In 2022, the Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at 11 volcanic centers in Alaska and in the Northern Mariana Islands. Eruptive activity in Alaska consisted of repeated small, ash-producing, phreatomagmatic explosions from Mount Young on Semisopochnoi Island; the eruption of a thick lava flow within the summit crater at Great Sitkin Volcano; and weak explosive activity and the eruption of small, channelized flows at Pavlof Volcano. Uplift and an increase in seismicity were detected at Mount Edgecumbe, a long-dormant volcano in southeastern Alaska. Anomalous seismicity was also detected at three other volcanoes, including Trident Volcano, Takawangha volcano, and Davidof volcano. Other activity documented in 2022 includes ash resuspension events at Mount Katmai and Aniakchak Crater, and Mount Cleveland had a period of unrest, but no eruptive activity took place. In the Commonwealth of the Northern Marianas Islands, hydroacoustic detections and a submarine plume observed in satellite data at Ahyi seamount indicated underwater eruptive activity there.
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During an effusive eruption crisis the initial advance of a lava flow is typically the primary focus of model forecasts and hazard management efforts. Flow branching and lateral expansion of lava flows can pose significant dangers within evolving flow fields throughout the duration of an eruption and are an underappreciated hazard. We use field monitoring, infrasound, time lapse imagery, and lidar data collected during the 2018 lower East Rift Zone eruption of Kīlauea (Hawai‘i) to track the origins, progression, and implications of a flow branching event caused by catastrophic levee failure. Our analyses show that surges in effusion rate, rheologic transitions between pāhoehoe and ‘a‘ā flow regimes, slope-breaks, pre-existing topographic highs, and the structure of perched levee walls all played a role in the failure of the levee and subsequent re-routing of the lava flow. Failure of perched lava structures leads to an acutely hazardous situation because lava impounded by the structure can rapidly inundate the landscape. This is the first time a levee failure event has been observed in such detail with numerous monitoring techniques; this unprecedented level of observation provides quantifiable insights into levee failure processes that have important implications for hazard mitigation and an improved understanding of lava flow emplacement dynamics.
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The MCS is the earliest comprehensive source of 2024 mineral production data for the world. More than 90 individual minerals and materials are covered by two-page synopses.
Abbreviations and units of measure and definitions of selected terms used in the report are in Appendix A and Appendix B, respectively. Reserves and resources information is in Appendix C, which includes “Part A—Resource and Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is in Appendix D.
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2025 are welcomed.
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","tableOfContents":"A thorough understanding of fluvial sediment transport is essential for addressing key environmental issues such as aquatic habitat degradation, flooding, excess nutrients, and challenges with river restoration. Fluvial sediment samples are valuable for addressing these concerns, but their collection is often impractical across all rivers and timeframes of interest. In addition, previously used analytical and numerical methods have not allowed for the transfer of knowledge from sites that have data to sites that do not have data. To overcome this limitation, the U.S. Geological Survey developed machine learning models to predict suspended-sediment concentrations and bedload transport in Minnesota rivers that lack physical sediment data and integrated them into the U.S. Geological Survey StreamStats web application.
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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Expanding human enterprise leading to resource subsidies for generalist species has resulted in widespread increases in common raven (Corvus corax) populations across the Western U.S. Ravens are an efficient predator and increased population abundance has led to adverse effects to multiple sensitive prey species. In regions where problematic interactions between ravens and their prey exist, managers seek efficient and effective tools for monitoring and controlling expanding raven populations. We previously developed a Rapid Assessment Function (RAF) for managers to quickly estimate raven population density and assess the need for management actions. We developed the RAF for the Great Basin (GB RAF) by first estimating raven density using robust distance sampling protocols with >30,000 raven point count surveys from sagebrush ecosystems in California, Nevada, Idaho, and Oregon across 131 field sites and years. We then used the relationship between raven density estimates from distance sampling and n ravens observedsite-year/ n surveyssite-year (that is, raven index) at each site-year combination to develop a function that accounts for detection probability and adjusts simple counts to provide a prediction of ‘true’ density. Our function produced reliable density estimates given approximately 50–100 surveys, thereby reducing the field-based and analytical efforts typically needed to estimate raven density, facilitating more efficient raven management in open sagebrush habitats. In this study, we sought to test our original GB RAF using data from sagebrush ecosystems outside of the Great Basin. Using raven point count data from two field site units in Washington state collected from 2016 to 2023, we calculated density estimates from distance sampling methods, comparable to what was done for previous analyses. We then used the GB RAF to generate predictions of density and compared those values to the more robust estimates from distance sampling. Additionally, we developed modified RAFs specifically for Washington data (WA RAFs) to assess how well they predicted raven density compared to the GB RAF. We found the detection curves estimated for the Washington sites largely aligned with those used to generate the original GB RAF. Furthermore, the estimates from the GB RAF exhibited similar or higher correlation with densities calculated from distance models (Pearson’s r = 0.73) than the modified WA RAFs with 1.33 km and 1.25 km truncation distances (Pearson’s r = 0.63 and 0.73, respectively). Producing an equivalently performing modified WA RAF would likely necessitate more data to reduce estimation error and produce more reliable estimates. These results provide evidence for the applicability of our GB RAF for more widespread use within sagebrush ecosystems, possibly negating the need for locally developed RAFs. Continued assessments of the GB RAF outside of the Great Basin would further verify its applicability across the sagebrush biome.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2025.01.27.635125","usgsCitation":"Brussee, B.E., O’Neil, S.T., Atamian, M., Leingang, C., and Coates, P.S., 2025, Evaluation of a rapid assessment function to aid monitoring and management of common ravens (Corvus corax) in Washington state: BioRxiv, https://doi.org/10.1101/2025.01.27.635125.","productDescription":"27 p.","ipdsId":"IP-167957","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":489959,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1101/2025.01.27.635125","text":"Publisher Index Page"},{"id":482499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atamian, Michael T.","contributorId":351491,"corporation":false,"usgs":false,"family":"Atamian","given":"Michael T.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":928685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leingang, Colin G.","contributorId":351492,"corporation":false,"usgs":false,"family":"Leingang","given":"Colin G.","affiliations":[{"id":83997,"text":"Yakima Training Center","active":true,"usgs":false}],"preferred":false,"id":928686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928687,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263339,"text":"70263339 - 2025 - Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning","interactions":[],"lastModifiedDate":"2025-02-06T15:53:28.669956","indexId":"70263339","displayToPublicDate":"2025-01-29T09:49:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning","docAbstract":"Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.
