The segment of the Upper Missouri River between Fort Peck Dam and the headwaters of Lake Sakakawea is home to a population of the endangered Scaphirhynchus albus (pallid sturgeon). Lack of population growth (recruitment failure) has been attributed to inadequate dispersal distance of larvae between spawning locations and the headwaters of Lake Sakakawea, where conventional wisdom holds that anoxic water-quality conditions are lethal to larval sturgeon. River-management objectives to recover pallid sturgeon in this segment therefore focus on increasing available drift distance, decreasing drift rate, increasing larval development rate, or a combination of these objectives. Understanding of channel morphological conditions along this about 400-kilometer segment may provide insight into upstream spawning habitat potential (where sturgeon likely spawn) and into geomorphic factors that may contribute to flow complexity, hence drift rate. This report documents a continuous geomorphic classification of the Fort Peck segment of the Upper Missouri River using remotely sensed datasets to provide contextual information about spatial variations potentially affecting pallid sturgeon reproductive ecology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235109","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Missouri River Recovery Program","usgsCitation":"Jacobson, R.B., Elliott, C.M., and Bulliner, E., 2023, Geomorphic classification framework for assessing reproductive ecology of Scaphirhynchus albus (pallid sturgeon), Fort Peck segment, Upper Missouri River, Montana and North Dakota: U.S. Geological Survey Scientific Investigations Report 2023–5109, 15 p., https://doi.org/10.3133/sir20235109.","productDescription":"Report: vi, 15 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-155746","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":421828,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92HVKT3","text":"USGS data release","linkHelpText":"Geomorphic variables for classification of the Upper Missouri River, Montana and North Dakota"},{"id":421829,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235109/full","linkFileType":{"id":5,"text":"html"}},{"id":421827,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5109/images/"},{"id":421824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5109/coverthb.jpg"},{"id":421825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5109/sir20235109.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5109"},{"id":421826,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5109/sir20235109.XML","linkFileType":{"id":8,"text":"xml"}}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Fort Peck segment, Upper Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.31605153925373,\n 48.4037537878354\n ],\n [\n -106.52859788507773,\n 48.4037537878354\n ],\n [\n -106.60549394122786,\n 47.047516337061694\n ],\n [\n -103.30750753301488,\n 47.08228719733623\n ],\n [\n -103.31605153925373,\n 48.4037537878354\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
Anadromous fish returning to the lower American River are restricted to 36 kilometers of free-flowing river between Nimbus Dam and American River’s confluence with the Sacramento River, California. Salmon in the American River provide an important freshwater recreational fishery. However, annual salmon production in the American River in recent years has been low relative to the mid-1990s (Surface Water Resources, Inc., 2001). To investigate the low production of fall-run Chinook salmon (Oncorhynchus tshawytscha), the Bureau of Reclamation requested that the U.S. Geological Survey apply the Stream Salmonid Simulator (S3) model to the population of fall-run Chinook salmon on the American River.
The American River was chosen among seven candidate Sacramento Basin rivers for S3 application. The American River was selected because of its management and public interest, recently low anadromous fish production, and rich time series of key demographic data needed for S3 application. Data that were not available, however, were empirical estimates on juvenile salmon habitat suitability in the American River. Therefore, a large component of applying S3 to the American River was devoted to the estimation of juvenile salmon habitat suitability and capacity. This entailed snorkeling the lower American River for 3 weeks in March 2021 during the early out-migration period for juvenile Chinook salmon. These efforts were fruitful and showed that the typically small fish (<55 millimeters) in the American River preferred much shallower depths than predicted by habitat suitability criteria derived from the literature for this population. Having empirical estimates on juvenile salmon in the American River provided a solid foundation from which to simulate the population using the S3 model.
The S3 model is a spatially explicit population model that runs on a daily time step to simulate redd superimposition, egg maturation, fry emergence and the subsequent growth, survival, and emigration of juvenile Chinook salmon from the river. The key features of this model relevant to this report include (1) a temperature-dependent bioenergetics model driving daily growth rates; (2) density-dependent dynamics that are influenced by the effect of flow on suitable habitat area; and (3) within-year habitat, river flow, and water temperature effects specific to spawning, egg incubation, and fry, parr, and smolt life stages. We used estimates of spawning escapement and geo-referenced redd locations to quantify the spatial and temporal distribution of female spawners for brood years 2014–19. These estimates of female spawners initiate the simulation of each year’s juvenile salmon emergence and emigration over a spatial domain extending from Nimbus Dam to the river’s confluence with the Sacramento River.
Using weekly estimates of juvenile salmon abundance and size (fork length) that passed the Watt Avenue fish trap (river kilometer 14.7), we calibrated the S3 model by estimating three key demographic parameters for each year, y: (1) Sy, the average daily survival probability, (2) M0y, the intercept for density-dependence in movement, representing the average daily probability of remaining in a habitat at zero abundance, and (3) Cy, the average daily proportion of maximum consumption. These parameters were obtained by minimizing the Mallow’s distance (Lupu and others, 2017) between distributions of weekly abundances and sizes of fish at the traps and weekly simulated abundances and sizes (by S3). Investigation of model fit showed excellent agreement between simulated annual abundances and the abundance of fish passing the fish trap. However, when we compared weekly abundances at the fish trap, S3 under-predicted peaks and over-predicted troughs in the time series of weekly abundances at the fish trap. Thus, some unknown within-year effects have yet to be identified and incorporated in the S3 model. Identifying these important effects and incorporating them in the S3 model would help explain the lack of fit between estimated and simulated weekly abundances.
We estimated parameters for 6 years that included a wide range of female spawner abundances (3,057–10,753) and water year types (Critical–Wet). We contrast our estimated parameters to the corresponding number of female spawners and the water year type for the Sacramento Valley. By happenstance, years having higher annual spawner abundances concurred with Critical to Dry water year types. Estimates of survival trended lower with higher spawner abundances and Critical to Dry conditions. In contrast, the extremely wet water year of 2017 had the lowest M0y, suggesting less density-dependence in fish movement, and the lowest Cy, suggesting lower average consumption in this year. When this high-flow year was excluded, a trend towards higher probabilities of fish remaining in a habitat at low abundance and lower proportions of maximum consumption was apparent from Critical to Wet conditions, but only 5 years of data were included. Except for 2017, daily proportions of maximum consumption were relatively high (Cy > 0.83), suggesting that fish were feeding at reasonably high proportions relative to the expected maximum consumption as defined by the “Wisconsin” bioenergetics model (Stewart and Ibarra, 1991).
Survival estimates from fry emergence to outmigration at the Sacramento River confluence were generally low when integrated over time. The highest daily survival probability was Sy = 0.93 in 2019, or 50 percent total mortality after 10 days. In contrast, our lowest daily survival probability was Sy = 0.74 in 2015, or 95 percent total mortality after 10 days. Consequently, even our highest estimated daily survival probability might be considered low. This is especially true given that Sy was estimated over a relatively short distance (<14.7 kilometers) from emergence to the Watt Avenue fish trap. Several factors, including our assumed and relatively high daily egg survival rate of 0.9975, could influence juvenile survival estimates. For example, an egg survival rate of 0.9975 results in 3-percent total mortality after 10 days. Egg mortality estimates used in S3 calibration were approximated from egg survivorship studies in the Yakima River, Washington (Johnson and others, 2012), and remains one of the greater uncertainties in S3 when estimating survival across life stages. By including bona fide estimates of egg survival in S3 simulations, the validity of the S3’s current daily egg survival rate could be assessed specifically for the American River. Tagging studies also could provide S3 with direct estimates of juvenile survival and movement; survival during egg incubation then could be estimated indirectly via model fitting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231060","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., Hatton, T.W., Smith, C.D., and Hannon, J.M., 2023, Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California: U.S. Geological Survey Open-File Report 2023–1060, 35 p., https://doi.org/10.3133/ofr20231060.","productDescription":"ix, 35 p.","onlineOnly":"Y","ipdsId":"IP-141661","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":421858,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.XML"},{"id":421857,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1060/images"},{"id":421856,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1060"},{"id":421855,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1060"},{"id":421854,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1060/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"American River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.5064051133351,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.727216763718815\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
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Efforts to incorporate bioavailability adjustments into regulatory water quality criteria in the United States have included four major procedures: hardness-based single-linear regression equations, water-effect ratios (WERs), biotic ligand models (BLMs), and multiple-linear regression models (MLRs) that use dissolved organic carbon, hardness, and pH. The performance of each with copper (Cu) is evaluated, emphasizing the relative performance of hardness-based versus MLR-based criteria equations. The WER approach was shown to be inherently highly biased. The hardness-based model is in widest use, and the MLR approach is the US Environmental Protection Agency's (USEPA's) present recommended approach for developing aquatic life criteria for metals. The performance of criteria versions was evaluated with numerous toxicity datasets that were independent of those used to develop the MLR models, including olfactory and behavioral toxicity, and field and ecosystem studies. Within the range of water conditions used to develop the Cu MLR criteria equations, the MLR performed well in terms of predicting toxicity and protecting sensitive species and ecosystems. In soft waters, the MLR outperformed both the BLM and hardness models. In atypical waters with pH <5.5 or >9, neither the MLR nor BLM predictions were reliable, suggesting that site-specific testing would be needed to determine reliable Cu criteria for such settings. The hardness-based criteria performed poorly with all toxicity datasets, showing no or weak ability to predict observed toxicity. In natural waters, MLR and BLM criteria versions were strongly correlated. In contrast, the hardness-criteria version was often out of phase with the MLR and, depending on waterbody and season, could be either strongly overprotective or underprotective. The MLR-based USEPA-style chronic criterion appears to be more generally protective of ecosystems than other models.
