Regional mapping of actively deforming landslides, including measurements of landslide velocity, is integral for hazard assessments in paraglacial environments. These inventories are also critical for describing the potential impacts that the warming effects of climate change have on slope instability in mountainous and cryospheric terrain. The objective of this study is to identify slow-moving landslides in the Prince William Sound region, southcentral Alaska, United States, which has had rapid deglaciation since the mid-1800s, and assess their tsunamigenic plausibility. We use an automated time series persistent scatterer interferometric synthetic aperture radar processing method with 7 years of Sentinel-1 data (2016–22) to identify 43 slow-moving slopes with average velocities ranging from approximately 0.2 to 21 millimeters per year. Landslide presence is confirmed using aerial imagery and previous landslide inventory records. We assess the tsunamigenic plausibility of the landslides using empirically derived estimates of landslide mobility based on modeled landslide volumes. Of the identified landslides, our preliminary analysis suggests that 11 have tsunamigenic potential if they were to fail rapidly and catastrophically. Although our estimate of tsunamigenic plausibility is preliminary and can be refined with additional observations and analyses, it can be used to prioritize ongoing and future hazard assessment, surveillance, and research efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231099","collaboration":"Prepared in collaboration with Southern Methodist University","programNote":"Landslide Hazards Program","usgsCitation":"Schaefer, L.N., Kim, J., Staley, D.M., Lu, Z., and Barnhart, K.R., 2024, Satellite interferometry landslide detection and preliminary tsunamigenic plausibility assessment in Prince William Sound, southcentral Alaska: U.S. Geological Survey Open-File Report 2023–1099, 22 p., https://doi.org/10.3133/ofr20231099.","productDescription":"v, 22 p.","onlineOnly":"Y","ipdsId":"IP-155368","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":499202,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115975.htm","linkFileType":{"id":5,"text":"html"}},{"id":424451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1099/coverthb.jpg"},{"id":424866,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1099/ofr20231099.xml"},{"id":424865,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1099/images"},{"id":424452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1099/ofr20231099.pdf","text":"Report","size":"11.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1099"},{"id":424970,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231099/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1099"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.54873591535403,\n 61.68671968753719\n ],\n [\n -149.54873591535403,\n 59.52701043286805\n ],\n [\n -143.89688082106878,\n 59.52701043286805\n ],\n [\n -143.89688082106878,\n 61.68671968753719\n ],\n [\n -149.54873591535403,\n 61.68671968753719\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
Conservation prioritization frameworks are used worldwide to identify species at greatest risk of extinction and to allocate limited resources across regions, species, and populations. Conservation prioritization can be impeded by ecological knowledge gaps and data deficiency, especially in freshwater species inhabiting highly complex aquatic ecosystems. Therefore, we developed a flexible approach that calculates a species' imperilment risk based on the conservation principles of resiliency, redundancy, and representation (i.e., the “three R's”). Our approach organizes data on species traits, distributions, population connectivity, and threats within a Bayesian belief network capable of predicting resiliency and redundancy within representative ecological settings. Empirical data and expert judgment inform the model to provide robust and repeatable risk assessments for rare and data-deficient species. The model calculates resiliency at hierarchical spatial scales from distributional trends and population strength. Redundancy is estimated from the connectivity and quantities of extant populations. Resiliency, redundancy, and species' inherent vulnerability based on species traits collectively estimate extirpation risk within each unique ecological setting. Extirpation risks across ecological settings characterize representation and are aggregated to estimate global imperilment risk. We demonstrate the model's utility with Piebald Madtom (Noturus gladiator), a species petitioned for listing under the U.S. Endangered Species Act. Our results revealed that resiliency, redundancy, and extirpation risks can vary spatially across the species' range while identifying populations where additional sampling could disproportionally reduce uncertainty in estimated global imperilment risk. Our approach could standardize and expedite conservation status assessments, identify opportunities for early management intervention of at-risk species and populations, and strategically reduce uncertainty by focusing monitoring and research on priority information gaps.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4738","usgsCitation":"Dunn, C.G., Schumann, D.A., Colvin, M., Sleezer, L.J., Wagner, M., Jones-Farrand, D., Rivenbark, E., McRae, S., and Evans, K., 2024, Using resiliency, redundancy, and representation in a Bayesian belief network to assess imperilment of riverine fishes: Ecosphere, v. 15, e4738, 21 p., https://doi.org/10.1002/ecs2.4738.","productDescription":"e4738, 21 p.","ipdsId":"IP-137173","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4738","text":"Publisher Index Page"},{"id":432153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2024-01-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schumann, David A.","contributorId":267261,"corporation":false,"usgs":false,"family":"Schumann","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Colvin, Michael E.","contributorId":264842,"corporation":false,"usgs":false,"family":"Colvin","given":"Michael E.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sleezer, Logan John 0000-0002-5787-8629","orcid":"https://orcid.org/0000-0002-5787-8629","contributorId":331489,"corporation":false,"usgs":true,"family":"Sleezer","given":"Logan","email":"","middleInitial":"John","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":907432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Matthew 0000-0002-3987-072X","orcid":"https://orcid.org/0000-0002-3987-072X","contributorId":221861,"corporation":false,"usgs":false,"family":"Wagner","given":"Matthew","affiliations":[{"id":40445,"text":"Student contractor to the U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":907435,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones-Farrand, D. Todd","contributorId":54713,"corporation":false,"usgs":true,"family":"Jones-Farrand","given":"D. Todd","affiliations":[],"preferred":false,"id":907433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rivenbark, Erin","contributorId":340546,"corporation":false,"usgs":false,"family":"Rivenbark","given":"Erin","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907437,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McRae, Sarah","contributorId":340663,"corporation":false,"usgs":false,"family":"McRae","given":"Sarah","affiliations":[{"id":81646,"text":"South Atlantic-Gulf and Mississippi-Basin Unified Interior Regions","active":true,"usgs":false}],"preferred":false,"id":907436,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Evans, Kristine","contributorId":217902,"corporation":false,"usgs":false,"family":"Evans","given":"Kristine","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":907434,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70253131,"text":"70253131 - 2024 - Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances","interactions":[],"lastModifiedDate":"2024-04-19T12:21:26.738112","indexId":"70253131","displayToPublicDate":"2024-01-24T07:19:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances","docAbstract":"Two independent machine learning techniques, boosted regression trees and artificial neural networks, were used to examine the physicochemical and meteorological variables that affect the seasonal growth and decline of harmful algal blooms (HABs) in a shallow, hypereutrophic lake in southern Oregon. High temporal resolution data collected at four monitoring locations were aggregated into daily timesteps to create two response variables: (1) daily maximum pH (pHmax), representing HAB growth, and (2) daily minimum dissolved oxygen (DOmin), representing HAB decline. Predictors included meteorological and physical data, estimates of external phosphorus loading, and previous-year average nutrient concentrations, and excluded HAB biomass and internal phosphorus loading. The predictors that captured seasonal changes in both pHmax and DOmin were temperature, inflows, lake-surface elevation, and external phosphorus loading, while short-term changes were captured by measures of stratification, temperature, and wind speed. The pHmax models had similar fits with leave-one-year-out cross-validation (LOYO-CV) R2 values of 0.2–0.43 (median = 0.40). The DOmin models for the deeper locations had LOYO-CV R2 values of 0.27–0.43 compared to 0.1–0.25 for the shallower locations. Model performance was affected by variability due to patchiness of HABs, measurement uncertainty, and advection.
