Spatial data provided by the U.S. Department of Agriculture National Resource Conservation Service representing implementation at the field-level for a selection of agricultural conservation practices were incorporated within a spatially referenced regression model to estimate their effects on nitrogen loads in streams in the Chesapeake Bay watershed. Conservation practices classified as “high-impact” were estimated to be effective (p = 0.017) at reducing contemporary nitrogen loads to streams of the Chesapeake Bay watershed in areas where groundwater ages are estimated to be less than 14-years old. Watershed-wide, high-impact practices were estimated to reduce nitrogen loads to streams by 1.45%, with up to 60% reductions in areas with shorter groundwater ages and larger amounts of implementation. Effects of “other-impact” practices and practices in areas with groundwater ages of 14 years or more showed less evidence of effectiveness. That the discernable impact of high-impact practices was limited to areas with a median groundwater age of less than 14 years does not imply that conservation practices are not effective in areas with older groundwater ages. A model recalibrated using high-impact agricultural conservation practice data summarized by county suggests effects may also be detectable using implementation data available at such coarser resolution. Despite increasing investment, effects of agricultural conservation practices on regional water quality remain difficult to quantify due to factors such as groundwater travel times, varying modes-of-action, and the general lack of high-quality spatial datasets representing practice implementation.
The U.S. Geological Survey and University of Wisconsin–Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at mainstem Plum Creek and west Plum Creek in northeastern Wisconsin. These two subbasins cover 88 percent of the Plum Creek Basin (Hydrologic Unit Code 12), which is a subbasin of the lower Fox River Basin. A published total maximum daily load report for the lower Fox River Basin rated Plum Creek as one of the greatest contributors of total suspended solids (TSS) and total phosphorus (TP) draining into the lower Fox River. To reduce TSS and TP exports from Plum Creek, additional cropland conservation practices and watercourse protections were applied between 2012 and 2020. To detect water-quality trends, data were collected during 2010 to 2020 at mainstem Plum Creek and 2013 to 2020 at west Plum Creek.
The project used two methods to evaluate CMP effectiveness. The first method focused on evaluating water-quality changes between initial and post-CMP implementation periods during rain- or snowmelt-induced runoff events (hereafter referred to as “events”). In this approach random-forest models were developed to account for environmental factors which influence water quality. Model residuals from the two time periods were compared to determine the significance of water-quality changes associated with CMP implementation for mainstem and west Plum Creek Basins. The second method used a Weighted Regressions on Time, Discharge, and Season time-series approach to examine changes in water quality during the entire study period in mainstem Plum Creek. Results from both methods indicated there were minimal water-quality changes in TSS concentrations and flow-normalized delivery during runoff events during the 10-year period from 2010 to 2020; however, TP concentrations during low streamflow (less than 3 cubic feet per second [ft3/s]) may have decreased. The lack of observed improvement may be attributable to any of the following: variability in weather and hydrologic conditions, insufficient post-treatment data, additional cropland being converted to corn production, above average rainfall, streambank degradation, acute and legacy sources of phosphorus from farm fields, excessive/vulnerable manure applications and spills, and point-source discharges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235043","collaboration":"Prepared in cooperation with the University of Wisconsin-Green Bay and Outagamie County, Wisconsin","usgsCitation":"Horwatich, J.A., Fermanich, K., Pronschinske, M.A., Robertson, D.M., Kussow, S., Loken, L.C., Reneau, P.C., Freund, J., and Komiskey, M.J., 2023, Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20: U.S. Geological Survey Scientific Investigations Report 2023–5043, 31 p., https://doi.org/10.3133/sir20235043.","productDescription":"Report: ix, 31 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130579","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5043/coverthb.jpg"},{"id":416707,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500920,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114718.htm","linkFileType":{"id":5,"text":"html"}},{"id":416709,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92A0H98","text":"USGS data release","linkHelpText":"Water quality and estimated changes in the Plum Creek watershed 2010–2020 (data release and model archive)"},{"id":416708,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5043/images"},{"id":416706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5043"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Plum Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.30723441433527,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.40323167054055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
To help support remedial efforts at the former Badger Army Ammunition Plant the U.S. Geological Survey built and calibrated a transient groundwater flow model using the Newton Raphson formulation (MODFLOW–NWT) of the U.S. Geological Survey’s modular three-dimensional finite-difference code. The model simulates the groundwater flow system at the site from 1984 to 2020. The former Badger Army Ammunition Plant is a 7,275-acre site in Sauk County, Wisconsin. The plant produced smokeless gunpower and solid rocket propellent as munitions components. Peak production periods were during World War II, the Korean War, and the Vietnam War. Subsequent groundwater contamination investigations have found four plumes at the site. A health risk assessment identified at least one contaminant of concern for human health risk present in three of the plumes: the propellant burning ground plume, the deterrent burning ground plume, and the central plume. A cooperative study began between the U.S. Army Environmental Command and U.S. Geological Survey to better understand the groundwater flow system at the former Badger Army Ammunition Plant. Field data, including aquifer tests, streamflow measurements, continuous groundwater elevations, and groundwater gradients with the Wisconsin River were collected and used to inform and calibrate the groundwater flow model. The model was used to assess the variability of the groundwater system over the study period, the components of the groundwater budget, and groundwater flow directions from identified source areas towards the Wisconsin River. Model performance assessment focused on using particle tracking to compare groundwater flowpaths that originate in the contaminant source areas to the observed plume footprints. This focus on plume behavior geometry should help constrain the advective component of a future groundwater transport model of the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235040","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Haserodt, M.J., Reeves, H.W., Nielsen, M.G., Schachter, L.A., Corson-Dosch, N.T., and Feinstein, D.T., 2023, Simulation of groundwater flow at the former Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5040, 140 p., https://doi.org/10.3133/sir20235040.","productDescription":"Report: viii, 140 p.; 3 Data Releases; Dataset","numberOfPages":"152","onlineOnly":"Y","ipdsId":"IP-135445","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416676,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2IDV0","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model archive used to simulate potential annual recharge for the former Badger Army Ammunition Plant study area, Prairie du Sac, Wisconsin, 1980 to 2020"},{"id":416672,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":416671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.pdf","text":"Report","size":"106 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5040"},{"id":416670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5040/coverthb.jpg"},{"id":500916,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114707.htm","linkFileType":{"id":5,"text":"html"}},{"id":416678,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":416675,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TSI73","text":"USGS data release","linkHelpText":"Slug test analysis results from unconsolidated and bedrock aquifers at Badger Army Ammunition Plant, Sauk County, Wisconsin, 2020"},{"id":416674,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5040/images"},{"id":416677,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LNRILT","text":"USGS data release","linkHelpText":"Groundwater model archive for the former Badger Army Ammunition Plant, Wisconsin"},{"id":416712,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235040/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"former Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.76922975135027,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.38657213852542\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed field mapping difficult. While recent advances in remote sensing have facilitated mapping of surficial materials and landforms, manual map creation has remained a time-intensive task.
The development of convolutional neural networks (CNNs) for image classification has provided a new opportunity for rapid characterization of digital elevation models, thus enabling efficient mapping of surficial materials and landforms. We have developed a methodology that leverages existing geologic maps and high-resolution (1–3 m) lidar data to train a U-Net CNN to classify alluvium and exposed bedrock in previously glaciated regions. Coupled with U.S. Geological Survey-developed geomorphometry tools capable of approximating stream incision depths, these classifications can be used to estimate the minimum thicknesses of stream-proximal hillslope sediments in areas where streams have undergone minimal incision into bedrock.
