operations included testing two configurations of a rotary pressure-coring tool in a gas hydrate–bearing formation. In the first hole, an extended core barrel (cutting shoe) configuration of the Pressure Coring Tool with Ball Valve (PCTB-CS) was deployed, and in the second hole, the PCTB face bit configuration (PCTB-FB) was deployed. The PCTB-CS successfully recovered and maintained pressure for only one core out of eight deployments. A series of incremental modifications were made during and after the PCTB-CS deployment period that impacted the operations of the subsequent PCTB-FB deployments. Thus, in the second hole, the PCTB-FB successfully recovered and maintained pressure within the hydrate stability zone for 11 cores out of 13 deployments. The PCTB cored gas hydrate–bearing sandy silt interbedded with non–hydrate-bearing clayey silt within the main reservoir. The PCTB also recovered long intervals of unbroken, high-quality core with preserved sedimentary structures. We recovered one pressure core 130 m (437 ft) above the main hydrate reservoir in the silty clay. Pressure coring is the only available technology for recovering intact cores from sediment that is normally disturbed by gas expansion, dissolution, or dissociation; this allows a wide range of scientific measurements to be obtained with minimal disturbance to the core sediment fabric. Analysis of pressure cores has the potential to illuminate the in situ properties, gas saturation, and gas composition of a wide range of reservoirs including unconventional shale systems.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/02262019036","usgsCitation":"Thomas, C., Phillips, S.C., Flemings, P., Santra, M., Hammon, H., Collett, T., Cook, A., Pettigrew, T., Mimitz, M., Holland, M., and Schultheiss, P., 2020, Pressure coring operations during The University of Texas-Gulf of Mexico 2-1 (UT-GOM2-1) Hydrate Pressure Coring Expedition in Green Canyon Block 955, northern Gulf of Mexico: AAPG Bulletin, v. 104, no. 9, p. 1877-1901, https://doi.org/10.1306/02262019036.","productDescription":"24 p.","startPage":"1877","endPage":"1901","ipdsId":"IP-106754","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":379085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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Austin","active":true,"usgs":false}],"preferred":false,"id":800597,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flemings, Peter B.","contributorId":242641,"corporation":false,"usgs":false,"family":"Flemings","given":"Peter B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":800576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Santra, Manasij","contributorId":242642,"corporation":false,"usgs":false,"family":"Santra","given":"Manasij","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":800577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammon, Helen","contributorId":242643,"corporation":false,"usgs":false,"family":"Hammon","given":"Helen","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":800578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220806,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":800573,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cook, Ann","contributorId":242644,"corporation":false,"usgs":false,"family":"Cook","given":"Ann","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":800598,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pettigrew, Tom","contributorId":242645,"corporation":false,"usgs":false,"family":"Pettigrew","given":"Tom","affiliations":[{"id":48042,"text":"Pettigrew Engineering","active":true,"usgs":false}],"preferred":false,"id":800599,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mimitz, Mike","contributorId":242646,"corporation":false,"usgs":false,"family":"Mimitz","given":"Mike","affiliations":[{"id":48495,"text":"Geotek Coring","active":true,"usgs":false}],"preferred":false,"id":800600,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Holland, Melanie","contributorId":242647,"corporation":false,"usgs":false,"family":"Holland","given":"Melanie","affiliations":[{"id":48495,"text":"Geotek Coring","active":true,"usgs":false}],"preferred":false,"id":800601,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schultheiss, Peter","contributorId":242648,"corporation":false,"usgs":false,"family":"Schultheiss","given":"Peter","affiliations":[{"id":48496,"text":"Geotek","active":true,"usgs":false}],"preferred":false,"id":800602,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70270772,"text":"70270772 - 2020 - Assessing the distribution and habitat needs of the Least Darter and sympatric species of the Ozark and Arbuckle Mountain ecoregions","interactions":[],"lastModifiedDate":"2025-08-28T15:18:35.030108","indexId":"70270772","displayToPublicDate":"2020-09-30T10:12:46","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-146-2020","title":"Assessing the distribution and habitat needs of the Least Darter and sympatric species of the Ozark and Arbuckle Mountain ecoregions","docAbstract":"Limited information is known about factors driving the distribution of Least Darter in Oklahaoma. The Least Darter occurs in the Ozark Highland and Arbuckle Uplift ecoregions of Oklahoma which represent the southern extent of its range. Least Darter was historically recorded in Oklahoma from groundwater-fed