Monitoring trends in large mammal populations is a fundamental component of wildlife management and conservation. However, direct estimates of population size and vital rates of large mammals can be logistically challenging and expensive. Indicators that reflect trends in abundance, therefore, can be valuable tools for supporting population monitoring. Polar bears have a relatively simple life history such that a few key variables may be effective indicators for tracking changes in body condition and recruitment that affect abundance. Direct estimates of polar bear abundance are difficult to obtain due to their large home ranges in remote Arctic habitats. Changes in abundance associated with environmental conditions appear to affect polar bears largely via effects on female body condition which influence reproduction and cub survival (i.e., recruitment). Loss of sea ice habitat is further limiting researcher access for population monitoring creating a need for alternative approaches. Here we used relationships established from eight years (2008–2017) of data collected on 439 polar bears in the Chukchi Sea, to transform previously published individual-based relationships with annually available sea ice, atmospheric circulation, and prey body condition variables to predict annual mean body condition and recruitment during 2018–2022. Although annual sample sizes were limited for verifying predicted body condition and recruitment via techniques such as cross-validation, in most cases predicted annual means were closely correlated with observed means for 2008–2017. Summer sea ice and prey body condition remained within or increased relative to levels observed during 2008–2017 and predicted polar bear body condition and recruitment during 2018–2022 were largely within or above observed annual means during 2008–2017. A lack of trend in environmental and ecological variables or polar bear body condition and recruitment metrics during 2008–2022 is suggestive that the Chukchi Sea polar bear population was likely stable during this time. Our results provide support for developing models that predict important population parameters of large mammals based on environmental and ecological indicators. Given that trend information is lacking for 10 of the 19 recognized polar bear populations and is outdated for others, the use of environmental and ecological indicators may be particularly useful for augmenting direct estimates of polar bear vital rates in between periods of data collection. Although demographic assessments for polar bears have primarily focused on correlations with sea ice availability, our study and others highlight that prey health is also an important indicator of polar bear population status.
Improved understanding of wildlife population connectivity among protected area networks can support effective planning for the persistence of wildlife populations in the face of land use and climate change. Common approaches to estimating connectivity often rely on small samples of individuals without considering the spatial structure of populations, leading to limited understanding of how individual movement links to demography and population connectivity. Recently developed spatial capture-recapture (SCR) models provide a framework to formally connect inference about individual movement, connectivity, and population density, but few studies have applied this approach to empirical data to support connectivity planning.
We used mark-recapture data collected from 924 genetic detections of 598 American black bears (Ursus americanus) in 2004 with SCR ecological distance models to simultaneously estimate density, landscape resistance to movement, and population connectivity in Glacier National Park northwest Montana, USA. We estimated density and movement parameters separately for males and females and used model estimates to calculate predicted density-weighted connectivity surfaces.
Model results indicated that landscape structure influences black bear density and space use in Glacier. The mean density estimate was 16.08 bears/100 km2 (95% CI 12.52–20.6) for females and 9.27 bears/100 km2 (95% CI 7.70–11.14) for males. Density increased with forest cover for both sexes. For male black bears, density decreased at higher grizzly bear (Ursus arctos) densities. Drainages, valley bottoms, and riparian vegetation decreased estimates of landscape resistance to movement for male and female bears. For males, forest cover also decreased estimated resistance to movement, but a transportation corridor bisecting the study area strongly increased resistance to movement presenting a barrier to connectivity.