","language":"English","publisher":"MDPI","doi":"10.3390/rs17030457","usgsCitation":"Shastry, A.R., Cerovski-Darriau, C., Coltin, B., and Stock, J.D., 2025, Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning: Remote Sensing, v. 17, no. 3, 457, 11 p., https://doi.org/10.3390/rs17030457.","productDescription":"457, 11 p.","ipdsId":"IP-153917","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":487628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17030457","text":"Publisher Index Page"},{"id":481746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.41245311245856,\n 35.20197552578807\n ],\n [\n -117.96299074904582,\n 36.06858120494961\n ],\n [\n -118.86530937366612,\n 37.63884023254646\n ],\n [\n -119.85965473348287,\n 38.80651233617289\n ],\n [\n -120.17114612624695,\n 40.23030133169971\n ],\n [\n -120.73602418336918,\n 40.662012753561754\n ],\n [\n -122.36739903137283,\n 40.400491599532984\n ],\n [\n -120.74692405664294,\n 38.0147515126105\n ],\n [\n -119.36615838296214,\n 35.979191454701876\n ],\n [\n -118.41245311245856,\n 35.20197552578807\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Shastry, Apoorva Ramesh 0000-0002-3996-4857","orcid":"https://orcid.org/0000-0002-3996-4857","contributorId":317867,"corporation":false,"usgs":true,"family":"Shastry","given":"Apoorva","email":"","middleInitial":"Ramesh","affiliations":[{"id":227,"text":"Earth Surface Dynamics Program","active":true,"usgs":true}],"preferred":true,"id":926515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cerovski-Darriau, Corina 0000-0002-0543-0902","orcid":"https://orcid.org/0000-0002-0543-0902","contributorId":221159,"corporation":false,"usgs":true,"family":"Cerovski-Darriau","given":"Corina","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coltin, Brian","contributorId":350636,"corporation":false,"usgs":false,"family":"Coltin","given":"Brian","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":926517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stock, Jonathan D. 0000-0001-8565-3577 jstock@usgs.gov","orcid":"https://orcid.org/0000-0001-8565-3577","contributorId":3648,"corporation":false,"usgs":true,"family":"Stock","given":"Jonathan","email":"jstock@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":926518,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263130,"text":"70263130 - 2025 - Forecasting sea otter recolonization: Insights from isotopic analysis of modern and zooarchaeological populations","interactions":[],"lastModifiedDate":"2025-01-30T15:28:06.452327","indexId":"70263130","displayToPublicDate":"2025-01-29T09:23:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18342,"text":"Proceedings of the Royal Society B, Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting sea otter recolonization: Insights from isotopic analysis of modern and zooarchaeological populations","docAbstract":"Retrospective datasets offer essential context for conservation by revealing species’ ecological roles before industrial-era human impacts. We analysed isotopic compositions of pre-industrial and modern sea otters (Enhydra lutris) to reconstruct pre-extirpation ecology and offer insights for management. Our study focuses on southeast Alaska (SEAK), where sea otters are recolonizing, and northern Oregon, where translocations are being considered. We measured bulk bone collagen δ13C and δ15N values and essential amino acid δ13C values of extirpated sea otters from archaeological contexts, and bulk isotopic values from vibrissae of modern SEAK sea otters. We compare these results with published isotopic data of potential prey and additional archaeological datasets. In SEAK, our data show pre-industrial sea otter populations consumed infaunal bivalves and used soft-sediment (33%) and kelp forest habitats (67%), with sub-regional variation. We anticipate current populations will expand into this historical niche, and conflict with regional traditional/subsistence bivalve fisheries will persist. In northern Oregon, isotopic data from extirpated sea otters indicate past consumption of low trophic level invertebrates and a stronger reliance on kelp forests (88%) rather than soft-sediment habitats, highlighting the importance of kelp forests for future translocations. Our work exemplifies the value of historical ecology in informing conservation strategies for recovering species.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rspb.2024.1682","usgsCitation":"Elliott Smith, E.A., Moss, M., Wellman, H., Gill, V., Monson, D., and Newsome, S.D., 2025, Forecasting sea otter recolonization: Insights from isotopic analysis of modern and zooarchaeological populations: Proceedings of the Royal Society B, Biological Sciences, v. 292, no. 2039, 20241682, 12 p., https://doi.org/10.1098/rspb.2024.1682.","productDescription":"20241682, 12 p.","ipdsId":"IP-162071","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":499595,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11775623/","text":"External Repository"},{"id":481502,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.26359877499726,\n 46.308565616179806\n ],\n [\n -124.26359877499726,\n 45.25430222380987\n ],\n [\n -123.48250866767472,\n 45.25430222380987\n ],\n [\n -123.48250866767472,\n 46.308565616179806\n ],\n [\n -124.26359877499726,\n 46.308565616179806\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -134.6258986222449,\n 59.17859339162297\n ],\n [\n -137.37101203559084,\n 58.25218310558745\n ],\n [\n -133.21606440626744,\n 54.52221277170722\n ],\n [\n -131.50965539994962,\n 54.47095791058172\n ],\n [\n -130.7340511065769,\n 55.94266269565489\n ],\n [\n -134.6258986222449,\n 59.17859339162297\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"292","issue":"2039","noUsgsAuthors":false,"publicationDate":"2025-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Elliott Smith, Emma A.","contributorId":140743,"corporation":false,"usgs":false,"family":"Elliott Smith","given":"Emma","email":"","middleInitial":"A.","affiliations":[{"id":13339,"text":"University of New Mexico, Albuquerque","active":true,"usgs":false}],"preferred":false,"id":925637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moss, Madonna L.","contributorId":350305,"corporation":false,"usgs":false,"family":"Moss","given":"Madonna L.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":925638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wellman, Hannah P.","contributorId":350306,"corporation":false,"usgs":false,"family":"Wellman","given":"Hannah P.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":925639,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gill, Verena A.","contributorId":140658,"corporation":false,"usgs":false,"family":"Gill","given":"Verena A.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":925640,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":925641,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newsome, Seth D.","contributorId":81640,"corporation":false,"usgs":false,"family":"Newsome","given":"Seth","email":"","middleInitial":"D.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":925642,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262822,"text":"sir20245105 - 2025 - Groundwater hydrology, groundwater and surface-water interactions, water quality, and groundwater-flow simulations for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2017–19","interactions":[],"lastModifiedDate":"2025-01-29T14:30:56.495951","indexId":"sir20245105","displayToPublicDate":"2025-01-28T12:40:00","publicationYear":"2025","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":"2024-5105","displayTitle":"Groundwater Hydrology, Groundwater and Surface-Water Interactions, Water Quality, and Groundwater-Flow Simulations for the Wet Mountain Valley Alluvial Aquifer, Custer and Fremont Counties, Colorado, 2017–19","title":"Groundwater hydrology, groundwater and surface-water interactions, water quality, and groundwater-flow simulations for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2017–19","docAbstract":"In 2017, the U.S. Geological Survey, in cooperation with the Upper Arkansas Water Conservancy District, began a study to provide a comprehensive analysis of the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado. The study included collection of data pertaining to groundwater hydrology, groundwater and surface-water interactions, and water quality in the alluvial aquifer. In addition to providing foundational information on the hydrology of the alluvial aquifer, a numerical groundwater-flow model was developed to estimate the potential effects of additional storage of groundwater in the alluvial aquifer.
Groundwater-level elevation data from 30 wells were used to estimate groundwater-flow directions in the alluvial aquifer, which were generally from the southwest to northeast, away from the Sangre de Cristo Mountains and towards perennial streams in the center of the valley. Although some seasonal variation was apparent in groundwater-level elevation records, no statistically significant seasonal trends were indicated. Statistically significant long-term trends were indicated in groundwater-level elevation records for 8 of the 30 wells, and of these wells with statistically significant trends, all but 1 indicated a negative trend of groundwater-level elevations. Spatial evaluation of wells with statistically significant negative groundwater-level elevation trends showed many are in areas of denser well drilling for domestic or other uses, indicating increasing groundwater use could potentially be causing groundwater-level elevation declines. There were instances of wells with no statistically significant groundwater-level elevation trends also located in areas of greater density of well completions. Additional investigations may be necessary to more fully characterize the processes responsible for negative groundwater-level elevation trends.
Streamflow gain or loss calculations were completed for low flow in 2017–19 and for high flow in 2018 in nine reaches of streams within the study area. Stream reaches of the upper Texas Creek, upper Grape Creek, upper-middle Grape Creek, and Taylor Creek displayed consistent streamflow loss in each period from 2017 to 2019. These stream reaches represent long-term sources of recharge to the alluvial aquifer. Streamflow gain or loss varies through time in other stream reaches (lower Texas Creek, lower-middle Grape Creek, lower Grape Creek below Westcliffe, and lower Grape Creek above DeWeese Reservoir). The temporally variable behavior indicates these stream reaches may be sources of groundwater recharge or areas of groundwater discharge, likely depending on temporal dynamics between the elevation of the water table and the stream.
Water-quality samples were collected from 10 groundwater wells and 10 stream sites during September through November 2019. All groundwater and stream samples were analyzed for major and trace elements and stable isotopes of water. A subset of groundwater samples was also analyzed for the environmental tracers sulfur hexafluoride, tritium, and noble gases. Comparison of water-quality results to U.S. Environmental Protection Agency drinking water-quality standards indicated no constituents exceeded primary standards for human health. Spatial evaluation of water quality indicated the concentrations of various constituents are likely controlled by groundwater and surface-water interactions and by spatial variability in bedrock geology underlying the alluvial aquifer. Specifically, streams shown to gain from groundwater had water chemistry constituent compositions similar to groundwater, whereas streams exiting the Sangre de Cristo Mountains tended to have compositions consistent with snowmelt. Groundwater geochemistry appeared to be partially controlled by oxidation-reduction processes and by proximity to igneous rocks in the Wet Mountains. Environmental tracers used to estimate groundwater age indicated all sampled groundwater contained tracers representing modern recharge (approximately less than 65 years old) but mixing of premodern recharge (approximately more than 65 years old) also occurs. Spatial evaluation of environmental tracers indicated large faults may be conduits for upwelling of older groundwater. No trends were observed in groundwater age with well depth, indicating all sampled wells are located within the zone of active groundwater flow. The presence of modern groundwater in wells with statistically significant negative groundwater-level elevation trends indicates groundwater storage depletions may be partially offset by capture of modern recharge. Repeated sampling of groundwater age would be necessary, however, to determine if any trends in groundwater age exist, which may indicate changing groundwater recharge, storage, or discharge. Additional investigations could also consider quantifying groundwater age in deeper wells to more fully define the depth of active groundwater flow.