streams. Our study objectives were to determine the distribution of Least Darter and a subset of congeners across the two ecoregions of Oklahoma and assess factors driving patch occupancy of Least Darter at a fine spatial scale. We used temporally replicated snorkel surveys conducted in July through October 2018-2019 to determine occupany by Least Darter. We snorkeled and seined for four species in each reach including two life stages of Smallmouth Bass (subadult and adult). We sampled 153 sites (i.e., riffle-pool complexes) nested within 61 stream reaches (i.e., 200-500-m long) in the Arbuckle Uplift and Ozark Highland ecoregions. Detection probability was similar between ecoregions. Least Darter was detected at more sites when snorkeling compared to seining (24 versus 18). Smallmouth Bass, Redspot Chub and Southern Redbelly Dace were typically 2-3 times more likely to be detected by snorkeling than by seining. We found relationships between occupancy and habitat parameters that were both shared among species but also species-specific. Least Darter occurrence probability in the Ozark Highlands was lower than in the Arbuckle Uplift. Occurrence probability was higher for subadult Smallmouth Bass and Southern Redbelly Dace in 2018 compared to 2019. Occurrence probabilities of both Least Darter and Southern Redbelly Dace were higher in cooler habitat patches. Southern Redbelly Dace was negatively associated with a higher proportion of pool habitat across a reach. Lastly, subadult Smallmouth Bass and Redspot Chub were more likely to occur in deeper pools and in larger streams (i.e., drainage area). We sampled one study reach (~150-m long with shallow riffles or a waterfall on each end) in the Arbuckle Uplift (winter and summer sampling) and Ozark Highland (winter sampling) ecoregions to determine fine-scale habitat selection during the thermally harsh seasons. We developed transects across the reaches to quantify depth, velocity, substrate, cover and water temperature. We found Least Darter used higher water column velocities and shallower water depths with little vegetation during the winter. The average water depth used was similar during summer and winter (~ 20 cm deep). Least Darter used denser vegetation during the summer and tended to avoid coarse substrates in both seasons. If the conservation of Least Darter is a management goal, actions to mitigate increasing stream water temperatures (e.g., protection of springs and riparian corridors) and protecting stream morphologies that facilitate species separations (i.e., allow for a wide range of water depth and velocities) may be beneficial (e.g., fencing cattle from streams, promoting natural bankful flows during spring)
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css59114986","usgsCitation":"Brewer, S., Sewdberg, D., Mollenhauer, R., and Dattilo, J., 2020, Assessing the distribution and habitat needs of the Least Darter and sympatric species of the Ozark and Arbuckle Mountain ecoregions: Cooperator Science Series CSS-146-2020, 63 p., https://doi.org/10.3996/css59114986.","productDescription":"63 p.","ipdsId":"IP-124601","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":495008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Ozark and Arbuckle Mountain ecoregions","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.48968658701178,\n 37.07593633608397\n ],\n [\n -98.34921377661617,\n 37.07593633608397\n ],\n [\n -98.34921377661617,\n 33.75112170322656\n ],\n [\n -94.48968658701178,\n 33.75112170322656\n ],\n [\n -94.48968658701178,\n 37.07593633608397\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":947043,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sewdberg, D.","contributorId":360422,"corporation":false,"usgs":false,"family":"Sewdberg","given":"D.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mollenhauer, R.","contributorId":276144,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"R.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947045,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dattilo, J.","contributorId":274267,"corporation":false,"usgs":false,"family":"Dattilo","given":"J.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947046,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217168,"text":"70217168 - 2020 - Impacts of grade control structure installations on hydrology and sediment transport as an adaptive management strategy","interactions":[],"lastModifiedDate":"2021-01-08T15:59:46.702087","indexId":"70217168","displayToPublicDate":"2020-09-30T09:44:59","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"ST-2017-1751-01","title":"Impacts of grade control structure installations on hydrology and sediment transport as an adaptive management strategy","docAbstract":"The goal of this research was to examine the impacts of Grade Control Structure (GCS) installations at the Heard Scout Pueblo (HSP) study site in the City of Phoenix, Arizona, USA. The study site is around a high-use trail system and is comprised of eroded and incised channels that conduct high flows and associated sediments into a residential neighborhood downstream, a noted stormwater control problem. We established baseline conditions associated with rainfall/runoff response before structures were installed so we could have some data for comparison afterwards.