Density-weighed connectivity surfaces highlighted areas important for population connectivity that were distinct from areas with high potential connectivity. For black bears in Glacier and surrounding landscapes, consideration of both vegetation and valley topography could inform the placement of underpasses along the transportation corridor in areas characterized by both high population density and potential connectivity. Our study demonstrates that the SCR ecological distance model can provide biologically realistic, spatially explicit predictions to support movement connectivity planning across large landscapes.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-023-00445-7","usgsCitation":"Carroll, S.L., Schmidt, G.M., Waller, J.S., and Graves, T., 2024, Evaluating density-weighted connectivity of black bears (Ursus americanus) in Glacier National Park with spatial capture–recapture models: Movement Ecology, v. 12, 8, 18 p., https://doi.org/10.1186/s40462-023-00445-7.","productDescription":"8, 18 p.","ipdsId":"IP-152033","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":440652,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-023-00445-7","text":"Publisher Index Page"},{"id":424846,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -115.27217583291136,\n 49.293498450490404\n ],\n [\n -115.27217583291136,\n 47.6179024821721\n ],\n [\n -112.5475664579116,\n 47.6179024821721\n ],\n [\n -112.5475664579116,\n 49.293498450490404\n ],\n [\n -115.27217583291136,\n 49.293498450490404\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-01-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Sarah L","contributorId":300618,"corporation":false,"usgs":false,"family":"Carroll","given":"Sarah","email":"","middleInitial":"L","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":893258,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Greta M","contributorId":300615,"corporation":false,"usgs":false,"family":"Schmidt","given":"Greta","email":"","middleInitial":"M","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":893259,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waller, John S.","contributorId":167055,"corporation":false,"usgs":false,"family":"Waller","given":"John","email":"","middleInitial":"S.","affiliations":[{"id":16272,"text":"National Park Service, Glacier National Park, West Glacier, MT","active":true,"usgs":false}],"preferred":false,"id":893260,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":893261,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250152,"text":"ofr20231084 - 2024 - Report of the River Master of the Delaware River for the period December 1, 2013–November 30, 2014","interactions":[],"lastModifiedDate":"2026-01-28T17:37:12.683393","indexId":"ofr20231084","displayToPublicDate":"2024-01-22T14:20:00","publicationYear":"2024","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":"2023-1084","displayTitle":"Report of the River Master of the Delaware River for the Period December 1, 2013–November 30, 2014","title":"Report of the River Master of the Delaware River for the period December 1, 2013–November 30, 2014","docAbstract":"A Decree of the Supreme Court of the United States, entered June 7, 1954 (New Jersey v. New York, 347 U.S. 995), established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes the diversion of water from the Delaware River Basin and requires compensating releases from specific reservoirs owned by New York City to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master provide reports to the Court, not less frequently than annually. This report is the 61st annual report of the River Master of the Delaware River. The report covers the 2014 River Master report year, which is the period from December 1, 2013, to November 30, 2014.
During the report year, precipitation in the upper Delaware River Basin was 42.40 inches or 95 percent of the long-term average. On December 1, 2013, combined useable storage in New York’s Pepacton, Cannonsville, and Neversink Reservoirs in the upper Delaware River Basin was 200.133 billion gallons or 73.9 percent of the combined capacity of 270.8 billion gallons. The reservoirs were at about 99.7 percent of usable capacity on May 31, 2014. Combined storage in the Pepacton, Cannonsville, and Neversink Reservoirs decreased below 80 percent of combined capacity in late August. The lowest combined storage was 151.730 billion gallons or 56 percent of combined capacity on November 24, 2014. Delaware River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and the State of New Jersey fully complied with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the flow objective for the Delaware River at Montague, New Jersey, on 94 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year.
Water quality in the Delaware River estuary between streamgages at Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at several locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231084","isbn":"978-1-4113-4543-0","programNote":"Water Availability and Use Science Program","usgsCitation":"Russell, K.L., Andrews, W.J., DiFrenna, V.J., Norris, J.M., and Mason, R.R., Jr., 2024, Report of the River Master of the Delaware River for the period December 1, 2013–November 30, 2014: U.S. Geological Survey Open-File Report 2023–1084, 98 p., https://doi.org/10.3133/ofr20231084.","productDescription":"xii, 98 p.","numberOfPages":"98","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123859","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"links":[{"id":499192,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115976.htm","linkFileType":{"id":5,"text":"html"}},{"id":422830,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1084/ofr20231084.XML"},{"id":422831,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1084/images/"},{"id":422832,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231084/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1084"},{"id":422833,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1084/coverthb.jpg"},{"id":422834,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1084/ofr20231084.pdf","text":"Report","size":"9.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1084"}],"country":"United States","state":"New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.3534603634281,\n 39.372074240175664\n ],\n [\n -74.00,\n 39.372074240175664\n ],\n [\n -74.00,\n 43.02029898998293\n ],\n [\n -76.3534603634281,\n 43.02029898998293\n ],\n [\n -76.3534603634281,\n 39.372074240175664\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Delaware River Master
Office of the Delaware River Master
U.S. Geological Survey
120 Route 209
South Milford, PA 18337
Widely separated basalt lava-flow outcrops in north-central Oregon, USA, expose products of a single eruptive episode. A Pliocene lava flow, here informally termed the Tetherow basalt, issued from vents near Redmond, in the Deschutes basin of Oregon, as a plains-forming basalt now exposed in continuous outcrops northward for 60 km. A similar basalt crops out 47 km farther north, near Maupin, within what was then a slightly incised ancestral Deschutes River canyon. The northernmost outcrops of this lava flow lie on Fulton Ridge, in the Dalles basin, near the confluence of the Deschutes and Columbia Rivers. Complementary lines of evidence confirm these rocks are all from the same volcanic eruption. Outcrops in the Deschutes and Dalles basins are chemically similar high-titanium basalts, petrographically similar to each other and distinct from other lava flows in the area. Paleomagnetic directions from 11 scattered sites are similar and indistinguishable by various tests for a common mean. Three new 40Ar/39Ar ages indicate the Tetherow basalt eruption occurred between 5.5 Ma and 5.0 Ma, likely at ca. 5.2 Ma. The widely separated outcrops of this lava flow span 160–180 km along the ancestral Deschutes River and downstream Columbia River. The lava flow’s length and erupted volume of 15–20 km3 are extraordinarily large in a non-flood-basalt setting. This lava flow provides a datum with which to describe regional physiographic history, assess incision rates, and infer tectonic history. Spanning different depositional basins, the Tetherow basalt is a useful chronologic and stratigraphic marker bed.
