Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113307","usgsCitation":"Enderlin, E., Elkin, C., Gendreau, M., Marshall, H., O'Neel, S., McNeil, C., Florentine, C., and Sass, L., 2022, Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions: Remote Sensing of Environment, v. 283, 113307, 17 p., https://doi.org/10.1016/j.rse.2022.113307.","productDescription":"113307, 17 p.","ipdsId":"IP-141547","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113307","text":"Publisher Index Page"},{"id":486323,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1WHK","text":"USGS data release","linkHelpText":"Point Raw Glaciological Data: Ablation Stake, Snow Pit, and Probed Snow Depth Data on USGS Benchmark Glaciers"},{"id":408693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Idaho","otherGeospatial":"Reynolds Creek Experimental Watershed, Wolverine Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.9169563546656,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.36296558422342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.84207609134586,\n 60.42699332401864\n ],\n [\n -148.8772978314237,\n 60.46763185035496\n ],\n [\n -148.92384084509808,\n 60.44126913255184\n ],\n [\n -148.95214402908923,\n 60.43009729404224\n ],\n [\n -148.9219539661653,\n 60.37666770702921\n ],\n [\n -148.9112616522131,\n 60.37542411458642\n ],\n [\n -148.84207609134586,\n 60.42699332401864\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":855759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elkin, Colten","contributorId":298508,"corporation":false,"usgs":false,"family":"Elkin","given":"Colten","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gendreau, Madeline","contributorId":298509,"corporation":false,"usgs":false,"family":"Gendreau","given":"Madeline","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, H. P.","contributorId":298510,"corporation":false,"usgs":false,"family":"Marshall","given":"H. P.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":855763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNeil, Christopher J. 0000-0003-4170-0428 cmcneil@usgs.gov","orcid":"https://orcid.org/0000-0003-4170-0428","contributorId":5803,"corporation":false,"usgs":true,"family":"McNeil","given":"Christopher J.","email":"cmcneil@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":855764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":855766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":855765,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70259706,"text":"70259706 - 2022 - Exploring declustering methodology for addressing geothermal exploration bias","interactions":[],"lastModifiedDate":"2024-10-21T12:29:48.729606","indexId":"70259706","displayToPublicDate":"2022-10-21T07:28:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Exploring declustering methodology for addressing geothermal exploration bias","docAbstract":"Geothermal resources assessments use data that are unevenly distributed in space, with more data collected in areas with known thermal features. To meet the assumptions for geostatistical modeling (e.g., variography and kriging) such as having a random sample representative of the population, declustering may be needed to correct for spatial sample bias. Several declustering methods exist and to understand how best to use these methods, we apply these to real data and samples of that data. The work described herein summarizes the application of cell-based declustering to shallow temperature data (~20 cm) collected in a survey across a thermal feature in the Lower Geyser Basin, Yellowstone National Park, Wyoming. The sample dataset is a regular grid (3-m spacing) of temperatures across a 72-m square area, providing a shallow, subsurface temperature dataset collected with minimal spatial bias (a few grid locations near a hot spring could not be sampled). To test the influence of sample clustering on geothermal estimates, this dense dataset is sub-sampled irregularly to evaluate bias on temperature estimation. Three sampling strategies were tested: a simple random sample, a stratified random sample, and a stratified biased random sample. The naive mean (before declustering) values for each dataset were compared to the post-declustering mean to evaluate the effectiveness of declustering on correcting the mean for spatial bias. For the limited number of sample datasets evaluated, we found that although cell-based declustering did partially correct the mean, some bias remained (i.e., the estimate was improved, but not fully corrected). It is possible that the procedure documented herein (applied here to only a few random samples) could be applied to many random samples, so that robust conclusions might be drawn (e.g., Is there always some remaining bias in declustered estimates? Does it depend on the number of sample points?). In particular, bias could be evaluated for persistency, and uncertainty could be evaluated.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Lindsey, C.R., Price, A.N., and Burns, E.R., 2022, Exploring declustering methodology for addressing geothermal exploration bias: Geothermal Resources Council Transactions, v. 46, p. 1109-1119.","productDescription":"11 p.","startPage":"1109","endPage":"1119","ipdsId":"IP-141054","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":463063,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034661"},{"id":463064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lindsey, Cary Ruth","contributorId":345373,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Price, Adam N. 0000-0002-7211-4758","orcid":"https://orcid.org/0000-0002-7211-4758","contributorId":295971,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":916396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916397,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241136,"text":"70241136 - 2022 - Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","interactions":[],"lastModifiedDate":"2023-03-13T12:07:56.782111","indexId":"70241136","displayToPublicDate":"2022-10-21T07:05:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","docAbstract":"Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long-term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines.
Previous research suggests that predation by piscivorous colonial waterbirds may negatively influence the survival of Lost River Suckers (LRS) Deltistes luxatus and Shortnose Suckers (SNS) Chasmistes brevirostris in the Upper Klamath Basin (UKB), USA. However, estimates of predation from past studies, which were based on suckers with PIT tags, represent minimum estimates of sucker mortality because analyses did not account for the proportion of tags that were consumed by birds and deposited beyond their breeding colony. To address this uncertainty, we fed PIT-tagged suckers to American white pelicans Pelecanus erythrorhynchos to estimate deposition probabilities. A hierarchical Bayesian model was then used to estimate predation rates (percentage of available tagged fish that were consumed) on juvenile suckers that were released as part of the Sucker Assisted Rearing Program (SARP) and on wild juvenile and adult LRS and SNS during 2009–2020. Pelican deposition probabilities were estimated to be 0.47 (95% credible interval = 0.36–0.60), indicating that for every 100 tags consumed, 47 tags on average were deposited on breeding colonies by birds. Deposition-corrected estimates of predation rates were approximately two times greater than those previously reported and ranged annually from 4.3% (95% credible interval = 2.9–6.7%) to 8.5% (6.3–12.7%) on SARP juvenile suckers, from 4.3% (0.9–13.2%) to 10.5% (3.8–24.5%) on wild juvenile suckers, and from 0.1% (<0.1–0.3%) to 7.2% (2.8–16.4%) on adult suckers, depending on species and location. Results suggest that predation by colonial waterbirds, although not the original cause of sucker declines, was a substantial source of sucker mortality in some years. Future studies should consider models that jointly estimate both predation and survival and models that include environmental factors that potentially influence sucker susceptibility to avian predators in the UKB.
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 2 (April–June), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed throughout the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at the lower orbit of 697 kilometers on May 5, 2022, extending the science mission to allow for essential data to be acquired during the 2022 Northern Hemisphere fire and growing season. Additional information about the Landsat 7 orbit lowering is here:
https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers.
Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Recreational hunting has been the dominant game management and conservation mechanism in the United States for the past century. However, there are numerous modern-day issues that reduce the viability and efficacy of hunting-based management, such as fewer hunters, overabundant wildlife populations, limited access, and emerging infectious diseases in wildlife. Quantifying the drivers of recreational harvest by hunters could inform potential management actions to address these issues, but this is seldom comprehensively accomplished because data collection practices limit some analytical applications (e.g., differing spatial scales of harvest regulations and harvest data). Additionally, managing large-scale issues, such as infectious diseases, requires collaborations across management agencies, which is challenging or impossible if data are not standardized. Here we discuss modern issues with the prevailing wildlife management framework in the United States from an analytical point of view with a case study of white-tailed deer (Odocoileus virginianus) in the Midwest. We have four aims: (1) describe the interrelated processes that comprise hunting and suggest improvements to current data collections systems, (2) summarize data collection systems employed by state wildlife management agencies in the Midwestern United States and discuss potential for large-scale data standardization, (3) assess how aims 1 and 2 influence managing infectious diseases in hunted wildlife, and (4) suggest actionable steps to help guide data collection standards and management practices. To achieve these goals, Wisconsin Department of Natural Resources disseminated a questionnaire to state wildlife agencies (Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, Wisconsin), and we report and compare their harvest management structures, data collection practices, and responses to chronic wasting disease. We hope our “call to action” encourages re-evaluation, coordination, and improvement of harvest and management data collection practices with the goal of improving the analytical potential of these data. A deeper understanding of the strengths and deficiencies of our current management systems in relation to harvest and management data collection methods could benefit the future development of comprehensive and collaborative management and research initiatives (e.g., adaptive management) for wildlife and their diseases.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.943411","usgsCitation":"Brandell, E., Storm, D., Van Deelen, T., Walsh, D.P., and Turner, W.C., 2022, A call to action: Standardizing white-tailed deer harvest data in the Midwestern United States and implications for quantitative analysis and disease management: Frontiers in Ecology and Evolution, v. 19, 943411, 19 p., https://doi.org/10.3389/fevo.2022.943411.","productDescription":"943411, 19 p.","ipdsId":"IP-139769","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":467155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.943411","text":"Publisher Index Page"},{"id":466006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, 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\"}}]}","volume":"19","noUsgsAuthors":false,"publicationDate":"2022-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Brandell, Ellen E.","contributorId":347965,"corporation":false,"usgs":false,"family":"Brandell","given":"Ellen E.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storm, Daniel J.","contributorId":347966,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Deelen, Timothy R.","contributorId":347967,"corporation":false,"usgs":false,"family":"Van Deelen","given":"Timothy R.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":922781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922782,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263097,"text":"70263097 - 2022 - Editorial: Habitat and distribution models of marine and estuarine species: Advances for a sustainable future","interactions":[],"lastModifiedDate":"2025-01-29T16:14:43.221849","indexId":"70263097","displayToPublicDate":"2022-10-19T10:12:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Editorial: Habitat and distribution models of marine and estuarine species: Advances for a sustainable future","docAbstract":"No abstract available.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.1050548","usgsCitation":"Fabrizio, M., Henderson, M., Rose, K., and Petitgas, P., 2022, Editorial: Habitat and distribution models of marine and estuarine species: Advances for a sustainable future: Frontiers in Marine Science, v. 9, 1050548., 4 p., https://doi.org/10.3389/fmars.2022.1050548.","productDescription":"1050548., 4 p.","ipdsId":"IP-145479","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":489758,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.1050548","text":"Publisher Index Page"},{"id":481462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2022-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Fabrizio, Mary C.","contributorId":350223,"corporation":false,"usgs":false,"family":"Fabrizio","given":"Mary C.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":925507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rose, Kenneth","contributorId":350225,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925508,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petitgas, Pierre","contributorId":350227,"corporation":false,"usgs":false,"family":"Petitgas","given":"Pierre","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925509,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259709,"text":"70259709 - 2022 - A modern multicentennial record of radiocarbon variability from an exactly dated bivalve chronology at the Tree Nob site (Alaska Coastal Current)","interactions":[],"lastModifiedDate":"2024-10-19T13:12:35.431924","indexId":"70259709","displayToPublicDate":"2022-10-19T08:09:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3225,"text":"Radiocarbon","active":true,"publicationSubtype":{"id":10}},"title":"A modern multicentennial record of radiocarbon variability from an exactly dated bivalve chronology at the Tree Nob site (Alaska Coastal Current)","docAbstract":"Quantifying the marine radiocarbon reservoir effect, offsets (ΔR), and ΔR variability over time is critical to improving dating estimates of marine samples while also providing a proxy of water mass dynamics. In the northeastern Pacific, where no high-resolution time series of ΔR has yet been established, we sampled radiocarbon (14C) from exactly dated growth increments in a multicentennial chronology of the long-lived bivalve, Pacific geoduck (Paneopea generosa) at the Tree Nob site, coastal British Columbia, Canada. Samples were taken at approximately decadal time intervals from 1725 CE to 1920 CE and indicate average ΔR values of 256 ± 22 years (1σ) consistent with existing discrete estimates. Temporal variability in ΔR is small relative to analogous Atlantic records except for an unusually old-water event, 1802–1812. The correlation between ΔR and sea surface temperature (SST) reconstructed from geoduck increment width is weakly significant (r2 = .29, p = .03), indicating warm water is generally old, when the 1802–1812 interval is excluded. This interval contains the oldest (–2.1σ) anomaly, and that is coincident with the coldest (–2.7σ) anomalies of the temperature reconstruction. An additional 32 14C values spanning 1952–1980 were detrended using a northeastern Pacific bomb pulse curve. Significant positive correlations were identified between the detrended 14C data and annual El Niño Southern Oscillation (ENSO) and summer SST such that cooler conditions are associated with older water. Thus, 14C is generally relatively stable with weak, potentially inconsistent associations to climate variables, but capable of infrequent excursions as illustrated by the unusually cold, old-water 1802–1812 interval.
Hydrologic and geophysical components interact to produce streambed scour. This study investigates methods for improving the utility of estimates of hydrologic flow in streams and rivers used when evaluating potential pier scour over the design-life of highway bridges in Virginia. Recent studies of streambed composition identify potential bridge design cost savings when attributes of cohesive soil and weathered rock unique to certain streambeds are considered within the bridge planning design. To achieve potential cost savings, however, attributes and effects of scour forces caused by water movement across the streambed surface must be accurately described and estimated.