We validate this approach in the context of the Neversink River watershed, a subbasin of the Delaware River Basin and significant water source for New York City. Evaluation of deep learning model performance demonstrates substantial agreement with manually drawn maps of alluvium and exposed bedrock. Validation of the minimum sediment thickness map using borehole data and passive seismic measurements shows the greatest performance for shallow materials and decreased performance in deep sediments, as well as in areas where bedrock exposures were too small to be resolved by lidar. To resolve these issues and create more accurate surficial maps, we are training new CNNs with additional geologic data and exploring advanced approaches for estimating depths of stream incision.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2023.100116","usgsCitation":"Odom, W.E., and Doctor, D.H., 2023, Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learning-derived surficial material maps: Applied Computing and Geosciences, v. 18, 100116, 11 p., https://doi.org/10.1016/j.acags.2023.100116.","productDescription":"100116, 11 p.","ipdsId":"IP-146769","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443651,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2023.100116","text":"Publisher Index Page"},{"id":417128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.31774531759645,\n 38.65648371068133\n ],\n [\n -74.83711116001508,\n 39.00784172038104\n ],\n [\n -74.58034795534637,\n 39.29641683375996\n ],\n [\n -74.84560023131485,\n 39.59878756424641\n ],\n [\n -74.57402157309659,\n 39.81435898583538\n ],\n [\n -74.36164212699806,\n 40.42339954973025\n ],\n [\n -74.7398677925508,\n 40.67483290305228\n ],\n [\n -74.20311730788073,\n 41.49081439956416\n ],\n [\n -73.87900349307475,\n 42.134764710063195\n ],\n [\n -74.40404082594178,\n 42.53787114018144\n ],\n [\n -75.19513563542162,\n 42.478156559974536\n ],\n [\n -75.74165548596034,\n 41.860885020202005\n ],\n [\n -76.19243039302005,\n 41.151909429148475\n ],\n [\n -76.53910969789781,\n 40.49637767696336\n ],\n [\n -76.22016361968275,\n 40.00185213900514\n ],\n [\n -75.6922644432239,\n 39.71427259376151\n ],\n [\n -75.61094038798961,\n 39.383028287456796\n ],\n [\n -75.31774531759645,\n 38.65648371068133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, William E. 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","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":872923,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244178,"text":"70244178 - 2023 - Bringing the Nature Futures Framework to life: Creating a set of illustrative narratives of nature futures","interactions":[],"lastModifiedDate":"2023-06-06T11:51:36.740586","indexId":"70244178","displayToPublicDate":"2023-05-04T06:49:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5318,"text":"Sustainability Science","active":true,"publicationSubtype":{"id":10}},"title":"Bringing the Nature Futures Framework to life: Creating a set of illustrative narratives of nature futures","docAbstract":"To halt further destruction of the biosphere, most people and societies around the globe need to transform their relationships with nature. The internationally agreed vision under the Convention of Biological Diversity—Living in harmony with nature—is that “By 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people”. In this context, there are a variety of debates between alternative perspectives on how to achieve this vision. Yet, scenarios and models that are able to explore these debates in the context of “living in harmony with nature” have not been widely developed. To address this gap, the Nature Futures Framework has been developed to catalyse the development of new scenarios and models that embrace a plurality of perspectives on desirable futures for nature and people. In this paper, members of the IPBES task force on scenarios and models provide an example of how the Nature Futures Framework can be implemented for the development of illustrative narratives representing a diversity of desirable nature futures: information that can be used to assess and develop scenarios and models whilst acknowledging the underpinning value perspectives on nature. Here, the term illustrative reflects the multiple ways in which desired nature futures can be captured by these narratives. In addition, to explore the interdependence between narratives, and therefore their potential to be translated into scenarios and models, the six narratives developed here were assessed around three areas of the transformative change debate, specifically, (1) land sparing vs. land sharing, (2) Half Earth vs. Whole Earth conservation, and (3) green growth vs. post-growth economic development. The paper concludes with an assessment of how the Nature Futures Framework could be used to assist in developing and articulating transformative pathways towards desirable nature futures.
The National Park Service (NPS), in collaboration with the U.S. Geological Survey (USGS), recognizes the need to quantify the sediment budget of the barrier islands within the Gulf Islands National Seashore (GINS) to understand the coastal processes affecting island resiliency. To achieve this goal, identifying and quantifying the physical parameters that drive long-term change is necessary to model the processes that are both generative and terminal in island evolution and capture island response to long-term human alteration and climatic patterns. For example, measuring change across periods of storminess is more effective at assessing island resiliency than measuring change resulting from a single storm impact. Understanding changes to the physical environment over time is key to successfully predicting island responses to future storm impacts, human alteration, and sea-level rise and is necessary for effective decision making and management response. Yet, the diversity of factors affecting natural and cultural resources necessitates a strategic approach to data collection priorities that can inform sediment budget quantification and integrated resource management.
This study sought to advance sediment budget modeling efforts by conducting a “Needs Assessment Workshop” at the GINS. The purpose of the workshop was to identify and prioritize the specific research and data needs regarding the sediment budget at the GINS that can enhance the NPS efforts to conserve the islands’ natural resources, cultural resources, and the facilities and infrastructure that support both conservation and visitor use of those resources. This effort explored two research questions: (1) “what research and data needs exist for the sediment budget at Gulf Islands National Seashore” (research question 1) and (2) “how can research to address these needs capitalize on regional partnerships to advance natural and cultural resource conservation at Gulf Islands National Seashore” (research question 2)? The workshop was conducted virtually in a two-part, two-day series.
The workshop series was organized by researchers from North Carolina State University in collaboration with NPS and USGS staff and was facilitated by National Oceanographic and Atmospheric Administration staff. The workshop series (two paired, sequential, partial-day workshops) addressed two target audiences: (1) NPS and USGS staff (April workshop) and (2) regional Federal, State, county, and nongovernmental organization staff, including NPS and USGS staff (May workshop). A total of four workshop sessions were held, comprising two sessions with each target audience.
The workshop series intended to identify sediment management research and data needs that could enhance natural and cultural resource stewardship at the GINS. One objective was to share information about regional sediment transport and management, available sediment management plans, and predictive modeling capabilities, including geomorphologic and hydrodynamic predictive models. This information was shared through a series of presentations by park managers and NPS and USGS researchers that identified park issues and available capabilities and data. The second objective was to elicit research and data needs, with a primary goal of assessing the importance and urgency of the identified needs. This assessment was partly determined by requesting that the workshop participants identify and prioritize research themes through polls, comments, and discussion.
The polls explicitly asked participants to qualitatively evaluate the importance (not at all, slightly, somewhat, very, or extremely) and urgency (not at all, slightly, somewhat, very, or extremely) of the thematically grouped research and data needs. These evaluations were plotted and shared during the workshop to visualize how the relative importance (x-axis) and relative urgency (y-axis) of each “need,” relative to other needs, to identify the most necessary (importance) and time-sensitive (urgent) items, thereby allowing an enhanced, holistic understanding of the sediment budget at GINS. Results of the poll are published as a USGS data release.
The assessment results revealed that the most important and urgent research and data needs included mapping (for example, elevation, habitat, and cultural resources), a regional sediment budget and management plan, and the dynamic modeling of sediment processes. During the workshop, these issues were visualized using scatter plots to demonstrate the relative importance and urgency of each theme, provide descriptive statistics, and elicit discussion. This format of iterative presentation, discussion, and prioritization allowed the project team to effectively accomplish their objective of identifying important and urgent research needs for natural and cultural resource stewardship at the GINS. Through the workshop, it was determined that expanded communication with the broader research community was needed to coordinate research activities and streamline potential funding opportunities and that research and policy should be integrated through a structured decision-making process.
At the conclusion of the workshop, an administered poll showed that the presentations effectively identified data and research needs and that the goals of the workshop were achieved. The results suggest that this type of needs-assessment workshop can effectively identify existing research capabilities and data, determine and prioritize research and data needs, and address how these efforts can use regional partnerships to aid natural and cultural resource conservation and management at National Parks.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221087","programNote":"Coastal/Marine Hazards and Resources Program","usgsCitation":"Seekamp, E., Flocks, J., Hotchkiss, C., York, L., and Irick, K., 2023, Gulf Islands National Seashore regional sediment budget research and data needs—Workshop series summary: U.S. Geological Survey Open-File Report 2022–1087, 46 p., https://doi.org/10.3133/ofr20221087.","productDescription":"Report: vii, 46 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127837","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416372,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1087/coverthb.jpg"},{"id":416373,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1087/ofr20221087.pdf","text":"Report","size":"2.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1087"},{"id":416375,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1087/ofr20221087.XML"},{"id":416376,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1087/images/"},{"id":416377,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JG3J7B","text":"USGS data release","linkHelpText":"Gulf Islands National Seashore 2020 workshop-attendee survey results"},{"id":499720,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114708.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida, Mississippi","otherGeospatial":"Gulf Islands National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.47993451563534,\n 30.4372165405142\n ],\n [\n -89.47993451563534,\n 30.14326231135827\n ],\n [\n -86.39410371358161,\n 30.14326231135827\n ],\n [\n -86.39410371358161,\n 30.4372165405142\n ],\n [\n -89.47993451563534,\n 30.4372165405142\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South St.