A numerical groundwater-flow model was developed to estimate components of the water budget, simulate groundwater and surface-water interactions, and evaluate the potential effects of aquifer storage and recovery. Simulated groundwater-level elevations from the calibrated groundwater-flow model are similar to the observed pattern of groundwater-level elevations with higher elevations in the western part of the study area along the Sangre de Cristo Mountains. Simulated water-budget components indicate most of the recharge to the alluvial aquifer is derived from streamflow losses, which is consistent with observations of losing streams along the mountain front. The largest groundwater discharge component of the alluvial aquifer was to streams in the center of the valley, where observations of stream gain or loss indicated the predominance of gaining conditions. Comparison of groundwater and surface-water interactions between the calibrated groundwater-flow model for 2000-19 (the base-case model) and a simulation including additional recharge, representing potential aquifer storage and recovery operations, indicated the additional recharge distributed throughout the area had minimal effects on streamflow in the nearby Grape Creek. An analysis of subregional groundwater budgets showed approximately 54 percent of the additional recharge flowed back to nearby Grape Creek, and the other 46 percent was distributed laterally into adjacent cells in the alluvial aquifer. The comparison of simulations and subregional water budget show the additional recharge did not substantially alter groundwater-level elevations or basin wide groundwater storage. Although the analysis of additional recharge provided in the numerical groundwater-flow model considers only one of many possible recharge scenarios, the model provides a useful tool that could be modified for various scenarios to understand potential effects of managed aquifer recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245105","collaboration":"Prepared in cooperation with the Upper Arkansas Water Conservancy District","usgsCitation":"Newman, C.P., Russell, C.A., Kisfalusi, Z.D., and Paschke, S.S., 2025, Groundwater hydrology, groundwater and surface-water interactions, water quality, and groundwater-flow simulations for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2017–19: U.S. Geological Survey Scientific Investigations Report 2024–5105, 62 p., https://doi.org/10.3133/sir20245105.","productDescription":"Report: vii, 62 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-125470","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":481114,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5105/coverthb.jpg"},{"id":481115,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5105/sir20245105.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5105"},{"id":481144,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9342SSP","text":"USGS data release","linkHelpText":"Environmental tracer model for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2019"},{"id":481145,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AAUGNY","text":"USGS data release","linkHelpText":"Groundwater-flow model of the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado"},{"id":481407,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5105/images"},{"id":481408,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5105/sir20245105.xml"},{"id":481417,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245105/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5105"}],"country":"United States","state":"Colorado","county":"Custer County, Fremont County","otherGeospatial":"Upper Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.1667,\n 38.5\n ],\n [\n -105.1667,\n 37.9167\n ],\n [\n -105.9167,\n 37.9167\n ],\n [\n -105.9167,\n 38.5\n ],\n [\n -105.1667,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
The Morley Nelson Snake River Birds of Prey National Conservation Area (NCA), in southwestern Idaho, USA supports a large population of breeding Prairie Falcons (Falco mexicanus). Abundance of Prairie Falcons in the NCA was previously monitored in 1976–1978 and 1990–1994. That research indicated maximum counts for each period in 1976 and 1992 and a possible population decline across that time span. We assessed the abundance and nesting success of Prairie Falcons in the NCA in 2002–2003 and 2019–2021, and we compared results to data from before 2000 to assess possible population change. Number of nesting pairs increased over 45 years from peak counts of 206, 193, and 217 in the 1970s, 1990s, and early 2000s, respectively, to 257 in 2021. Increases were not concentrated in one region, but widely distributed across the study area. Rates of nesting success in 2002–2003 and 2019–2021 averaged 57 ± 11.8% (SD) at 49.8 ± 3.3 nests observed each year and did not differ from pre-2000 rates. Finally, our analysis showed that in all 10 years in which a full census was conducted, a sampling approach to surveys would have been effective at estimating the number of falcons nesting within the NCA. Prairie Falcons are of conservation concern because of possible population declines in parts of their range. These results illustrate an area with apparently increasing numbers of this important species and highlight the importance of long-term surveys for tracking population fluctuations and the value of a national conservation area for providing raptor breeding habitat.
","language":"English","publisher":"The Raptor Research Foundation, Inc.","doi":"10.3356/jrr2395","usgsCitation":"Alsup, S., Belthoff, J.R., Steenhof, K., Kochert, M.N., and Katzner, T., 2025, Prairie Falcon (Falco mexicanus) abundance in a National Conservation Area in Idaho has increased since the 1970s–1990s: Journal of Raptor Research, v. 59, no. 1, p. 1-13, https://doi.org/10.3356/jrr2395.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-160247","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":498250,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr2395","text":"Publisher Index Page"},{"id":481494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"59","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alsup, Steven","contributorId":245281,"corporation":false,"usgs":false,"family":"Alsup","given":"Steven","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":925723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":925724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steenhof, Karen karen_steenhof@usgs.gov","contributorId":203439,"corporation":false,"usgs":false,"family":"Steenhof","given":"Karen","email":"karen_steenhof@usgs.gov","affiliations":[],"preferred":false,"id":925725,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kochert, Michael N. 0000-0002-4380-3298 mkochert@usgs.gov","orcid":"https://orcid.org/0000-0002-4380-3298","contributorId":3037,"corporation":false,"usgs":true,"family":"Kochert","given":"Michael","email":"mkochert@usgs.gov","middleInitial":"N.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":925726,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":925727,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262914,"text":"70262914 - 2025 - Hotter temperatures alter riparian plant outcomes under regulated river conditions","interactions":[],"lastModifiedDate":"2025-01-28T15:42:49.175773","indexId":"70262914","displayToPublicDate":"2025-01-27T09:39:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Hotter temperatures alter riparian plant outcomes under regulated river conditions","docAbstract":"Climate change and river regulation alter environmental controls on riparian plant occurrence and cover worldwide. Simultaneous changes to river flow and air temperature could result in unanticipated plant responses to novel environmental conditions. Increasing temperature could alter riparian plant response to hydrology and other factors, while river regulation may exacerbate environmental stress through novel flows like those resulting from power generation. Further, plant establishment and growth may require differing conditions, which may be decoupled by novel conditions. Using a large dataset that spans a natural 5°C mean annual temperature (MAT) gradient and a Bayesian model that integrates plant occurrence and cover, we address four questions: (1) Does hotter MAT modify plant response to hydrology, substrate composition, topography, and cover of co-occurring plant species? (2) Does the timing of hydropower tides benefit some species over others? (3) Does dam-induced erosion hinder riparian species more than upland species? (4) Do occurrence and cover respond to different environmental variables, allowing for decoupling of life history processes? We addressed these questions with data collected along 364 km of the Colorado River downstream of Glen Canyon Dam, Arizona, United States of America. Occurrence and cover class were recorded in >10,000 plots from 2016 to 2020, along with environmental covariates that repeat across the climate gradient. For 36 species, plant occurrence and cover were modeled with respect to MAT, hydrology, substrate, topography, other plant cover, and their interactions with MAT. There were four key results. (1) Increasing MAT will not only directly influence plants but will mediate their responses to the environment, including greater dependence on stable water supplies. (2) The timing of hydropower tides shapes plant community composition. (3) Dam-related erosion has an outsized effect on riparian species, which could lead to a loss of regionally unique plant species. (4) For all species, the most important covariates driving occurrence differed from those for cover, suggesting the potential for these life stages to be decoupled. Not only will climate change and river regulation independently alter plant distributions, interactions among hotter temperature, dam-controlled flow patterns, and limited fine sediments will determine which species flourish or perish under future conditions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1645","usgsCitation":"Palmquist, E.C., Ogle, K., Butterfield, B.J., Whitham, T.G., Allan, G.J., and Shafroth, P., 2025, Hotter temperatures alter riparian plant outcomes under regulated river conditions: Ecological Monographs, v. 95, no. 1, e1645, 21 p., https://doi.org/10.1002/ecm.1645.","productDescription":"e1645, 21 p.","ipdsId":"IP-159047","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":481415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River