To improve our understanding of hydrothermal activity on the Yellowstone Plateau volcanic field, we collected and analyzed a large data set of δ2H, δ18O, and the 3H concentrations of circum-neutral and alkaline waters. We find that (a) hot springs are fed by recharge throughout the volcanic plateau, likely focused through fractured, permeable tuff units. Previous work had stressed the need for light δ2H water recharge restricted to the northern part of the plateau or recharge during past cold periods. However, new data from the Y-7 drill hole suggests that recharge is not restricted to a certain area or a cold period. (b) δ18O values of thermal waters in the geyser basins are shifted from the global meteoric water line by temperature-dependent water-rock reactions with higher subsurface temperatures resulting in a greater shift. (c) Large temporal variations in the isotopic composition of meteoric water recharge and small temporal variability in the isotopic composition of hot spring discharge implies that the volume of groundwater in, and around the Yellowstone caldera is substantially larger than the volume of annual water recharge. (d) Hot springs discharged through different rhyolitic units correlate with identifiable differences in δ2H and δ18O compositions, 3H concentrations, and water chemistry that imply equilibration at different temperatures and travel along different flow paths. (e) Based on measured 3H concentrations, we calculate that hot spring waters in the central part of the geyser basins mostly contain <2% post-1950 meteoric water, whereas waters discharged at the basin margins contain larger fractions of post-1950s meteoric water.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GC012230","usgsCitation":"Hurwitz, S., McCleskey, R., Jurgens, B., Lowenstern, J.B., Clor, L., and Hunt, A.G., 2025, The systematics of stable hydrogen (δ2H) and oxygen (δ18O) isotopes and tritium (3H) in the hydrothermal system of the Yellowstone Plateau volcanic field, USA: Geochemistry, Geophysics, Geosystems, v. 26, no. 7, e2025GC012230, 19 p., https://doi.org/10.1029/2025GC012230.","productDescription":"e2025GC012230, 19 p.","ipdsId":"IP-174864","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":492080,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gc012230","text":"Publisher Index Page"},{"id":491895,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone Plateau volcanic field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.05101027973069,\n 44.981940603343645\n ],\n [\n -111.05101027973069,\n 43.58270636700257\n ],\n [\n -109.52640476499235,\n 43.58270636700257\n ],\n [\n -109.52640476499235,\n 44.981940603343645\n ],\n [\n -111.05101027973069,\n 44.981940603343645\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":942478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":942479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":942480,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lowenstern, Jacob B. 0000-0003-0464-7779 jlwnstrn@usgs.gov","orcid":"https://orcid.org/0000-0003-0464-7779","contributorId":2755,"corporation":false,"usgs":true,"family":"Lowenstern","given":"Jacob","email":"jlwnstrn@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":942481,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clor, Laura E. 0000-0003-2633-5100","orcid":"https://orcid.org/0000-0003-2633-5100","contributorId":209969,"corporation":false,"usgs":true,"family":"Clor","given":"Laura E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":942482,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":174135,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":942483,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270037,"text":"70270037 - 2025 - Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA","interactions":[],"lastModifiedDate":"2025-08-08T15:33:12.382534","indexId":"70270037","displayToPublicDate":"2025-07-03T10:29:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9346,"text":"Science of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA","docAbstract":"Lidar and structure from motion-derived digital elevation and surface models have widespread application. Consideration of a topographic model's vertical root mean squared error (RMSEz) and systematic directional bias is important for many of these applications, particularly landscape change detection and measurement. Due to logistic, resource, and time constraints, wide area remotely sensed topographic surveys are not always accompanied by an in situ checkpoint network for validating and characterizing survey error. Here we describe and test a method for automatically generating synthetic elevation checkpoints in bulk across hundreds of kilometers using a publicly available lidar-derived DEM time-series, road vector network, and landcover classification map. Our method produced 6000–10,000 synthetic checkpoints across the developed barrier island coastline of North Carolina. These checkpoints characterized vertical error metrics in a statistically similar way as in situ checkpoints when assessing the vertical accuracy of a contemporary lidar-derived DEM and produced RMSEz metrics an average of 0.018 m from the RMSEz of historical lidar DEMs published with tested accuracy metrics. This new method has the potential to A) lower the cost and time required to validate new remotely sensed topographic surveys by reducing or eliminating the field work associated with in situ checkpoint surveys, B) provide a means of retroactively assessing the absolute vertical accuracy and systematic bias of historical topographic datasets that were not published with tested accuracy metrics, and C) generate reference networks to assess and correct spatially variable patterns of vertical bias in topographic datasets.