Climate warming is expected to increase global methane (CH4) emissions from wetland ecosystems. Although in situ eddy covariance (EC) measurements at ecosystem scales can potentially detect CH4 flux changes, most EC systems have only a few years of data collected, so temporal trends in CH4 remain uncertain. Here, we use established drivers to hindcast changes in CH4 fluxes (FCH4) since the early 1980s. We trained a machine learning (ML) model on CH4 flux measurements from 22 [methane-producing sites] in wetland, upland, and lake sites of the FLUXNET-CH4 database with at least two full years of measurements across temperate and boreal biomes. The gradient boosting decision tree ML model then hindcasted daily FCH4 over 1981–2018 using meteorological reanalysis data. We found that, mainly driven by rising temperature, half of the sites (n = 11) showed significant increases in annual, seasonal, and extreme FCH4, with increases in FCH4 of ca. 10% or higher found in the fall from 1981–1989 to 2010–2018. The annual trends were driven by increases during summer and fall, particularly at high-CH4-emitting fen sites dominated by aerenchymatous plants. We also found that the distribution of days of extremely high FCH4 (defined according to the 95th percentile of the daily FCH4 values over a reference period) have become more frequent during the last four decades and currently account for 10–40% of the total seasonal fluxes. The share of extreme FCH4 days in the total seasonal fluxes was greatest in winter for boreal/taiga sites and in spring for temperate sites, which highlights the increasing importance of the non-growing seasons in annual budgets. Our results shed light on the effects of climate warming on wetlands, which appears to be extending the CH4 emission seasons and boosting extreme emissions.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.17131","usgsCitation":"Feron, S., Malhotra, A., Bansal, S., Fluet-Chouinard, E., McNicol, G., Knox, S., Delwiche, K., Cordero, R., Ouyang, Z., Zhang, Z., Poulter, B., and Jackson, R., 2024, Recent increases in annual, seasonal, and extreme methane fluxes driven by changes in climate and vegetation in boreal and temperate wetland ecosystems: Global Change Biology, v. 30, no. 1, e17131, 18 p., https://doi.org/10.1111/gcb.17131.","productDescription":"e17131, 18 p.","ipdsId":"IP-158799","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":440670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.17131","text":"Publisher Index Page"},{"id":424864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Feron, Sarah","contributorId":330045,"corporation":false,"usgs":false,"family":"Feron","given":"Sarah","affiliations":[{"id":78774,"text":"University of Groningen, Netherlands","active":true,"usgs":false}],"preferred":false,"id":892305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malhotra, Avni","contributorId":330047,"corporation":false,"usgs":false,"family":"Malhotra","given":"Avni","affiliations":[{"id":37399,"text":"University of Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":892306,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":892307,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fluet-Chouinard, Etienne","contributorId":217392,"corporation":false,"usgs":false,"family":"Fluet-Chouinard","given":"Etienne","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":892308,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McNicol, Gavin 0000-0002-6655-8045","orcid":"https://orcid.org/0000-0002-6655-8045","contributorId":260536,"corporation":false,"usgs":false,"family":"McNicol","given":"Gavin","email":"","affiliations":[],"preferred":false,"id":892309,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knox, Sarah 0000-0003-2255-5835","orcid":"https://orcid.org/0000-0003-2255-5835","contributorId":167493,"corporation":false,"usgs":false,"family":"Knox","given":"Sarah","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":892310,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delwiche, Kyle","contributorId":330044,"corporation":false,"usgs":false,"family":"Delwiche","given":"Kyle","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":892311,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cordero, Raul","contributorId":333264,"corporation":false,"usgs":false,"family":"Cordero","given":"Raul","email":"","affiliations":[],"preferred":false,"id":892312,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ouyang, Zutao","contributorId":260556,"corporation":false,"usgs":false,"family":"Ouyang","given":"Zutao","email":"","affiliations":[],"preferred":false,"id":892313,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zhang, Zhen 0000-0003-0899-1139","orcid":"https://orcid.org/0000-0003-0899-1139","contributorId":149173,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":892314,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Poulter, Benjamin","contributorId":330088,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":892315,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jackson, Robert B.","contributorId":330089,"corporation":false,"usgs":false,"family":"Jackson","given":"Robert B.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":892316,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70251280,"text":"70251280 - 2024 - Scattered tree death contributes to substantial forest loss in California","interactions":[],"lastModifiedDate":"2024-02-02T13:04:23.863538","indexId":"70251280","displayToPublicDate":"2024-01-20T07:00:59","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Scattered tree death contributes to substantial forest loss in California","docAbstract":"In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups ( ≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires.
The Flood Risk Management Project was initiated in 2008 in the Fargo-Moorhead metropolitan area to reduce flood risk, flood damages, and flood protection costs in the Fargo-Moorhead metropolitan area. In cooperation with the U.S. Army Corps of Engineers, the U.S. Geological Survey initiated a water-quality monitoring study to describe the water-quality characteristics of the Red River of the North and its tributaries in the Fargo-Moorhead metropolitan area during the preconstruction period of the Flood Risk Management Project from October 1, 2019, to October 1, 2022. The monitoring study included the collection of discrete and continuous water-quality data and streamflow monitoring at selected sites that integrated and enhanced existing monitoring programs within the study area.
Discrete samples collected at 10 sites in the Fargo-Moorhead metropolitan area were analyzed for major ions, trace elements, nutrients, suspended sediment, pesticides, and fecal indicator bacteria. In general, major ion concentrations were higher at sites on the tributaries (Wild Rice, Sheyenne, and Maple Rivers) compared to sites on the Red River of the North. In general, bicarbonate, calcium, magnesium, and sulfate represented most of the dissolved ions measured in samples collected at the 10 sites. Calcium, chloride, fluoride, potassium, silica, and sodium were also measured in samples, but they represented a smaller portion of the total dissolved ions. Sulfate was the most dominant dissolved ion that had the highest concentrations among the major ions measured in samples.
A total of 18 trace elements were analyzed in discrete samples. Several of the trace elements had concentrations below the laboratory reporting level in all of the samples, including antimony, beryllium, cadmium, chromium, silver, and thallium. Sites on the Wild Rice River generally had the highest concentrations of arsenic, barium, boron, manganese, and nickel compared to the other sites.
Nutrients analyzed in discrete samples included filtered and unfiltered concentrations of ammonia, nitrate plus nitrite, phosphorus, and organic carbon. The median filtered ammonia concentration at most sites was less than the laboratory reporting level of 0.03 milligram per liter as nitrogen except for the Sheyenne River at Harwood, North Dakota (U.S. Geological Survey [USGS] station 05060400), and Red River of the North near Georgetown, Minnesota (USGS station 05062130). The lowest median unfiltered nitrate plus nitrite concentration was measured at sites on the Red River of the North upstream from the Fargo-Moorhead metropolitan area and the highest median was at sites on the Red River of the North downstream from the Fargo-Moorhead metropolitan area compared to all other sites. The increase in nitrate plus nitrite concentrations could reflect the effect of the wastewater-treatment plant discharge that enters the Red River of the North upstream from the site located downstream from the Fargo-Moorhead metropolitan area and from urban runoff. Phosphorus (unfiltered) concentrations were generally higher at sites on the Maple and Sheyenne Rivers compared to the other sites and were higher at sites on the Red River of the North downstream from the Fargo-Moorhead metropolitan area compared to sites upstream on the Red River of the North.