Innovative monitoring equipment, including video cameras and pressure transducers (to calculate discharge); digital terrain models, sediment samplers and sediment chains (to measure erosion and deposition); soil moisture sensors in monitoring wells (to document infiltration and potential recharge); and weather stations (to track temperature and relative humidity) were established and a small Unmanned Aircraft System (sUAS) survey was completed by July, 11, 2017, in time for the typical summer monsoon season which officially runs from June 15th to September 30th. Only one pre-GCS installation rain event incurred a significant flow event (October 13, 2018).
Natural Channel Design (NCD), a landscape restoration company with decades of experience, was hired through a competitive bid process to develop a novel layout of ~30 GCS installations (sills, modified one-rock dams (ORD), and plugs, as well as a modified Zuni-bowl). The American Conservation Experience (ACE) hand-built the structures based on these designs in the main channel from November 13, 2018 through December 1, 2018. ACE built another ten structures in locations adjacent to the channel from January 15 through January 18, 2019. NCD worked with the landscape forensics to identify a historic channel and reinstate it using GCS.
A surface-water model was also applied, using some of the baseline measurements (terrain and hydraulic conductivity) to track the flows of water and potential infiltration associated with rainfall events before GCS installation, to assist NCD in their design. The same model was applied using the installed GCS locations to simulate impacts of the structures on flow and infiltration. Our model was able to predict the slight reduction and delay in peak flows for small events and simulate infiltration, which was measured and occurred in the channel. Results demonstrated that structures could increase infiltration by ~15% over time. More data describing geomorphology and hydrology after repeated rainfall events will allow for increased analyses.
Innovative monitoring, including the large‐scale particle image velocimetry (LSPIV) were invaluable to this research. Given the arid-land location and added drought conditions, the water levels were not high enough to compute, even using the continuous slope-area method, so discharge was calculated solely using the LSPIV. The careful redundancy of data acquisition is extremely important when studying dryland hydrology.
Weather data indicated that the HSP GCS installations created roughly a three-degree microclimate cooling effect for at least two days following rainfall events, as compared with the untreated channel. The cooling was attributed to increased moisture, evaporation, and latent heat expulsion from the evaporation.