This study explores the potential for improving estimates of the hydrologic component, namely hydrologic flow, afforded by empirically based deterministic, probabilistic, and statistical modeling of flows using streamgage data from 10 selected sites in Virginia. Methods are described and tools are provided that may assist with estimating hydrological components of flow duration and potential cumulative stream power for bridge designs in specific settings, and calculation of comprehensive projections of anticipated individual bridge pier scour rates. Examples of hydrologic properties needed to determine the rates of streambed scour are described for sites spanning a range of basin sizes and locations in Virginia. Deterministic, probabilistic, and statistical modeling methods are demonstrated for estimating hydrological components of streambed scour over a bridge design lifespan. Eight tools provide examples of streamflow analysis using daily and instantaneous streamflow data collected at 10 study sites in Virginia. Tool 1 provides a generalized system dynamics model of streamflow and sediment motion that may be used to estimate hydrologic flow over time. Tool 2 illustrates at-a-station hydraulic geometry using methods pioneered by Leopold and others. Tool 3 provides a system dynamics model developed to test the use of Monte-Carlo sampling of instantaneous streamflow measurements to augment and increase precision of site-specific period-of-record daily-flow values useful for driving stream-power and streambed scour estimates. Tool 4 integrates deterministic modeling, maximum likelihood logistic regression, and Monte-Carlo sampling to identify probable hydrologic flows. Tool 5 provides instantaneous flow hydrologic envelope profiles, using measured instantaneous flow data integrated with measured daily-flow value data. Tool 6 provides precise estimates of hydrologic flow over entire data time-series suitable for driving scour simulation models. Tool 7 provides a threshold of flow and probability of time-under-load interactive calculator that allows selection of a desired bridge design lifespan, ranging from 1 to 250 years, and identification of a flow interval of interest. Tool 8 provides a flow-random sampling interactive tool, developed to facilitate easy access to large datasets of randomly sampled flow data measurements from unique locations for purposes of computing and testing future models of bridge pier scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225059","collaboration":"Prepared in cooperation with the Virginia Department of Transportation","usgsCitation":"Austin, S.H., 2022, Virginia Bridge Scour Pilot Study—Hydrological Tools: U.S. Geological Survey Scientific Investigations Report 2022–5059, 46 p., https://doi.org/10.3133/sir20225059.","productDescription":"Report: vii, 46 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-137495","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":408481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5059/coverthb.jpg"},{"id":408486,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957ABZN","text":"USGS data release","linkHelpText":"Virginia bridge scour pilot study streamflow data"},{"id":408485,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.XML"},{"id":408487,"rank":7,"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":503375,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113768.htm","linkFileType":{"id":5,"text":"html"}},{"id":408484,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5059/images/"},{"id":408483,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225059/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5059"},{"id":408482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.pdf","text":"Report","size":"9.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5059"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.28857421875,\n 39.554883059924016\n ],\n [\n -80.39794921875,\n 38.18638677411551\n ],\n [\n -80.4638671875,\n 37.52715361723378\n ],\n [\n -77.49755859375,\n 37.59682400108367\n ],\n [\n -77.32177734375,\n 39.53793974517628\n ],\n [\n -78.28857421875,\n 39.554883059924016\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Planning stormwater projects requires estimates of current and future extreme precipitation depths for events with specified return periods and durations. In this study, precipitation data from four downscaled climate datasets are used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the period 2050–89 primarily on the basis of Representative Concentration Pathways 4.5 and 8.5 emission scenarios from the Coupled Model Intercomparison Project Phase 5. The four downscaled climate datasets are (1) the Coordinated Regional Downscaling Experiment (CORDEX) dataset, (2) the Localized Constructed Analogs (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, and (4) the Jupiter Intelligence Weather Research and Forecasting Model (JupiterWRF) dataset. Change factors—multiplicative changes in expected extreme precipitation magnitude from current to future period—were computed for grid cells from the downscaled climate datasets containing National Oceanic and Atmospheric Administration Atlas 14 stations in central and south Florida. Change factors for specific durations and return periods may be used to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the period 2050–89 on the basis of changes in extreme precipitation derived from downscaled climate datasets. Model culling was implemented to select downscaled climate models that best captured observed historical patterns of precipitation extremes in central and south Florida.
Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally greater than one and increasing with return period. In general, median change factors were higher for the south-central Florida climate region (1.05–1.55 depending on downscaled climate dataset, duration, and return period) than for the south Florida climate region (1–1.4 depending on downscaled climate dataset, duration, and return period) when considering best performing models for both areas, indicating a projected overall increase in future extreme precipitation events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225093","collaboration":"Prepared in cooperation with the South Florida Water Management District","usgsCitation":"Irizarry-Ortiz, M.M., Stamm, J.F., Maran, C., and Obeysekera, J., 2022, Development of projected depth-duration frequency curves (2050–89) for south Florida: U.S. Geological Survey Scientific Investigations Report 2022–5093, 114 p., https://doi.org/10.3133/sir20225093.","productDescription":"Report: xii, 114 p.; 1 Table; Data Release","numberOfPages":"130","onlineOnly":"Y","ipdsId":"IP-134493","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":435652,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3LEIL","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida (ver 2.0, May 2024)"},{"id":408474,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P935WRTG","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida"},{"id":408473,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.csv","text":"Table 1.1","size":"18.6 kB","linkFileType":{"id":7,"text":"csv"}},{"id":408468,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5093/coverthb.jpg"},{"id":408469,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.pdf","text":"Report","size":"23.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5093"},{"id":408470,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.XML"},{"id":408471,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5093/images"},{"id":408472,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.xlsx","text":"Table 1.1","size":"50.0 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":408853,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225093/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":503557,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113770.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.111572265625,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 24.327076540018634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
In this report, we apply the stream salmonid simulator (S3) to coho salmon (Oncorhynchus kisutch) in the Klamath River Basin by extending the original model to account for life history and disease dynamics specific to coho salmon. This version of S3 includes tracking of three separate life-history strategies representing the different time periods and ages at which fish leave natal tributaries such as the Scott and Shasta Rivers (age-0 spring, age-0 fall, or age-1 smolt). Once fish leave their natal tributaries and enter the Klamath River, the deterministic life-stage-structured population model simulates daily growth, movement, and survival. We extend the model to include non-natal tributary dynamics, where spring age-0 fish entry to non-natal tributaries is simulated based on environmental conditions in the main-stem Klamath River. Fish that use non-natal tributaries then reenter the Klamath River during the winter or spring as smolts and actively migrate downstream. We also consider the life history strategy where fish rear in natal tributaries and enter the Klamath River as age-1 smolts. In addition to simulating different life history pathways that coho salmon may take, we model disease dynamics, incorporating new information on Ceratonova shasta related infection and mortality. We incorporate competitive interactions between juvenile coho and Chinook salmon (Oncorhynchus tshawytscha) by simulating density-dependent movement dynamics in response to Chinook salmon abundance.
Model simulations suggest that total abundance and survival to the ocean differed between life-history strategies. In general, spring age-0 fish that leave their natal tributaries in their first spring had lower survival compared with fish that remained in natal tributaries and out-migrated later. Spring age-0 fish also had higher disease related mortality, owing to their residence in the main-stem Klamath River overlapping with periods of elevated C. shasta spore concentrations. Age-0 fish leaving their natal tributaries in the fall had near-zero disease related mortality. Most non-natal tributary use occurred at upstream tributary locations and was variable between the brood years depending on passage timing and environmental conditions. The inclusion of Chinook salmon in simulations resulted in decreased abundance and survival of Coho salmon reaching the ocean. In addition, we developed an R package to facilitate use of and continued development of S3 as a tool to guide management of juvenile salmonid populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221071","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Bureau of Reclamation","usgsCitation":"Dodrill, M.J., Perry, R.W., Som, N.A., Manhard, C.V., and Alexander, J.D., 2022, Extending the Stream Salmonid Simulator to accommodate the life history of coho salmon (Oncorhynchus kisutch) in the Klamath River Basin, Northern California: U.S. Geological Survey Open-File Report 2022–1071, 70 p., https://doi.org/10.3133/ofr20221071.","productDescription":"viii, 70 p.","onlineOnly":"Y","ipdsId":"IP-129401","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":408507,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1071/ofr20221071.XML"},{"id":408506,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1071/images"},{"id":408505,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221071/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1071"},{"id":408503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1071/coverthb.jpg"},{"id":408504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1071/ofr20221071.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1071"}],"country":"United States","state":"California","otherGeospatial":"Klamath River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.16748046874999,\n 41.071069130806414\n ],\n [\n -121.915283203125,\n 41.071069130806414\n ],\n [\n -121.915283203125,\n 42.037054301883806\n ],\n [\n -124.16748046874999,\n 42.037054301883806\n ],\n [\n -124.16748046874999,\n 41.071069130806414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The horizontal‐to‐vertical spectral ratios from microtremor (mHVSR) data obtained at 196 seismic stations in California are used to evaluate three alternative microtremor‐based proxies for site amplification for use in ground‐motion models (GMMs): the site fundamental period (