Suspended-sediment concentrations were generally highest at sites in the Sheyenne River and lowest in the upstream Red River of the North sites. Suspended-sediment concentration was highly variable in samples collected at the 10 sites, mostly influenced by the occurrence of snowmelt and rainfall-runoff events. The Sheyenne River near Kindred, N. Dak. (USGS station 05059000) had the largest range in sediment concentrations in samples collected at the 10 sites. For all sites other than the Sheyenne River near Kindred, N. Dak., 95 percent or more of the suspended sediment had particle diameter sizes less than 0.0625 millimeter in 50 percent of the samples (median).
Of the 102 pesticides and pesticide degradates analyzed, 45 constituents had no detectable concentrations in any of the 17 samples collected at five sites. The remaining 57 pesticides had at least one detection in the samples collected at the five sites. The sites on the Wild Rice River (near Abercrombie, N. Dak., USGS station 05053000, and near St. Benedict, N. Dak., USGS station 05053500) and Sheyenne River near Kindred, N. Dak., had fewer pesticide detections compared to the Maple River below Mapleton, N.Dak. (USGS station 05060100) and the Red River of the North at Fargo, N. Dak (USGS station 05054000) and near Georgetown, Minn.
Patterns in annual loads generally followed the same pattern as streamflow at the 10 sites for water years 2020–22. A water year is the 12-month period from October 1 to September 30 and is designated by the calendar year in which it ends. The greatest loads for all constituents were delivered at the two downstream sites on the Red River of the North; sites that also had the highest annual streamflows among the sites and the greatest loads were delivered in water year 2020 when the highest streamflows occurred at the sites. Likewise, the least loads for most constituents were at the Maple River and were least in 2021 compared to the other years because of low-streamflow conditions.
Water-quality measurements continuously recorded at the Red River of the North at Hickson, N. Dak. (USGS station 05051522); Red River of the North at Fargo, N. Dak.; and Red River of the North near Georgetown, Minn. included water temperature, specific conductance, dissolved oxygen, pH, and turbidity. Specific conductance values were similar for the Red River of the North near Hickson, N. Dak., and Red River of the North at Fargo, N. Dak., when compared to the Red River of the North near Georgetown, Minn. that had higher values than the other two sites. Dissolved oxygen concentrations and pH were similar among the three sites on the Red River. The patterns in turbidity were mostly related to streamflow conditions and were similar among the three sites on the Red River of the North.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235136","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, St. Paul District","usgsCitation":"Galloway, J.M., Nustad, R.A., and Wheeling, S., 2024, Water-quality characteristics of the Red River of the North and tributaries in the Fargo-Moorhead metropolitan area, North Dakota, 2019–22: U.S. Geological Survey Scientific Investigations Report 2023–5136, 76 p., https://doi.org/10.3133/sir20235136.","productDescription":"Report: vii, 76 p.; Data Release; Dataset","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-155202","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":499398,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115957.htm","linkFileType":{"id":5,"text":"html"}},{"id":424499,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":424498,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B0S8L0","text":"USGS data release","linkHelpText":"Data and scripts used in water-quality characteristics of the Red River of the North and tributaries in the Fargo-Moorhead metropolitan area, North Dakota 2019–22"},{"id":424494,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5136/sir20235136.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5036"},{"id":424493,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5136/coverthb.jpg"},{"id":424497,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235136/full"},{"id":424495,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5136/sir20235136.XML"},{"id":424496,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5136/images/"}],"country":"United States","state":"Minnesota, North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.01554629866006,\n 47.455361951934975\n ],\n [\n -98.01554629866006,\n 46.13927557479718\n ],\n [\n -95.6424994236601,\n 46.13927557479718\n ],\n [\n -95.6424994236601,\n 47.455361951934975\n ],\n [\n -98.01554629866006,\n 47.455361951934975\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
The 1 September 1886 Charleston, South Carolina, earthquake was one of the largest preinstrumental earthquakes in eastern North America for which extensive contemporaneous observations were documented. The distribution of shaking was mapped shortly after the earthquake, and reconsidered by several authors in the late twentieth century, but has not been reconsidered with a modern appreciation for issues associated with macroseismic data interpretation. Detailed contemporary accounts have also never been used to map the distribution of numerical shaking intensities in the near field. In this study we reconsider macroseismic data from far‐field accounts as well as detailed accounts of damage in the near field, estimating modified Mercalli intensity values at 1297 locations including over 200 definite “not felt” reports that delineate the overall felt extent. We compare the results to the suite of ground‐motion models for eastern North America selected by the National Seismic Hazard Model, using a recently proposed mainshock rupture model and an average site condition for the locations at which intensities are estimated. The comparison supports the moment magnitude estimate, 7.3, from a recently proposed rupture model (Bilham and Hough, 2023). A ShakeMap constrained by model predictions and estimated intensities further illustrates this consistency, which we show is insensitive to rupture model details. Given the uncertainty of calibration relations for magnitudes close to 7, the overall intensity distribution provides a good characterization of shaking but cannot improve the independent moment magnitude estimate. We also identify a previously unrecognized early large aftershock that occurred 9–10 min after the mainshock, for which we estimate magnitude ∼5.6.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230224","usgsCitation":"Hough, S.E., and Bilham, R., 2024, The 1886 Charleston, South Carolina, earthquake: Intensities and ground motions: Bulletin of the Seismological Society of America, v. 114, no. 3, p. 1658-1679, https://doi.org/10.1785/0120230224.","productDescription":"22 p.","startPage":"1658","endPage":"1679","ipdsId":"IP-157642","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","city":"Charleston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.5,\n 33.3\n ],\n [\n -80.5,\n 32.65\n ],\n [\n -79.9,\n 32.65\n ],\n [\n -79.9,\n 33.3\n ],\n [\n -80.5,\n 33.3\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"114","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bilham, Roger","contributorId":225117,"corporation":false,"usgs":false,"family":"Bilham","given":"Roger","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":927606,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250914,"text":"sir20235126 - 2024 - Flood of October 31 to November 3, 2019, in the East Canada Creek, West Canada Creek, and Sacandaga River basins in central New York","interactions":[],"lastModifiedDate":"2026-01-30T19:22:05.254094","indexId":"sir20235126","displayToPublicDate":"2024-01-17T07:10:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5126","displayTitle":"Flood of October 31 to November 3, 2019, in the East Canada Creek, West Canada Creek, and Sacandaga River Basins in Central New York","title":"Flood of October 31 to November 3, 2019, in the East Canada Creek, West Canada Creek, and Sacandaga River basins in central New York","docAbstract":"Between October 31 and November 3, 2019, historic flooding in localized areas of the Mohawk Valley and southern Adirondack region in central New York State resulted in one fatality and an estimated $33 million in damages. Flooding resulted from high-intensity, hyperlocal rainfall in the region within a 24-hour period between October 31 and November 1, 2019, at the end of a much wetter than average October. In that 24-hour period, rainfall amounts largely ranged from 2 to 5 inches in the most heavily affected parts of the region, but a maximum rainfall amount for the region of 7 inches was recorded in Speculator, New York. This rainfall total for a 24-hour period for this location is estimated to have between a 200- and 500-year recurrence interval. The most severe flooding to result from the rainfall was mainly in the Sacandaga River basin, which is within the upper Hudson River basin, and in the East and West Canada Creek basins, which are within the Mohawk River basin.