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.srs.2025.100252","usgsCitation":"Seymour, A.C., Kranenburg, C.J., and Doran, K., 2025, Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA: Science of Remote Sensing, v. 12, 100252, 16 p., https://doi.org/10.1016/j.srs.2025.100252.","productDescription":"100252, 16 p.","ipdsId":"IP-160786","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":494186,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.srs.2025.100252","text":"Publisher Index Page"},{"id":493850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.25473663967196,\n 36.645619157350026\n ],\n [\n -76.25473663967196,\n 35.102166390295025\n ],\n [\n -75.37748509576826,\n 35.102166390295025\n ],\n [\n -75.37748509576826,\n 36.645619157350026\n ],\n [\n -76.25473663967196,\n 36.645619157350026\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Seymour, Alexander C. 0000-0002-7680-6102","orcid":"https://orcid.org/0000-0002-7680-6102","contributorId":238616,"corporation":false,"usgs":true,"family":"Seymour","given":"Alexander","email":"","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. 0000-0002-2955-0167 ckranenburg@usgs.gov","orcid":"https://orcid.org/0000-0002-2955-0167","contributorId":169234,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine","email":"ckranenburg@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doran, Kara S. 0000-0001-8050-5727","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":292448,"corporation":false,"usgs":true,"family":"Doran","given":"Kara S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945219,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70268792,"text":"sir20255049 - 2025 - Completion summary for monitor wells NRF-17 and NRF-18 at the Naval Reactors Facility, Idaho National Laboratory, Idaho","interactions":[],"lastModifiedDate":"2026-01-26T19:27:33.044408","indexId":"sir20255049","displayToPublicDate":"2025-07-03T07:22:30","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":"2025-5049","displayTitle":"Completion Summary for Monitor Wells NRF-17 and NRF-18 at the Naval Reactors Facility, Idaho National Laboratory, Idaho","title":"Completion summary for monitor wells NRF-17 and NRF-18 at the Naval Reactors Facility, Idaho National Laboratory, Idaho","docAbstract":"The U.S. Geological Survey (USGS)—in cooperation with the U.S. Department of Energy (DOE) for the Naval Reactors Laboratory Field Office that supports operations for the Naval Reactors Facility (NRF) located at the Idaho National Laboratory (INL)—drilled and constructed well NRF-17 (formerly borehole USGS 151) and well NRF-18 (formerly borehole USGS 152) for stratigraphic framework analyses and water-quality monitoring at the Idaho National Laboratory (INL) near the NRF, in southeastern Idaho. Borehole USGS 151 was continuously cored from about 48 to 1,070 feet (ft) below land surface (BLS); rotary drilled from approximately 1,070 to 1,720 ft BLS; and re-drilled to complete construction as a monitor well NRF-17, completed to 461 ft BLS. Borehole USGS 152 was continuously cored from approximately 19 to 1,259 ft BLS; rotary drilled from approximately 1,259 to 1,630 ft BLS; and re-drilled to complete construction as a monitor well NRF-18, completed to 450 ft BLS.
Geophysical data were examined with photographed core material to record lithologic descriptions and to suggest zones where groundwater flow was anticipated. Basalt flows varied from highly fractured to dense, with high-to-low vesiculation. Well NRF-17 generally was constructed in mostly dense basalt (greater than 75 percent), and well NRF-18 was constructed in primarily fractured and (or) vesicular basalt. In well NRF-17, the well capacity is directly affected by the limited amount of fractured basalt, which serves as the primary pathway for groundwater. This effect was observed during the pumping test conducted after the well's final construction.
Single-well aquifer tests were done at wells NRF-17 and NRF-18 to provide estimates of transmissivity and hydraulic conductivity after final well construction and initial well development. Estimated values of transmissivity and hydraulic conductivity for well NRF-17 were 8.81 feet squared per day (ft2/d) and 1.04×10-2 feet per day (ft/d), respectively. Estimated values of transmissivity and hydraulic conductivity for well NRF-18 were 4.77×103 ft2/d and 5.61 ft/d, respectively. The NRF-17 pump test resulted in 19.41 ft of measured drawdown at a sustained average pumping rate of 3.3 gallons per minute (gal/min). The NRF-18 pump test resulted in 0.55 ft of measured drawdown at a sustained average pumping rate of 31.0 gal/min.