Pyrethroids, a class of commonly used insecticides, are frequently detected in aquatic environments, including estuaries. The influence that salinity has on organism physiology and the partitioning of hydrophobic chemicals, such as pyrethroids, has driven interest in how toxicity changes in saltwater compared to freshwater. Early life exposures in fish to pyrethroids cause toxicity at environmentally relevant concentrations, which can alter behavior. Behavior is a highly sensitive endpoint that influences overall organism fitness and can be used to detect toxicity of environmentally relevant concentrations of aquatic pollutants. Inland Silversides (Menidia beryllina), a commonly used euryhaline model fish species, were exposed from 5 days post fertilization (~1-day pre-hatch) for 96 h to six pyrethroids: bifenthrin, cyfluthrin, cyhalothrin, cypermethrin, esfenvalerate and permethrin. Exposures were conducted at three salinities relevant to brackish, estuarine habitat (0.5, 2, and 6 PSU) and across 3 concentrations, either 0.1, 1, 10, and/or 100 ng/L, plus a control. After exposure, Inland Silversides underwent a behavioral assay in which larval fish were subjected to a dark and light cycle stimuli to determine behavioral toxicity. Assessment of total distanced moved and thigmotaxis (wall hugging), used to measure hyper/hypoactivity and anxiety like behavior, respectively, demonstrate that even at the lowest concentration of 0.1 ng/L pyrethroids can induce behavioral changes at all salinities. We found that toxicity decreased as salinity increased for all pyrethroids except permethrin. Additionally, we found evidence to suggest that the relationship between log KOW and thigmotaxis is altered between the lower and highest salinities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.159398","usgsCitation":"Hutton, S., Siddiqui, S., Pedersen, E., Markgraf, C., Segarra, A., Hladik, M.L., Connon, R., and Brander, S.M., 2022, Comparative behavioral ecotoxicology of Inland Silverside larvae exposed to pyrethroids across a salinity gradient: Science of the Total Environment, v. 857, no. Part 3, 159398, 12 p., https://doi.org/10.1016/j.scitotenv.2022.159398.","productDescription":"159398, 12 p.","ipdsId":"IP-143936","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":408606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"857","issue":"Part 3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hutton, Sara","contributorId":298401,"corporation":false,"usgs":false,"family":"Hutton","given":"Sara","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siddiqui, Samreen","contributorId":298402,"corporation":false,"usgs":false,"family":"Siddiqui","given":"Samreen","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pedersen, Emily","contributorId":298404,"corporation":false,"usgs":false,"family":"Pedersen","given":"Emily","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markgraf, Christopher","contributorId":298406,"corporation":false,"usgs":false,"family":"Markgraf","given":"Christopher","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Segarra, Amelie 0000-0002-0551-0013","orcid":"https://orcid.org/0000-0002-0551-0013","contributorId":251846,"corporation":false,"usgs":false,"family":"Segarra","given":"Amelie","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":855529,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":203857,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855530,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Connon, Richard E","contributorId":152478,"corporation":false,"usgs":false,"family":"Connon","given":"Richard E","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":855531,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brander, Susanne M.","contributorId":187546,"corporation":false,"usgs":false,"family":"Brander","given":"Susanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":855532,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237634,"text":"70237634 - 2022 - It’s time for focused in situ studies of planetary surface-atmosphere interactions","interactions":[],"lastModifiedDate":"2022-10-17T14:20:50.197661","indexId":"70237634","displayToPublicDate":"2022-10-17T09:12:39","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"It’s time for focused in situ studies of planetary surface-atmosphere interactions","docAbstract":"A critical gap in planetary observations has been in situ characterization of extra-terrestrial, present-day atmospheric and surface environments and activity. While some surface activity has been observed and some in situ meteorological measurements have been collected by auxiliary instruments on Mars, existing information is insufficient to conclusively characterize the natural processes via concurrent and high-resolution measurement of environmental drivers and activity. Thus, many atmospheric, aeolian, and other surface processes models – which are used to generate key constraints on science and exploration in many areas of planetary investigation—such as surface exposure/erosion estimates, landscape interpretation, and modeling dust storm development—remain untested under non-Earth conditions.\nAnalogous terrestrial processes are often studied intensively via numerical modeling that integrates empirical results from laboratory and/or field studies of process-response interactions between the atmosphere and relevant surface landforms. Incorporation of such in situ measurements into model development has significantly advanced our understanding of atmosphere-surface interactions and related geomorphic processes on Earth, and is poised to do so on other planets. However, to date, such testing and refinement have not been possible in other planetary environments, partially because investigations of this sort require new technologies, mission architectures, and operations designs (e.g., different from large rovers focused on geochemical investigations) to fully address the key gaps in our understanding while keeping cost and risk low.\nFortunately, technological developments in the areas of surface access, instrumentation, and onboard processing/memory now enable small spacecraft to accommodate meteorological and aeolian instrumentation that could collect the needed measurements to fill this critical gap while remaining within typical small spacecraft resource budgets. Furthermore, maturity of our understanding of the broader geologic and atmospheric context on Mars provides a ready framework for ingestion of discrete ground truth measurements into our understanding of the broader and multi-scale martian natural systems and processes. These advancements make addressing key science questions with novel mission concepts feasible, promising results that would significantly advance our understanding of extraterrestrial surface-atmosphere interactions. This summary follows from a community-generated white paper for the ongoing Planetary Science/Astrobiology Decadal Survey, small spacecraft concept development at JPL, and numerous JPL and community discussions.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2022 IEEE Aerospace Conference (AERO)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 IEEE Aerospace Conference","conferenceDate":"March 5-12, 2022","conferenceLocation":"Big Sky, Montana, United States","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/AERO53065.2022.9843357","usgsCitation":"Diniega, S., Barba, N., Giersch, L., Jackson, B., Soto, A., Banfield, D., Day, M.D., Doran, G., Dundas, C., Mischna, M., Rafkin, S., Smith, I.B., Sullivan, R., Swann, C., Titus, T.N., Walker, I.J., Widmer, J., Burr, D., Mandrake, L., Vriend, N., and Williams, K.E., 2022, It’s time for focused in situ studies of planetary surface-atmosphere 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1967–2021","interactions":[],"lastModifiedDate":"2026-03-31T21:17:44.416924","indexId":"pp1874","displayToPublicDate":"2022-10-17T08:45:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1874","displayTitle":"Lessons Learned from Wetlands Research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021","title":"Lessons learned from wetlands research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021","docAbstract":"Depressional wetlands in the Prairie Pothole Region of North America have a long history of investigation owing to their importance in maintaining migratory-bird populations, especially waterfowl. One area of particularly intensive study is the Cottonwood Lake study area in Stutsman County, North Dakota. Studies at the Cottonwood Lake study area began in 1967 and continue through the present (2022). During this period of scientific discovery, meteorological conditions at the Cottonwood Lake study area varied greatly and included one of the most severe droughts of the 20th century and one of the wettest periods in the past 500 years.
Persistent wet conditions that began in 1993 have contributed to state changes in many of the study area’s larger wetlands to lake-like conditions, whereas the smaller wetlands returned to seasonally ponded conditions during relatively dry years interspersed within the longer-term wet period. Additionally, some nonwetland areas of the study area developed wetland plant, hydrology, and soil characteristics during the 1993-to-present (2022) wet period. The persistently high stages of water in the larger wetlands since 1993 contributed to a buildup of dissolved solids and increases in salinity with time following an initial decrease in salinity caused by the dilution of dissolved solids within a larger volume of water. During 2021, drought conditions similar to the 1988 to 1992 period may develop if conditions persist. However, meteorological changes during the past 30 years have persisted long enough to be considered a change in climate conditions at the study area and, if such wet conditions continue, would represent a change from conditions that occurred in the past two millennia.