Streamflow, stage, and reservoir elevation data, collected by the U.S. Geological Survey, are documented in this report. Flooding resulted in new peak streamflow records at five of six U.S. Geological Survey streamgages in the region that have periods of record of at least 20 years, including at three streamgages that have been in operation for about 100 years. At the sixth streamgage, this flooding resulted in the second highest peak streamflow in its 71-year period of record. For all six streamgages, estimates of flood magnitudes for selected annual exceedance probabilities were updated using the peak streamflows from the flooding. Additionally, the annual exceedance probabilities for the six respective peak streamflows were all estimated to be less than 1 percent (greater than a 100-year recurrence interval). At three of those six streamgages, however, previous annual peak streamflows of comparable magnitudes (within 10 percent) have also happened within the past 20 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235126","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Graziano, A.P., Gazoorian, C.L., Smith, T.L., and Lilienthal, A.G., III, 2024, Flood of October 31 to November 3, 2019, in the East Canada Creek, West Canada Creek, and Sacandaga River basins in central New York: U.S. Geological Survey Scientific Investigations Report 2023–5126, 37 p., https://doi.org/10.3133/sir20235126.","productDescription":"Report: vii, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129517","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":499390,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115944.htm","linkFileType":{"id":5,"text":"html"}},{"id":424346,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SCOJ7M","text":"USGS data release","linkHelpText":"Flood-frequency data for six selected streamgages following the central New York flood of October 31–November 3, 2019"},{"id":424342,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5126/sir20235126.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5126"},{"id":424341,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5126/coverthb.jpg"},{"id":424345,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5126/images/"},{"id":424344,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5126/sir20235126.XML"},{"id":424343,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235126/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5126"}],"country":"United States","state":"New York","otherGeospatial":"East Canada Creek basin, West Canada Creek basin, Sacandaga River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.89510221171278,\n 42.85733224203008\n ],\n [\n -73.69783658671246,\n 42.85733224203008\n ],\n [\n -73.69783658671246,\n 44.15608967292573\n ],\n [\n -75.89510221171278,\n 44.15608967292573\n ],\n [\n -75.89510221171278,\n 42.85733224203008\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Survival of out-migrating juvenile salmonids (Oncorhynchus spp.) through the Sacramento-San Joaquin River Delta averages less than 33 percent, depending on water flow through the delta, and is partially governed by the distribution of fish among three Sacramento River distributaries: Sutter, Steamboat, and Georgiana sloughs. Behavioral altering structures in the junctions of the distributaries can effectively increase entrainment into favorable routes, thereby increasing through-delta (Verona to Chips Island, California) survival. The effectiveness of these structures, hence forth called “behavioral barriers,” are dependent on shape, length, location, barrier type, and water velocity, which is governed by Sacramento River discharge (hereinafter referred to as “flow”).
We developed a machine learning tool to optimize behavioral barrier designs at up to three junctions within the Sacramento-San Joaquin Delta for improving through-delta survival of juvenile winter-run Chinook salmon (Oncorhynchus tshawytscha). This barrier optimization tool (BOT) works by evolving barrier solutions in one to three junctions by repeatedly simulating survival of populations of Sacramento River origin fish as they pass through the Delta. Over approximately 6,000 simulations per junction, the BOT converges on barrier designs that result in the greatest average survival given simulated environmental conditions. Survival at each iteration of the model is simulated using a modified version of the salmon travel time and routing simulation (STARS) model. In the BOT, STARS is modified by replacing probabilistic route determinations with an individual based model (IBM) that simulates fish behavior to predict the entrainment rates in each junction. The IBM allows the flexibility to explore how entrainment changes with evolving barrier designs. We used juvenile winter-run-sized Chinook salmon catch data collected at Knights Landing from 1997 to 2011 to create realistic arrival and spatial distributions of simulated fish within the BOT that varied among water years (hereafter years). We demonstrated the capabilities of the BOT by comparing optimized barrier solutions and their resulting simulated improvement in survival among three scenarios that differed in the number of junctions with barriers (Georgiana Slough, Steamboat Slough, or both) and the barrier operational period (early: November 1–March 15, or late: January 1–April 30). In this initial demonstration of the BOT we only considered a bioacoustic fish fence (BAFF) at Georgiana Slough and a floating fish guidance structure (FFGS) at Steamboat Slough.