LANDFIRE Help Desk
Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Due to Idaho’s inland location approximately 350 miles from the Pacific Ocean and its 80 recognized mountain ranges, the State’s climate varies widely, with maritime influence in the northern and western parts of Idaho and continental influence on the eastern side. The weather in the abundant mountains is unpredictable and often associated with natural hazards such as severe thunder and lightning storms leading to flooding, landslides, and wildfires. Issues important to Idaho’s economy include river, stream, and forest resource management, and infrastructure and construction management. Idaho participated in the U.S. Geological Survey 3D Elevation Program (3DEP) in 2016, the State’s first 3DEP project. The success of this project led to development of the Idaho Statewide Lidar Plan. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233035","usgsCitation":"Carlson, T., 2023, The 3D Elevation Program—Supporting Idaho’s economy: U.S. Geological Survey Fact Sheet 2023–3035, 2 p., https://doi.org/10.3133/fs20233035.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146166","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":421592,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3035/fs20233035.XML"},{"id":421591,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3035/images/"},{"id":421590,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233035/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Avalanche forecasters and snow scientists use physically based snow stratigraphy models to fill spatial and temporal gaps in field-based snow profile observations. These models generate stratigraphy predictions using meteorological input from automated weather stations (AWS) or numerical weather prediction (NWP) models. The choice of input data is often determined by data availability or convenience instead of giving full consideration to the most appropriate source for a particular application. For example, while AWS may provide weather observations that better represent a particular site, they have large up-front costs and require specialized personnel to service and maintain. The goal of this study is to quantify the accuracy of snow stratigraphy produced by the SNOWPACK model driven by different input data, with a particular focus on cost-benefit analysis for operational avalanche forecasting. We generate modeled snow profiles at a field site in the Bridger Range of southwestern Montana, USA, using a) observations from an AWS at the field site and b) NWP output from the NOAA High-Resolution Rapid Refresh (HRRR) model. Validation data consist of a season-long time series of 10 manual snow profiles. We use dynamic time-warping (DTW) to quantify the overall and grain-type categorized similarities between modeled and in-situ observed profiles that are collocated in time and in space. Based on the similarity results, we present a cost-benefit analysis that considers the cost of installing and maintaining an AWS alongside the improved representation of snow depth, grain size, and weak layer types.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Palomaki, R.T., and Miller, Z., 2023, Assessing snowpack stratigraphy accuracy based on different input data: Insights for operations avalanche forecasting, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 287-294.","productDescription":"8 p.","startPage":"287","endPage":"294","ipdsId":"IP-157004","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421974,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=2889","linkFileType":{"id":5,"text":"html"}},{"id":421975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bridger Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94066722835859,\n 45.83656619518504\n ],\n [\n -110.94066722835859,\n 45.83190909512919\n ],\n [\n -110.92970520296628,\n 45.83190909512919\n ],\n [\n -110.92970520296628,\n 45.83656619518504\n ],\n [\n -110.94066722835859,\n 45.83656619518504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Palomaki, Ross T. 0000-0002-3304-9914","orcid":"https://orcid.org/0000-0002-3304-9914","contributorId":299761,"corporation":false,"usgs":false,"family":"Palomaki","given":"Ross","email":"","middleInitial":"T.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":881596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":881597,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248045,"text":"70248045 - 2023 - Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting","interactions":[],"lastModifiedDate":"2023-10-18T15:42:28.662716","indexId":"70248045","displayToPublicDate":"2023-10-08T10:31:48","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting","docAbstract":"Wet snow avalanches are predicted to increase in frequency with climate change and are often difficult to forecast. Improving our understanding of wet snow avalanche timing will help with current forecasting challenges. The onset of wet snow avalanching is closely tied to the temporal progression of liquid water flow through the seasonal snowpack. Measuring the flow of water through the snowpack in-situ is difficult due to the spatial variability of snow depth and structure. However, physical snowpack models can potentially simulate this process. The accuracy of snowpack models is heavily dependent upon the quality of the meteorological input data. A thorough investigation of model output differences using several different meteorological inputs for forecasting water movement and wet snow avalanches has not yet been thoroughly investigated. Here, we evaluate indicators of regional wet snow avalanches produced by the SNOWPACK model using different meteorological input. We compare the accuracy of SNOWPACK modeled outputs driven by two different numerical weather prediction (NWP) forecast models: the High-Resolution Deterministic Prediction System (HRDPS) and the North American Model (NAMnest). We leverage hourly automated weather station data, daily operational avalanche observations along the Going-to-the-Sun Road in Glacier National Park, Montana, United States, and in-situ snow stratigraphy and wetness profile observations to validate the SNOWPACK modeled outputs. This research is directly applicable to avalanche forecasting operations and future avalanche research as wet snow avalanche timing evolves due to climate change.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Miller, Z., Horton, S., Mitterer, C., and Peitzsch, E.H., 2023, Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 264-271.","productDescription":"8 p.","startPage":"264","endPage":"271","ipdsId":"IP-157003","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421971,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=2886","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park, Going-to-the-Sun Road study area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.55234262299646,\n 48.73306362472809\n ],\n [\n -113.74164451916323,\n 48.73306362472809\n ],\n [\n -113.74164451916323,\n 48.642925680389254\n ],\n [\n -113.55234262299646,\n 48.642925680389254\n ],\n [\n -113.55234262299646,\n 48.73306362472809\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":881607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horton, Simon 0000-0003-2936-8688","orcid":"https://orcid.org/0000-0003-2936-8688","contributorId":328885,"corporation":false,"usgs":false,"family":"Horton","given":"Simon","email":"","affiliations":[{"id":78515,"text":"Avalanche Canada, Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":881608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitterer, Christoph 0000-0002-2268-8016","orcid":"https://orcid.org/0000-0002-2268-8016","contributorId":328886,"corporation":false,"usgs":false,"family":"Mitterer","given":"Christoph","email":"","affiliations":[{"id":78516,"text":"Avalanche Forecasting Service Tyrol","active":true,"usgs":false}],"preferred":false,"id":881609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":881610,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249586,"text":"70249586 - 2023 - The relationship between whumpf observations and avalanche activity in Colorado, USA","interactions":[],"lastModifiedDate":"2023-10-18T15:14:19.422926","indexId":"70249586","displayToPublicDate":"2023-10-08T10:10:47","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The relationship between whumpf observations and avalanche activity in Colorado, USA","docAbstract":"Triggering whumpfs is a primary indicator of unstable snowpack conditions. Although backcountry travelers and avalanche forecasters rely on whumpfs as a warning sign of potential avalanches, there is little formal research to confirm this relationship. This study investigated the temporal correlation between whumpfs and avalanche activity in data from Colorado's Front Range and southern San Juan Mountains between the winters of 2010/11 and 2022/23. To assess changing conditions over a variety of seasons, we compared the timing of whumpfs and avalanches to the total snow depth at a representative site. We used a 13-inch (33 cm) rolling-window average snow depth versus the median for observed whumpfs, and small avalanches (D1 to D1.5), and large to very large avalanches (D2 and greater). Our results support informal observations that whumpfs are important indicators of avalanche activity, especially at shallower snow depths. Later in the season, when snow depths are deeper and basal weak layers become more difficult to trigger, whumpfs become less common even during periods of increasing avalanche activity. Some of our results may be due to the thin, weak, and wind-affected snow in the Colorado Front Range, where whumpfing typically occurs due to collapsing basal depth hoar. Our findings are important for backcountry travelers assessing stability and for backcountry avalanche forecasters communicating conditions to the public. Our data show that although whumpfs generally indicate unstable conditions and correlate with avalanche activity, the largest avalanches of the winter may not always be preceded by whumpfing.