","language":"English","publisher":"Bureau of Reclamation","usgsCitation":"Tosline, D., Norman, L., Greimann, B.P., Cederberg, J., Huang, V., and Ruddell, B., 2020, Impacts of grade control structure installations on hydrology and sediment transport as an adaptive management strategy: Final Report ST-2017-1751-01, iv, 65 p.","productDescription":"iv, 65 p.","ipdsId":"IP-121918","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":382021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382013,"type":{"id":15,"text":"Index Page"},"url":"https://data.usbr.gov/catalog/4414/item/6298"}],"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 -112.09899902343749,\n 33.293803558346596\n ],\n [\n -111.9784927368164,\n 33.293803558346596\n ],\n [\n -111.9784927368164,\n 33.38529959859565\n ],\n [\n -112.09899902343749,\n 33.38529959859565\n ],\n [\n -112.09899902343749,\n 33.293803558346596\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tosline, Deborah","contributorId":247510,"corporation":false,"usgs":false,"family":"Tosline","given":"Deborah","affiliations":[{"id":49564,"text":"Reclamation, Hydrologist / Program Manager","active":true,"usgs":false}],"preferred":false,"id":807809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":807810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greimann, Blair P.","contributorId":247511,"corporation":false,"usgs":false,"family":"Greimann","given":"Blair","email":"","middleInitial":"P.","affiliations":[{"id":49565,"text":"Reclamation, Hydraulic Engineer","active":true,"usgs":false}],"preferred":false,"id":807811,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cederberg, Jay 0000-0001-6649-7353","orcid":"https://orcid.org/0000-0001-6649-7353","contributorId":219724,"corporation":false,"usgs":true,"family":"Cederberg","given":"Jay","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807812,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huang, Victor","contributorId":247512,"corporation":false,"usgs":false,"family":"Huang","given":"Victor","email":"","affiliations":[{"id":49565,"text":"Reclamation, Hydraulic Engineer","active":true,"usgs":false}],"preferred":false,"id":807813,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruddell, Benjamin L.","contributorId":247513,"corporation":false,"usgs":false,"family":"Ruddell","given":"Benjamin L.","affiliations":[{"id":49567,"text":"Northern Arizona University, Professor","active":true,"usgs":false}],"preferred":false,"id":807814,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228928,"text":"70228928 - 2020 - Using video survey to examine the effect of habitat on gag grouper encounter","interactions":[],"lastModifiedDate":"2022-03-08T14:43:20.629834","indexId":"70228928","displayToPublicDate":"2020-09-30T08:32:37","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using video survey to examine the effect of habitat on gag grouper encounter","docAbstract":"Gag is a reef fish that was declared overfished in the Gulf of Mexico (GOM) in 2009. Although Gag are no longer listed as overfished, fisheries managers are concerned that stocks may not be recovering. Our objective was to identify habitat characteristics important to Gag, and their effect on the probability of Gag occurrence. We obtained data from three separate fisheries-independent video surveys that sampled in the eastern GOM from 2010-2017: the National Atmospheric and Oceanic Administration (NOAA) Panama City, FL Office, the NOAA Southeast Area Monitoring and Assessment Program, and the Florida Fish and Wildlife Research Institute. We ran a separate mixed effects logistic regression for each survey, and used Akaike’s Information Criteria to determine the best fitting models. Some variables - percent rock coverage, vertical relief, latitude, and depth - were present in all confidence models. Depth did not have the same relationship with Gag across all surveys, suggesting that shallower habitats (<50 m) might be more suitable for juveniles, whereas deeper habitats (>50 m) might be more suitable for adults. Managers may be able to help Gag and encourage their recovery by using these data to establish or expand protected areas throughout shallower waters.
","conferenceTitle":"Annual Meeting of the American Fisheries Society. Virtual","conferenceDate":"Aug 28 - Sep 3, 2020","language":"English","usgsCitation":"Alvarez, G., Gandy, D., Irwin, B., Jennings, C.A., and Fox, A., 2020, Using video survey to examine the effect of habitat on gag grouper encounter, Annual Meeting of the American Fisheries Society. Virtual, Aug 28 - Sep 3, 2020, 3 p.","productDescription":"3 p.","ipdsId":"IP-119340","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.26416015625,\n 24.8\n ],\n [\n -81.2548828125,\n 24.8\n ],\n [\n -81.2548828125,\n 30.637912028341123\n ],\n [\n -88.26416015625,\n 30.637912028341123\n ],\n [\n -88.26416015625,\n 24.8\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alvarez, G.","contributorId":280041,"corporation":false,"usgs":false,"family":"Alvarez","given":"G.