Water-quality samples collected from the two wells were analyzed for cations, anions, metals, nutrients, volatile organic compounds, stable isotopes, and radionuclides. Water samples for select inorganic constituents showed concentrations consistent with signatures from tributary valley groundwater with influences from ephemeral surface-water recharge from the Big Lost River. Water-quality samples analyzed for stable isotopes of oxygen and hydrogen are consistent with signatures from tributary valley groundwater and surface-water recharge inputs to the aquifer. No measured water-quality results were greater than their respective maximum contaminant levels for public drinking-water supplies. Inorganic and nutrient water-quality results for well NRF-17 and well NRF-18 suggest the groundwater in this area is potentially affected by industrial wastewater disposal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255049","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22264","usgsCitation":"Twining, B.V., Treinen, K.C., and Zingre, J.A., 2025, Completion summary for monitor wells NRF-17 and NRF-18 at the Naval Reactors Facility, Idaho National Laboratory, Idaho: U.S. Geological Survey Scientific Investigations Report\n2025–5049, 37 p., https://doi.org/10.3133/sir20255049.","productDescription":"Report: vii, 37 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-159224","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":491691,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5049/images"},{"id":491690,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13URUXF","text":"USGS data release","description":"USGS data release","linkHelpText":"Single-well aquifer test data from wells NRF-17 and NRF-18, Idaho National Laboratory, Idaho"},{"id":499046,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118682.htm","linkFileType":{"id":5,"text":"html"}},{"id":491692,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5049/sir20255049.XML"},{"id":491688,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5049/sir20255049.pdf","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5049"},{"id":491687,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5049/coverthb.jpg"},{"id":491689,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255049/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5049"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory, Naval Reactors Facility","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.5,\n 44\n ],\n [\n -113.5,\n 44\n ],\n [\n -113.5,\n 43.25\n ],\n [\n -112.5,\n 43.25\n ],\n [\n -112.5,\n 44\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
The U.S. Geologic Survey National Earthquake Information Center (NEIC) monitors global seismicity, producing a catalog of earthquake source parameters in near-real-time to provide information that can help mitigate the societal impact of earthquakes. The NEIC commonly relies on teleseismic observations to constrain earthquake source parameters (e.g., location, depth, magnitude, and mechanism) due to a lack of local and regional observations. For these ‘teleseismic’ events, depth phase (i.e., pP, sP) arrival time observations provide the best estimate on source depth. However, depth phases are often difficult to accurately identify and/or pick. Therefore, NEIC relies on waveform modeling, such as those determined from W-phase (Mww), body wave (Mwb), and regional (Mwr) moment tensor estimations, to provide constraints on source depth. While depth estimates from these approaches are informative, higher frequency observations provide more precise estimates because depth phases are more prominently observed at higher frequencies. Here, we present NEIC’s relatively high-frequency (~0.04 to 1 Hz) teleseismic waveform modeling approach, termed Synthetic Depth Phase Modeling (SynDepth), for determining source depth. SynDepth was developed to provide NEIC with a tool that enables rapid, accurate, and quantifiable estimates of earthquake source depth in cases where locator depths are not reliable. This relatively simple and fast procedure searches over 1 km-incremented source depths and an expanding triangular source-time function to find the best-fitting solution. We compare automatic SynDepth solutions for a dataset of 1,216 earthquakes (M5.5-M7.6) between 2017 and 2021 to NEIC-derived depth estimates from other methods. Our approach provides a robust depth estimate for earthquakes lacking local arrival time data, and it minimizes the need for analyst review of depth-phase picks (pP, sP) or using predefined ‘fixed’ depths.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0220240372","usgsCitation":"Yeck, W.L., Herrmann, R., Patton, J., Barnhart, W.D., and Benz, H.M., 2025, Estimating earthquake source depth using teleseismic broadband waveform modeling at the USGS National Earthquake Information Center: Seismological Research Letters, v. 96, no. 6, p. 3643-3655, https://doi.org/10.1785/0220240372.","productDescription":"13 p.","startPage":"3643","endPage":"3655","ipdsId":"IP-167539","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":491836,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":942024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrmann, Robert B.","contributorId":80255,"corporation":false,"usgs":false,"family":"Herrmann","given":"Robert B.","affiliations":[],"preferred":false,"id":942025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patton, John 0000-0003-0142-5118","orcid":"https://orcid.org/0000-0003-0142-5118","contributorId":218681,"corporation":false,"usgs":true,"family":"Patton","given":"John","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, William D. 