downstream of Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.21176601160529,\n 36.979125233919234\n ],\n [\n -113.97825341279814,\n 36.979125233919234\n ],\n [\n -113.97825341279814,\n 35.65153018969767\n ],\n [\n -111.21176601160529,\n 35.65153018969767\n ],\n [\n -111.21176601160529,\n 36.979125233919234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"95","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":925281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ogle, Kiona","contributorId":248351,"corporation":false,"usgs":false,"family":"Ogle","given":"Kiona","email":"","affiliations":[],"preferred":false,"id":925282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":925283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitham, Thomas G.","contributorId":174327,"corporation":false,"usgs":false,"family":"Whitham","given":"Thomas","email":"","middleInitial":"G.","affiliations":[{"id":27416,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Nothern Arizona University, Flagstaff, AZ 86011 USA","active":true,"usgs":false}],"preferred":false,"id":925284,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allan, Gerard J.","contributorId":189075,"corporation":false,"usgs":false,"family":"Allan","given":"Gerard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":925285,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":925286,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263392,"text":"70263392 - 2025 - American alligators (Alligator mississippiensis) as wetland ecosystem carbon stock regulators","interactions":[],"lastModifiedDate":"2025-02-10T15:34:48.266876","indexId":"70263392","displayToPublicDate":"2025-01-27T09:11:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"displayTitle":"American alligators (Alligator mississippiensis) as wetland ecosystem carbon stock regulators","title":"American alligators (Alligator mississippiensis) as wetland ecosystem carbon stock regulators","docAbstract":"Blue carbon refers to organic carbon sequestered by oceanic and coastal ecosystems. This stock has gained global attention as a high organic carbon repository relative to other ecosystems. Within blue carbon ecosystems, tidally influenced wetlands alone store a disproportionately higher amount of organic carbon than other blue carbon systems. North America harbors 42% of tidally influenced global wetland area, which has been identified as a critical carbon stock in the context of climate change mitigation. However, quantified associations between vertebrate biota and carbon sequestration within ecosystems are in their infancy and have been incidental, given that microbial trophic levels are thought to drive nutrient dynamics. Here, we assess the relationship between American alligator (Alligator mississippiensis) demography and tidally influenced wetland soil carbon stock among habitats at continental, biogeographically-relevant, and local scales. We used soil core profile data from the Smithsonian’s Coastal Carbon Network and filtered for continuous core profiles in tidally influenced wetland areas along the Gulf and Atlantic Coasts of the United States. Results indicate that American alligator presence is positively correlated with soil carbon stock across habitats within their native distribution. Further, American alligator demographic variables are positively correlated with soil carbon stock at local scales. These conclusions are concordant with previous findings that apex predators, through trophic cascade theory, play a key role in regulating soil carbon stock and that alligators are functional apex predators in carbon dynamics and a key commercialized natural resource.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-87369-x","usgsCitation":"Murray, C., Coleman, T., Gabel, W., and Krauss, K., 2025, American alligators (Alligator mississippiensis) as wetland ecosystem carbon stock regulators: Scientific Reports, v. 15, 3423, 13 p., https://doi.org/10.1038/s41598-025-87369-x.","productDescription":"3423, 13 p.","ipdsId":"IP-167548","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":487632,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-87369-x","text":"Publisher Index Page"},{"id":481859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Florida, Georgia, Louisiana, Maryland, Maine, Massachusetts, New Jersey, New York, North Carolina, Rhode Island, South Carolina, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.44395344413132,\n 30.417303608437123\n ],\n [\n -94.44395344413132,\n 28.50890824220714\n ],\n [\n -88.29666972342,\n 28.50890824220714\n ],\n [\n -88.97117895302259,\n 30.065684962095915\n ],\n [\n -94.44395344413132,\n 30.417303608437123\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.41409842555348,\n 30.137378875847332\n ],\n [\n -83.15674989349813,\n 28.77488765689182\n ],\n [\n -82.66413534911965,\n 26.994908834669644\n ],\n [\n -80.92490820370587,\n 24.61736802235329\n ],\n [\n -79.56037819551399,\n 25.457561329918803\n ],\n [\n -80.0094167016866,\n 28.870796985014195\n ],\n [\n -81.19075794212608,\n 30.94494224760247\n ],\n [\n -75.41770980017554,\n 35.361259497066754\n ],\n [\n -75.64770077017783,\n 35.9085071161131\n ],\n [\n -76.42569049628423,\n 35.82596730807093\n ],\n [\n -81.54105365351762,\n 31.937521767286142\n ],\n [\n -81.86610550469868,\n 29.662292005548764\n ],\n [\n -80.30356040767145,\n 26.92283892564656\n ],\n [\n -81.00711935646325,\n 25.546735859879604\n ],\n [\n -81.58611069419317,\n 26.461835391473628\n ],\n [\n -82.21883851154212,\n 28.064621313026365\n ],\n [\n -82.43807153375221,\n 29.27522927528912\n ],\n [\n -84.04429695303452,\n 30.39215823864386\n ],\n [\n -84.41409842555348,\n 30.137378875847332\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.43208983783808,\n 43.61139375779942\n ],\n [\n -71.21521441288681,\n 41.89215865023837\n ],\n [\n -73.46558191102486,\n 41.36562353853671\n ],\n [\n -74.3997705518226,\n 40.706929834414495\n ],\n [\n -74.7195061996966,\n 39.68414536456231\n ],\n [\n -78.05401190048184,\n 39.656621840215934\n ],\n [\n -76.83423840549077,\n 38.66706149261154\n ],\n [\n -74.8980092791069,\n 38.43842848979952\n ],\n [\n -73.52840490435234,\n 39.804939734313535\n ],\n [\n -69.27998936050872,\n 41.16998983302173\n ],\n [\n -70.29445317948046,\n 42.58120245732246\n ],\n [\n -70.43208983783808,\n 43.61139375779942\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Murray, Christopher M.","contributorId":350717,"corporation":false,"usgs":false,"family":"Murray","given":"Christopher M.","affiliations":[{"id":83816,"text":"Department of Biological Sciences, Southeastern Louisiana University, Hammond, LA, USA","active":true,"usgs":false}],"preferred":false,"id":926778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coleman, Tyler S. 0000-0001-7472-1976","orcid":"https://orcid.org/0000-0001-7472-1976","contributorId":350490,"corporation":false,"usgs":false,"family":"Coleman","given":"Tyler S.","affiliations":[{"id":83754,"text":"Southeastern Lousiana University","active":true,"usgs":false}],"preferred":false,"id":926779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gabel, Wray","contributorId":350718,"corporation":false,"usgs":false,"family":"Gabel","given":"Wray","affiliations":[{"id":83816,"text":"Department of Biological Sciences, Southeastern Louisiana University, Hammond, LA, USA","active":true,"usgs":false}],"preferred":false,"id":926780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":926781,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266202,"text":"70266202 - 2025 - Integrated analysis of marked and count data to characterizefine-scale stream fish movement","interactions":[],"lastModifiedDate":"2025-04-30T15:07:09.143764","indexId":"70266202","displayToPublicDate":"2025-01-27T07:51:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Integrated analysis of marked and count data to characterizefine-scale stream fish movement","docAbstract":"Immigration and emigration are key demographic processes of animal population dynamics. However,\n3 we have limited knowledge on how fine-scale movement varies over space and time. We developed a\n4 Bayesian integrated population model using individual mark-recapture and count data to characterize\n5 fine-scale movement of stream fish at 20-m resolution every two months for 28 months. Our study\n6 targeted small-bodied fish, for which imperfect capture was accounted for (bluehead chub Nocomis\n7 leptocephalus, creek chub Semotilus atromaculatus and mottled sculpin Cottus bairdii). Based on\n8 data from 2,021 individuals across all species, we found that proportions of immigrants averaged\n9 30-42% among the study species, but they varied over space and time. Creek chub immigrants\n10 increased during warmer intervals when individuals grew more and transitioned between body size\n11 classes, suggesting that immigration was due to ontogenetic habitat shifts. There was a weak pattern\n12 across the species that individuals were more likely to leave 20-m sections when flow was higher.