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":835931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gandy, D.","contributorId":280042,"corporation":false,"usgs":false,"family":"Gandy","given":"D.","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":835932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Brian J. 0000-0002-0666-2641","orcid":"https://orcid.org/0000-0002-0666-2641","contributorId":280043,"corporation":false,"usgs":true,"family":"Irwin","given":"Brian J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835933,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jennings, Cecil A. 0000-0002-6159-6026 jennings@usgs.gov","orcid":"https://orcid.org/0000-0002-6159-6026","contributorId":874,"corporation":false,"usgs":true,"family":"Jennings","given":"Cecil","email":"jennings@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835934,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, Adam","contributorId":288127,"corporation":false,"usgs":false,"family":"Fox","given":"Adam","affiliations":[],"preferred":false,"id":835935,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224335,"text":"70224335 - 2020 - Assessing plot-scale impacts of land use on overland flow generation in Central Panama","interactions":[],"lastModifiedDate":"2021-09-23T12:24:52.951151","indexId":"70224335","displayToPublicDate":"2020-09-30T07:22:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Assessing plot-scale impacts of land use on overland flow generation in Central Panama","docAbstract":"Land use in Panama has changed dramatically with ongoing deforestation and conversion to cropland and cattle pastures, potentially altering the soil properties that drive the hydrological processes of infiltration and overland flow. We compared plot-scale overland flow generation between hillslopes in forested and actively cattle-grazed watersheds in Central Panama. Soil physical and hydraulic properties, soil moisture and overland flow data were measured along hillslopes of each land-use type. Soil characteristics and rainfall data were input into a simple, 1-D representative model, HYDRUS-1D, to simulate overland flow that we used to make inferences about overland flow response at forest and pasture sites. Runoff ratios (overland flow/rainfall) were generally higher at the pasture site, although no overall trends were observed between rainfall characteristics and runoff ratios across the two land uses at the plot scale. Saturated hydraulic conductivity (Ks) and bulk density were different between the forest and pasture sites (p < 10−4). Simulating overland flow in HYDRUS-1D produced more outputs similar to the overland flow recorded at the pasture site than the forest site. Results from our study indicate that, at the plot scale, Hortonian overland flow is the main driver for overland flow generation at the pasture site during storms with high-rainfall totals. We infer that the combination of a leaf litter layer and the activation of shallow preferential flow paths resulting in shallow saturation-excess overland flow are likely the main drivers for plot scale overland flow generation at the forest site. Results from this study contribute to the broader understanding of the delivery of freshwater to streams, which will become increasingly important in the tropics considering freshwater resource scarcity and changing storm intensities.
The prediction of wave runup, as well as its components, time-averaged setup and the time-varying swash, is a key element of coastal storm hazard assessments, as wave runup controls the transitions between morphodynamic response types such as dune erosion and overwash, and the potential for flooding by wave overtopping. While theoretically able to simulate the dominant low-frequency swash, previous studies using the infragravity-wave–resolving model XBeach (XBSB) have shown an underestimation of the observed swash variance and wave runup, which was in part related to the absence of incident-band swash motions in the model. Here, we use an incident-band wave-resolving, non-hydrostatic version of the XBeach model (XBNH) to simulate wave runup observed during the SandyDuck '97 experiment on an intermediate–reflective sandy beach. The results show that the XBNH model describes wave runup and the individual setup and swash components well. We subsequently examine differences in wave runup prediction between the XBSB and XBNH models and find that the XBNH model is a better predictor of wave runup than XBSB for this beach, which is due to better predictions of both the incident-band and infragravity-band swash. For a range of beach states from reflective to dissipative it is shown that incident-band swash is underestimated by XBSB relative to XBNH, in particular for reflective conditions. Infragravity-band swash is shown to be lower in XBSB than XBNH for most conditions, including dissipative conditions for which the mean difference is 16% of the deep water wave height. The difference in infragravity-band swash in XBNH relative to XBSB is shown to mainly be the result of processes occurring outside the swash zone, but approximately 15% of the difference is caused by explicitly resolving incident-band wave motions within the swash zone, such as swash-swash interactions, which inherently cannot be simulated by wave-averaged models.
The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that estimate daily water-level data at ungaged locations, and applications that generate derived hydrologic data across the freshwater part of the Greater Everglades landscape. Version 3 (V3) of the EDEN interpolation surface-water model is the most recent update, replacing the version 2 (V2) model released in 2011.