During the period of study covered in this report (1967–2021), biotic communities responded in a variety of ways to subtle and marked changes in ponded-water depths, permanence, and salinity among the different wetland types in the study area. This report provides background information on the Cottonwood Lake study area and its context within the Prairie Pothole Region, documents techniques used to quantify environmental conditions and biotic communities, describes major trends that have been observed, presents significant findings as “lessons learned,” discusses recent modeling advances, and highlights key messages to managers. The Wetland Continuum concept was used as a framework to place research findings within an ecological context and to highlight the dynamic nature of prairie-pothole wetland ecosystems.
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Area","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":2033,\"properties\":{\"name\":\"Stutsman\",\"state\":\"ND\"},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.2669,47.3268],[-98.8466,47.327],[-98.8392,47.327],[-98.8232,47.3272],[-98.8152,47.3271],[-98.4991,47.327],[-98.467,47.3266],[-98.4677,47.2402],[-98.4685,46.9788],[-98.4412,46.9789],[-98.4396,46.6296],[-98.7894,46.6294],[-99.0379,46.6309],[-99.1616,46.6317],[-99.4122,46.6316],[-99.4498,46.6319],[-99.4477,46.8044],[-99.4476,46.9788],[-99.4821,46.9795],[-99.4824,47.0089],[-99.4822,47.0162],[-99.4821,47.0249],[-99.4826,47.0396],[-99.4827,47.1558],[-99.4801,47.3267],[-99.2669,47.3268]]]}}]}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Aftershocks do not uniformly surround a mainshock, and instead occur in spatial clusters. Spatially variable physical properties of the crust may influence the spatial distribution of aftershocks. I study four aftershock sequences in Southern California (1992 Landers, 1999 Hector Mine, 2010 El Mayor—Cucapah, and 2019 Ridgecrest) to investigate which physical properties are spatially correlated with aftershock occurrence. I find that aftershocks correlate with several properties, including measures of stress and stress change from the mainshock, fault structure, kinematics, seismic velocity, and heat flow. Aftershock spatial density exhibits an order of magnitude or more variation as a function of these properties. I determine simple empirical relations between each of the properties and the aftershock spatial density, and use these relations to construct new spatial models that describe aftershock locations. The new spatial models are a significant improvement over a simple base model, but do not fully capture the dense spatial clustering of aftershocks. Numerous spatially varying physical properties exhibit no (or poor) correlation with aftershock spatial density, including temperature, rock composition, and rheological properties that might be expected to control aftershock occurrence. These results suggest that while spatially variable physical properties appear to influence aftershock locations, more work is necessary in order to establish the connections between aftershock occurrence and the causative physical properties.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024727","usgsCitation":"Hardebeck, J.L., 2022, Physical properties of the crust influence aftershock locations: JGR Solid Earth, v. 10, no. 127, e2022JB024727, 23 p., https://doi.org/10.1029/2022JB024727.","productDescription":"e2022JB024727, 23 p.","ipdsId":"IP-134643","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":412026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Baja California, California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.78554692822775,\n 36.37592047497546\n ],\n [\n -121.78554692822775,\n 32.070443661872034\n ],\n [\n -114.04108196964091,\n 32.070443661872034\n ],\n [\n -114.04108196964091,\n 36.37592047497546\n ],\n [\n -121.78554692822775,\n 36.37592047497546\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"127","noUsgsAuthors":false,"publicationDate":"2022-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":861758,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237635,"text":"70237635 - 2022 - Tectonic subsidence modeling of diachronous transition from backarc to retroarc basin development and uplift during Cordilleran orogenesis, Patagonian-Fuegian Andes","interactions":[],"lastModifiedDate":"2022-10-17T13:34:17.854179","indexId":"70237635","displayToPublicDate":"2022-10-17T08:24:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Tectonic subsidence modeling of diachronous transition from backarc to retroarc basin development and uplift during Cordilleran orogenesis, Patagonian-Fuegian Andes","docAbstract":"Backstripped tectonic basin subsidence histories are critical for interpreting phases of lithospheric deformation and paleoenvironmental change from the stratigraphic record. This study presents new subsidence modeling of the Rocas Verdes Backarc Basin (RVB) and Magallanes-Austral retroarc foreland basin (MAB) of southernmost South America to evaluate along-strike changes in tectonic subsidence related to the Late Jurassic through Miocene history of the Southern Andes. We compiled composite stratigraphic sections for seven basin localities that span 47°–54°S from published sedimentological records of paleoenvironment, paleobathymetry, and geochronology. Modeling results resolve regional trends in basin tectonic subsidence, uplift, and sedimentation rate that influenced the depositional environment during five broad phases of RVB-MAB development: (a) Late Jurassic tectonic subsidence and basin deepening associated with rift-related backarc extension that postdated regional diachronous rift-related magmatism. (b) Southward younging of Early to Late Cretaceous pronounced acceleration in tectonic subsidence interpreted as the initiation of flexural loading and development of the MAB foreland basin system. (c) Late Cretaceous (ca. 85–70 Ma) tectonic uplift within the central foredeep ∼49° to 52°S, coeval with a shift from slope to shelf deposition at these latitudes. (d) A protracted period of low-magnitude basin uplift and relative tectonic quiescence during the Paleogene, with the exception of southernmost localities; and (e) Synchronous latest Oligocene-early Miocene tectonic subsidence linked to basin deepening and transgression across the northern and central basin sectors. Backstripped tectonic subsidence analysis corroborates existing interpretations for orogenic development in the RVB-MAB and sheds new light on complex polyphase basin histories where extension precedes convergence.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021TC006891","usgsCitation":"VanderLeest, R.A., Fosdick, J.C., Malkowski, M., Romans, B.W., Ghiglione, M.C., Schwartz, T.M., and Sickmann, Z.T., 2022, Tectonic subsidence modeling of diachronous transition from backarc to retroarc basin development and uplift during Cordilleran orogenesis, Patagonian-Fuegian Andes: Tectonics, v. 41, no. 10, e2021TC006891, 29 p., https://doi.org/10.1029/2021TC006891.","productDescription":"e2021TC006891, 29 p.","ipdsId":"IP-129371","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":446107,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021tc006891","text":"External Repository"},{"id":408377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina, Chile","otherGeospatial":"Andes Mountains, Magallanes-Austral retroarc foreland basin, Patagonia, Rocas Verdes Backarc Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.76391601562499,\n -54.1881554810715\n ],\n [\n -68.719482421875,\n -53.82011176955965\n ],\n [\n -70.72998046875,\n -53.59250480903936\n ],\n [\n -72.158203125,\n -52.29504228453733\n ],\n [\n -72.037353515625,\n -50.90303283111256\n ],\n [\n -71.861572265625,\n -50.06419173665909\n ],\n [\n -71.905517578125,\n -49.48953847306649\n ],\n [\n -71.641845703125,\n -48.2100321223404\n ],\n [\n -71.56494140625,\n -47.10752278534248\n ],\n [\n -71.455078125,\n -46.62680639535518\n ],\n [\n -73.212890625,\n -46.82261666880492\n ],\n [\n -73.58642578125,\n -48.056053763981225\n ],\n [\n -73.970947265625,\n -49.2032427441791\n ],\n [\n -74.02587890625,\n -50.52041218671901\n ],\n [\n -73.8720703125,\n -51.48138289610098\n ],\n [\n -73.41064453125,\n -52.72963909783716\n ],\n [\n -73.212890625,\n -53.3767749750602\n ],\n [\n -71.575927734375,\n -54.15600109028492\n ],\n [\n -70.46630859375,\n -54.81967870427068\n ],\n [\n -66.148681640625,\n -54.98391819036322\n ],\n [\n -65.9619140625,\n -54.463652645044775\n ],\n [\n -66.76391601562499,\n -54.1881554810715\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-13","publicationStatus":"PW","contributors":{"authors":[{"text":"VanderLeest, Rebecca A.","contributorId":229447,"corporation":false,"usgs":false,"family":"VanderLeest","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":854741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fosdick, Julie C.","contributorId":297956,"corporation":false,"usgs":false,"family":"Fosdick","given":"Julie","email":"","middleInitial":"C.