\n13 Water-column species (bluehead chub and creek chub) were more likely to immigrate into and stay\n14 in deeper sections with more pool area. Across all species and occasions, number of immigrants\n15 to stream sections did not decrease with number of individuals that survived and stayed in the\n16 same sections. Thus, the habitat did not appear saturated, and our data provided no evidence that\n17 intra-specific interactions affected fine-scale movement at our fish densities. In conclusion, high\n18 turnover rates characterized fish movement among stream sections and their variation was associated\n19 with temporal and spatial shifts in abiotic conditions.","language":"English","publisher":"Springer Nature","doi":"10.1007/s00442-024-05639-3","usgsCitation":"Kanno, Y., Pregler, K., and Kim, S., 2025, Integrated analysis of marked and count data to characterizefine-scale stream fish movement: Oecologia, v. 207, 25, 15 p., https://doi.org/10.1007/s00442-024-05639-3.","productDescription":"25, 15 p.","ipdsId":"IP-162172","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Clemson University Experimental Forest, Indian Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.82048747799679,\n 34.68143669803426\n ],\n [\n -82.82048747799679,\n 34.67911153920382\n ],\n [\n -82.81055950407821,\n 34.67911153920382\n ],\n [\n -82.81055950407821,\n 34.68143669803426\n ],\n [\n -82.82048747799679,\n 34.68143669803426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"207","noUsgsAuthors":false,"publicationDate":"2025-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kanno, Yoichiro","contributorId":353979,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":934906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pregler, Kasey Celene 0000-0002-0664-9594","orcid":"https://orcid.org/0000-0002-0664-9594","contributorId":353980,"corporation":false,"usgs":true,"family":"Pregler","given":"Kasey Celene","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":934907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Seoghyun","contributorId":353981,"corporation":false,"usgs":false,"family":"Kim","given":"Seoghyun","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":934908,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263128,"text":"70263128 - 2025 - Towards mobile wind measurements using joust configured ultrasonic anemometer for applications in gas flux quantification","interactions":[],"lastModifiedDate":"2025-01-30T15:22:48.042166","indexId":"70263128","displayToPublicDate":"2025-01-26T08:14:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18351,"text":"Drones","active":true,"publicationSubtype":{"id":10}},"title":"Towards mobile wind measurements using joust configured ultrasonic anemometer for applications in gas flux quantification","docAbstract":"Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates are sensitive to the accuracy of wind speed and direction measurements. In this study, we examined how filtering and correcting sUAS-based wind measurements affects data accuracy by comparing data from a miniature ultrasonic anemometer mounted on a sUAS in a joust configuration to highly accurate wind data taken from a nearby eddy covariance flux tower (aka the Tower). These corrections had a small effect on wind speed error, but reduced wind direction errors from 50° to >120° to 20–30°. A concurrent experiment examining the amount of error due to the sUAS and the Tower not being co-located showed that the impact of this separation was 0.16–0.21
“Leaky weirs” are rock structures installed in dryland streams, which are anchored into exposed bedrock, loosely cemented, and designed to allow water to slowly pass through. They are being tested at a ranch in southeastern Arizona, USA, to restore and conserve the historic range and desert wetlands. Data are collected to assess how leaky weirs impact surface water, subsurface water, and groundwater recharge—including stream discharge, timing, and depth of infiltration, and groundwater elevations. Three adjacent watersheds, two with outlets just below leaky weirs and one with leaky weirs farther upstream, were instrumented with water-level loggers, wildlife cameras, and crest stage instruments with temperature sensors in the soil. As most groundwater recharge is assumed to be focused along the mountain fronts in this region, mountain-block recharge is also evaluated to differentiate between the two using isotope analyses. Finally, a single, late-season flood event is scrutinized to consider the leaky weir effect on all monitored components in the water budget. Results indicated groundwater flow is primarily from the mountains to the east via older, regional mountain-block recharge. However, the development of shallow alluvial aquifers is supported by the leaky weirs, that slow flows, capture permeable sediments, and allow infiltration, thus enhancing mountain-front recharge. In turn, these new pockets of water help support the restoration of historic wetlands. Sediment accumulates where leaky weirs are installed, reducing flashy peak flows, and resulting in a series of infiltration ponds along the channel that support vegetation during growing seasons and recharge the shallow aquifer during non-growing seasons.
","language":"English","publisher":"Springer","doi":"10.1007/s13201-025-02371-y","usgsCitation":"Norman, L., Uhlman, K., Coy, H., Wilson, N., Bennett, A., Gray, F., and Ehrenberg, K., 2025, “Leaky weirs” capture alluvial deposition and enhance seasonal mountain-front recharge in dryland streams: Applied Water Science, v. 15, 29, 27 p., https://doi.org/10.1007/s13201-025-02371-y.","productDescription":"29, 27 p.","ipdsId":"IP-157242","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":488063,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13201-025-02371-y","text":"Publisher Index Page"},{"id":481927,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.333333,\n 32.283333\n ],\n [\n -109.916667,\n 32.283333\n ],\n [\n -109.916667,\n 31.5\n ],\n [\n -109.333333,\n 31.5\n ],\n [\n -109.333333,\n 32.283333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-01-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":927001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uhlman, Kristine;","contributorId":167093,"corporation":false,"usgs":false,"family":"Uhlman","given":"Kristine;","email":"","affiliations":[{"id":17599,"text":"Texas Bureau of Economic Geology","active":true,"usgs":false}],"preferred":false,"id":927002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coy, Hanna","contributorId":350793,"corporation":false,"usgs":false,"family":"Coy","given":"Hanna","affiliations":[{"id":83830,"text":"U.S. Geological Survey, Arizona Water Science Center (Ret.)","active":true,"usgs":false}],"preferred":false,"id":927003,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Natalie R. 0000-0001-5145-1221","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":202534,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":927004,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennett, Andrew M.","contributorId":350794,"corporation":false,"usgs":false,"family":"Bennett","given":"Andrew M.","affiliations":[{"id":83831,"text":"Contracter","active":true,"usgs":false}],"preferred":false,"id":927005,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gray, Floyd 0000-0002-0223-8966","orcid":"https://orcid.org/0000-0002-0223-8966","contributorId":201529,"corporation":false,"usgs":true,"family":"Gray","given":"Floyd","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":927006,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ehrenberg, Kurt T.","contributorId":350795,"corporation":false,"usgs":false,"family":"Ehrenberg","given":"Kurt T.","affiliations":[{"id":83832,"text":"U.S. Geological Survey, Arizona Water Science Center, 520 N. 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Statistics were computed for active (through September 30, 2021) and discontinued USGS streamgages with 10 or more years of daily mean streamflow record. Streamflow at each streamgage was assessed for degree of human alteration owing to dams and diversions before streamflow statistics were computed. Streamflow records from 615 streamgages were used to compute basic, seasonal, and flow-duration statistics; streamflow records from 387 streamgages were used to compute n-day statistics, which are streamflow statistics describing streamflow over a number of days (n), and statistics that can be used for regional regression. The streamflow statistics are provided in a USGS data publication that accompanies this report and through the USGS StreamStats web-based application (https://www.usgs.gov/streamstats).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245104","collaboration":"Prepared in cooperation with Wyoming Water Development Office","usgsCitation":"Armstrong, D.W., Lange, D.A., and Chase, K.J., 2025, Methods to determine streamflow statistics based on data through water year 2021 for selected streamgages in or near Wyoming: U.S. Geological Survey Scientific Investigations Report 2024–5104, 10 p., https://doi.org/10.3133/sir20245104.","productDescription":"Report: v, 10 p.; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148941","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":492669,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118406.htm","linkFileType":{"id":5,"text":"html"}},{"id":481019,"rank":6,"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":481017,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20245104/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024–5104."},{"id":481016,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5104/sir20245104.XML","linkFileType":{"id":8,"text":"xml"}},{"id":481015,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5104/images"},{"id":481014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5104/sir20245104.pdf","text":"Report","size":"5.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5104"},{"id":481013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5104/coverthb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.10935768359315,\n 45.03387726366623\n ],\n [\n -111.10935768359315,\n 40.99279378165886\n ],\n [\n -104.05893115935177,\n 40.99279378165886\n ],\n [\n -104.05893115935177,\n 45.03387726366623\n ],\n [\n -111.10935768359315,\n 45.03387726366623\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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Global observations at 30-m ground sampling distance (GSD) are now possible at a cadence of 1-3 days by combining Landsat 8 and 9 with Sentinel-2A and -2B satellites. Previous studies characterizing pixel-level Landsat-class measurement frequency used data from different sources but offered little information on observation availability after rigorous quality screening. This study examined the coverage frequency of HLS V2.0 data for 2022, the first year all four satellites data were available. These data have had quality control filtering and harmonization, and therefore reflect the spatial-temporal distribution of usable observations. On average, HLS data provide observations every 1.6 days at the global scale, and 2.2 days in the data-scarce tropical regions, regardless of cloud cover. The global mean and median cloud-free observations were 69 and 64, respectively. The frequency of good-quality observations varies geographically and seasonally due to changes in satellite swath overlap, cloud frequency, and solar illumination. High latitudes (>~75°N) exhibit the highest number of cloud-free observations between March and September. However, data are unavailable during winter months due to low solar elevation angles and boreal regions have a lower number of clear observations in the summer months. The tropical regions have the lowest number of clear observations. More frequent HLS observations could improve terrestrial monitoring. We mapped the monthly and weekly number of clear observations globally to show where HLS data could support monthly or sub-weekly time series applications.