The primary revision for the V3 model is the switch to the R programming language to create a more efficient and portable EDEN code relative to V2, without reliance on proprietary software. Using R, the interpolation script runs over 10 times faster and is more easily updated, for example, to accommodate changes in the gage network or to incorporate R software updates. Additional revisions made for the V3 model include updates to the interpolation model, the gage network, and groundwater-level estimations. The EDEN model domain in the Greater Everglades and Big Cypress National Preserve is divided into subdomains that are based on hydrologic boundaries. In the V3 model, the number of subdomains was increased from five to eight, which allows hydrologic boundaries, such as levees and canals, to be better represented in the interpolation scheme. Five pseudogages were added to constrain the water-level surface at subdomain boundaries. Changes made to the water-level gage network between the implementation of the V2 and V3 models are incorporated, and groundwater-level estimations are added, which are important information for hydrologic and ecological studies.
Summary model performance statistics indicate similar accuracy in water-level surfaces generated by the V3 and V2 models, with a root mean square error of 4.78 centimeters for both interpolation models against independent water-level measurements. Providing stability and continuity for the EDEN user community, the V3 model closely replicates the V2 model, with a root mean square difference of 3.87 centimeters for interpolated surfaces from April 1, 2014, to March 31, 2018. The additional groundwater levels provide a realistic estimate of the saturated groundwater surface continuous with the surface-water surface for Water Conservation Areas 2A and 2B from 2000 to 2011. This continuous surface is a more accurate estimation of the spatial distribution of water in the hydrologic system than before, providing needed information for ecological studies in areas where depth to water table affects habitats. Development of the EDEN V3 model advances the tools available to scientists and resource managers for guiding large-scale field operations, describing hydrologic changes, and supporting biological and ecological assessments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205083","collaboration":"USGS Greater Everglades Priority Ecosystems Science ProgramDirector, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
We measured food availability and diet composition of juvenile salmonids over multiple years and seasons before and during the world’s largest dam removal on the Elwha River, Washington State. We conducted these measurements over three sediment-impacted sections (the estuary and two sections of the river downstream of each dam) and compared these to data collected from mainstem tributaries not directly affected by the massive amount of sediment released from the reservoirs. We found that sediment impacts from dam removal significantly reduced invertebrate prey availability, but juvenile salmon adjusted their foraging so that the amount of energy in diets was similar before and during dam removal. This general pattern was seen in both river and estuary habitats, although the mechanisms driving the change and the response differed between habitats. In the estuary, the dietary shifts were related to changes in invertebrate assemblages following a hydrological transition from brackish to freshwater caused by sediment deposition at the river’s mouth. The loss of brackish invertebrate species caused fish to increase piscivory and rely on new prey sources such as plankton. In the river, energy provided to fish by Ephemeroptera, Plecoptera, and Trichoptera taxa before dam removal was replaced first by terrestrial invertebrates, and then by sediment-tolerant taxa such as Chironomidae. The results of our study are consistent with many others that have shown sharp declines in invertebrate density during dam removal. Our study further shows how those changes can move through the food web and affect fish diet composition, selectivity, and energy availability. As we move further along the dam removal response trajectory, we hypothesize that food web complexity will continue to increase as annual sediment load now approaches natural background levels, anadromous fish have recolonized the majority of the watershed between and above the former dams, and revegetation and microhabitats continue to develop in the estuary.