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":854742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Malkowski, Matthew A.","contributorId":221753,"corporation":false,"usgs":false,"family":"Malkowski","given":"Matthew A.","affiliations":[{"id":40415,"text":". Department of Geological Sciences, Stanford University, Stanford CA 94305","active":true,"usgs":false}],"preferred":false,"id":854743,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romans, Brian W.","contributorId":297958,"corporation":false,"usgs":false,"family":"Romans","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854744,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ghiglione, Matias C.","contributorId":297961,"corporation":false,"usgs":false,"family":"Ghiglione","given":"Matias","email":"","middleInitial":"C.","affiliations":[{"id":64468,"text":"CONICET-Universidad de Buenos Aires","active":true,"usgs":false}],"preferred":false,"id":854745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwartz, Theresa Maude 0000-0001-6606-4072","orcid":"https://orcid.org/0000-0001-6606-4072","contributorId":245180,"corporation":false,"usgs":true,"family":"Schwartz","given":"Theresa","email":"","middleInitial":"Maude","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":854746,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sickmann, Zachary T.","contributorId":292770,"corporation":false,"usgs":false,"family":"Sickmann","given":"Zachary","email":"","middleInitial":"T.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":854747,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70259621,"text":"70259621 - 2022 - A geophysical characterization of structure and geology of the Northern Granite Springs Valley Geothermal System, Northwestern Nevada","interactions":[],"lastModifiedDate":"2024-10-17T12:21:18.923074","indexId":"70259621","displayToPublicDate":"2022-10-17T07:20:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"A geophysical characterization of structure and geology of the Northern Granite Springs Valley Geothermal System, Northwestern Nevada","docAbstract":"The northern Granite Springs Valley in northwestern Nevada is the focus of recent studies for its potential for hosting undiscovered geothermal resources. Although the area lacks definitive surface manifestations of an active hydrothermal system, previous studies identify this region as having potential for hosting a blind geothermal resource, based on elevated subsurface temperatures and a favorable structural framework of the area. As part of the Nevada Play Fairway Project, we conducted high resolution geophysical surveys to better characterize the valley’s geothermal resources. This included ground magnetic, gravity, magnetotelluric, and rock property studies aimed at mapping and modeling subsurface geology and structure. \nVarious derivative and filtering methods were employed to delineate buried faults and contacts from gravity and magnetic data. A depth to basement gravity inversion reveals that the basin is deepest on the west side of the valley. Flanking the basin to the east is a prominent gravity high interpreted as an intra-basin horst. A new high-resolution ground magnetic survey reveals a prominent elongate NW-trending magnetic high, interpreted as an unexposed subsurface dike swarm situated near the boundary between the basin and horst and confined to basement. \nGeophysical models help constrain basin fill comprised of Cenozoic sediments and volcanic rocks. These overlie Mesozoic crystalline basement that, in the west, consists of Cretaceous granitic intrusives and, to the east, dominantly Mesozoic metasedimentary rocks. The contact between these basement lithologies is not certain but inferred to coincide with the geophysically mapped dike swarm. This is partly supported by the fact that the dikes, as projected along strike to the northwest, intersect the contact between the Cretaceous intrusions and older Mesozoic basement rocks to the north of the study area.\nAlthough the age of the inferred dike swarm is not known, the trend of the anomaly is consistent with some of the Tertiary dikes in the nearby Sahwave Range, suggesting emplacement predated or was coeval with early development of the basin. The coincidence of the geothermal system, horst, dike swarm, and terminating normal fault zone suggests that basin tectonics and hydrothermal activity were influenced by both pre-existing basement structure and recent deformation. This relationship may pertain more generally to other hydrothermal settings throughout the Great Basin. If so, future efforts focused on mapping basement geology and structure may prove important to understanding underlying structural controls on geothermal systems.\nThis work is supporting the next phase of research involving additional 3D geophysical and geologic modeling under the U.S. Department of Energy funded INGENIOUS project. The focus of this new work is on the western flank and structural corners of the horst block, based on evidence from detailed geophysical structural mapping, new shallow temperature data, and detailed 3D geologic and geophysical modeling, all aimed at identifying sites for temperature gradient drilling that may intersect zones with sufficient permeability and temperature to support geothermal development.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Glen, J.M., Peacock, J., Earney, T.E., Schermerhorn, W., Siler, D.L., Faulds, J., and DeAngelo, J., 2022, A geophysical characterization of structure and geology of the Northern Granite Springs Valley Geothermal System, Northwestern Nevada: Geothermal Resources Council Transactions, v. 46, p. 700-720.","productDescription":"21 p.","startPage":"700","endPage":"720","ipdsId":"IP-131199","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":462928,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034630"},{"id":462943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916024,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schermerhorn, William 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":303003,"corporation":false,"usgs":false,"family":"Schermerhorn","given":"William","affiliations":[{"id":65593,"text":"formerly at USGS","active":true,"usgs":false}],"preferred":false,"id":916025,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916026,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Faulds, James","contributorId":299513,"corporation":false,"usgs":false,"family":"Faulds","given":"James","affiliations":[{"id":64865,"text":"Great Basin Center for Geothermal Energy; Nevada Bureau of Mines and Geology; University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":916027,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916028,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237654,"text":"70237654 - 2022 - Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States","interactions":[],"lastModifiedDate":"2023-11-08T16:36:31.345339","indexId":"70237654","displayToPublicDate":"2022-10-17T07:17:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5836,"text":"Journal of Hydrology X","onlineIssn":"2589-9155","active":true,"publicationSubtype":{"id":10}},"title":"Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States","docAbstract":"While nonstationary flood frequency analysis (NSFFA) methods have proliferated, few studies have rigorously compared them for modeling changes in both the central tendency and variability of annual peak-flow series, also known as the annual maximum series (AMS), in hydrologically diverse areas. Through Monte Carlo experiments, we appraise five methods for updating estimates of 10- and 100-year floods at gauged sites using synthetic records based on sample moments and change trajectories of observed AMS in the conterminous United States (CONUS). We compare two methods that consider changes in both central tendency and variability - a Gamma generalized linear model estimated with weighted least squares and the Generalized Additive Model for Location, Scale, Shape (GAMLSS) - with a distribution-free approach (quantile regression), and baseline cases assuming stationarity or only changes in central tendency.