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Konigsberg, J., Simenhois, R., Birkeland, K.W., Peitzsch, E.H., Chabot, D., and Greene, E.M., 2023, The relationship between whumpf observations and avalanche activity in Colorado, USA, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 1032-1038.","productDescription":"7 p.","startPage":"1032","endPage":"1038","ipdsId":"IP-156518","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421968,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=3008","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","otherGeospatial":"Front Range Mountains, San Juan Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.44815485949579,\n 40.97327281710409\n ],\n [\n -106.44815485949579,\n 39.064936136772985\n ],\n [\n -105.28491868786489,\n 39.064936136772985\n ],\n [\n -105.28491868786489,\n 40.97327281710409\n ],\n [\n -106.44815485949579,\n 40.97327281710409\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.51362840857615,\n 37.99145028406636\n ],\n [\n -108.51362840857615,\n 37.16200987030051\n ],\n [\n -106.20006250061266,\n 37.16200987030051\n ],\n [\n -106.20006250061266,\n 37.99145028406636\n ],\n [\n -108.51362840857615,\n 37.99145028406636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Konigsberg, Jason","contributorId":330955,"corporation":false,"usgs":false,"family":"Konigsberg","given":"Jason","email":"","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":886321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simenhois, Ron","contributorId":330956,"corporation":false,"usgs":false,"family":"Simenhois","given":"Ron","email":"","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":886322,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birkeland, Karl W.","contributorId":173366,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","middleInitial":"W.","affiliations":[{"id":27213,"text":"USDA Forest Service National Avalanche Center, Bozeman, MT, USA","active":true,"usgs":false}],"preferred":false,"id":886323,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chabot, Doug","contributorId":330957,"corporation":false,"usgs":false,"family":"Chabot","given":"Doug","email":"","affiliations":[{"id":79076,"text":"Gallatin National Forest Avalanche Center","active":true,"usgs":false}],"preferred":false,"id":886324,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greene, Ethan M.","contributorId":330958,"corporation":false,"usgs":false,"family":"Greene","given":"Ethan","middleInitial":"M.","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":886326,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249584,"text":"70249584 - 2023 - Mapping a glide avalanche with terrestrial lidar in Glacier National Park, USA","interactions":[],"lastModifiedDate":"2023-10-18T15:08:24.195224","indexId":"70249584","displayToPublicDate":"2023-10-08T10:03:57","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mapping a glide avalanche with terrestrial lidar in Glacier National Park, USA","docAbstract":"Thorough documentation of large avalanche events is important for forecasting efforts, infrastructure planning, and investigating the processes involved in avalanche formation and release. However, due in part to the isolated and dangerous nature of avalanche terrain, collecting in-situ, spatially continuous, and quantitative information surrounding avalanches remains difficult. Advances in remote sensing continue to address this knowledge gap. For example, terrestrial laser scanners (TLSs) can produce snow depth measurements at fine spatial resolutions over large areas. Repeat data acquisitions between precipitation events also allow for depth quantification atop an interface, as well as precise estimations of release volume and runout area after avalanche failure. Here, we explore the benefits of TLS-derived documentation from a large avalanche event by examining the development and release of a glide avalanche that occurred in Glacier National Park, Montana, USA, during the spring of 2022. Three sets of lidar point cloud data were acquired in the Haystack Creek drainage, focused on a well-known glide avalanche site. Lidar scans were collected after glide cracks emerged but prior to glide failure, and shortly (~ 1.5 days) after avalanche occurrence, in addition to a snow-free scan later in the year. With this temporal dataset, we were able to account for and visualize the spatial variability of snow depth across the avalanche start zone, such that we could precisely calculate the release volume (18674 m3) and average start zone depth (3.3 m) of the avalanche. Furthermore, TLS data were used to map the extent of the runout area and entrainment zone.
","largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","conferenceTitle":"International Snow Science Workshop 2023","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Dillon, J.W., Miller, Z., Peitzsch, E.H., and Hammonds, K.D., 2023, Mapping a glide avalanche with terrestrial lidar in Glacier National Park, USA, in Proceedings, International Snow Science Workshop 2023, p. 1431-1436.","productDescription":"6 p.","startPage":"1431","endPage":"1436","ipdsId":"IP-156879","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421966,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=3073","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.741667,\n 48.744444\n ],\n [\n -113.741667,\n 48.736111\n ],\n [\n -113.723611,\n 48.736111\n ],\n [\n -113.723611,\n 48.744444\n ],\n [\n -113.741667,\n 48.744444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dillon, James W.","contributorId":330951,"corporation":false,"usgs":false,"family":"Dillon","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":886311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammonds, Kevin D.","contributorId":330952,"corporation":false,"usgs":false,"family":"Hammonds","given":"Kevin","email":"","middleInitial":"D.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":886314,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249583,"text":"70249583 - 2023 - Temporal evolution of slab and weak layer properties during the transition from dry to wet snowpack conditions","interactions":[],"lastModifiedDate":"2023-10-18T14:58:26.08698","indexId":"70249583","displayToPublicDate":"2023-10-08T09:49:06","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Temporal evolution of slab and weak layer properties during the transition from dry to wet snowpack conditions","docAbstract":"Wet-snow slab avalanches are destructive and may become more prevalent in a warming climate. This type of avalanche remains challenging to forecast because the underlying processes leading to wet-snow slab avalanche release are poorly understood. In this study, we examine the temporal evolution of weak layer and slab liquid water content (LWC), critical cut length, and propagation saw test (PST) results during the season's first critical melt period at our study site in the Madison Mountains of southwest Montana. We used snowpack profiles and in-situ weather station data to initialize and force the 1-D physics-based snow cover model SNOWPACK throughout the winter and spring seasons. We then used a high-resolution numerical weather model to force SNOWPACK simulations to forecast the onset of the transition from dry to wet conditions. From April 10-12, 2023, we conducted 67 PSTs, 1053 LWC measurements, 20 hardness profiles, and a full snow profile each morning and early evening. During the first two days of sampling, we observed a transition from low to high propagation propensity with decreasing cut lengths and increasing LWC. On Day 3, we observed consistently low propagation propensity, even as LWC levels remained elevated and comparable to the preceding period of high propagation propensity. This indicates that there is a point where the relationship we observed through the first two days between increasing LWC, increasing propagation propensity, and decreasing cut length no longer holds. Our results further suggest PST propagation mode may help pinpoint the onset, peak, and decline of wet-snow fracture propagation propensity.
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Healthy forests can reduce the incidence of dangerous slab avalanches, slow avalanches when in motion, shorten their runout distances, and act as a safety buffer for backcountry users, infrastructure, and transportation corridors. Since 1984, wildfire area in the seasonal snow zone of the western United States has increased by 70% throughout the seasonal snow zone, creating significant changes to avalanche prone mountains and their connected communities. A major unknown is the impact a reduction of forested area due to forest fires will have on avalanche occurrence. We hypothesize increased potential for avalanching in forested areas impacted by wildfire. Reduced tree cover may make previously heavily forested terrain more susceptible to avalanching. Increases in the size of avalanche start zones, paths, and runouts due to forest fires may increase the destructive size of avalanches and create cascading ecological effects within the adjacent forested terrain. Forest fires may therefore increase the likelihood of avalanche release, resulting in further loss of tree cover and increased avalanche area as well as decreased protection for human infrastructure. In this study, we quantify avalanche area changes before and after the Ross Fork wildfire (2022) in Sawtooth National Forest, Idaho, USA. We utilized satellite imagery, a digital elevation model and historical fire spatial data to quantify and characterize avalanche area changes within the fire perimeter using the Auto-ATES workflow (Sykes et al., 2022). We found decreases in forest coverage that contributed to widespread increases in potential avalanche release areas, avalanche tracks, and potential runout zones throughout the study area as well as the creation of new potential avalanche release areas and a substantial decrease in non-avalanche connected terrain within the fire perimeter. These preliminary findings help inform avalanche and snow safety professionals as well as land managers working in wildfire-prone forested areas about potential post-wildfire changes in avalanche terrain.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Miller, Z., Sykes, J., Guinn, M., VandenBos, B., Savage, S., and Peitzsch, E.H., 2023, Spatial extent of forested avalanche terrain impacted by wildfire across the Sawtooth National Forest, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 272-279.","productDescription":"8 p.","startPage":"272","endPage":"279","ipdsId":"IP-156644","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421961,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"http://arc.lib.montana.edu/snow-science/item/2887","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","otherGeospatial":"Sawtooth National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.25979416843813,\n 44.25122593623942\n ],\n [\n -115.25979416843813,\n 43.47618673393339\n ],\n [\n -113.88619800565512,\n 43.47618673393339\n ],\n [\n -113.88619800565512,\n 44.25122593623942\n ],\n [\n -115.25979416843813,\n 44.25122593623942\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sykes, John","contributorId":330953,"corporation":false,"usgs":false,"family":"Sykes","given":"John","email":"","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":886316,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guinn, Megan","contributorId":330954,"corporation":false,"usgs":false,"family":"Guinn","given":"Megan","email":"","affiliations":[{"id":79075,"text":"USDA Forest Service Chugach National Forest Avalanche Center","active":true,"usgs":false}],"preferred":false,"id":886317,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"VandenBos, Benjamin","contributorId":209940,"corporation":false,"usgs":false,"family":"VandenBos","given":"Benjamin","email":"","affiliations":[{"id":38032,"text":"U.S.D.A. Forest Service Sawtooth National Forest Avalanche Center, Ketchum, Idaho, USA","active":true,"usgs":false}],"preferred":false,"id":886319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Savage, Scott","contributorId":209938,"corporation":false,"usgs":false,"family":"Savage","given":"Scott","email":"","affiliations":[{"id":38032,"text":"U.S.D.A. Forest Service Sawtooth National Forest Avalanche Center, Ketchum, Idaho, USA","active":true,"usgs":false}],"preferred":false,"id":886318,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886320,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249594,"text":"70249594 - 2023 - Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types","interactions":[],"lastModifiedDate":"2023-10-18T14:32:35.897212","indexId":"70249594","displayToPublicDate":"2023-10-08T08:28:37","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types","docAbstract":"Large-magnitude snow avalanches pose a hazard to humans and infrastructure worldwide. Analyzing the spatiotemporal behavior of avalanches and the contributory climate factors is important for understanding historical variability in climate-avalanche relationships as well as improving avalanche forecasting. This study uses established dendrochronological methods to develop long-term regional avalanche chronologies for three different climate types: high-latitude maritime climate of southeast Alaska, intermountain climate of the northern Rocky Mountains, and continental climate of Colorado. In the maritime study area, we collected 434 cross sections throughout six avalanche paths near Juneau, Alaska. This resulted in 2706 identified avalanche growth disturbances between year 1720 and 2018 Common Era (CE), which allowed us to reconstruct 82 years with large magnitude avalanche activity across three sub-regions. By combining this tree-ring derived avalanche dataset with a suite of climate and atmospheric variables and applying a generalized linear model to fit a binomial regression, we found February and March precipitation and the Oceanic Niño Index (ONI) were significant predictors of large magnitude avalanche activity in the southeast Alaska study area. In the intermountain climate study area, tree-rings from 647 trees exhibited 2134 avalanche-related growth disturbances in the northern Rocky Mountains of northwest Montana from 1867 to 2019. The data show that the amount of snowpack across the northern Rocky Mountain region is directly related to avalanche probability. Coincident with warming and regional snowpack reductions, a decline of ~ 14% (~ 2% per decade) in overall large magnitude avalanche probability is apparent through the period 1950–2017 CE. In the continental climate of Colorado, we sampled 24 avalanche paths throughout the state and collected 1188 total samples with 4135 identified growth disturbances from 1698 to 2019. Preliminary results suggest years with large magnitude avalanche activity across the sub-regions of this study area are generally characterized by stormy winters with above average snowpack development but that early and late winter temperature and precipitation also play an important role in large avalanche activity. Characterizing historical climate-avalanche relationships across different climate types provides a broad baseline for understanding potential future changes in avalanche activity. Overall, this work helps forecasters and planners better understand the influence of climate on large magnitude avalanche frequency, and how potential changes in avalanche character and occurrence will affect their operations in the context of a warming climate.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Peitzsch, E.H., Pederson, G.T., Martin, J.T., Hood, E., Greene, E.M., Birkeland, K.W., Elder, K., Wolken, G., Kichas, N.E., Stahle, D.K., and Harley, J., 2023, Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 547-553.","productDescription":"7 p.","startPage":"547","endPage":"553","ipdsId":"IP-156827","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science 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Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hood, Eran","contributorId":106802,"corporation":false,"usgs":false,"family":"Hood","given":"Eran","affiliations":[],"preferred":false,"id":886377,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Greene, Ethan M.","contributorId":330958,"corporation":false,"usgs":false,"family":"Greene","given":"Ethan","middleInitial":"M.","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":886378,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birkeland, Karl W.","contributorId":173366,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","middleInitial":"W.","affiliations":[{"id":27213,"text":"USDA Forest Service National Avalanche Center, Bozeman, MT, USA","active":true,"usgs":false}],"preferred":false,"id":886379,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Elder, Kelly","contributorId":174398,"corporation":false,"usgs":false,"family":"Elder","given":"Kelly","email":"","affiliations":[{"id":5121,"text":"U.S. Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843","active":true,"usgs":false}],"preferred":false,"id":886380,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wolken, Gabriel","contributorId":305685,"corporation":false,"usgs":false,"family":"Wolken","given":"Gabriel","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":886381,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kichas, Nickolas E.","contributorId":221182,"corporation":false,"usgs":false,"family":"Kichas","given":"Nickolas","email":"","middleInitial":"E.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":886383,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stahle, Daniel Kent 0000-0003-1252-5990","orcid":"https://orcid.org/0000-0003-1252-5990","contributorId":224403,"corporation":false,"usgs":true,"family":"Stahle","given":"Daniel","email":"","middleInitial":"Kent","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886382,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Harley, John","contributorId":292933,"corporation":false,"usgs":false,"family":"Harley","given":"John","email":"","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":886384,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70249592,"text":"70249592 - 2023 - Using tree rings to compare Colorado’s 2019 avalanche cycle to previous large avalanche cycles","interactions":[],"lastModifiedDate":"2023-10-18T14:39:11.096438","indexId":"70249592","displayToPublicDate":"2023-10-08T07:18:42","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using tree rings to compare Colorado’s 2019 avalanche cycle to previous large avalanche cycles","docAbstract":"Large magnitude avalanches (size ≥D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a historically large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a 2-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. The extensive number of downed trees from this avalanche cycle allowed us to collect 1188 cross-sections and cores from 1023 unique trees within 24 avalanche paths across the state. We recorded 4135 growth disturbances in these samples. These data comprise the largest known avalanche tree-ring dataset in the world. We employed a strategic nested sampling design to account for scale by including several individual avalanche paths within a given drainage to create sub-regions and then sampled six major sub-regions (counties) throughout the greater region (state). We identified 76 avalanche years within 24 individual avalanche paths from 1698 to 2020. Large magnitude empirical avalanche event frequency varied across paths and sub-regions. Our results indicate the most widespread avalanche cycle in our study area prior to 2019 occurred in 1899, where 12 avalanche paths show evidence of large magnitude avalanche activity. Historical records also highlight 1899 as a year with widespread and large magnitude avalanche activity. These results indicate the avalanche cycle of March 2019 was of similar magnitude. Understanding the spatial extent and return frequency of large magnitude avalanche cycles across multiple spatial scales, from individual paths to an entire state, helps avalanche forecasters improve their products and mitigation strategies and assists infrastructure planners when designing and planning in avalanche terrain.
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Switzerland","active":true,"usgs":false}],"preferred":false,"id":886363,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eckert, Nicolas","contributorId":330971,"corporation":false,"usgs":false,"family":"Eckert","given":"Nicolas","email":"","affiliations":[{"id":27334,"text":"Universite Grenoble Alpes","active":true,"usgs":false}],"preferred":false,"id":886364,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Birkeland, Karl W.","contributorId":173366,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","middleInitial":"W.","affiliations":[{"id":27213,"text":"USDA Forest Service National Avalanche Center, Bozeman, MT, USA","active":true,"usgs":false}],"preferred":false,"id":886365,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Elder, Kelly","contributorId":174398,"corporation":false,"usgs":false,"family":"Elder","given":"Kelly","email":"","affiliations":[{"id":5121,"text":"U.S. Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843","active":true,"usgs":false}],"preferred":false,"id":886366,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70256570,"text":"70256570 - 2023 - Positive but un-sustained wildlife community responses to reserve expansion and mammal reintroductions in South Africa","interactions":[],"lastModifiedDate":"2024-08-19T12:12:25.494441","indexId":"70256570","displayToPublicDate":"2023-10-07T07:03:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Positive but un-sustained wildlife community responses to reserve expansion and mammal reintroductions in South Africa","docAbstract":"The creation and expansion of protected areas, coupled with wildlife reintroductions, are increasingly used as conservation measures to combat wildlife declines worldwide. Although these types of restoration efforts are expected be beneficial to wildlife populations, variable species management and interactions among species within complex food webs have the potential to lead to unintended species-specific responses to reserve expansion which can counteract the anticipated positive effects. We used a multi-season camera trap study to investigate community-wide responses of wildlife to a reserve expansion and associated wildlife releases in South Africa. We analyzed the camera trap data using community occupancy and N-mixture models to assess how the occupancy and intensity of use of individual species changed in the four seasons following reserve expansion. We found species-specific responses to reserve expansion, although responses were generally positive in occupancy and intensity of use but un-sustained. The apex predator, the lion (Panthera leo) and the majority of managed herbivores exhibited sustained or delayed positive responses, whereas most subordinate predators and unmanaged herbivores had short-lived, fluctuating, or neutral responses. Interactive effects of top-down suppression, competitive pressure, and increased space and food resources likely resulted in the temporally-variable responses of most species. Although no species responded negatively to reserve expansion and mammal reintroductions, the lack of sustained positive responses for most species indicates the complexities of implementing conservation actions to benefit multiple wildlife species. Our results highlight the importance of monitoring the entire wildlife community following management actions such as reserve expansion or wildlife reintroductions.