Petersburg, FL 33701
Although freshwater mussels are imperiled and identified as key conservation priorities, limited bioaccumulation information is available on these organisms for contaminants of emerging concern. In the present study we investigated the bioaccumulation of per- and polyfluoroalkyl substances (PFAS) in the model freshwater pond mussel Sagittunio subrostratus because mussels provide important ecosystem services and are important components of aquatic systems where PFAS occur. In the present study we selected four representative perfluorinated carboxylic acids and sulfonic acids, then determined the bioaccumulation kinetics of freshwater mussels in a controlled laboratory study. Because uptake (ku) and elimination (ke) rate constants and time to steady state are important parameters for food web bioaccumulation models, we derived bioaccumulation kinetic parameters following exposure to perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), and perfluorodecanoic acid (PFDA) at 10 µg/L and perfluoroundecanoic acid (PFUnDA) at 1 µg/L during a 14-day uptake period followed by a 7-day elimination period. Kinetic and ratio-based bioaccumulation factors (BAFs) were subsequently calculated, for example ratio-based BAFs for mussel at day 7 were determined for PFHxS (0.24 ± 0.08 L/kg), PFOS (7.73 ± 1.23 L/kg), PFDA (4.80 ± 1.21 L/kg), and PFUnDA (84.0 ± 14.4 L/kg). We generally observed that, for these four model PFAS, freshwater mussels have relatively low BAF values compared with other aquatic invertebrates and fish.
Groundwater in the karst groundwater system at Area B of Fort Detrick in Frederick County, Maryland, is contaminated with chlorinated solvents from the past disposal of laboratory wastes. In cooperation with U.S. Army Environmental Command and U.S. Army Garrison Fort Detrick, the U.S. Geological Survey performed a 3-year study to refine the conceptual model of groundwater flow in and around Area B of Fort Detrick at the site- to regional-scale. The investigation was designed to review the geologic setting, assess the temporal variability of the hydrologic system, evaluate the potential for interbasin groundwater flow, determine the degree of vertical connectivity of the aquifer, characterize the sources and timing of groundwater recharge, and identify if dyes from previous tracer tests continue to drain from the aquifer. This study established a continuous hydrologic monitoring network of 12 water level gages, 2 streamgages, a precipitation gage, and in situ fluorometric monitoring. A water budget analysis was performed using hydrologic monitoring data and a soil-water balance model constructed for the study. In this study each individual water budget term is calculated using available data or through modeling, and a water budget residual term is calculated. If the water budget residual term is small relative to the uncertainty of the underlying data, then an additional import or export of water (in other words, interbasin transfer) is not needed to fully describe the hydrologic system. Groundwater and spring samples from 20 locations were collected in a 2019 synoptic geochemical sampling event and analyzed for a suite of analytes that included groundwater age tracer constituents.
The karst groundwater system was found to be highly responsive to hydrologic events, with strong water level and stream base flow responses to individual storm events and a historic wet period in 2017 and 2018. The water budget analysis included historic flooding in May 2018, though more typical hydrologic patterns were observed in 2019 and 2020. During most evaluated intervals, the water budget residual was less than the estimated uncertainty on the residual for the two Carroll Creek watersheds, which suggested no substantial net interbasin flow occurs from these watersheds. The watershed difference area, a region that includes Area B, had a significant negative water budget residual, which may be the result of a net interbasin import of groundwater or the result of focused groundwater recharge not simulated by the soil-water balance model. Geochemical analysis and groundwater age dating reveals shallow groundwater (approximately less than [<] 150 feet deep) appears to be relatively young (approximately <30 years) and to be recharged in the vicinity of Area B. In the deep groundwater sampled in this study (approximately greater than [>] 150 feet deep), older groundwater from a differing recharge source, based on stable isotopes and noble gas analyses, is observed and interpreted to represent less direct connectivity to the surface and increased proportions of water recharged to the north and (or) west of Area B. A clustering analysis to reveal groupings within the suite of geochemical data was used to define seven groups. The groupings generally show that wells in similar depths and lateral aquifer positions generally cluster together, with some exceptions. Although limited by suspended sediments, the in situ fluorometric monitoring at springs did not detect any dye leaving the system above the limit of detection for the method. Dye was only detected above the limit of detection in one well, which was used as an injection well during a previous dye tracer test.
The results of this study support and refine the conceptual site model of groundwater hydrology at Area B. The geologic and geophysical log review in this study agrees with prior assessments of physical controls on groundwater flow. A literature review of mid-Atlantic karst studies identified similar controls reported in these environments. The additional characterization of hydrologic responsiveness in this study suggests that hydrologic conditions and events are important considerations when interpreting potentiometric surfaces and contaminant trends over time and highlights the importance of continuous hydrologic monitoring. There is evidence to suggest that either intense focused groundwater recharge occurs in the vicinity of Area B or net along-valley groundwater interbasin flow from the upper study watershed enters the lower watershed and discharges to Carroll Creek. Geochemical analyses also suggest that water recharged from Catoctin Mountain and the elevated areas to the north and (or) west of the site may be present in the older and deeper Area B groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225054","collaboration":"Prepared in cooperation with U.S. Army Environmental Command and U.S. Army Garrison, Fort Detrick","usgsCitation":"Goodling, P.J., Fleming, B.J., Solder, J., Soroka, A., and Raffensperger, J., 2023, Hydrogeologic characterization of Area B, Fort Detrick, Maryland: U.S. Geological Survey Scientific Investigations Report 2022–5054, 128 p., https://doi.org/10.3133/sir20225054.","productDescription":"Report: xiv, 128 p.; 2 Data Releases","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124092","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":435349,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DUFZY7","text":"USGS data release","linkHelpText":"Supporting Datasets for Hydrogeological Characterization of Ft. Detrick Area B, Maryland"},{"id":500936,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114709.htm","linkFileType":{"id":5,"text":"html"}},{"id":416517,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GTTX8Q","text":"USGS data release","linkHelpText":"Soil water balance model developed for Maryland and Pennsylvania"},{"id":416516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AYWBXU","text":"USGS data release","linkHelpText":"Supporting datasets for hydrogeological characterization of Area B, Fort Detrick, Maryland"},{"id":416515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.pdf","text":"Report","size":"51.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5054"},{"id":416514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5054/coverthb.jpg"},{"id":416562,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5054"},{"id":416564,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5054/images/"},{"id":416563,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.XML"}],"country":"United States","state":"Maryland","otherGeospatial":"Fort Detrick","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.4693386578843,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.458154924593\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
In recent years, coral populations in the western Atlantic have undergone widespread declines from climate change, anthropogenic stressors, and infectious disease outbreaks. The pillar coral, Dendrogyra cylindrus, has been one of the most affected species, prompting its listing as threatened under the United States Endangered Species Act in 2014 and critically endangered under the IUCN Red List in 2022. However, due to its natural rarity, it is particularly difficult to study using conventional long-term monitoring studies or less common paleontological investigations. Here, we document for the first time, the multi-century persistence of D. cylindrus on high-latitude nearshore reefs off southeast Florida during the late Holocene. Using high-precision uranium–thorium (U-Th) dating, we constrain the ages of well-preserved subfossil D. cylindrus colonies recovered from newly described coral death assemblages. We also describe specific morphological characteristics and taphonomic indicators reflecting their unique depositional environment. Our findings demonstrate long-term persistence of D. cylindrus in southeast Florida, despite geographical isolation and historical rarity in the region.
Conservation and management of biological systems involves decision-making over time, with a generic goal of sustaining systems and their capacity to function in the future. We address four persistent and difficult conservation challenges: (1) prediction of future consequences of management, (2) uncertainty about the system's structure, (3) inability to observe ecological systems fully, and (4) nonstationary system dynamics. We describe these challenges in terms of dynamic systems subject to different sources of uncertainty, and we present a basic Markovian framework that can encompass approaches to all four challenges. Finding optimal conservation strategies for each challenge requires issue-specific structural features, including adaptations of state transition models, uncertainty metrics, valuation of accumulated returns, and solution methods. Strategy valuation exhibits not only some remarkable similarities among approaches but also some important operational differences. Technical linkages among the models highlight synergies in solution approaches, as well as possibilities for combining them in particular conservation problems. As methodology and computing software advance, such an integrated conservation framework offers the potential to improve conservation outcomes with strategies to allocate management resources efficiently and avoid negative consequences.