‘Trend-space’ plots identify realistic AMS changes for which modeling trends in both central tendency and variability were warranted based on fractional root mean squared errors (fRMSE). They also reveal statistical properties of AMS under which NSFFA models perform especially well or poorly. For instance, quantile regression performed especially well (poorly) under strong negative (positive) skewness. Although the nonstationary LP3 distribution accommodates most AMS with trends well, the sensitivity of NSFFA model performance to different sample moments and trends suggests the need for more flexibility in prescribing design-flood adjustments in the CONUS. A follow-up comparison of regional NSFFA models pooling at-site AMS would further illuminate NSFFA guidance, especially for AMS with properties less conducive to NSFFA modeling, such as positive skewness and increasing variability.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2021.100115","usgsCitation":"Hecht, J., Barth, N.A., Ryberg, K.R., and Gregory, A., 2022, Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States: Journal of Hydrology X, v. 17, 100115, 24 p., https://doi.org/10.1016/j.hydroa.2021.100115.","productDescription":"100115, 24 p.","ipdsId":"IP-129280","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":446110,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2021.100115","text":"Publisher Index Page"},{"id":435655,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PVRCDS","text":"USGS data release","linkHelpText":"Data for simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States"},{"id":408467,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.32031249999997,\n 24.5271348225978\n ],\n [\n -65.91796875,\n 24.5271348225978\n ],\n [\n -65.91796875,\n 50.958426723359935\n ],\n [\n -128.32031249999997,\n 50.958426723359935\n ],\n [\n -128.32031249999997,\n 24.5271348225978\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hecht, Jory Seth","contributorId":298019,"corporation":false,"usgs":true,"family":"Hecht","given":"Jory Seth","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":854875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barth, Nancy A. 0000-0002-7060-8244 nabarth@usgs.gov","orcid":"https://orcid.org/0000-0002-7060-8244","contributorId":298020,"corporation":false,"usgs":true,"family":"Barth","given":"Nancy","email":"nabarth@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":854876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gregory, Angela 0000-0002-9905-1240","orcid":"https://orcid.org/0000-0002-9905-1240","contributorId":45018,"corporation":false,"usgs":true,"family":"Gregory","given":"Angela","email":"","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854938,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237663,"text":"70237663 - 2022 - Formation of orogenic gold deposits by progressive movement of a fault-fracture mesh through the upper crustal brittle-ductile transition zone","interactions":[],"lastModifiedDate":"2022-10-18T11:56:10.635076","indexId":"70237663","displayToPublicDate":"2022-10-17T06:48:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Formation of orogenic gold deposits by progressive movement of a fault-fracture mesh through the upper crustal brittle-ductile transition zone","docAbstract":"Orogenic gold deposits are comprised of complex quartz vein arrays that form as a result of fluid flow along transcrustal fault zones in active orogenic belts. Mineral precipitation in these deposits occurs under variable pressure conditions, but a mechanism explaining how the pressure regimes evolve through time has not previously been proposed. Here we show that extensional quartz veins at the Garrcon deposit in the Abitibi greenstone belt of Canada preserve petrographic characteristics suggesting that the three recognized paragenetic stages formed within different pressure regimes. The first stage involved the growth of interlocking quartz grains competing for space in fractures held open by hydrothermal fluids at supralithostatic pressures. Subsequent fluid flow at fluctuating pressure conditions caused recrystallization of the vein quartz and the precipitation of sulfide minerals through wall-rock sulfidation, with some of the sulfide minerals containing microscopic gold. These pressure fluctuations between supralithostatic to near-hydrostatic conditions resulted in the post-entrapment modification of the fluid inclusion inventory of the quartz. Late fluid flow occurred at near-hydrostatic conditions and resulted in the formation of fluid inclusions that have not been affected by post-entrapment modification as pressure conditions never returned to supralithostatic conditions. This late fluid flow is interpreted to have formed the texturally late, coarse native gold that occurs along quartz grain boundaries and in open spaces. The systematic evolution of the pressure regimes in orogenic gold deposits such as Garrcon can be explained by relative movement of fault-fracture meshes across the base of the upper crustal brittle-ductile transition zone. We conclude that early vein quartz in orogenic deposits is precipitated at near-lithostatic conditions whereas the paragenetically late gold is introduced at distinctly lower pressure.
This study characterized the concentration and quality of dissolved organic carbon (DOC) in the McKenzie River, a relatively undeveloped watershed in western Oregon, and its link to forming disinfection by-products (DBPs) in treated drinking water. The study aimed to identify the primary source(s) of DOC in source water for the Eugene Water & Electric Board’s (EWEB) conventional treatment plant on the McKenzie River near river mile 11, upstream of Hayden Bridge. The two classes of regulated compounds examined—trihalomethanes (THMs) and haloacetic acids (HAAs)—form when organic carbon in raw source water reacts with chlorine and (or) bromine during water treatment.
The objectives of the study were to:
Eleven storms were sampled synoptically in upstream-to-downstream fashion to provide a “snapshot” of water quality conditions at four sites on the McKenzie River from Frissell Bridge (6 miles downstream from Trail Bridge Reservoir) to the EWEB water treatment plant at Hayden Bridge and nine contributing tributaries. Storms included late summer and early autumn “first flush” events and late autumn, winter, and spring storms spanning a range in streamflows from 3,000 to 26,000 cubic feet per second as measured in the main stem McKenzie River at the EWEB water intake.
Water samples were analyzed for DOC concentrations and optical properties (fluorescence and ultraviolet absorbance [UVA]) across a range of wavelengths to characterize the quantity and quality of dissolved organic matter (DOM) in the McKenzie River at the drinking water intake and upstream locations. Paired sets of source and finished water samples were collected at the EWEB treatment plant to identify DOC quality parameters in raw source water that might predict DBP concentrations in finished drinking water.
DOC concentrations were relatively low in the McKenzie River (0.4–3 milligrams per liter [mg/L]; average 1.5 mg/L) but much higher in the tributaries. The highest DOC concentrations occurred during “first flush” storms in October 2012 and September 2013; the highest value (16 mg/L) was measured at the 52nd Street stormwater outfall. The average DOC concentration in the lower basin-tributaries was 3.8 mg/L; three middle basin tributaries—Quartz, Gate, and Haagen Creeks, which drain private forestland with less coniferous forest compared with other higher elevation tributaries— had slightly lower average DOC concentrations (2.8 mg/L). These middle-basin watersheds may be important sources of DOC and DBP precursors to the McKenzie River, even more so than the lower basin tributaries, depending on their flows (and loads). This is particularly true after the September 2020 Holiday Farm fire, which burned much of this area.
DOC concentrations increased 68 percent in the McKenzie River between the uppermost reference site at Frissell Bridge and Vida; this includes drainage from Quartz Creek, Blue River Lake and Cougar Reservoir, which all contributed DOC to the main stem. In contrast, the lowermost tributaries draining most of the agricultural and urban land did not have a large effect on DOC in the McKenzie River despite their higher DOC concentrations because of their presumed relatively low streamflows and, consequently, DOC loads. Apart from the continuous flow monitors in the McKenzie River and some tributaries (Blue River and South Fork McKenzie River, and streamflow at Hayden Bridge and Vida, Camp Creek and some other locations), streamflow was not assessed during sample collection for this study. This lack of streamflow data precludes a detailed analysis of loads, which is discussed in the future studies section.
All DBP concentrations in finished drinking water were less than EPA maximum contaminant levels (MCLs) of 0.080 mg/L for the four trihalomethanes (THM4) and 0.060 mg/L for five haloacetic acids (HAA5). During the 11 storm sampling events the maximum summed concentrations were about 0.040 mg/L for both THM4 and HAA5. Compliance monitoring samples, collected separately by EWEB, yielded some higher concentrations—0.046 mg/L THM4 and 0.047 HAA5—during the December 2012 storm. The corresponding benchmark quotient (BQ) values, which indicate how close a measured DBP concentration is to the MCL, were 0.58 and 0.78, respectively, for THM4 and HAA5. Compared with a similar 2007–08 McKenzie River study that did not target storm events, concentrations of THM4 and HAA5 in finished water were 68 percent and 33 percent higher, respectively, during the current study.