The increase in simulated through-delta fish survival ranged from 1.0 to 6.3 percent among the optimized barrier designs. The most effective Georgiana Slough barrier design predicted improved survival by 6.3 percent and was chosen by the California Department of Water Resources (DWR) as the Georgiana Slough salmon migratory barrier planned for operation annually from 2023 to 2030 at Georgiana Slough in response to the 2020 California Department of Fish and Wildlife’s (CDFW) Incidental Take Permit Minimization Measure 8.9.1 (California Department of Fish and Wildlife [CDFW], 2020). When barriers were simulated in both junctions, the percentages of simulated winter-run Chinook salmon interacting with a barrier at Steamboat or Georgiana sloughs were 95 percent given the early operational period and 48 percent given the late operational period. When barriers were simulated at both sloughs, the optimal barrier at Steamboat Slough effectively routed fish into the Sacramento River. This is because the Georgiana Slough barrier reduced routing into Georgiana Slough where survival is low, which resulted in higher survival for fish routed down the Sacramento River at Steamboat Slough than fish routed down Steamboat Slough. Whereas when no barrier was simulated at Georgiana Slough, the optimized barrier at Steamboat Slough routed fish into Steamboat Slough. This is because survival was higher through Steamboat Slough than the Sacramento River and Georgiana Slough combined. The greatest improvement in survival (6.3 percent) was predicted over the earlier operational period with only a barrier at Georgiana Slough.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231095","collaboration":"Prepared in cooperation with the California Department of Water Resources","usgsCitation":"Swyers, N.M., Blake, A., Stumpner, P., Burau, J.R., Burdick, S.M., and Anwar, M.S., 2024, A machine learning tool for design of behavioral fish barriers in the Sacramento-San Joaquin River Delta: U.S. Geological Survey Open-File Report 2023–1095, 38 p., https://doi.org/10.3133/ofr20231095.","productDescription":"ix, 38 p.","onlineOnly":"Y","ipdsId":"IP-151594","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":424660,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231095/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1095"},{"id":424362,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1095/images"},{"id":424363,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1095/ofr20231095.XML"},{"id":424359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1095/ofr20231095.jpg"},{"id":424360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1095/ofr20231095.pdf","text":"Report","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1095"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.4,\n 38.5\n ],\n [\n -122.4,\n 38.0\n ],\n [\n -121.8,\n 38.0\n ],\n [\n -121.8,\n 38.5\n ],\n [\n -122.4,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543-1598
Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.
Plant macrofossils from packrat (Neotoma spp.) middens provide direct evidence of past vegetation changes in arid regions of North America. Here we describe the newest version (version 5.0) of the U.S. Geological Survey (USGS) North American Packrat Midden Database. The database contains published and contributed data from 3,331 midden samples collected in southwest Canada, the western United States, and northern Mexico, with samples ranging in age from 48 ka to the present. The database includes original midden-sample macrofossil counts and relative-abundance data along with a standardized relative-abundance scheme that makes it easier to compare macrofossil data across midden-sample sites. In addition to the midden-sample data, this version of the midden database includes calibrated radiocarbon (14C) ages for the midden samples and plant functional type (PFT) assignments for the midden taxa. We also provide World Wildlife Fund ecoregion assignments and climate and bioclimate data for each midden-sample site location. The data are provided in tabular (.xlsx), comma-separated values (.csv), and relational database (.mdb) files.
In recent years, the Mississippi River Deltaic Plain (MRDP) has experienced the highest rates of wetland loss in the USA. Although the process of vertical drowning has been heavily studied in coastal wetlands, less is known about the relationship between elevation change and land loss in wetlands that are experiencing lateral erosion and the contribution of erosion to land loss in the MRDP. We quantified relationships of elevation change and land change in ten submerging tidal wetlands and found that, despite significant land loss, elevation trajectories in seven of the land loss study sites were positive. Furthermore, we observed an acceleration in elevation gain preceding the conversion from vegetated marsh to open water.
To identify regional contributions of lateral erosion to land loss, we quantified the relationship of elevation change and land change in 159 tidal marsh sites in the MRDP. Approximately half the sites were persistently losing land, and 82% of these sites were vulnerable to erosion, identifying erosion as a dominant mechanism of coastal wetland loss in this region. Notably, the sites that were vulnerable to erosion were experiencing land loss while also gaining elevation, and sites with the highest land loss exhibited accelerating elevation gain. Together, these data illustrate that (1) erosion is a dominant mechanism of wetland loss in the MRDP, (2) accelerated elevation gain is an indicator of erosion, and (3) consideration of elevation change trajectories within the context of land change is critical for providing accurate coastal wetland vulnerability assessments.