","language":"English","publisher":"IEEE Xplore","doi":"10.1109/LGRS.2025.3533923","usgsCitation":"Zhou, Q., Neigh, C., Ju, J., Dabney, P., Cook, B., Zhu, Z., Crawford, C., Gascon, F., Strobl, P., and Sridhar, M., 2025, Towards seamless global 30-meter terrestrial monitoring: Evaluating 2022 cloud free coverage of harmonized Landsat and Sentinel-2 (HLS) V2.0: IEEE Geoscience and Remote Sensing Letters, v. 22, 5000505, 5 p., https://doi.org/10.1109/LGRS.2025.3533923.","productDescription":"5000505, 5 p.","ipdsId":"IP-164632","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":488360,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/lgrs.2025.3533923","text":"Publisher Index Page"},{"id":481614,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang","contributorId":350371,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":63570,"text":"Science Systems and Applications Inc","active":true,"usgs":false}],"preferred":false,"id":925890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neigh, Christopher","contributorId":350372,"corporation":false,"usgs":false,"family":"Neigh","given":"Christopher","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":925891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ju, Junchang","contributorId":350375,"corporation":false,"usgs":false,"family":"Ju","given":"Junchang","affiliations":[{"id":83726,"text":"University of Maryland College Park","active":true,"usgs":false}],"preferred":false,"id":925892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dabney, Philip","contributorId":350376,"corporation":false,"usgs":false,"family":"Dabney","given":"Philip","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":925893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, Bruce","contributorId":350378,"corporation":false,"usgs":false,"family":"Cook","given":"Bruce","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":925894,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Zhe","contributorId":350380,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":925895,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":925896,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gascon, Ferran","contributorId":350381,"corporation":false,"usgs":false,"family":"Gascon","given":"Ferran","affiliations":[{"id":38836,"text":"European Space Agency","active":true,"usgs":false}],"preferred":false,"id":925897,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Strobl, Peter","contributorId":350382,"corporation":false,"usgs":false,"family":"Strobl","given":"Peter","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":925898,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sridhar, Madhu","contributorId":350383,"corporation":false,"usgs":false,"family":"Sridhar","given":"Madhu","affiliations":[{"id":83729,"text":"University of Alabama Huntsville","active":true,"usgs":false}],"preferred":false,"id":925899,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70263376,"text":"70263376 - 2025 - Improving hydroacoustic methods for monitoring suspended-sand flux and grain size in sediment-laden rivers","interactions":[],"lastModifiedDate":"2025-02-07T20:16:15.850263","indexId":"70263376","displayToPublicDate":"2025-01-23T13:10:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Improving hydroacoustic methods for monitoring suspended-sand flux and grain size in sediment-laden rivers","docAbstract":"Suspended-sand concentration and grain-size data in rivers provide valuable information on the catchment's dynamics for scientists and river managers. Producing continuous measurements of suspended-sand concentrations remains a scientific challenge due to their high spatial and temporal variability. Traditional methods such as sediment-rating curves may be highly uncertain, and optical turbidity is insensitive to coarse particles when there are many fine particles. Surrogate hydroacoustic methods aim to improve sand concentration measurements. These single- or dual-frequency acoustic methods use acoustic attenuation and/or backscatter to estimate fine-sediment (i.e., silt and clay) and/or sand concentration and possibly grain size. New methods have recently been developed and applied in rivers exhibiting a wide range of sediment conditions in North America but not independently tested elsewhere by other researchers. In this article, we apply, adapt and evaluate hydroacoustic methods to continuously estimate suspended-sand concentration and grain size in an Alpine river with high suspended-sediment concentrations. From the example of the River Isère at Grenoble Campus, France, we show that the hydroacoustic methods adapted to local conditions may yield valuable sand concentration estimates consistent with traditional measurements. Compared with prior knowledge, limited additional information on the grain size can be obtained due to high uncertainties. Hydroacoustic concentration estimates are more sensitive to real changes in concentration at the event scale than traditional rating-curve methods that relate concentration to discharge only. These findings open the perspective for facilitated sand concentration monitoring at a higher temporal resolution with decreased field work.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.6056","usgsCitation":"Marggraf, J., Le Coz, J., Camenen, B., Lauters, F., Dramais, G., Pierrefeu, G., and Topping, D.J., 2025, Improving hydroacoustic methods for monitoring suspended-sand flux and grain size in sediment-laden rivers: Earth Surface Processes and Landforms, v. 50, no. 1, e6056, 23 p., https://doi.org/10.1002/esp.6056.","productDescription":"e6056, 23 p.","ipdsId":"IP-166620","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":487630,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.6056","text":"Publisher Index Page"},{"id":481810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France","otherGeospatial":"Grenoble Campus","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 5.661882051323261,\n 45.223790952419705\n ],\n [\n 5.661882051323261,\n 45.1819511476435\n ],\n [\n 5.74135891360703,\n 45.1819511476435\n ],\n [\n 5.74135891360703,\n 45.223790952419705\n ],\n [\n 5.661882051323261,\n 45.223790952419705\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-01-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Marggraf, Jessica","contributorId":350702,"corporation":false,"usgs":false,"family":"Marggraf","given":"Jessica","affiliations":[{"id":83813,"text":"RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":926698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Le Coz, Jerome","contributorId":350703,"corporation":false,"usgs":false,"family":"Le Coz","given":"Jerome","affiliations":[{"id":83813,"text":"RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":926699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Camenen, Benoıt","contributorId":350704,"corporation":false,"usgs":false,"family":"Camenen","given":"Benoıt","affiliations":[{"id":83813,"text":"RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":926700,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauters, Francois","contributorId":350705,"corporation":false,"usgs":false,"family":"Lauters","given":"Francois","affiliations":[{"id":83814,"text":"Service Etudes Eau Environnement, EDF, 134 Rue de l’´etang, Saint Martin le Vinoux, 38950, France","active":true,"usgs":false}],"preferred":false,"id":926701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dramais, Guillaume 0000-0002-2703-9314","orcid":"https://orcid.org/0000-0002-2703-9314","contributorId":238955,"corporation":false,"usgs":false,"family":"Dramais","given":"Guillaume","email":"","affiliations":[{"id":47837,"text":"Ph.D. student, IRSTEA, Flagstaff, Arizona","active":true,"usgs":false}],"preferred":false,"id":926702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pierrefeu, Gilles","contributorId":238958,"corporation":false,"usgs":false,"family":"Pierrefeu","given":"Gilles","email":"","affiliations":[{"id":47841,"text":"Senior Engineer, CNR, Lyon, France","active":true,"usgs":false}],"preferred":false,"id":926703,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":926704,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70262404,"text":"sir20245114 - 2025 - Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in Missouri, 2022–23","interactions":[],"lastModifiedDate":"2025-07-21T17:49:26.38695","indexId":"sir20245114","displayToPublicDate":"2025-01-23T07:38:12","publicationYear":"2025","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":"2024-5114","displayTitle":"Bathymetric Contour Maps, Surface Area and Capacity Tables, and Bathymetric Change Maps for Selected Water-Supply Lakes in Missouri, 2022–23","title":"Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in Missouri, 2022–23","docAbstract":"Bathymetric data were collected at 13 water-supply lakes around the periphery of Missouri by the U.S. Geological Survey in cooperation with the Missouri Department of Natural Resources and various local agencies, as part of a multiyear effort to establish or update the surface area and capacity tables for the surveyed lakes. Surveys were carried out during the months of April and May in 2022 and 2023. All but two of the lakes had been surveyed previously by the U.S. Geological Survey, and the recent surveys were compared to the earlier surveys to document the changes in the bathymetric surface and capacity of the lake.