Due to the high dilution rates in the McKenzie River main stem, many of the individual fluorescence excitation-emission measurements were low (<0.1 Raman units) and approached analytical detection limits. Parallel factor analysis (PARAFAC) resulted in a five-component model (C1–C5) that represents five unique organic fluorophores. Components C1, C2, and C3 represent DOM associated with soil-derived, humic-like, more degraded organic matter. In contrast, components C4 and C5 represent “fresher” DOM, derived from terrestrial and aquatic plants, including algae and cyanobacteria that are common in the McKenzie River and its tributaries and reservoirs. The fluorescence data and PARAFAC modeling suggest that most of the DOC in the McKenzie River originated from terrestrial sources (primarily components C1 and C2). The largest increases in DOC in the main stem occurred in the reach upstream of Vida, from inflows by Quartz Creek, Blue River, South Fork McKenzie River, and other tributaries.
Concentrations of DBPs in EWEB’s finished drinking water were positively correlated with DOC concentrations in raw source water (THM4, p<0.05; HAA5, p<0.01) for paired samples collected 12−24 hours apart. DOC concentrations were significantly positively correlated (p<0.001) with laboratory-based fluorescent dissolved organic matter (fDOM) measurements, suggesting fDOM as a useful parameter for monitoring and predicting DOC concentration in surface water and DBP concentrations in finished water.
Of all the PARAFAC components in surface water, C5 had the highest correlations with DBPs in finished water (rho = 0.77–0.84, p<0.01), followed by components C1 and C2 (rho = 0.75 and 0.71, respectively, p<0.01). This C5 carbon is associated with recently produced DOM, possibly from decomposed terrestrial and aquatic vegetation. Model loadings of these three components were considerably higher in the sampled tributaries relative to the main stem McKenzie River, with most of the observed increases in the main stem apparent at Vida. This points to Quartz Creek or other tributaries in the reach between Frissell Bridge and the sampling site near Vida (South Fork McKenzie and Blue Rivers) as potentially key contributors of DOM source material that leads to the production of DBPs in treated drinking water. A limited load analysis showed that the reservoirs contributed 8–37 percent of the instantaneous DOC loads observed at Vida at the time of sampling, which suggests other sources such as Quartz Creek and other streams in the reach between Frissell Bridge and Vida are more important.
Random forest analyses identified PARAFAC components C1 and C5 and fluorescence peaks A, C, M, T and N as the best predictors for HAA5 concentrations in finished drinking water, explaining 62.5 percent of the variation. The best predictors for THM4 were C1, C4 + C5, and peaks T, A, and N, which explained 33 percent of the variation.
Several land cover and vegetation classes were correlated with DOC concentration and other optical measurements. The percentage of evergreen forest in each of the subwatersheds sampled was negatively correlated (p<0.001) with DOC concentration and many optical indicators of DOM quantity: UVA254, fDOM, and all of the fluorescence peaks. In contrast, mixed (deciduous) forest was positively correlated (p<0.001) with DOC, fDOM, UVA254, and several fluorescence peaks, demonstrating the importance of deciduous leaf fall in generating DOC and DBP precursors.
The high level of human activities in the middle and lower portion of the basin—including timber harvesting and road construction on private forestland, agricultural, rural, industrial, and urban development—have resulted in the greatest loss in native coniferous and mixed deciduous forests in the basin. DOC loading from these tributaries and reservoir releases, which contain DOC from terrestrial and aquatic productivity, both enrich the McKenzie River. Concentrations of DOC increased an average of 71 percent (range 30–120 percent) in the McKenzie River between Frissell Bridge, the upstream reference site, and Vida. PARAFAC components C1, C2, and C5—which were correlated with DBPs in finished water—increased, on average, 109–136 percent (range 20–250 percent) in this same Frissell-to-Vida reach. These increases occur from input of tributaries in the middle basin such as Quartz Creek and others, as noted above.
Future monitoring, field, and lab studies can improve our understanding of seasonal and spatial sources of organic carbon contributing DBP precursors to the McKenzie River and allow detection of long-term trends resulting from the recent Holiday Farm Fire, which burned 173,393 acres of forestland, including riparian areas along the main stem, and numerous structures, homes, and outbuildings in September 2020. Future studies could examine DOC fluxes and flushing of carbon from the watershed, investigate the role of precipitation amount and intensity in mobilizing carbon and sediment, and evaluate impacts to aquatic communities and human health as part of a post-fire assessment. Other areas ripe for study include evaluating the impacts of potential temperature increases on carbon sequestration and decomposition in the burned and unburned forests and identifying practices that foster sequestration of carbon in forest soils.
The use of fluorescence sensors such as fDOM to monitor the concentration and composition of raw water supplies may be improved for detection of specific DBP precursors, to provide continuous and real-time information to treatment plant operators. Future studies that monitor DOM amount and quality, and DBP Formation Potential (FP), particularly during storm events, paired with streamflow measurements, as suggested above, could help identify areas that contribute high DOC loads and thus help managers identify the key areas to focus restoration activities. Other studies could examine treatment options for currently regulated DBPs and potentially unregulated compounds, including advanced biological treatments for their removal.
This study was a collaboration between the U.S. Geological Survey (USGS) and EWEB in Eugene, Oregon, with additional funding provided from USGS Cooperative Matching Funds Program.
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U.S. Geological Survey
Oil spills can inflict mortality and injury on bird populations; many of these deaths involve starvation resulting from thermoregulatory costs incurred by oiling of birds’ feathers. However, the fates and responses of sublethally oiled birds are poorly known. Due to this knowledge gap and the potential for birds to die far from the spill site, resource risk and injury assessors need tools to make informed estimates for delayed deaths and lost reproductive capacity in these birds. Focusing on the thermoregulatory cost of oiled feathers, we present a model addressing one facet of the effects of sublethal oiling on birds. Using mallard-like ducks as a model organism, we combined values from previous laboratory studies of oiled birds with a modified version of an existing temperature-influenced avian migration energetics model. Using this model, we examined the potential effects of oiling on general migration patterns, changes in energetic gains required to compensate for oiling, and starvation. We assessed all metrics across multiple oiling severities; we assessed starvation across both oiling severity and body condition. Median estimates for delays in spring migration were one to two months for trace and lightly oiled birds, and we predicted arrested spring migration in moderately oiled birds. Median estimates of required increases in energetic gains to offset costs of increased thermoregulation ranged from 20.3% to 88.6% depending on severity of oiling. We predicted starvation within four weeks for most combinations of oiling severity and body condition at the median predicted minimum wintering temperature of unoiled birds (-4.9°C). However, at the average winter temperature of the southernmost model latitude (10.8°C), we predicted only moderately oiled birds in less-than-excellent body condition had the potential to starve within a four-week time frame. Due to the potential for even trace oiling to delay spring migration and decrease body condition, the thermoregulatory costs of sublethal oiling during spring migration could reduce a bird's reproductive capacity. Future research integrating this initial energetics-based model into a spatially explicit, population scale migration model could provide additional insight into the potential effects of sublethal oiling on reproduction and survival. Such an integrated model could strengthen risk predictions and injury assessments for birds subjected to sublethal oiling.