Riverine exports of silicon (Si) influence global carbon cycling through the growth of marine diatoms, which account for ∼25% of global primary production. Climate change will likely alter river Si exports in biome-specific ways due to interacting shifts in chemical weathering rates, hydrologic connectivity, and metabolic processes in aquatic and terrestrial systems. Nonetheless, factors driving long-term changes in Si exports remain unexplored at local, regional, and global scales. We evaluated how concentrations and yields of dissolved Si (DSi) changed over the last several decades of rapid climate warming using long-term data sets from 60 rivers and streams spanning the globe (e.g., Antarctic, tropical, temperate, boreal, alpine, Arctic systems). We show that widespread changes in river DSi concentration and yield have occurred, with the most substantial shifts occurring in alpine and polar regions. The magnitude and direction of trends varied within and among biomes, were most strongly associated with differences in land cover, and were often independent of changes in river discharge. These findings indicate that there are likely diverse mechanisms driving change in river Si biogeochemistry that span the land-water interface, which may include glacial melt, changes in terrestrial vegetation, and river productivity. Finally, trends were often stronger in months outside of the growing season, particularly in temperate and boreal systems, demonstrating a potentially important role of shifting seasonality for the flux of Si from rivers. Our results have implications for the timing and magnitude of silica processing in rivers and its delivery to global oceans.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GB007678","usgsCitation":"Jankowski, K.J., Johnson, K., Sethna, L.R., Julian, P., Wymore, A.S., Shogren, A.J., Thomas, P., Sullivan, P.L., McKnight, D.M., McDowell, W.H., Heindel, R.C., Jones, J.B., Wollheim, W.M., Abbott, B., Deegan, L.A., and Carey, J.C., 2023, Long-term changes in concentrations and yield of riverine dissolved silicon from the poles to the tropics: Global Biogeochemical Cycles, v. 37, no. 9, e2022GB007678, 22 p., https://doi.org/10.1029/2022GB007678.","productDescription":"e2022GB007678, 22 p.","ipdsId":"IP-148189","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":441929,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gb007678","text":"Publisher Index Page"},{"id":435153,"rank":2,"type":{"id":30,"text":"Data 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The lakes were surveyed in March through May 2021. Ten of the lakes had been surveyed previously by the USGS, and the recent surveys were compared to the earlier surveys to document the changes in the bathymetric surface and capacity of the lakes.
Bathymetric data were collected using a high-resolution multibeam mapping system mounted on a boat. Supplemental depth data at five of the lakes were collected in shallow areas with an acoustic Doppler current profiler on a remote-controlled boat. Data points from the various sources were exported at a gridded data resolution appropriate to each lake, either 0.82 foot, 1.64 feet, or 3.28 feet. Data outside the multibeam survey extent and greater than the surveyed water-surface elevation were obtained from data collected using aerial light detection and ranging (lidar) point cloud data. A linear enforcement technique was used to add points to the dataset in areas of sparse data (the upper ends of coves where the water was shallow or aquatic vegetation precluded data acquisition) based on surrounding multibeam and upland data values. The various point datasets were used to produce a three-dimensional triangulated irregular network surface of the lake-bottom elevations for each lake. A surface area and capacity table was produced from the three-dimensional surface for each lake showing surface area and capacity at specified lake water-surface elevations. Various quality-assurance tests were conducted to ensure quality data were collected with the multibeam, including beam angle checks and patch tests. Additional quality-assurance tests were conducted on the gridded bathymetric data from the survey, the bathymetric surface created from the gridded data, and the contours created from the bathymetric survey.
If there were data from a previous bathymetric survey for a given lake, a bathymetric change map was generated from the elevation difference between the previous survey and the 2021 bathymetric survey data points. After reconciling any vertical datum disagreement between the previous survey data and the 2021 survey datum, coincident points between the surveys were identified, and a bathymetric change map was generated using the coincident point data.
The mean elevation change between all repeat surveys at most lakes was positive, indicating sedimentation. Relative to previous surveys, the change in capacity at the primary spillway elevation ranged from a 7.7-percent decrease at Memphis Reservoir to a 3.9-percent increase at Old Lake (Bowling Green West). The mean bathymetric change ranged from 0.03 foot at Hazel Creek and 0.07 foot at Shelbina Lake and Bowling Green Reservoir (Jack Floyd Memorial Lake) to 0.63 at Memphis Lake (Lake Showme) and 0.88 at Memphis Reservoir. The time-averaged mean bathymetric change ranged from 0.002 foot per year at Hazel Creek Lake to 0.044 foot per year at Memphis Reservoir. The computed volumetric sedimentation rate generally ranged from 0.14 to 6.80 acre-feet per year at Shelbina Lake and Memphis Lake (Lake Showme), respectively; however, Forest Lake had a substantially larger sedimentation rate of 17.0 acre-feet per year. Some changes observed in some bathymetric change maps are believed to result from the difference in data collection equipment and techniques between the previous and present bathymetric surveys, whereas other erosional features around the perimeter of certain lakes may be the result of wave action during low-water years.
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Rivers integrate processes occurring throughout their watersheds and are therefore sentinels of change across broad spatial scales. River chemistry also regulates ecosystem function across Earth’s land–ocean continuum, exerting control from the micro- (for example, local food web) to the macro- (for example, global carbon cycle) scale. In the rapidly warming Arctic, a wide range of processes—from permafrost thaw to biological uptake and transformation—might reasonably alter river water chemistry. Here we use data from major rivers that collectively drain two-thirds of the Arctic Ocean watershed to assess widespread change in biogeochemical function within the pan-Arctic basin from 2003 to 2019. While the oceanward flux of alkalinity and associated ions increased markedly over this time frame, nitrate and other inorganic nutrient fluxes declined. Fluxes of dissolved organic carbon showed no overall trend. This divergence in response indicates the perturbation of multiple processes on land, with implications for biogeochemical cycling in the coastal ocean. We anticipate that these findings will facilitate refinement of conceptual and numerical models of current and future functioning of Arctic coastal ecosystems and spur research on scale-dependent change across the river-integrated Arctic domain.
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Despite its critical population status, a statistically robust analysis of the species’ demographic rates utilizing all data has yet to be completed (Benscoter et al. 2021). Furthermore, long-term population processes in response to hydrologic and environmental conditions have not been evaluated for this species. To address these substantial gaps in knowledge, 30 years of demographic data were synthesized to assess population dynamics of this imperiled species using an integrated population model (IPM). Three demographic data types (range-wide counts, capture-mark-recapture, nest monitoring) were incorporated into a unified IPM to evaluate demographic processes and predict population trajectories. The following were calculated: (1) annual estimates of annual population size, survival, and fecundity; (2) coefficient estimates of hydrologic and environmental variables on survival and fecundity; (3) estimates of annual population growth and their correlation with demographic rates; and (4) a Bayesian population viability analysis (BPVA) that includes predicted population size, demographic rates, and extinction risks ten years into the future.","language":"English","publisher":"U.S. Fish and Wildlife Service","collaboration":"U.S. Fish and Wildlife Service and National Park Service","usgsCitation":"Martinez, M.T., D’Acunto, L., and Romanach, S., 2023, Long-term demographic analysis of the Cape Sable seaside sparrow (1992–2021), 25 p.","productDescription":"25 p.","ipdsId":"IP-157496","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":422231,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":422220,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/161341"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martinez, Marisa Takada 0000-0002-1915-6019","orcid":"https://orcid.org/0000-0002-1915-6019","contributorId":304805,"corporation":false,"usgs":true,"family":"Martinez","given":"Marisa","email":"","middleInitial":"Takada","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":887094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":887095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":223479,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":887096,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249337,"text":"70249337 - 2023 - The sands of time: Predicting sea level rise impacts to barrier island habitats","interactions":[],"lastModifiedDate":"2023-10-04T21:13:43.827511","indexId":"70249337","displayToPublicDate":"2023-10-04T13:28:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"The sands of time: Predicting sea level rise impacts to barrier island habitats","docAbstract":"Coastal beach ecosystems support critical habitat for numerous species and are vulnerable to sea level rise. Sand beaches are spatially and temporally dynamic, making it difficult to accurately predict future habitat loss – estimates that are crucial as species are being assessed for protection. We mapped sand beach habitat on 12 focal barrier islands and low-lying beaches off the Gulf Coast of Florida, USA using two methods for comparison - a remotely sensed land cover and hand-digitized aerial imagery that was collected concurrently with digital elevation data. We then compared estimates of beach habitat lost to sea level rise between the two methods and compared this sensitivity to control mangrove islands. Predictions suggest that most of the beach habitat in our study areas will be underwater within the next century. These beaches represent critical nesting habitat for vulnerable vertebrate species such as the snowy plover (Charadrius nivosus) and the loggerhead turtle (Caretta caretta). Importantly, we found that for some islands, using remotely sensed land cover data led us to under- or over-estimate the amount of beach habitat lost to sea level rise relative to the digitized data because of the temporal mismatch between land cover and elevation data. In contrast, we found relatively little difference between methods in the amount of mangrove forest lost to sea level rise on nearby control islands. We suggest that using land cover data collected at the same time as elevation data can produce more accurate predictions of beach habitat inundation from sea level rise.