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0239198","usgsCitation":"Morley, S.A., Foley, M.M., Duda, J.J., Beirne, M.M., Paradis, R.L., Johnson, R.C., McHenry, M.L., Elofson, M., Sampson, E.M., McCoy, R.E., Stapleton, J., and Pess, G.R., 2020, Shifting food web structure during dam removal—Disturbance and recovery during a major restoration action: PLoS ONE, v. 15, no. 9, e0239198, 34 p., https://doi.org/10.1371/journal.pone.0239198.","productDescription":"e0239198, 34 p.","ipdsId":"IP-117389","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":455196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0239198","text":"Publisher Index Page"},{"id":378966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River, Olympic Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.64974975585938,\n 47.81960975604292\n ],\n [\n -123.38882446289061,\n 47.81960975604292\n ],\n [\n -123.38882446289061,\n 48.16333749877855\n ],\n [\n -123.64974975585938,\n 48.16333749877855\n ],\n [\n -123.64974975585938,\n 47.81960975604292\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Morley, Sarah A.","contributorId":148956,"corporation":false,"usgs":false,"family":"Morley","given":"Sarah","email":"","middleInitial":"A.","affiliations":[{"id":17601,"text":"NOAA Fisheries, Northwest Fisheries Science Center, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":800243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foley, Melissa M 0000-0002-5832-6404","orcid":"https://orcid.org/0000-0002-5832-6404","contributorId":238117,"corporation":false,"usgs":false,"family":"Foley","given":"Melissa","email":"","middleInitial":"M","affiliations":[{"id":47699,"text":"San Francisco Estuary Institute, Richmond, CA","active":true,"usgs":false}],"preferred":false,"id":800244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":800245,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beirne, Mathew M","contributorId":241958,"corporation":false,"usgs":false,"family":"Beirne","given":"Mathew","email":"","middleInitial":"M","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800246,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paradis, Rebecca L","contributorId":241960,"corporation":false,"usgs":false,"family":"Paradis","given":"Rebecca","email":"","middleInitial":"L","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800247,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Rachelle Carina 0000-0003-1480-4088","orcid":"https://orcid.org/0000-0003-1480-4088","contributorId":241962,"corporation":false,"usgs":true,"family":"Johnson","given":"Rachelle","email":"","middleInitial":"Carina","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":800248,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McHenry, Michael L.","contributorId":39672,"corporation":false,"usgs":false,"family":"McHenry","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":800249,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Elofson, Mel","contributorId":241966,"corporation":false,"usgs":false,"family":"Elofson","given":"Mel","email":"","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800250,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sampson, Earnest M","contributorId":241968,"corporation":false,"usgs":false,"family":"Sampson","given":"Earnest","email":"","middleInitial":"M","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800251,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McCoy, Randall E","contributorId":241971,"corporation":false,"usgs":false,"family":"McCoy","given":"Randall","email":"","middleInitial":"E","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800252,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Stapleton, Justin","contributorId":241974,"corporation":false,"usgs":false,"family":"Stapleton","given":"Justin","email":"","affiliations":[{"id":47700,"text":"Natural Resources Department, Lower Elwha Klallam Tribe, Port Angeles, WA","active":true,"usgs":false}],"preferred":false,"id":800253,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pess, George R.","contributorId":13501,"corporation":false,"usgs":false,"family":"Pess","given":"George","email":"","middleInitial":"R.","affiliations":[{"id":6578,"text":"National Marine Fisheries Service, Seattle, WA 98112, USA","active":true,"usgs":false}],"preferred":false,"id":800254,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70214968,"text":"70214968 - 2020 - The distribution of woody species in relation to climate and fire in Yosemite National Park, California, USA","interactions":[],"lastModifiedDate":"2020-10-03T15:26:50.097038","indexId":"70214968","displayToPublicDate":"2020-09-29T10:24:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The distribution of woody species in relation to climate and fire in Yosemite National Park, California, USA","docAbstract":"The effects of climate on plant species ranges are well appreciated, but the effects of other processes, such as fire, on plant species distribution are less well understood. We used a dataset of 561 plots 0.1 ha in size located throughout Yosemite National Park, in the Sierra Nevada of California, USA, to determine the joint effects of fire and climate on woody plant species. We analyzed the effect of climate (annual actual evapotranspiration [AET], climatic water deficit [Deficit]) and fire characteristics (occurrence [BURN] for all plots, fire return interval departure [FRID] for unburned plots, and severity of the most severe fire [dNBR]) on the distribution of woody plant species.