Many microorganisms secrete secondary metabolites with antibiotic properties; however, there is debate whether the secretions evolved as a means to gain a competitive edge or as a chemical signal to coordinate community growth. The objective of this research was to investigate if select antibiotics acted as a weapon or as a chemical signal by exposing communities of aquatic cave bacteria to increasing concentrations of antibiotics. Water samples were collected from six cave locations where actinobacterial mats appeared to be plentiful. Bacterial growth was measured using colony counts on 10 % tryptic soy agar augmented with increasing concentrations of erythromycin, tetracycline, kanamycin, gentamicin, or quaternary ammonia compounds (QAC). Colony counts generally decreased as the gentamicin, kanamycin and QAC dose increased. In contrast, the colony numbers increased on agar plates supplemented with 0.01 mg L−1, 0.10 mg L−1 and 1.00 mg L−1 erythromycin or tetracycline. A 10.00 mg L−1 dose of each antibiotic treatment reduced bacteria colonies by 98 % or more. Community-level physiological capabilities were evaluated using Ecolog plates inoculated with cave water dosed with either 0.00 mg L−1 or 0.10 mg L−1 of erythromycin. Incubation with the antibiotic almost doubled the number of food substrates used in the first 24 hours. There was a significant increase in the use of acetyl glucosamine, arginine, and putrescine when bacteria were exposed to 0.10 mg L−1 erythromycin triggered by the antibiotic acting as a chemical messenger. Principal component analysis confirmed a shift in substrate preferences when erythromycin was added. A conceptual ecological model is proposed based on the response of aquatic cave bacteria to sublethal antibiotics.
","language":"English","publisher":"National Speleological Society","doi":"10.4311/2022MB0106","usgsCitation":"Byl, T.D., Byl, P.K., Byl, J.P., and Toomey, R., 2023, Stimulation of aquatic bacteria from Mammoth Cave, Kentucky, by sublethal concentrations of antibiotics: Journal of Cave and Karst Studies, v. 85, no. 1, p. 16-27, https://doi.org/10.4311/2022MB0106.","productDescription":"12 p.","startPage":"16","endPage":"27","ipdsId":"IP-065135","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":443673,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.4311/2022mb0106","text":"Publisher Index Page"},{"id":435353,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J1018X","text":"USGS data release","linkHelpText":"Average well color development data for water samples from six locations within the historic section of Mammoth Cave National Park, Kentucky"},{"id":418004,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Mammoth Cave","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.2828185616811,\n 37.28844220519747\n ],\n [\n -86.2828185616811,\n 37.09470580139313\n ],\n [\n -85.96018319973803,\n 37.09470580139313\n ],\n [\n -85.96018319973803,\n 37.28844220519747\n ],\n [\n -86.2828185616811,\n 37.28844220519747\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"85","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byl, Thomas D. 0000-0001-6907-9149 tdbyl@usgs.gov","orcid":"https://orcid.org/0000-0001-6907-9149","contributorId":583,"corporation":false,"usgs":true,"family":"Byl","given":"Thomas","email":"tdbyl@usgs.gov","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byl, Petra Kim 0000-0002-9168-2603","orcid":"https://orcid.org/0000-0002-9168-2603","contributorId":304716,"corporation":false,"usgs":false,"family":"Byl","given":"Petra","email":"","middleInitial":"Kim","affiliations":[{"id":66150,"text":"Biological Oceanography University of Hawaii at Mānoa School of Ocean and Earth Science and Technology Department of Oceanography","active":true,"usgs":false}],"preferred":false,"id":875145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byl, Jacob P. 0000-0001-7998-9795","orcid":"https://orcid.org/0000-0001-7998-9795","contributorId":304724,"corporation":false,"usgs":false,"family":"Byl","given":"Jacob","email":"","middleInitial":"P.","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":875146,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toomey, Rickard III","contributorId":306230,"corporation":false,"usgs":false,"family":"Toomey","given":"Rickard","suffix":"III","email":"","affiliations":[],"preferred":false,"id":875147,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249412,"text":"70249412 - 2023 - Forecasting sea level rise-driven inundation in diked and tidally restricted coastal lowlands","interactions":[],"lastModifiedDate":"2023-10-06T15:44:12.015642","indexId":"70249412","displayToPublicDate":"2023-05-01T10:38:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting sea level rise-driven inundation in diked and tidally restricted coastal lowlands","docAbstract":"Diked and drained coastal lowlands rely on hydraulic and protective infrastructure that may not function as designed in areas with relative sea-level rise. The slow and incremental loss of the hydraulic conditions required for a well-drained system make it difficult to identify if and when the flow structures no longer discharge enough water, especially in tidal settings where two-way flows occur through the dike. We developed and applied a hydraulic mass-balance model to quantify how water levels in the diked and tidally restricted coastal wetlands and water bodies dynamically respond to sea-level rise, specifically applied to the Herring River Estuary in MA, USA, from 2020 to 2100. Sensitivity testing of the model parameters indicated that primary outcomes were not sensitive to many of the chosen input values, though the terrestrial water input rate to the estuary and the flow coefficient for the hydraulic infrastructure were important. The relative importance of parameters, however, is expected to be site specific. We introduced a drainability metric that quantifies the net water volume drained over every tidal cycle to monitor and forecast how rising water levels on either side of the dike affected the net draining or impounding conditions of the system. Ensembles of model results across parameter and sea-level scenario uncertainties indicated that substantial impoundment of the Herring River Estuary was expected within ~ 20 years with the existing flow structures, a sluice and two flap gates. Simulations with up to three additional gates did not dampen this trend toward impoundment, suggesting that rising impounded water levels are likely even with major construction upgrades. Increasingly impounded diked coastal waterbodies present a hydrologic challenge with socioecological implications due to projected flooding and ecosystem impacts. Solutions to this challenge may be to allow coastal wetland restoration pathways or require substantial and recurring infrastructure improvement projects.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-023-01174-1","usgsCitation":"Befus, K.A., Kurnizki, A., Kroeger, K.D., Eagle, M.J., and Smith, T.P., 2023, Forecasting sea level rise-driven inundation in diked and tidally restricted coastal lowlands: Estuaries and Coasts, v. 46, no. 6, p. 1157-1169, https://doi.org/10.1007/s12237-023-01174-1.","productDescription":"13 p.","startPage":"1157","endPage":"1169","ipdsId":"IP-145736","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1007/s12237-023-01174-1","text":"Publisher Index Page"},{"id":421746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Herring River Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.06356706053543,\n 41.92990690084844\n ],\n [\n -70.0599719567331,\n 41.93094108028947\n ],\n [\n -70.05877358879943,\n 41.93322334800044\n ],\n [\n -70.05954054427677,\n 41.93450708770192\n ],\n [\n -70.05915706653809,\n 41.9355411925597\n ],\n [\n -70.05752728614821,\n 41.93611172599333\n ],\n [\n -70.057239677844,\n 41.93678922781737\n ],\n [\n -70.05786282916928,\n 41.937965924404836\n ],\n [\n -70.05656859180117,\n 41.937751981185585\n ],\n [\n -70.0528776185643,\n 41.939463506843225\n ],\n [\n -70.05426772536764,\n 41.94014097305953\n ],\n [\n -70.057239677844,\n 41.93889300339495\n ],\n [\n -70.05862978464737,\n 41.93903562973523\n ],\n [\n -70.05934880540781,\n 41.93871472002016\n ],\n [\n -70.05915706653809,\n 41.93760935197429\n ],\n [\n -70.06025956503724,\n 41.93746672244353\n ],\n [\n -70.06131412881953,\n 41.935826459914495\n ],\n [\n -70.06323151751364,\n 41.93464972385124\n ],\n [\n -70.06275217033973,\n 41.93340164672611\n ],\n [\n -70.06529271035981,\n 41.93233184689518\n ],\n [\n -70.06356706053543,\n 41.92990690084844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Befus, Kevin A.","contributorId":299488,"corporation":false,"usgs":false,"family":"Befus","given":"Kevin","email":"","middleInitial":"A.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":885528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurnizki, A","contributorId":330654,"corporation":false,"usgs":false,"family":"Kurnizki","given":"A","email":"","affiliations":[{"id":78949,"text":"epartment of Civil and Architectural Engineering, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":885529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":885530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":885531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Timothy P.","contributorId":220144,"corporation":false,"usgs":false,"family":"Smith","given":"Timothy","email":"","middleInitial":"P.