Bathymetric data were collected using a high-resolution multibeam mapping system mounted on a boat. Supplemental depth data at three 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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245114","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Rivers, B.C., Huizinga, R.J., and Waite, G.J., 2025, Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in Missouri, 2022–23: U.S. Geological Survey Scientific Investigations Report 2024–5114, 70 p., https://doi.org/10.3133/sir20245114.","productDescription":"Report: vii, 70 p.; 13 Plates: 24.00 x 30.00 inches or smaller; 2 Data Releases","numberOfPages":"82","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-149403","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":492661,"rank":8,"type":{"id":36,"text":"NGMDB Index 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Some of the most biologically diverse coastal wetlands and estuaries are found along the Great Lakes, but the spatial extent and timing of river-related inundation and sedimentation vary greatly among natural and altered systems. We used hydrologic data, geomorphic change detection, and satellite imagery to study inundation and sedimentation patterns in the naturally dynamic estuary of the Bad River (Mashkiiziibii) that flows into Lake Superior (Anishinaabeg-gichigami), and the Kakagon River (Ogaakaagaang-ziibii) that flows into a sheltered bay (Chi-Kaamigong). In 2016, an extreme summer flood (annual exceedance probability < 0.2 %) caused total inundation of the 46-km2 estuary. Floods from the sediment-rich Bad River, with an annual exceedance probability of ≤ 50 %, have overflowed into the upper wetlands and channels of the Kakagon River about 60 times over the last 75 years, including 20 floods during the most recent 10-year wet period. Sedimentation patterns were associated with proximity to river channels, shoreline erosion, and wind action. Early winter ice-up coupled with a storm surge and an early spring snowmelt into the iced-over bay changed inundation duration and sedimentation patterns. Climate-change projections for more intense rainfall and warmer temperatures will likely cause more frequent flooding and sedimentation; however, patterns may differ depending on the timing of the floods relative to storm surges and ice formation, or other factors. The approach of integrating readily available data helped give a broader temporal and spatial context to the possible causes for inundation and sedimentation, some expected and others not, in natural and restored estuaries of the Great Lakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102458","usgsCitation":"Fitzpatrick, F., Vaughan, A., Dantoin, E.D., Sterner, S.P., Reneau, P., and Roland, C., 2025, Effects of river floods and sedimentation on a naturally dynamic Great Lakes estuary: Journal of Great Lakes Research, v. 51, no. 1, 102458, 19 p., https://doi.org/10.1016/j.jglr.2024.102458.","productDescription":"102458, 19 p.","ipdsId":"IP-163441","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":488275,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102458","text":"Publisher Index Page"},{"id":483809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bad River, Chequamegon Bay, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.86741881591443,\n 46.74517791310441\n ],\n [\n -90.86741881591443,\n 46.59429411429369\n ],\n [\n -90.55858891028036,\n 46.59429411429369\n ],\n [\n -90.55858891028036,\n 46.74517791310441\n ],\n [\n -90.86741881591443,\n 46.74517791310441\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fitzpatrick, Faith 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209191,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vaughan, Angus 0000-0001-9900-4658","orcid":"https://orcid.org/0000-0001-9900-4658","contributorId":302333,"corporation":false,"usgs":true,"family":"Vaughan","given":"Angus","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":931770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dantoin, Eric D. 0000-0002-8561-2924 edantoin@usgs.gov","orcid":"https://orcid.org/0000-0002-8561-2924","contributorId":2278,"corporation":false,"usgs":true,"family":"Dantoin","given":"Eric","email":"edantoin@usgs.gov","middleInitial":"D.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sterner, Shelby P. 0000-0002-3103-7960","orcid":"https://orcid.org/0000-0002-3103-7960","contributorId":292246,"corporation":false,"usgs":true,"family":"Sterner","given":"Shelby","email":"","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reneau, Paul 0000-0002-1335-7573","orcid":"https://orcid.org/0000-0002-1335-7573","contributorId":217293,"corporation":false,"usgs":true,"family":"Reneau","given":"Paul","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931773,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roland, Collin 0000-0003-1004-0746","orcid":"https://orcid.org/0000-0003-1004-0746","contributorId":343660,"corporation":false,"usgs":true,"family":"Roland","given":"Collin","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931774,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70268258,"text":"70268258 - 2025 - Metabolic interactions underpinning high methane fluxes across terrestrial freshwater wetlands","interactions":[],"lastModifiedDate":"2025-06-18T14:49:21.713746","indexId":"70268258","displayToPublicDate":"2025-01-22T09:42:21","publicationYear":"2025","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":"Metabolic interactions underpinning high methane fluxes across terrestrial freshwater wetlands","docAbstract":"Current estimates of wetland contributions to the global methane budget carry high uncertainty, particularly in accurately predicting emissions from high methane-emitting wetlands. Microorganisms drive methane cycling, but little is known about their conservation across wetlands. To address this, we integrate 16S rRNA amplicon datasets, metagenomes, metatranscriptomes, and annual methane flux data across 9 wetlands, creating the Multi-Omics for Understanding Climate Change (MUCC) v2.0.0 database. This resource is used to link microbiome composition to function and methane emissions, focusing on methane-cycling microbes and the networks driving carbon decomposition. We identify eight methane-cycling genera shared across wetlands and show wetland-specific metabolic interactions in marshes, revealing low connections between methanogens and methanotrophs in high-emitting wetlands. Methanoregula emerged as a hub methanogen across networks and is a strong predictor of methane flux. In these wetlands it also displays the functional potential for methylotrophic methanogenesis, highlighting the importance of this pathway in these ecosystems. Collectively, our findings illuminate trends between microbial decomposition networks and methane flux while providing an extensive publicly available database to advance future wetland research.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-025-56133-0","usgsCitation":"Bechtold, E., Ellenbogen, J., Villa, J.A., de Melo Ferreira, D., Oliverio, A., Kostka, J., Rich, V.I., Varner, R.K., Bansal, S., Ward, E.J., Bohrer, G., Borton, M., Wrighton, K.C., and Wilkins, M., 2025, Metabolic interactions underpinning high methane fluxes across terrestrial freshwater wetlands: Nature Communications, v. 16, 944, 15 p., https://doi.org/10.1038/s41467-025-56133-0.","productDescription":"944, 15 p.","ipdsId":"IP-166828","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":491016,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-025-56133-0","text":"Publisher Index 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These conditions are defined by a minimum, maximum, and optimal ranges of single and combined factors. Forays into environmental conditions outside the minimum or maximum tolerance of a species (i.e., thresholds) are predicted to have large effects on a species' population and may help predict population resilience in the face of changing conditions. Here, we explore ecological thresholds for an important fisheries species and ecosystem engineer, Crassostrea virginica (eastern oyster). In coastal Louisiana, extreme freshwater inputs from rivers and precipitation events impact estuarine salinity, which is a key driver of oyster population dynamics. Using daily salinity and monthly oyster abundance monitoring data across Louisiana estuaries, we explore low salinity exposure threshold levels for oysters. Two statistical approaches were applied, with each model highlighting a different operational definition of a threshold: random forest models identified a threshold as an abrupt change in the oyster abundance- salinity relationship, while Bayesian models identified an increased probability of oyster abundance dropping below a critical threshold, defined here as less than 50% of the 5-year mean. All model results indicate oysters in coastal Louisiana experience low salinity exposure thresholds, defined as the number of consecutive summer days of salinity levels less than 5. However, actual number of days and salinity threshold differed by statistical approach, oyster life stage, and estuary highlighting the multiple dimensions defining ecological thresholds. While thresholds are considered important benchmarks to inform management and assess population or ecosystem vulnerability, our results reveal the need to carefully relate threshold definition to management goals and to acknowledge that thresholds may be highly context dependent.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.70759","usgsCitation":"La Peyre, M., Wang, H., Sable, S.E., Wu, W., Li, B., Comba, D., Perez, C., Bates, M., and Swam, L.M., 2025, Multiple dimensions define thresholds for population resilience of the eastern oyster, Crassostrea virginica: Ecology and Evolution, v. 15, no. 1, e70759, 17 p., https://doi.org/10.1002/ece3.70759.","productDescription":"e70759, 17 p.","ipdsId":"IP-166145","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":488493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.70759","text":"Publisher Index Page"},{"id":484861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.11822914557477,\n 30.785757926614025\n ],\n [\n -93.93935746486115,\n 30.785757926614025\n ],\n [\n -93.93935746486115,\n 28.547047787509243\n ],\n [\n -89.11822914557477,\n 28.547047787509243\n ],\n [\n -89.11822914557477,\n 30.785757926614025\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":934187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, H. 