0000-0003-0498-1697 wbarnhart@usgs.gov","orcid":"https://orcid.org/0000-0003-0498-1697","contributorId":294678,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":942027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942028,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268796,"text":"70268796 - 2025 - 2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska","interactions":[],"lastModifiedDate":"2025-07-08T16:16:15.639994","indexId":"70268796","displayToPublicDate":"2025-07-02T09:12:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska","docAbstract":"Alaska's coastal communities face growing landslide hazards owing to glacier retreat and extreme weather intensified by the warming climate, yet hazard monitoring remains challenging. As part of ongoing experimental monitoring in Prince William Sound, we detected three large landslides (0.5–2.3 M m3) at Surprise Inlet on 20 September 2024, within the span of an hour. These events were identified in near real-time through seismic data and later confirmed using satellite imagery, tidal records, and infrasound. The landslides generated a modest tsunami, and a 4 cm wave was recorded by a tide gauge 18 km away, marking the first recorded landslide to reach water since monitoring began in this region in 2021. Here, we examine the detection and interpretation of these landslides using multiple data sources and modeling. We demonstrate the effectiveness of this regional seismic monitoring system and show how complementary instrumentation, where available, can enhance detection capabilities.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL115911","usgsCitation":"Karasozen, E., West, M.E., Barnhart, K.R., Lyons, J.J., Nichols, T., Schaefer, L.N., Bahng, B., Ohlendorf, S., Staley, D.M., and Wolken, G.J., 2025, 2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska: Geophysical Research Letters, v. 52, no. 13, e2025GL115911, 11 p., https://doi.org/10.1029/2025GL115911.","productDescription":"e2025GL115911, 11 p.","ipdsId":"IP-176964","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":492061,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl115911","text":"Publisher Index Page"},{"id":491813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.69339399398933,\n 61.18214394792622\n ],\n [\n -148.69339399398933,\n 59.91280847695441\n ],\n [\n -145.64673816140657,\n 59.91280847695441\n ],\n [\n -145.64673816140657,\n 61.18214394792622\n ],\n [\n -148.69339399398933,\n 61.18214394792622\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"13","noUsgsAuthors":false,"publicationDate":"2025-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Karasozen, Ezgi 0000-0003-1140-1427","orcid":"https://orcid.org/0000-0003-1140-1427","contributorId":244223,"corporation":false,"usgs":false,"family":"Karasozen","given":"Ezgi","email":"","affiliations":[{"id":6606,"text":"Colorado School of 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jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":942017,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, Terry","contributorId":357616,"corporation":false,"usgs":false,"family":"Nichols","given":"Terry","affiliations":[{"id":85473,"text":"National Tsunami Warning Center, Palmer, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":942018,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schaefer, Lauren N. 0000-0003-3216-7983","orcid":"https://orcid.org/0000-0003-3216-7983","contributorId":241997,"corporation":false,"usgs":true,"family":"Schaefer","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942019,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bahng, Bohyun","contributorId":357617,"corporation":false,"usgs":false,"family":"Bahng","given":"Bohyun","affiliations":[{"id":85473,"text":"National Tsunami Warning Center, Palmer, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":942020,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ohlendorf, Summer","contributorId":357618,"corporation":false,"usgs":false,"family":"Ohlendorf","given":"Summer","affiliations":[{"id":85473,"text":"National Tsunami Warning Center, Palmer, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":942021,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942022,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wolken, Gabriel J.","contributorId":221149,"corporation":false,"usgs":false,"family":"Wolken","given":"Gabriel","email":"","middleInitial":"J.","affiliations":[{"id":40336,"text":"Alaska Department of Natural Resources: Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":942023,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70268143,"text":"sir20235101 - 2025 - Hydraulic conductivity and transmissivity estimates from slug tests in wells within the Mississippi Alluvial Plain, Arkansas and Mississippi, 2020","interactions":[],"lastModifiedDate":"2026-01-26T19:15:04.764709","indexId":"sir20235101","displayToPublicDate":"2025-07-02T07:37:05","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":"2023-5101","displayTitle":"Hydraulic Conductivity and Transmissivity Estimates from Slug Tests in Wells Within the Mississippi Alluvial Plain, Arkansas and Mississippi, 2020","title":"Hydraulic conductivity and transmissivity estimates from slug tests in wells within the Mississippi Alluvial Plain, Arkansas and Mississippi, 2020","docAbstract":"During the spring and summer of 2020, the U.S. Geological Survey conducted single-well slug tests on selected observation wells within the Mississippi Alluvial Plain in Arkansas and Mississippi to estimate hydraulic conductivity and transmissivity values for the Mississippi River Valley alluvial and middle Claiborne aquifers. Well and aquifer data