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":885532,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243216,"text":"70243216 - 2023 - Incorporating uncertainty in susceptibility criteria into probabilistic liquefaction hazard analysis","interactions":[],"lastModifiedDate":"2023-05-09T15:31:01.80947","indexId":"70243216","displayToPublicDate":"2023-05-01T10:29:02","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Incorporating uncertainty in susceptibility criteria into probabilistic liquefaction hazard analysis","docAbstract":"Most conventional approaches for assessing liquefaction triggering hazards generally rely on simplified procedures that involve identifying liquefaction susceptible layers and calculating a factor of safety against liquefaction (FSL) in each layer. Such procedures utilize deterministic semi-empirical models for standard penetration test (SPT), cone penetrometer test (CPT), or shear wave velocity (Vs)-based subsurface data. This general approach largely neglects considerable uncertainties in ground shaking, as well as aleatory variabilities and epistemic uncertainties inherent to liquefaction susceptibility and triggering prediction. A more robust methodology introduced by Kramer and Mayfield (2007) is known as probabilistic liquefaction hazard analysis (PLHA), which integrates the full ground motion hazard space with probabilistic forms of liquefaction triggering models (e.g., Boulanger and Idriss 2014), resulting in the computation of FSL profiles with consistent return periods. Multiple PLHA computational platforms have been developed over the years, with the computational framework from Makdisi (2021) serving as the basis for a new Liquefaction Hazard Tool under development at the U.S. Geologic Survey (USGS).\nDespite significant improvements in recent years to the availability of seismic hazard data and probabilistic triggering and effects models, the issue of incorporating uncertainty in characterizing liquefaction susceptibility remains a challenge. Most compositional susceptibility criteria (i.e., whether or not the soil exhibits sand-like behavior) currently in use are presented as deterministic bounds based on in-situ or laboratory test data; similarly, determination of soil saturation is often based on a single groundwater level from in-situ testing. As a result, the same types of binary decisions must be made in PLHA as in more conventional methods. With the expansion and availability of field and laboratory data pertaining to liquefaction through resources such as the Next Generation Liquefaction (NGL) project, there exists the potential for an improved set of susceptibility models for CPT, SPT, and Vs-based applications. Presented here is a brief discussion on how probabilistic susceptibility modeling can be accommodated in PLHA calculations, as well as how the use of multiple models can be leveraged within a logic tree to improve the representation of epistemic uncertainty in liquefaction hazard analysis.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"PEER workshop on liquefaction susceptibility, PEER report 2023-02","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Pacific Earthquake Engineering Research Center","doi":"10.55461/BPSK6314","usgsCitation":"Makdisi, A.J., 2023, Incorporating uncertainty in susceptibility criteria into probabilistic liquefaction hazard analysis, in PEER workshop on liquefaction susceptibility, PEER report 2023-02, p. 147-149, https://doi.org/10.55461/BPSK6314.","productDescription":"3 p.","startPage":"147","endPage":"149","ipdsId":"IP-143255","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":443677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.55461/bpsk6314","text":"Publisher Index Page"},{"id":416863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":871493,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70245189,"text":"70245189 - 2023 - Exploring the geology of the Midcontinent Rift under western Lake Superior using a preliminary velocity model of seismic line GLIMPCE C","interactions":[],"lastModifiedDate":"2023-09-28T14:31:13.326929","indexId":"70245189","displayToPublicDate":"2023-05-01T09:24:24","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Exploring the geology of the Midcontinent Rift under western Lake Superior using a preliminary velocity model of seismic line GLIMPCE C","docAbstract":"Seismic-reflection data were collected in the 1980s as part of the Great Lakes International Multidisciplinary Program on Crustal Evolution (GLIMPCE) to investigate the 1.1 Ga Midcontinent Rift System (MRS). GLIMPCE Line C crosses western Lake Superior from north to south shores (Fig. 1 inset). Many previous workers have interpreted the MRS in Line C as an asymmetric central graben filled with 10–20 km of subaerial basalt flows, overlain by 7-10 km of sedimentary section, and underlain by magmatic underplating. The central graben was interpreted to have formed from extensional normal faults, later reactivated as high-angle reverse faults. The northern part of Line C crosses over a prominent gravity low called the Grand Marais Ridge (GMR; Fig. 1 inset), previously interpreted as an Archean granitic basement high.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 69th ILSG annual meeting","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Institute on Lake Superior Geology 69th Annual Meeting","conferenceDate":"April 23-24, 2023","conferenceLocation":"Eau Claire, WI","language":"English","publisher":"Institute on Lake Superior Geology","usgsCitation":"Grauch, V.J., Heller, S.J., Stewart, E.K., and Woodruff, L.G., 2023, Exploring the geology of the Midcontinent Rift under western Lake Superior using a preliminary velocity model of seismic line GLIMPCE C, in Proceedings of the 69th ILSG annual meeting, Eau Claire, WI, April 23-24, 2023, p. 37-38.","productDescription":"2 p.","startPage":"37","endPage":"38","ipdsId":"IP-151610","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":421345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418275,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.lakesuperiorgeology.org/"}],"country":"United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.46510586137026,\n 47.59708757842765\n ],\n [\n -90.52262719746251,\n 47.033420504728184\n ],\n [\n -89.41926338696646,\n 47.03699042088601\n ],\n [\n -89.52384763440693,\n 47.67106063862627\n ],\n [\n -90.46510586137026,\n 47.59708757842765\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grauch, V. J. S. 0000-0002-0761-3489 tien@usgs.gov","orcid":"https://orcid.org/0000-0002-0761-3489","contributorId":886,"corporation":false,"usgs":true,"family":"Grauch","given":"V.","email":"tien@usgs.gov","middleInitial":"J. S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":875800,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heller, Samuel J. 0000-0002-6579-5620 sheller@usgs.gov","orcid":"https://orcid.org/0000-0002-6579-5620","contributorId":201350,"corporation":false,"usgs":true,"family":"Heller","given":"Samuel","email":"sheller@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":875801,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Esther K.","contributorId":247878,"corporation":false,"usgs":false,"family":"Stewart","given":"Esther","email":"","middleInitial":"K.","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":875802,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodruff, Laurel G. 0000-0002-2514-9923 woodruff@usgs.gov","orcid":"https://orcid.org/0000-0002-2514-9923","contributorId":2224,"corporation":false,"usgs":true,"family":"Woodruff","given":"Laurel","email":"woodruff@usgs.gov","middleInitial":"G.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":875803,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70246535,"text":"70246535 - 2023 - Subsurface characterization of the Duluth Complex and related intrusions from 3D modeling of gravity and magnetotelluric data","interactions":[],"lastModifiedDate":"2023-09-28T14:14:46.847767","indexId":"70246535","displayToPublicDate":"2023-05-01T09:14:04","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Subsurface characterization of the Duluth Complex and related intrusions from 3D modeling of gravity and magnetotelluric data","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 69th ILSG annual meeting","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Institute on Lake Superior Geology 69th Annual Meeting","conferenceDate":"April 24-25, 2023","conferenceLocation":"Eau Claire, WI","language":"English","publisher":"Institute on Lake Superior Geology","usgsCitation":"Peterson, D.E., Bedrosian, P.A., and Finn, C., 2023, Subsurface characterization of the Duluth Complex and related intrusions from 3D modeling of gravity and magnetotelluric data, in Proceedings of the 69th ILSG annual meeting, v. 69, Eau Claire, WI, April 24-25, 2023, p. 63-64.","productDescription":"2","startPage":"63","endPage":"64","ipdsId":"IP-151647","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":421344,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421343,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.lakesuperiorgeology.org/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Minnesota","otherGeospatial":"Duluth Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.33841098115533,\n 46.60889668098744\n ],\n [\n -92.06873570973505,\n 46.77233950080557\n ],\n [\n -91.20990186562136,\n 47.3068599853257\n ],\n [\n -90.74627730696767,\n 47.61551630804743\n ],\n [\n -89.53957926670478,\n 48.018149448034876\n ],\n [\n -90.80965661623506,\n 48.13833346173527\n ],\n [\n -92.12116043915276,\n 47.51332134203204\n ],\n [\n -92.89697287085113,\n 46.80490236888849\n ],\n [\n -92.53462077271212,\n 46.43611775146357\n ],\n [\n -92.33841098115533,\n 46.60889668098744\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"69","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, Dana E. 0000-0002-1941-265X","orcid":"https://orcid.org/0000-0002-1941-265X","contributorId":225536,"corporation":false,"usgs":true,"family":"Peterson","given":"Dana","email":"","middleInitial":"E.