Many species of wildlife alter their daily activity patterns in response to co-occurring species as well as the surrounding environment. Often smaller or subordinate species alter their activity patterns to avoid being active at the same time as larger, dominant species to avoid agonistic interactions. Human development can complicate interspecies interactions, as not all wildlife respond to human activity in the same manner. While some species may change the timing of their activity to avoid being active when humans are, others may be unaffected or may benefit from being active at the same time as humans to reduce predation risk or competition. To further explore these patterns, we used data from a coordinated national camera-trapping program (Snapshot USA) to explore how the activity patterns and temporal activity overlap of a suite of seven widely co-occurring mammalian mesocarnivores varied along a gradient of human development. Our focal species ranged in size from the large and often dominant coyote (Canis latrans) to the much smaller and subordinate Virginia opossum (Didelphis virginiana). Some species changed their activity based on surrounding human development. Coyotes were most active at night in areas of high and medium human development. Red fox (Vulpes vulpes) were more active at dusk in areas of high development relative to areas of low or medium development. However, because most species were primarily nocturnal regardless of human development, temporal activity overlap was high between all species. Only opossum and raccoon (Procyon lotor) showed changes in activity overlap with high overlap in areas of low development compared to areas of moderate development. Although we found that coyotes and red fox altered their activity patterns in response to human development, our results showed that competitive and predatory pressures between these seven widespread generalist species were insufficient to cause them to substantially alter their activity patterns.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0288477","usgsCitation":"McTigue, L., Lassiter, E.V., Shaw, M., Johansson, E., Wilson, K., and DeGregorio, B.A., 2024, Does daily activity overlap of seven mesocarnivores vary based on human development?: PLoS ONE, v. 19, no. 1, e0288477, 12 p., https://doi.org/10.1371/journal.pone.0288477.","productDescription":"e0288477, 12 p.","ipdsId":"IP-147648","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440736,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0288477","text":"Publisher Index Page"},{"id":432154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"McTigue, Leah","contributorId":310420,"corporation":false,"usgs":false,"family":"McTigue","given":"Leah","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lassiter, Ellery V.","contributorId":340666,"corporation":false,"usgs":false,"family":"Lassiter","given":"Ellery","email":"","middleInitial":"V.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaw, Mike","contributorId":340667,"corporation":false,"usgs":false,"family":"Shaw","given":"Mike","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907440,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johansson, Emily","contributorId":340668,"corporation":false,"usgs":false,"family":"Johansson","given":"Emily","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907441,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Ken","contributorId":340670,"corporation":false,"usgs":false,"family":"Wilson","given":"Ken","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907442,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907443,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70251382,"text":"70251382 - 2024 - Evolution of a lake margin recorded in the Sutton Island member of the Murray formation, Gale crater, Mars","interactions":[],"lastModifiedDate":"2024-02-08T12:46:13.419903","indexId":"70251382","displayToPublicDate":"2024-01-11T06:41:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7353,"text":"Journal of Geophysical Research - Planets","active":true,"publicationSubtype":{"id":10}},"title":"Evolution of a lake margin recorded in the Sutton Island member of the Murray formation, Gale crater, Mars","docAbstract":"This study uses data from the Mars Science Laboratory Curiosity rover to document the facies of the Sutton Island member of the Murray formation, interpret paleoenvironments, and establish key stratigraphic transitions at Gale crater. Two facies associations were identified: Facies Association 1 (FA1) and Facies Association 2 (FA2). Individual facies in FA1 include planar-laminated mudstone with minor intervals of planar sandstone, ripple cross-laminated sandstone, cross-stratified sandstone, and alternating laminated sandstone and mudstone. Meter-thick packages of planar-laminated mudstone in FA1 are interpreted to represent deposition in low-energy ponded environments along the lake margin. Straight- and curve-crested ripple cross-laminated facies are interpreted to represent current-influenced deposition. Cross-stratified sandstone facies consist of dm-thick sets that represent deposition in distal channels. Intercalated mm-scale mudstone and sandstone laminae represent waning flow conditions and possible channel abandonment. Facies in FA1 collectively represent deposition in a distal delta plain. FA2 is comprised of planar-laminated mudstone with minor sandstone and is interpreted to represent deposition in a lacustrine-basin setting by suspension settling linked to density flows. FA1 transitions upward into FA2, defining a rapid transgression substantial enough to facilitate the deposition of distal lake facies above delta plain facies. The abrupt transition from FA2 back to FA1 deltaic deposits is suggestive of forced regression. Facies in FA1 and FA2 are consistent with the prevalence of aqueous environments recorded in other Murray formation members and extend our understanding of the dynamic sedimentary processes that characterized ancient lacustrine systems at Gale crater.
Understanding pollinator networks requires species level data on pollinators. New photographic approaches to identification provide avenues to data collection that reduce impacts on declining bumblebee species, but limited research has addressed their accuracy. Using blind identification of 1418 photographed bees, of which 561 had paired specimens, we assessed identification and agreement across 20 bumblebee species netted in Montana, North Dakota, and South Dakota by people with minimal training. An expert identified 92.4% of bees from photographs, whereas 98.2% of bees were identified from specimens. Photograph identifiability decreased for bees that were wet or matted; bees without clear pictures of the abdomen, side of thorax, or top of thorax; bees photographed with a tablet, and for species with more color morphs. Across paired specimens, the identification matched for 95.1% of bees. When combined with a second opinion of specimens without matching identifications, data suggested a similar misidentification rate (2.7% for photographs and 2.5% specimens). We suggest approaches to maximize accuracy, including development of rulesets for collection of a subset of specimens based on difficulty of identification and to address cryptic variation, and focused training on identification that highlights detection of species of concern and species frequently confused in a study area.
Prices for battery-grade lithium have increased substantially since 2020, which is propelling the search for additional sources of this important element. Battery-grade lithium is predominately recovered from continental brines. Most crude oil and natural gas wells recover briny formation water, which may represent an additional source. Chemical analysis of these waters has been shown to indicate the presence of varying concentrations of lithium and related elements. This paper briefly reviews developments and literature supporting the presence of lithium in petroleum reservoir brines. It also describes the coverage and distribution of lithium data analyses in the United States Geological Survey National Produced Waters Geochemical Database (PWGD). It then addresses the question as to whether a lithium concentration can be accurately predicted using constituents of ion chemistry in produced brines from specific geologic formations. Four machine learning algorithms are employed to classify the commercial potential of lithium in oil field brines using data from oil wells recovering formation water from the Smackover Formation. The calibrated classification models are further applied to new (out-of-sample) data from the Marcellus Formation in the Appalachian Basin. Among the approaches considered, the predictive performance and wider applicability of the gradient boosted tree and the deep neural network models are determined to be the most promising. Finally, we discuss how the calibrated models could be applied to assure the quality of the data reported from chemical laboratory analysis and for imputation when lithium values are missing.
","language":"English","publisher":"Springer","doi":"10.1007/s13563-023-00409-8","usgsCitation":"Attanasi, E., Coburn, T., and Freeman, P., 2024, Machine learning approaches to identify lithium concentration in petroleum produced waters: Mineral Economics, v. 37, p. 477-497, https://doi.org/10.1007/s13563-023-00409-8.","productDescription":"21 p.","startPage":"477","endPage":"497","ipdsId":"IP-144611","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":424326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","noUsgsAuthors":false,"publicationDate":"2024-01-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":1809,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":891987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coburn, Timothy","contributorId":333122,"corporation":false,"usgs":false,"family":"Coburn","given":"Timothy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":891988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":891989,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}