Of 43 species that were present on at least two plots, 38 species occurred on five or more plots. Of those 38 species, models for the distribution of 13 species (34%) were significantly improved by including the variable for fire occurrence (BURN). Models for the distribution of 10 species (26%) were significantly improved by including FRID, and two species (5%) were improved by including dNBR. Species for which distribution models were improved by inclusion of fire variables included some of the most areally extensive woody plants. Species and ecological zones were aligned along an AET-Deficit gradient from cool and moist to hot and dry conditions.
In fire-frequent ecosystems, such as those in most of western North America, species distribution models were improved by including variables related to fire. Models for changing species distributions would also be improved by considering potential changes to the fire regime.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-020-00079-9","usgsCitation":"van Wagtendonk, J., Moore, P., Yee, J.L., and Lutz, J.A., 2020, The distribution of woody species in relation to climate and fire in Yosemite National Park, California, USA: Fire Ecology, v. 16, 22, 23 p., https://doi.org/10.1186/s42408-020-00079-9.","productDescription":"22, 23 p.","ipdsId":"IP-117438","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455198,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-020-00079-9","text":"Publisher Index Page"},{"id":379026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.13549804687501,\n 36.94989178681327\n ],\n [\n -118.24584960937499,\n 36.94989178681327\n ],\n [\n -118.24584960937499,\n 38.272688535980976\n ],\n [\n -120.13549804687501,\n 38.272688535980976\n ],\n [\n -120.13549804687501,\n 36.94989178681327\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2020-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"van Wagtendonk, Jan W.","contributorId":189573,"corporation":false,"usgs":false,"family":"van Wagtendonk","given":"Jan W.","affiliations":[],"preferred":false,"id":800466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Peggy E","contributorId":242603,"corporation":false,"usgs":false,"family":"Moore","given":"Peggy E","affiliations":[{"id":48478,"text":"retired USGS WERC employee","active":true,"usgs":false}],"preferred":false,"id":800467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":800468,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lutz, James A.","contributorId":139178,"corporation":false,"usgs":false,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":800469,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216489,"text":"70216489 - 2020 - Climate, sea level, and people - Changing South Florida's mangrove coast","interactions":[],"lastModifiedDate":"2020-11-23T14:35:27.724834","indexId":"70216489","displayToPublicDate":"2020-09-29T08:32:34","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Climate, sea level, and people - Changing South Florida's mangrove coast","docAbstract":"South Florida’s coast is a land of contrasts that appeals to almost everyone, whether they seek out quiet natural environments along the mangrove waterways and in the wilderness of the Everglades or vibrant international culture in Miami. Yet this paradise is threatened by a number of forces – changing climate, rising sea level, and too many people, to name a few. Florida’s past is filled with stories of dramatic change and resiliency, if we look at the geologic record. It also hints at the role of climate alone, in the absence of significant sea level change, in shaping the mangrove coast. Using our knowledge of present-day processes, such as impacts of storms on the mangroves, combined with our interpretation of the past geologic record, is the best way to anticipate future changes. The question is, have humans altered this landscape so much that the species and habitats have lost their natural resiliency, and if they have, what will happen to the people and the unique environments of south Florida?
","language":"English","publisher":"Springer","doi":"10.1007/978-3-030-52383-1_12","usgsCitation":"Wingard, G.L., 2020, Climate, sea level, and people - Changing South Florida's mangrove coast, p. 189-211, https://doi.org/10.1007/978-3-030-52383-1_12.","productDescription":"23 p.","startPage":"189","endPage":"211","ipdsId":"IP-112945","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":380685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.4853515625,\n 24.926294766395593\n ],\n [\n -79.89257812499999,\n 24.926294766395593\n ],\n [\n -79.89257812499999,\n 27.01998400798257\n ],\n [\n -82.4853515625,\n 27.01998400798257\n ],\n [\n -82.4853515625,\n 24.926294766395593\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Wingard, G. Lynn 0000-0002-3833-5207 lwingard@usgs.gov","orcid":"https://orcid.org/0000-0002-3833-5207","contributorId":605,"corporation":false,"usgs":true,"family":"Wingard","given":"G.","email":"lwingard@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":805400,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}