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":877082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":877083,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, Carol A. 0000-0002-6178-0405","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":229711,"corporation":false,"usgs":true,"family":"Finn","given":"Carol A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":877084,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70248070,"text":"70248070 - 2023 - Constraints on the composition and thermal structure of Ariel’s icy crust as inferred from its largest observed impact crater","interactions":[],"lastModifiedDate":"2023-09-05T13:15:19.774773","indexId":"70248070","displayToPublicDate":"2023-05-01T08:10:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"Constraints on the composition and thermal structure of Ariel’s icy crust as inferred from its largest observed impact crater","docAbstract":"The large graben-like troughs and smooth plains visible on the surface of Ariel are indicative of a period of high heat flow in the Uranian moon's past. High heat flows on icy moons like Ariel can also enable viscous flow that removes impact crater topography, a process called viscous relaxation. Here we use numerical modeling to investigate the conditions necessary to viscously relax Ariel's largest impact crater, Yangoor, which is 80 km in diameter and unusually shallow. If we assume that Ariel's crust consists of non-porous water ice, heat fluxes ≥60 mW m−2 are required to reduce an initially deep Yangoor-like crater to its current observed depth. Lower fluxes are required if a high-porosity (30%), low-conductivity surface layer several kilometers thick is assumed to exist, but in any case, fluxes in excess of 30 mW m−2 are necessary to substantially reduce Yangoor's topography. The inclusion of ammonia dihydrate has a negligible effect on our results despite decreasing the viscosity of Ariel's deep ice. Our results are consistent with previous inferences of high heat fluxes on Ariel, but exceed both expected radiogenic heat fluxes and known equilibrium tidal heat fluxes by an order of magnitude. If Yangoor's shallow depth is the result of tidal heating, then short-lived non-equilibrium tidal dissipation or some other source of energy is required. Notably, although our results do not require the presence of an ocean within Ariel, the thermal conditions necessary to viscously relax Yangoor also imply a relatively thin ice shell (∼10-km thick) if conductive heat transport is assumed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2023.115452","usgsCitation":"Bland, M.T., Beddingfield, C.B., Nordheim, T.A., Patthoff, D.A., and Vance, S.D., 2023, Constraints on the composition and thermal structure of Ariel’s icy crust as inferred from its largest observed impact crater: Icarus, v. 395, 115452, 11 p., https://doi.org/10.1016/j.icarus.2023.115452.","productDescription":"115452, 11 p.","ipdsId":"IP-144468","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":443681,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.icarus.2023.115452","text":"Publisher Index Page"},{"id":420471,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Ariel, Uranus, Yangoor","volume":"395","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bland, Michael T. 0000-0001-5543-1519 mbland@usgs.gov","orcid":"https://orcid.org/0000-0001-5543-1519","contributorId":146287,"corporation":false,"usgs":true,"family":"Bland","given":"Michael","email":"mbland@usgs.gov","middleInitial":"T.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":881746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beddingfield, Chloe B.","contributorId":328939,"corporation":false,"usgs":false,"family":"Beddingfield","given":"Chloe","email":"","middleInitial":"B.","affiliations":[{"id":78531,"text":"Seti Institute / NASA Ames","active":true,"usgs":false}],"preferred":false,"id":881747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nordheim, Tom A.","contributorId":328940,"corporation":false,"usgs":false,"family":"Nordheim","given":"Tom","email":"","middleInitial":"A.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":881748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Patthoff, Donald A.","contributorId":238744,"corporation":false,"usgs":false,"family":"Patthoff","given":"Donald","email":"","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":881749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vance, Steven D.","contributorId":328942,"corporation":false,"usgs":false,"family":"Vance","given":"Steven","email":"","middleInitial":"D.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":881750,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70246973,"text":"70246973 - 2023 - First investigations on lamprey responses to elevated total dissolved gas exposure and risk of gas bubble trauma","interactions":[],"lastModifiedDate":"2023-07-20T12:08:25.282241","indexId":"70246973","displayToPublicDate":"2023-04-30T07:06:31","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"First investigations on lamprey responses to elevated total dissolved gas exposure and risk of gas bubble trauma","docAbstract":"A flexible spill program in the federal Columbia River power system increased the total dissolved gas (TDG) water quality standards (i.e., the gas cap) from 120% to 125%. Spill is used to pass juvenile salmon (Oncorhynchus spp.) over dams, but it can generate elevated TDG, and exposed fish can develop gas bubble trauma (GBT) or experience mortality. Juvenile salmon are monitored for GBT through the Fish Passage Center’s (FPC), and under the flexible spill program, native non-salmonid fishes are also monitored. Pacific Lamprey (Entosphenus tridentatus) are exposed to elevated TDG, but nothing is known about their risk for GBT. This project is the first to evaluate GBT in lamprey, beginning with larval and juvenile lamprey in a controlled laboratory setting. These early life stages were chosen for this initial work because they have been shown to be more sensitive to GBT in other fish species. We modified the FPC protocol for GBT exams to be specific to lamprey and ranked bubbles in the mouth, eyes (juveniles only), gill pores, first and second dorsal fins, caudal fin, anal fin, vent, and body. We followed the FPC ranking criteria and assigned rank based on the proportion of the area occluded with bubbles, as 0=no bubbles, 1=1-5%, 2=6-25%, 3=26-50%, and 4=>50%. \n\nFour experiments were completed with larval lamprey from January to September 2022 using small (70 mm total length or less) and large (86 mm total length or greater) larvae in approximately equal proportions. Experiments included: (1) 130% TDG for 31 d, (2) 125% TDG for 91 d, (3) 130% TDG for 20 d with assessments of burrowing performance, and (4) 128-138% TDG for 3-4 d with assessments of predator avoidance ability and the corresponding untreated control groups.\n \nThe first and second experiments had similar study designs and findings. First, we tested an acute exposure at 130% TDG and then we tested a chronic exposure at 125%, to represent a full spill season. None of the controls (exposed to normally saturated water) experienced mortality or showed GBT signs. Few lamprey in the treatment groups (5% in Experiment 1; 0% in Experiment 2) showed GBT signs, and there were no mortalities (n=200 fish experiment 1; n=100 fish Experiment 2). Lamprey with GBT signs had bubbles on the body, with low severity ranks. During external exams for Experiment 2, we observed bubbles in the gut of several lamprey. The light coloration and transparency of the body made these observations possible, and we confirmed the finding with internal exams. From day 9 to day 91, 70.8% of the lamprey examined had bubbles in the gut. We observed five lamprey that were positively buoyant in the test tanks, and we likely underestimated the prevalence of floating as our procedures were not \ninitially designed to document this sign. \n\nIn our third experiment, burrowing performance was not significantly different between lamprey exposed to 130% TDG and controls. Mortality was 4.2% in the treatment group, but no GBT signs were observed. The proportion of lamprey with positive buoyancy increased through time, with 87.5% of fish floating on day 20 (end of the test). Bubbles in the gut were observed for some lamprey on each of five sampling dates (day 2 to 20), with prevalence ranging from 50-100%. Median burrow times ranged from 28 to 154 sec for treatment fish and from 40 to 100 sec for controls. We noted some atypical behaviors during burrow performance tests, including lamprey that were positively buoyant and unable to descend through 0.5 m of water to reach the sediment as well as lamprey that were unable to complete burrowing (within 10 min test period). These lamprey were so buoyant that they repeatedly floated to the surface of the water when they stopped or slowed their burrowing movements.\n \nPredator avoidance ability was assessed in our fourth experiment by exposing lamprey with GBT signs (floating) and controls to sculpin (Cottus spp.) until about 50% of the fish had been consumed or 2 h had passed. We completed five predation trials, testing the hypothesis that an equal proportion of treatment and control lamprey would be consumed. Treatment groups were generated by exposing 15 lamprey to 128-138% TDG for 3-4 d, until at least 10 lamprey were floating. Overall, 41 treatment and 46 control fish were eaten and there was no evidence that sculpin preferentially preyed upon lamprey with GBT signs. Additional tests with another predator are recommended. \n\nTwo experiments were completed with juvenile lamprey from March to November 2022: Experiment 5 exposed fish to 125% TDG for 10 d and Experiment 6 exposed fish to 125% for 16 d. Mortality rates for the treatment groups were 21.7% and 20.0% for these experiments, respectively, and few lamprey (4 per experiment) showed GBT signs. With the results from experiments pooled, bubbles were observed in all body areas with low severity ranks (means 0-1). We observed some exophthalmia and bubbles behind the gill pores, in addition to bubbles in the gut and fish floating. The presence of bubbles in or near the gill pores was the likely cause of death as exam findings included enlarged gill pore areas and restricted openings.