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2023.e02643","usgsCitation":"Koen, E.L., Barichivich, W., and Walls, S., 2023, The sands of time: Predicting sea level rise impacts to barrier island habitats: Global Ecology and Conservation, v. 47, e02643, 23 p., https://doi.org/10.1016/j.gecco.2023.e02643.","productDescription":"e02643, 23 p.","ipdsId":"IP-152002","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":441954,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2023.e02643","text":"Publisher Index Page"},{"id":435162,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RWLSCI","text":"USGS data release","linkHelpText":"Area of habitat still above water per decade of projected sea level rise for islands in two study areas off Florida's Gulf 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Solutions","active":true,"usgs":false}],"preferred":false,"id":885232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barichivich, William 0000-0003-1103-6861","orcid":"https://orcid.org/0000-0003-1103-6861","contributorId":215988,"corporation":false,"usgs":true,"family":"Barichivich","given":"William","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":885233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walls, Susan 0000-0001-7391-9155","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":216235,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":885234,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70248901,"text":"ofr20231059 - 2023 - Implementation plan of the National Cooperative Geologic Mapping Program strategy—Northeast region of the United States: New York and New England","interactions":[],"lastModifiedDate":"2026-02-11T21:33:09.483655","indexId":"ofr20231059","displayToPublicDate":"2023-10-04T11:45:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1059","displayTitle":"Implementation Plan of the National Cooperative Geologic Mapping Program Strategy—Northeast Region of the United States: New York and New England","title":"Implementation plan of the National Cooperative Geologic Mapping Program strategy—Northeast region of the United States: New York and New England","docAbstract":"Complexly deformed igneous, metamorphic, and sedimentary rocks form the bedrock of the Northeast region of the United States. Variably thick unconsolidated sediments deposited by glacial, fluvial, and eolian systems locally cover the bedrock. New geologic mapping focuses on areas lacking modern, detailed studies or syntheses, and contributes to existing framework research. This report addresses plans for the continued development of regionally and nationally consistent geologic maps and map databases, efforts to answer outstanding geologic questions, and societal concerns associated with related geologic topics, such as hazards, geologic and hydrologic resources, and environmental issues.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231059","programNote":"National Cooperative Geologic Mapping Program","usgsCitation":"Walsh, G.J., Thomas, M.A., Marvinney, R.G., Mabee, S.B., Chormann, F.H., Kozlowski, A., Gale, M.H., Kim, J., and Savage, B., 2023, Implementation plan of the National Cooperative Geologic Mapping Program strategy—Northeast region of the United States—New York and New England: U.S. Geological Survey Open-File Report 2023–1059, 21 p., https://doi.org/10.3133/ofr20231059","productDescription":"iv, 21 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\"}}]}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
MS 926A
Reston, VA 20192
Many geoscientific problems require us to exploit synergies of experimental and numerical approaches, which in turn lead to questions regarding the significance of experimental details for validation of numerical codes. We report results of an interlaboratory comparison regarding experimental determination of mechanical and hydraulic properties of samples from five rock types, three sandstone varieties with porosities ranging from 5% to 20%, a marble, and a granite. The objective of this study was to build confidence in the participating laboratories’ testing approaches and to establish tractable standards for several physical properties of rocks. We addressed the issue of sample-to-sample variability by investigating the variability of basic physical properties of samples of a particular rock type and by performing repeat tests. Compressive strength of the different rock types spans an order of magnitude and shows close agreement between the laboratories. However, differences among stress–strain relations indicate that the external measurement of axial displacement and the determination of system stiffness require special attention, apparently more so than the external load measurement. Furthermore, post-failure behavior seems to exhibit some machine-dependence. The different methods used for the determination of hydraulic permeability, covering six orders of magnitude for the sample suite, yield differences in absolute values and pressure dependence for some rocks but not for others. The origin of the differences in permeability, in no case exceeding an order of magnitude, correlate with the compressive strength and potentially reflect a convolution of end plug–sample interaction, sample-to-sample variability, heterogeneity on sample scale, and/or anisotropy, the last two aspects are notably not accounted for by the applied evaluation procedures. Our study provides an extensive data set apt for “benchmarking” considerations, be it regarding new laboratory equipment or numerical modeling approaches.
","language":"English","publisher":"Springer","doi":"10.1007/s12665-023-11173-x","usgsCitation":"Cheng, Y., Lockner, D., Duda, M., Morrow, C., Saffer, D., Song, I., and Renner, J., 2023, Interlaboratory comparison of testing hydraulic, elastic, and failure properties in compression: Lessons learned: Environmental Earth Sciences, v. 82, 509, 20 p., https://doi.org/10.1007/s12665-023-11173-x.","productDescription":"509, 20 p.","ipdsId":"IP-118956","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487585,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1007/s12665-023-11173-x","text":"Publisher Index Page"},{"id":482640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","noUsgsAuthors":false,"publicationDate":"2023-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Cheng, Yang","contributorId":211352,"corporation":false,"usgs":false,"family":"Cheng","given":"Yang","email":"","affiliations":[],"preferred":false,"id":929146,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lockner, David A. 0000-0001-8630-6833","orcid":"https://orcid.org/0000-0001-8630-6833","contributorId":257574,"corporation":false,"usgs":true,"family":"Lockner","given":"David A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929147,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duda, Mandy","contributorId":351625,"corporation":false,"usgs":false,"family":"Duda","given":"Mandy","affiliations":[{"id":84018,"text":"Ruhr-Universitat Bochum, Germany","active":true,"usgs":false}],"preferred":false,"id":929148,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morrow, Carolyn A.","contributorId":328522,"corporation":false,"usgs":false,"family":"Morrow","given":"Carolyn A.","affiliations":[{"id":37196,"text":"Retired USGS employee","active":true,"usgs":false}],"preferred":false,"id":929149,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Saffer, Demian","contributorId":351626,"corporation":false,"usgs":false,"family":"Saffer","given":"Demian","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":929150,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Song, Insun","contributorId":351627,"corporation":false,"usgs":false,"family":"Song","given":"Insun","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":929151,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Renner, Joerg","contributorId":351628,"corporation":false,"usgs":false,"family":"Renner","given":"Joerg","affiliations":[{"id":84018,"text":"Ruhr-Universitat Bochum, Germany","active":true,"usgs":false}],"preferred":false,"id":929152,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249378,"text":"70249378 - 2023 - Reimagining large river management using the Resist–Accept–Direct (RAD) framework in the Upper Mississippi River","interactions":[],"lastModifiedDate":"2023-10-05T11:57:11.26009","indexId":"70249378","displayToPublicDate":"2023-10-04T06:52:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1460,"text":"Ecological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Reimagining large river management using the Resist–Accept–Direct (RAD) framework in the Upper Mississippi River","docAbstract":"Large-river decision-makers are charged with maintaining diverse ecosystem services through unprecedented social-ecological transformations as climate change and other global stressors intensify. The interconnected, dendritic habitats of rivers, which often demarcate jurisdictional boundaries, generate complex management challenges. Here, we explore how the Resist–Accept–Direct (RAD) framework may enhance large-river management by promoting coordinated and deliberate responses to social-ecological trajectories of change. The RAD framework identifies the full decision space of potential management approaches, wherein managers may resist change to maintain historical conditions, accept change toward different conditions, or direct change to a specified future with novel conditions. In the Upper Mississippi River System, managers are facing social-ecological transformations from more frequent and extreme high-water events. We illustrate how RAD-informed basin-, reach-, and site-scale decisions could: (1) provide cross-spatial scale framing; (2) open the entire decision space of potential management approaches; and (3) enhance coordinated inter-jurisdictional management in response to the trajectory of the Upper Mississippi River hydrograph.
The RAD framework helps identify plausible long-term trajectories in different reaches (or subbasins) of the river and how the associated social-ecological transformations could be managed by altering site-scale conditions. Strategic reach-scale objectives may reprioritize how, where, and when site conditions could be altered to contribute to the basin goal, given the basin’s plausible trajectories of change (e.g., by coordinating action across sites to alter habitat connectivity, diversity, and redundancy in the river mosaic).
When faced with long-term systemic transformations (e.g., > 50 years), the RAD framework helps explicitly consider whether or when the basin vision or goals may no longer be achievable, and direct options may open yet unconsidered potential for the basin. Embedding the RAD framework in hierarchical decision-making clarifies that the selection of actions in space and time should be derived from basin-wide goals and reach-scale objectives to ensure that site-scale actions contribute effectively to the larger river habitat mosaic. Embedding the RAD framework in large-river decisions can provide the necessary conduit to link flexibility and innovation at the site scale with stability at larger scales for adaptive governance of changing social-ecological systems.