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":205508,"corporation":false,"usgs":true,"family":"Wang","given":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":934188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sable, Shaye E.","contributorId":257728,"corporation":false,"usgs":false,"family":"Sable","given":"Shaye","email":"","middleInitial":"E.","affiliations":[{"id":52096,"text":"Dynamic Solutions, LLC","active":true,"usgs":false}],"preferred":false,"id":934189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Wei","contributorId":353629,"corporation":false,"usgs":false,"family":"Wu","given":"Wei","affiliations":[{"id":12460,"text":"The University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":934190,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Bin","contributorId":47684,"corporation":false,"usgs":true,"family":"Li","given":"Bin","email":"","affiliations":[],"preferred":false,"id":934191,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Comba, Devin","contributorId":335897,"corporation":false,"usgs":false,"family":"Comba","given":"Devin","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":934192,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perez, Carlos","contributorId":353654,"corporation":false,"usgs":false,"family":"Perez","given":"Carlos","affiliations":[],"preferred":false,"id":934193,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bates, Melanie","contributorId":353630,"corporation":false,"usgs":false,"family":"Bates","given":"Melanie","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":934194,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Swam, Lauren M.","contributorId":341585,"corporation":false,"usgs":false,"family":"Swam","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":32913,"text":"Louisiana State University Agricultural Center","active":true,"usgs":false}],"preferred":false,"id":934195,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262605,"text":"70262605 - 2025 - Slow slip detectability in seafloor pressure records offshore Alaska","interactions":[],"lastModifiedDate":"2025-01-21T17:31:08.79207","indexId":"70262605","displayToPublicDate":"2025-01-21T11:21:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Slow slip detectability in seafloor pressure records offshore Alaska","docAbstract":"In subduction zones worldwide, seafloor pressure data are used to observe tectonic deformation, particularly from megathrust earthquakes and slow slip events (SSEs). However, such measurements are also sensitive to oceanographic circulation-generated pressures over a range of frequencies that conflate with tectonic signals of interest. Using seafloor pressure and temperature data from the Alaska Amphibious Community Seismic Experiment, and sea surface height data from satellite altimetry, we evaluate the efficacy of various seasonal and oceanographic pressure signal proxy corrections and conduct synthetic tests to determine their impact on the timing and amplitude prediction of ramp-like signals typical of SSEs. We find that subtracting out the first mode of the complex empirical orthogonal functions of the pressure records on either the shelf or slope yields signal root-mean-square error (RMS) reductions up to 73% or 80%, respectively. Additional correction with proxies that exploit the depth-dependent spatial coherence of pressure records provides cumulative variance reductions up to 83% and 93%, respectively. Our detectability tests show that the timing and amplitude of synthetic SSE-like ramps can be well constrained for ramp amplitudes ≥4 cm on the shelf and ≥2 cm on the slope, using a fully automated detector. The principal limits on detectability are residual abrupt changes in pressure that occur as part of the transition to and from summer to winter conditions but are not adequately characterized by our seasonal corrections, as well as the inability to properly account for instrumental drift, which is not readily separated from the seasonal signal.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024767","usgsCitation":"Fredrickson, E., Gomberg, J.S., Wilcock, W., Hautala, S., Hermann, A., and Johnson, H.P., 2025, Slow slip detectability in seafloor pressure records offshore Alaska: Journal of Geophysical Research, v. 128, no. 2, e2022JB024767, 24 p., https://doi.org/10.1029/2022JB024767.","productDescription":"e2022JB024767, 24 p.","ipdsId":"IP-143947","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481022,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jb024767","text":"Publisher Index Page"},{"id":480844,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -150.16349121912398,\n 58.54393007698937\n ],\n [\n -151.42167720993098,\n 59.98743881891275\n ],\n [\n -154.57208692356411,\n 59.65292298715761\n ],\n [\n -160.23909749826134,\n 56.65422767712158\n ],\n [\n -163.58127742066247,\n 55.65836547735071\n ],\n [\n -162.34836105867672,\n 53.57246789386025\n ],\n [\n -159.99664364329442,\n 53.66505587767077\n ],\n [\n -156.78037167628796,\n 53.84231267384558\n ],\n [\n -150.30294881264624,\n 55.9108113210971\n ],\n [\n -148.28571839994297,\n 57.58072409918367\n ],\n [\n -150.16349121912398,\n 58.54393007698937\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Fredrickson, Erik","contributorId":349722,"corporation":false,"usgs":false,"family":"Fredrickson","given":"Erik","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":924656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilcock, William","contributorId":171733,"corporation":false,"usgs":false,"family":"Wilcock","given":"William","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":924658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hautala, Susan","contributorId":194235,"corporation":false,"usgs":false,"family":"Hautala","given":"Susan","email":"","affiliations":[],"preferred":false,"id":924659,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hermann, Albert","contributorId":251790,"corporation":false,"usgs":false,"family":"Hermann","given":"Albert","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":924660,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, H. 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Removal strategies (e.g., hunting or culling) are used to control wildlife diseases across a wide range of systems, despite conflicting evidence of their effectiveness. This is especially true for chronic wasting disease (CWD), an untreatable, fatal prion disease threatening cervid populations across multiple countries, for which recreational harvest has been suggested as an important disease control strategy. Using observational data to evaluate whether harvest effectively limits CWD prevalence has been difficult because statistical relationships between harvest and disease prevalence can arise from a causal effect of harvest (i.e., harvest's impacts on prevalence via changes in transmission or demographic structure) or from a number of alternative mechanisms. For instance, correlations between harvest and disease prevalence can also be driven by disease's impacts on population size and harvest (i.e., reverse causality) or from confounding variables (e.g., habitat or geographic location) that impact both harvest rates and disease prevalence. We analyzed two decades of surveillance data (2000–2021) from 10 mule deer herds in Wyoming, using statistical approaches informed by causal inference theory, to test for the effects of harvest on CWD prevalence. Herds with consistently high harvest pressure across 20 years had significantly lower prevalence. Our models predicted that harvesting 40% of adult males per year across 20 years would maintain prevalence below 5% on average, whereas if only 20% of males were harvested in each year, prevalence would increase to >30% by year 20. Moreover, shifting the relative harvest pressure within a herd over a shorter period (3 years) reduced subsequent prevalence, albeit to a smaller degree. Although high harvest is unlikely to completely eradicate CWD, our analysis suggests that maintaining hunting pressure on adult males is an important tactic for slowing CWD epidemics within mule deer herds. Our study also provides guidance for future analyses of longitudinal surveillance data, including the importance of demographic data and appropriate time lags.
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Quantification of sediment-runoff dynamics at the event scale is integral for understanding source areas and transport of suspended-sediment through a watershed following wildfire. Here we used high-frequency turbidity and stream discharge data, coupled with discrete suspended-sediment measurements, in burned and unburned watersheds in the Southern Rocky Mountains and the western Cascades Range to evaluate the response of fine-grained (clay- and silt-sized particles) suspended-sediment. Hysteresis analysis was conducted on estimated suspended-sediment concentrations (using turbidity as a proxy) and streamflow through measurement of the difference in sediment concentration on the rising and falling limbs of the event hydrograph. All burned watersheds exhibited elevated fine suspended-sediment concentrations relative to concentrations found in pre-fire conditions. Changes to hysteretic response vary and may depend on a watershed's sediment connectivity limitations. Results suggest a watershed's inherent hillslope-to-channel (or lateral) connectivity is the primary factor controlling the relative magnitude of event-driven fine sediment fluxes in watersheds affected by wildfire. While wildfire did promote lateral connectivity through activation of hillslope sources, snowmelt, precipitation characteristics and antecedent conditions were more important drivers of hysteretic response than wildfire. For watersheds influenced by annual snowpack, we identified a predominantly clockwise hysteretic response during snowmelt and counterclockwise events during the late spring and summer months. There were also proportionally more counterclockwise events after wildfire in watersheds with high sediment connectivity. Results suggest contrasting wildfire-related sediment risk potential. Rivers in burned watersheds with high sediment connectivity may pose a higher risk to receiving waterbodies, such as larger tributaries or reservoirs, while rivers with low sediment connectivity may experience long-term sediment-related risk within the watershed above the outlet.
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