\n \nThis project provided the first insights into lamprey responses to elevated TDG, but substantial learning opportunities remain. Our findings highlight that lamprey are vulnerable to GBT, but the effects are generally sublethal and would not be detected using FPC exam procedures. For example, we observed larval and juvenile lamprey that had bubbles in the gut and/or were floating, although these conditions were not consistently linked. More data are needed, but we surmise that it takes some time for bubbles to form in sufficient quantity to create the floatation required to overcome the mass of the lamprey. Positive buoyancy in natural settings could have substantial impacts to the risk of mortality for lamprey. Future studies could test GBT risk for larval lamprey in burrows, investigate the influence of lamprey size, measure performance (e.g., burrowing, swimming, predator avoidance ability) after elevated TDG exposure, and describe the rate that GBT signs dissipate when lamprey are returned to normally saturated water.","language":"English","publisher":"Bonneville Power Administration","collaboration":"Bonneville Power Administration","usgsCitation":"Liedtke, T.L., Tiffan, K., Weiland, L.K., and Ekstrom, B.K., 2023, First investigations on lamprey responses to elevated total dissolved gas exposure and risk of gas bubble trauma, 39 p.","productDescription":"39 p.","ipdsId":"IP-151469","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":419180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419173,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P199259"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liedtke, Theresa L. 0000-0001-6063-9867 tliedtke@usgs.gov","orcid":"https://orcid.org/0000-0001-6063-9867","contributorId":2999,"corporation":false,"usgs":true,"family":"Liedtke","given":"Theresa","email":"tliedtke@usgs.gov","middleInitial":"L.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878424,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tiffan, Kenneth 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":217812,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878425,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weiland, Lisa K. 0000-0002-9729-4062 lweiland@usgs.gov","orcid":"https://orcid.org/0000-0002-9729-4062","contributorId":3565,"corporation":false,"usgs":true,"family":"Weiland","given":"Lisa","email":"lweiland@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878426,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ekstrom, Brian K. 0000-0002-1162-1780 bekstrom@usgs.gov","orcid":"https://orcid.org/0000-0002-1162-1780","contributorId":3704,"corporation":false,"usgs":true,"family":"Ekstrom","given":"Brian","email":"bekstrom@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":878427,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243276,"text":"70243276 - 2023 - So goes the snow: Alaska snowpack changes and impacts on pacific salmon in a warming climate","interactions":[],"lastModifiedDate":"2023-05-05T11:38:45.159335","indexId":"70243276","displayToPublicDate":"2023-04-30T06:36:59","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":691,"text":"Alaska Park Science","printIssn":"1545- 496","active":true,"publicationSubtype":{"id":10}},"title":"So goes the snow: Alaska snowpack changes and impacts on pacific salmon in a warming climate","docAbstract":"In Alaska’s watersheds, climate change is altering the nature and role of the snowpack, defined as snow accumulation that melts in spring. Generally, the amount of precipitation that falls as snow and the length of the snow-cover season both decrease as temperatures exceed 0°C (32°F) more frequently. The impacts of climate change on snowpack vary among watersheds. In southern, coastal parts of Alaska, large decreases in spring snowpack are expected by the mid-21st century, even with more winter precipitation because temperatures warm to above freezing, causing a shift from snow to rain or more melt during the winter. In contrast, modest early spring increases in the snowpack are expected in watersheds where temperatures remain below freezing. In these locations temperatures warm but remain cold enough for the extra winter precipitation to fall as snow, even though the snowpack will begin accumulating later in the fall and melt earlier in the spring as temperatures rise during those warmer seasons. Because potential impacts on hydrological and ecological systems will vary among watersheds, it is difficult to generalize the resulting ecological impacts at broad spatial scales. Here, we explore likely impacts on hydrology in critical anadromous fish habitat in southwest Alaska.","language":"English","publisher":"US National Park Service","usgsCitation":"Littell, J., Reynolds, J.H., Bartz, K.K., McAfee, S., and Hayward, G.D., 2023, So goes the snow: Alaska snowpack changes and impacts on pacific salmon in a warming climate: Alaska Park Science, v. 19, no. 1, p. 62-75.","productDescription":"14 p.","startPage":"62","endPage":"75","ipdsId":"IP-112750","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":416748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416743,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.nps.gov/articles/aps-19-1-10.htm"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -167.0502487962713,\n 69.32812262696825\n ],\n [\n -167.0502487962713,\n 63.68078746979131\n ],\n [\n -146.31697991983825,\n 63.68078746979131\n ],\n [\n -146.31697991983825,\n 69.32812262696825\n ],\n [\n -167.0502487962713,\n 69.32812262696825\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reynolds, Joel H.","contributorId":140498,"corporation":false,"usgs":false,"family":"Reynolds","given":"Joel","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":871777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartz, Krista K.","contributorId":200705,"corporation":false,"usgs":false,"family":"Bartz","given":"Krista","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":871778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAfee, Stephanie A.","contributorId":167115,"corporation":false,"usgs":false,"family":"McAfee","given":"Stephanie A.","affiliations":[{"id":24618,"text":"Department of Geography, University of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":871779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayward, Gregory D.","contributorId":209846,"corporation":false,"usgs":false,"family":"Hayward","given":"Gregory","email":"","middleInitial":"D.","affiliations":[{"id":38010,"text":"US Forest Service, Alaska Region","active":true,"usgs":false}],"preferred":false,"id":871780,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241229,"text":"sir20235006 - 2023 - Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results","interactions":[],"lastModifiedDate":"2026-03-02T18:01:49.725089","indexId":"sir20235006","displayToPublicDate":"2023-04-28T13:18:00","publicationYear":"2023","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":"2023-5006","displayTitle":"Magnitude and Frequency of Floods for Rural Streams in Georgia, South Carolina, and North Carolina, 2017—Results","title":"Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results","docAbstract":"Reliable estimates of the magnitude and frequency of floods are an important part of the framework for hydraulic-structure design and flood-plain management in Georgia, South Carolina, and North Carolina. Annual peak flows measured at U.S. Geological Survey streamgages are used to compute flood‑frequency estimates at those streamgages. However, flood‑frequency estimates also are needed at ungaged stream locations. A process known as regionalization was used to develop regression equations to estimate the magnitude and frequency of floods at ungaged locations.
A multistate approach was used to update estimates of the magnitude and frequency of floods in rural, ungaged basins in Georgia, South Carolina, and North Carolina. Annual peak-flow data through September 2017 were analyzed for 965 streamgages with 10 or more years of data on rural streams in Georgia, South Carolina, North Carolina, and adjacent parts of Alabama, Florida, Tennessee, and Virginia. Flood‑frequency estimates of the 50‑, 20‑, 10‑, 4‑, 2‑, 1‑, 0.5‑, and 0.2‑percent annual exceedance probability streamflows, which correspond to flood-recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively, were computed for the 965 streamgages following national guidelines. As part of the computation of flood‑frequency estimates for the streamgages, an updated value for the regional skew coefficient (0.048) was developed using a Bayesian generalized least squares regression model. The new regional skew has a mean square error or average variance of prediction of 0.092. Additionally, basin characteristics for these stations were computed using a geographical information system.
Exploratory analyses on the 965 streamgages confirmed the five hydrologic regions for Georgia, South Carolina, and North Carolina defined in a previous rural flood‑frequency study. From the 965 streamgages, streamgages with 30 or more years of record were used to complete a peak-flow trend analysis. Of the 965 streamgages, 164 streamgages were found to be redundant and were excluded from the regional regression analyses. Data from the remaining 801 streamgages (292 in Georgia, 75 in South Carolina, 303 in North Carolina, 15 in Alabama, 12 in Florida, 39 in Tennessee, and 65 in Virginia) were used in a regional regression analysis relating basin characteristics to flood‑frequency estimates. This analysis, based on generalized least squares regression, was used to develop a set of predictive equations to estimate the 50‑, 20‑, 10‑, 4‑, 2‑, 1‑, 0.5‑, and 0.2‑percent annual exceedance probability streamflows for rural, ungaged basins in Georgia, South Carolina, and North Carolina. The final set of predictive equations are all functions of drainage area and percentage of the drainage basin within each of the five hydrologic regions. Average errors of prediction for these regression equations range from 35.8 to 44.4 percent.