","language":"English","publisher":"Springer","doi":"10.1186/s13717-023-00460-x","usgsCitation":"Ward, N.K., Lynch, A., Beever, E.A., Booker, J., Bouska, K.L., Embke, H.S., Kocik, J.F., Kocik, J., Lemon, M.G., Lawrence, D.J., Limpinsel, D., Magee, M., Maitland, B.M., McKenna, O.P., Meier, A.R., Morton, J., Muehlbauer, J., Newman, R., Oliver, D.C., Rantala, H.M., Sass, G., Shultz, A.D., Thompson, L., and Wilkening, J.L., 2023, Reimagining large river management using the Resist–Accept–Direct (RAD) framework in the Upper Mississippi River: Ecological Processes, v. 12, 48, 20 p., https://doi.org/10.1186/s13717-023-00460-x.","productDescription":"48, 20 p.","ipdsId":"IP-151992","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":441957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13717-023-00460-x","text":"Publisher Index Page"},{"id":421668,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.89996697327331,\n 46.93330472296154\n ],\n [\n -94.89996697327331,\n 36.2596822615336\n ],\n [\n -86.46246697327341,\n 36.2596822615336\n ],\n [\n -86.46246697327341,\n 46.93330472296154\n ],\n [\n -94.89996697327331,\n 46.93330472296154\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2023-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, Nicole K.","contributorId":297294,"corporation":false,"usgs":false,"family":"Ward","given":"Nicole","email":"","middleInitial":"K.","affiliations":[{"id":64354,"text":"Virginia Tech, Department of Biological Sciences & Forest Resources & Environmental Conservation, Blacksburg, Virginia, USA","active":true,"usgs":false}],"preferred":false,"id":885384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":885385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":2934,"corporation":false,"usgs":true,"family":"Beever","given":"Erik","email":"ebeever@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":885386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Booker, Joshua","contributorId":200019,"corporation":false,"usgs":false,"family":"Booker","given":"Joshua","email":"","affiliations":[{"id":17755,"text":"U.S. Fish and Wildlife Service, Upper Midwest Environmental Sciences Center","active":true,"usgs":false}],"preferred":false,"id":885387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":885388,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":885389,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kocik, John F.","contributorId":315443,"corporation":false,"usgs":false,"family":"Kocik","given":"John","email":"","middleInitial":"F.","affiliations":[{"id":68326,"text":"NOAA Fisheries NEFSC Maine Field Station, 17 Godfrey Drive-Suite 1, Orono, Maine 04473, USA","active":true,"usgs":false}],"preferred":false,"id":885390,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kocik, Joshua","contributorId":330592,"corporation":false,"usgs":false,"family":"Kocik","given":"Joshua","email":"","affiliations":[{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":885391,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lemon, Mary Grace T.","contributorId":198501,"corporation":false,"usgs":false,"family":"Lemon","given":"Mary","email":"","middleInitial":"Grace T.","affiliations":[],"preferred":false,"id":885487,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lawrence, David J. 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R.","contributorId":215691,"corporation":false,"usgs":false,"family":"Meier","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":885488,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Morton, John M.","contributorId":245969,"corporation":false,"usgs":false,"family":"Morton","given":"John M.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":885397,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Muehlbauer, Jeffrey 0000-0003-1808-580X","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":221739,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":885398,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Newman, Robert","contributorId":248514,"corporation":false,"usgs":false,"family":"Newman","given":"Robert","affiliations":[],"preferred":false,"id":885399,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Oliver, Devon C.","contributorId":330594,"corporation":false,"usgs":false,"family":"Oliver","given":"Devon","email":"","middleInitial":"C.","affiliations":[{"id":65315,"text":"MN DNR","active":true,"usgs":false}],"preferred":false,"id":885400,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Rantala, Heidi M.","contributorId":330595,"corporation":false,"usgs":false,"family":"Rantala","given":"Heidi","email":"","middleInitial":"M.","affiliations":[{"id":65315,"text":"MN DNR","active":true,"usgs":false}],"preferred":false,"id":885401,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Sass, Greg G.","contributorId":244466,"corporation":false,"usgs":false,"family":"Sass","given":"Greg G.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":885402,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Shultz, Aaron D.","contributorId":303739,"corporation":false,"usgs":false,"family":"Shultz","given":"Aaron","email":"","middleInitial":"D.","affiliations":[{"id":16233,"text":"Great Lakes Indian Fish and Wildlife Commission","active":true,"usgs":false}],"preferred":false,"id":885403,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":885404,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Wilkening, Jennifer L. 0000-0001-8748-4578","orcid":"https://orcid.org/0000-0001-8748-4578","contributorId":127685,"corporation":false,"usgs":false,"family":"Wilkening","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7111,"text":"U. Colorado, Boulder, Dept. Ecology & Evol.Biol., PhD Student","active":true,"usgs":false}],"preferred":false,"id":885405,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70256518,"text":"70256518 - 2023 - Evaluating the spatial and temporal distribution and ecology of Bighead and Silver Carp and native fishes of the lower Red River basin","interactions":[],"lastModifiedDate":"2024-09-09T15:52:04.867925","indexId":"70256518","displayToPublicDate":"2023-10-03T10:49:10","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-153-2023","title":"Evaluating the spatial and temporal distribution and ecology of Bighead and Silver Carp and native fishes of the lower Red River basin","docAbstract":"We investigated the spatial and temporal distribution of Bighead Carp and Silver Carp (hereafter Carp) in the lower Red River basin of Arkansas. Our study objectives were: 1) determine the spatial and temporal extent of Bighead and Silver Carp in the Red River basin of Arkansas; 2) determine habitat associations of large river fish assemblages; and 3) summarize the demographics of Bighead and Silver Carp. We sampled 67 reaches in the lower Red River and its major tributaries for juvenile Carp and other small-bodied fishes (24 of the reaches were in the Arkansas portion of the Red River). We conducted repeated surveys in these reaches where the reaches were sampled 2-3 times over approximately 2 years representing 242 surveys (95 surveys in Arkansas). We completed adult Carp and native fish assemblage sampling across 61 reaches (22 reaches in Arkansas) where we also repeated surveys at these locations (245 total surveys, 100 surveys completed in Arkansas during the reporting period). We captured the most large-bodied fishes (including Carp) using gillnets and electrofishing, whereas fyke nets and seine hauls collected mainly smaller-bodied fishes. Hoop nets captured fewer fishes when compared to other gear types. We sampled 120,072 fishes, comprising 70 species and 41 genera, from the mainstem Red River in Arkansas. We used data associated with the entire catchment (including OK and TX data) to model the occupancy of adult fishes including both carp species. Carp tended to occupy reaches with the presence of slackwater habitat, that were deeper and narrower (lower habitat complexity), with higher discharge conditions, and were positively associated with chlorophyll-a concentrations. Adult and juvenile assemblage structure varied with reach scale attributes with notable differences among some taxonomically similar species. No carp under the age of 3 were sampled in the catchment. Bighead Carp and Silver Carp in the Red River catchment appear to live longer and grow larger than other populations. Silver Carp and Bighead Carp in the lower Red River had a theoretical maximum length (L_∞) of 920 and 1348-mm TL, respectively. The oldest sampled Silver Carp and Bighead Carp were age 14 and 17, respectively. Bighead Carp growth was positively associated with warmer air temperatures and negatively associated with discharge variability. Similarly, Silver Carp growth was positively associated with the warm air temperature and negatively associated with discharge variability. However, Silver Carp growth was also positively related to high discharge conditions and the variability of air temperature. Silver Carp annual mortality was relatively low and recruitment into the population appeared steady. It appears that Carp are likely coming from another catchment, have only limited or periodic successful reproduction in the study area, or spawn downriver in LA. Continued monitoring for reproductive success would be helpful. Moreover, if the goal is to greatly reduce or eliminate carp, then strategies to prevent further immigration would be ideal before reproduction occurs or becomes more successful. Targeted removal may then be useful for reducing numbers already in the catchment; however, there are also oxbow lakes that contain carp but appear only connected to the river during major floods (i.e., possible source locations).