Flood‑frequency estimates also were computed for 72 regulated (for example, a streamgage where flow is altered by a dam or weir) streamgages in Georgia, South Carolina, and North Carolina with 20 or more years of post-regulation record using data through water year 2019. The water year is the annual period from October 1 through September 30 and is designated by the year in which the period ends. Of the 72 regulated streamgages, 18 had pre-regulated periods of record that also were analyzed as part of this study. Flow adjustments were applied to historic peaks and large floods from the pre-regulated period, if available, for use in the post-regulation frequency analysis. Estimates of large floods provide valuable information in frequency analysis and, thus, were included in the post-regulation frequency analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235006","collaboration":"Prepared in cooperation with the Georgia Department of Transportation (Engineering Division, Office of Bridge Design and Maintenance), South Carolina Department of Transportation (Hydraulic Design Support Office), North Carolina Department of Transportation (Division of Highways, Hydraulics Unit), and the North Carolina Department of Crime Control and Public Safety (Division of Emergency Management, Floodplain Mapping Program)","usgsCitation":"Feaster, T.D., Gotvald, A.J., Musser, J.W., Weaver, J.C., Kolb, K.R., Veilleux, A.G., and Wagner, D.M., 2023, Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results: U.S. Geological Survey Scientific Investigations Report 2023–5006, 75 p., https://doi.org/10.3133/sir20235006.","productDescription":"Report: ix, 75 p.; 2 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-115205","costCenters":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":414229,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TSBPFS","text":"USGS data release","linkHelpText":"Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Data"},{"id":414230,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AQ2AX1","text":"USGS data release","linkHelpText":"Model archive for magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Juvenile salmonids migrate hundreds of kilometers from their natal streams to mature in the ocean. Throughout this migration, they respond to environmental cues such as local water velocities and other stimuli to direct and modulate their movements, often through heavily modified riverine and estuarine habitats. Management strategies in an uncertain future of climate change and altered land use regimes depend heavily on being able to reliably predict their ocean entry timings, route use, and survival rates through rivers and estuaries. We developed a spatially-explicit agent-based model of fish movement in response to hydrodynamic flows that uses movement dynamics gleaned from multi-dimensional tracking datasets of acoustically tagged juveniles moving through an urbanized, branched tidal estuary. We demonstrate how such models can be calibrated, and we apply it to the Sacramento-San Joaquin Delta in Central California. The quality of the out-of-sample validation of the model to predict juvenile salmon survival and route selection indicates that the model is versatile and flexible enough to be used in novel hydroclimatological conditions.
Because use of high-resolution hydrologic models is becoming more widespread and estimates are made over large domains, there is a pressing need for systematic evaluation of their performance. Most evaluation efforts to date have focused on smaller basins that have been relatively undisturbed by human activity, but there is also a need to benchmark model performance more comprehensively, including basins impacted by human activities. This study benchmarks the long-term performance of two process-oriented, high-resolution, continental-scale hydrologic models that have been developed to assess water availability and risks in the United States (US): the National Water Model v2.1 application of WRF-Hydro (NWMv2.1) and the National Hydrologic Model v1.0 application of the Precipitation–Runoff Modeling System (NHMv1.0). The evaluation is performed on 5390 streamflow gages from 1983 to 2016 (∼ 33 years) at a daily time step, including both natural and human-impacted catchments, representing one of the most comprehensive evaluations over the contiguous US. Using the Kling–Gupta efficiency as the main evaluation metric, the models are compared against a climatological benchmark that accounts for seasonality. Overall, the model applications show similar performance, with better performance in minimally disturbed basins than in those impacted by human activities. Relative regional differences are also similar: the best performance is found in the Northeast, followed by the Southeast, and generally worse performance is found in the Central and West areas. For both models, about 80 % of the sites exceed the seasonal climatological benchmark. Basins that do not exceed the climatological benchmark are further scrutinized to provide model diagnostics for each application. Using the underperforming subset, both models tend to overestimate streamflow volumes in the West, which could be attributed to not accounting for human activities, such as active management. Both models underestimate flow variability, especially the highest flows; this was more pronounced for NHMv1.0. Low flows tended to be overestimated by NWMv2.1, whereas there were both over and underestimations for NHMv1.0, but they were less severe. Although this study focused on model diagnostics for underperforming sites based on the seasonal climatological benchmark, metrics for all sites for both model applications are openly available online.
Sandy beaches are important resources providing recreation, tourism, habitat, and coastal protection. They evolve over various time scales due to local winds, waves, storms, and changes in sea level. A common method used to monitor change in sandy beaches is to measure the movement of the shoreline over time. Typically, the rate of change is estimated by fitting a linear regression through a time series of shoreline positions. To best manage the valuable resources within a coastal environment, accurate forecasts of shoreline position are needed. A simple way to estimate future shoreline position is to extrapolate a linear regression into the future, this method is often used to establish management guidelines like construction setback lines. A more recently developed shoreline forecasting technique utilizes the Kalman filter to assimilate shoreline data and modify the linear regression. This paper calculates the uncertainty and accuracy of both the extrapolated linear regression and Kalman filter forecasting methods for 10- and 20-year hindcasts using data collected at five diverse study areas. These data are inherently sparse (8–10 measurements per location, collected over 150 years) and are representative of the observed historical data available for the continental United States for this timeframe. Both methods produced similar results and had regionally averaged forecast accuracies of 5–16 m. We determined that the inaccuracy of the forecasts is largely due to the effects of shorter time scale variability. This variability is roughly proportional to the standard error of the linear regression, which is a useful measure of forecast uncertainty.
Nutrient limitation of tree growth can intensify when nutrients are lost to forest harvest, creating challenges for forest growth and sustainability. Forest harvest accelerates nutrient loss by removing nutrient-containing biomass and by increasing nutrient leaching, shaping patterns of nutrient depletion that cause long-term shifts in nutrient limitation. Nitrogen most frequently limits tree growth, but where nitrogen is abundant, nutrient limitation often shifts to phosphorus and base cations, depending on soil mineralogy. We used the process-based biogeochemical model NutsFor to evaluate how multiple elements can limit long-term forest growth via interactions between soil nitrogen (low vs. high nitrogen) and soil mineralogy (sedimentary vs. basaltic bedrock). Simulations were run for 525 years with 40-year harvest intervals for managed Douglas-fir forests of the Oregon Coast Range. In low nitrogen sites, nutrient limitation switched after several centuries from nitrogen to phosphorus, as cycles of forest growth and harvest depleted soil organic phosphorus pools. In contrast, high nitrogen sites displayed severe base cation depletion and reduced tree growth within only one to two rotations, with sedimentary bedrock sites limited by calcium and basaltic sites by both calcium and potassium. Harvesting stimulated the largest fractional losses of nitrogen and potassium across all simulations, and additionally of calcium in high nitrogen sites. These multi-element simulations of interactions among harvesting, soil nitrogen, and bedrock type provide a set of testable predictions to guide monitoring and changes in management aimed at sustaining long-term forest productivity across a wide range of soil biogeochemical conditions.
The 2016 moment magnitude 7.8 Kaikoura, New Zealand, earthquake occurred at the southern end of the Hikurangi subduction zone where the upper plate above the shallow megathrust is exposed sub-aerially. As a result, the substantial co-seismic deformation in the upper plate above the megathrust rupture was observed geologically and geodetically. We explore the relationship between this surface faulting and the subduction megathrust rupture and find that the greatest upper plate fault slip occurred coincident (in time and location) with the megathrust rupture. Models of Coulomb stress change demonstrate that these surface faults become positively loaded as the upper plate rebounds during the megathrust event, favoring fault slip. In addition, during the megathrust rupture these faults terminate against an uncoupled subduction plate interface. We simulate the effects of decoupling at the base of these faults and find that very large fault slip is an expected consequence of this decoupling, allowing near-complete strain release. In contrast, typical strike-slip faults, pinned at their base, would have lower amounts of fault slip. These two conditions—increased Coulomb stress and basal decoupling—combine to produce the extreme co-seismic upper plate faulting observed above the shallow Kaikoura megathrust earthquake. Similar conditions occur in other global subduction zones, but in most subduction zones the region above the coupled megathrust is underwater and poorly observed. Our analysis of the Kaikoura earthquake indicates a need to reevaluate patterns of strain accumulation and release in these regions, rather than assuming simple models of elastic rebound.