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Brewer, S.K., Dattilo, J., Ramsey, P., and Birdsall, B., 2023, Evaluating the spatial and temporal distribution and ecology of Bighead and Silver Carp and native fishes of the lower Red River basin: Cooperator Science Series FWS/CSS-153-2023, ii, 193 p.","productDescription":"ii, 193 p.","ipdsId":"IP-154807","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":431854,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/evaluating-spatial-and-temporal-distribution-and-ecology-bighead-and-silver-carp-and-native"},{"id":433628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":907783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dattilo, John","contributorId":341000,"corporation":false,"usgs":false,"family":"Dattilo","given":"John","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":907784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, Paul","contributorId":341001,"corporation":false,"usgs":false,"family":"Ramsey","given":"Paul","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":907785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birdsall, Ben","contributorId":341002,"corporation":false,"usgs":false,"family":"Birdsall","given":"Ben","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":907786,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256477,"text":"70256477 - 2023 - Striped bass exploitation in tailwater habitats of east-central Oklahoma","interactions":[],"lastModifiedDate":"2024-09-09T15:47:17.424251","indexId":"70256477","displayToPublicDate":"2023-10-03T10:41:23","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"FWS/CSS-152-2023","title":"Striped bass exploitation in tailwater habitats of east-central Oklahoma","docAbstract":"Striped Bass (Morone saxatilis) is naturally anadromous, but a few land-locked populations have been documented that are self-sustaining, including fish in the Arkansas River, Oklahoma. This rare population is the source of brood stock for the Oklahoma Department of Wildlife Conservation hatcheries and is an important sportfish stock. Striped Bass often congregate in tailwater habitats, where anecdotal observations indicate anglers can harvest numerous fish daily. This suggests the need to evaluate the sustainability of harvest in these locations. It is unknown what portion of fish from the Arkansas River population use tailwater habitats or the timing and duration of use. The objectives of this study were to: 1) determine size structure , abundance, and total mortality rate of Striped Bass in the tailwaters of Tenkiller Lake and Lake Eufaula; 2) determine the extent and timing of immigration and emigration of Striped Bass in tailwater habitats to determine the potential for overharvest when they congregate in tailwater areas; 3) estimate delayed hooking mortality of Striped Bass in spring and summer; and 4) using the above data and modeling simulations, determine the potential for growth overfishing of Striped Bass in the tailwater reaches. We sampled 2,730 Striped Bass using boat electrofishing and tagged with passive integrated transponder (PIT) tags to estimate demographic data using a capture-recapture model. A subset of these Striped Bass was tagged with angler reward tags (internal anchor tags, n = 681) and dual technology acoustic-radio telemetry tags (n = 111) to estimate exploitation and track movements, respectively. Anglers returned 116 tags from 2020 to 2022; and our angler reporting rate was estimated to be 14.3%. Annual harvest mortality is minimally 7% (unadjusted for reporting rate) but could be as high as 42% (i.e., adjusting for compliance; but this exceeds the measured total mortality rate (34.3%) so true exploitation is probably 7–34.3%). Our abundance estimates for Striped Bass varied seasonally (ranging from 782 to 38,597 seasonally) and had a high level of uncertainty likely due to relatively low recapture rates. Additionally, our results indicated that Striped Bass exhibited a strong fidelity to their respective habitats within seasons, with fidelity probabilities ranging from 0.98 to 1.00. Movement among segments was common among seasons, indicating these localized populations mix with a larger population annually. Striped Bass were primarily in tailwater habitats during summer. Delayed hooking mortality data were collected in summer 2022. Due to habitat conditions that year, angling catch rates were low. Twenty-nine Striped Bass were tagged, and only eight Striped Bass remained tagged long enough to be tracked at least one day. The total time tracked for these eight fish was between one and three days. There were no confirmed mortalities, treatment, or control. Because of the low sample size, literature values for delayed hooking mortality were also used to supplement field data in the models. The yield-per-recruit model indicated exploitation at 30% or higher leads to recruitment overfishing. A 600 mm minimum TL regulation and 25–30% exploitation rate achieve maximum yield (954 kg/1,000 recruits). Maximum yield related to an average size at harvest of 718-mm TL; thus, growth overfishing occurs for any regulation where average size of harvest is smaller than 718 mm (which the model predicted would occur for any minimum length < 600, and for minimum length = 600 if exploitation was > 30%, it never occurred with minimum length requirements > 650). Increasing the minimum length regulation improves size structure, but a maximum length regulation had minimal effect unless it was implemented at a sufficiently small size (i.e., < 700 mm). Although catch-and-release mortality can be relatively high at times in the literature, according to our model, it appears to have a small effect on size structure, except when exploitation rates are > 50% and a restrictive maximum size regulation (< 800 mm) is used. The current population appears sustainable, especially considering the annual mixing dynamics and apparently large population (though we see a lot of uncertainty in the population estimates). However, modeling indicates that if enhancing size structure is an agency priority, then implementing more restrictive regulations could be advantageous.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Vaisvil, A., Shoup, D., and Brewer, S.K., 2023, Striped bass exploitation in tailwater habitats of east-central Oklahoma: Cooperator Science Series FWS/CSS-152-2023, ii, 67 p.","productDescription":"ii, 67 p.","ipdsId":"IP-155654","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":431818,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/striped-bass-exploitation-tailwater-habitats-east-central-oklahoma"},{"id":433626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.68089573693297,\n 35.1807887620315\n ],\n [\n -94.68089573693297,\n 35.735103019942684\n ],\n [\n -95.40145518290683,\n 35.735103019942684\n ],\n [\n -95.40145518290683,\n 35.1807887620315\n ],\n [\n -94.68089573693297,\n 35.1807887620315\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vaisvil, Alex","contributorId":340784,"corporation":false,"usgs":false,"family":"Vaisvil","given":"Alex","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shoup, Daniel","contributorId":340785,"corporation":false,"usgs":false,"family":"Shoup","given":"Daniel","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907555,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70270685,"text":"70270685 - 2023 - Evaluating the spatial and temporal distribution and ecology of Bighead and Silver Carp and native fishes of the lower Red River basin","interactions":[],"lastModifiedDate":"2025-08-22T15:14:39.937639","indexId":"70270685","displayToPublicDate":"2023-10-03T10:05:53","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-153-2023","title":"Evaluating the spatial and temporal distribution and ecology of Bighead and Silver Carp and native fishes of the lower Red River basin","docAbstract":"We investigated the spatial and temporal distribution of Bighead Carp and Silver Carp (hereafter Carp) in the lower Red River basin of Arkansas. Our study objectives were: 1) determine the spatial and temporal extent of Bighead and Silver Carp in the Red River basin of Arkansas; 2) determine habitat associations of large river fish assemblages; and 3) summarize the demographics of Bighead and Silver Carp. We sampled 67 reaches in the lower Red River and its major tributaries for juvenile Carp and other small-bodied fishes (24 of the reaches were in the Arkansas portion of the Red River). We conducted repeated surveys in these reaches where the reaches were sampled 2-3 times over approximately 2 years representing 242 surveys (95 surveys in Arkansas). We completed adult Carp and native fish assemblage sampling across 61 reaches (22 reaches in Arkansas) where we also repeated surveys at these locations (245 total surveys, 100 surveys completed in Arkansas during the reporting period). We captured the most large-bodied fishes (including Carp) using gillnets and electrofishing, whereas fyke nets and seine hauls collected mainly smaller-bodied fishes. Hoop nets captured fewer fishes when compared to other gear types. We sampled 120,072 fishes, comprising 70 species and 41 genera, from the mainstem Red River in Arkansas. We used data associated with the entire catchment (including OK and TX data) to model the occupancy of adult fishes including both carp species. Carp tended to occupy reaches with the presence of slackwater habitat, that were deeper and narrower (lower habitat complexity), with higher discharge conditions, and were positively associated with chlorophyll-a concentrations. Adult and juvenile assemblage structure varied with reach scale attributes with notable differences among some taxonomically similar species. No carp under the age of 3 were sampled in the catchment. Bighead Carp and Silver Carp in the Red River catchment appear to live longer and grow larger than other populations. Silver Carp and Bighead Carp in the lower Red River had a theoretical maximum length (\uD835\uDC3F∞) of 920 and 1,348-mm TL, respectively. The oldest sampled Silver Carp and Bighead Carp were age 14 and 17, respectively. Bighead Carp growth was positively associated with warmer air temperatures and negatively associated with discharge variability. Similarly, Silver Carp growth was positively associated with warm air temperature and negatively associated with discharge variability. However, Silver Carp growth was also positively related to high discharge conditions and the variability of air temperature. Silver Carp annual mortality was relatively low and recruitment into the population appeared steady. It appears that Carp are likely coming from another catchment, have only limited or periodic successful reproduction in the study area, or spawn downriver in LA. Continued monitoring for reproductive success would be helpful. Moreover, if the goal is to greatly reduce or eliminate carp, then strategies that prevent further immigration before reproduction occurs or becomes more successful would be ideal. Targeted removal may then be useful for reducing numbers already in the catchment; however, there are also oxbow lakes that contain carp but appear only connected to the river during major floods (i.e., possible source locations).
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css88134777","usgsCitation":"Brewer, S., Dattilo, J., Ramsey, P., and Birdsall, B., 2023, Evaluating the spatial and temporal distribution and ecology of Bighead and Silver Carp and native fishes of the lower Red River basin: Cooperator Science Series CSS-153-2023, ii, 193 p., https://doi.org/10.3996/css88134777.","productDescription":"ii, 193 p.","ipdsId":"IP-177588","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":495039,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3996/css88134777","text":"Publisher Index Page"},{"id":494522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma, Texas","otherGeospatial":"lower Red River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.79873940117595,\n 34.31284035073031\n ],\n [\n -96.79873940117595,\n 33.02263338338369\n ],\n [\n -93.57412593043699,\n 33.02263338338369\n ],\n [\n -93.57412593043699,\n 34.31284035073031\n ],\n [\n -96.79873940117595,\n 34.31284035073031\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-10-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dattilo, John","contributorId":341000,"corporation":false,"usgs":false,"family":"Dattilo","given":"John","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":946821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, Paul","contributorId":341001,"corporation":false,"usgs":false,"family":"Ramsey","given":"Paul","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":946823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birdsall, Ben","contributorId":341002,"corporation":false,"usgs":false,"family":"Birdsall","given":"Ben","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":946824,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}