were collected from field measurements, well-construction reports, and published aquifer-thickness information. A total of 324 slug-in and slug-out tests were conducted on 48 wells by using mechanical slugs to displace the water column and submersible pressure transducers to record changes in water levels in the wells. Hydraulic conductivity of the aquifers in which the wells are screened was estimated by curve fitting the water-level-change data using aquifer test analysis software. Estimates of aquifer transmissivity were made by multiplying the estimated hydraulic conductivity value by the aquifer thickness at well locations. Mean hydraulic conductivity estimates for 44 observation wells screened in the Mississippi River Valley alluvial aquifer range from 3 to 401 feet per day, and mean transmissivity estimates range from 285 to 80,559 feet squared per day. Mean hydraulic conductivity estimates for four observation wells screened in units of the middle Claiborne aquifer range from 0.14 to 183 feet per day, and mean transmissivity estimates range from 55 to 67,913 feet squared per day. The results from these tests can be used to improve the understanding of water availability and groundwater migration, to refine groundwater models, and to ultimately provide stakeholders and decisionmakers better information for management of the groundwater resources within the Mississippi Alluvial Plain.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The Earth Resources Observation and Science Cal/Val Center of Excellence Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 4 (October–December) of 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251036","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Steinwand, D., Bresnahan, P., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Kaita, E., Levy, R., Miller, J., Ding, L., and Teixeira Pinto, C., 2025, ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2024: U.S. Geological Survey Open-File Report 2025–1036, 56 p., https://doi.org/10.3133/ofr20251036.","productDescription":"Report: viii, 56 p.; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-175052","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":491624,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":491621,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251036/full"},{"id":491620,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1036/images/"},{"id":491619,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1036/ofr20251036.XML"},{"id":491618,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1036/ofr20251036.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025–1036"},{"id":491617,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1036/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This report summarizes results from boat-based, high-resolution water-quality mapping surveys completed before, during, and after upgrades to the EchoWater Resource Recovery Facility (EchoWater Facility), the regional wastewater facility for the City of Sacramento and surrounding areas, near Elk Grove, California. Surveys were completed in the tidal aquatic environments of the Sacramento–San Joaquin Delta (Delta) in spring (May or June) 2018, 2020, 2021, and 2022. In each survey, a suite of in situ sensors were used to continuously (one measurement per second) measure water-quality conditions, nutrients, phytoplankton abundance, and species composition. In addition to in situ data collection, discrete water samples were collected about every 2 miles while underway for determination of phosphate, ammonium, and nitrate concentration. The boat stopped at about 30 locations to collect discrete samples for a suite of additional analytes, including phytoplankton enumeration. The four surveys represent snapshots in time across different phases of the EchoWater Facility Biological Nutrient Reduction (BNR) upgrade. The May 2018 survey represents conditions before the upgrade. The second survey (June 2020) represents conditions after implementation of the Nitrifying Sidestream Treatment. The third survey (May 2021) was completed immediately after the completion of the BNR upgrade and represents a transitional period, and the final survey (May 2022) represents post-upgrade conditions.
Relevant hydrologic and climatic context such as water-year type, X2 position (the distance from the Golden Gate Bridge to the point upstream where bottom salinity is 2 parts per thousand; Jassby and others, 1995), water export to import ratio, and management actions like the Delta Cross Channel gate operations are presented for each survey so they may be considered in comparisons among surveys. Differences in water-quality parameters, like turbidity, temperature, salinity, pH, and dissolved oxygen (DO) improve understanding of nutrient cycling and phytoplankton dynamics. Because the Delta is a complex system, we divided the study area into hydrologic zones to better examine general trends and obtain a broadscale view of differences among the 4 study years. Results are presented for each survey and parameter using box plots to compare the different hydrologic zones. We also present each parameter using contour maps by survey to display gradients across the system.
The most evident change to water quality in the Delta across surveys is related to the EchoWater Facility BNR upgrade, which included nitrification and denitrification processes. Through this upgrade, effluent ammonium (NH4+) concentrations were reduced by more than 95 percent (from about 2,000 micromolars [μM] to below the reporting limit of 35 μM), and nitrate (NO3−) concentrations increased from near zero to about 500 μM; therefore, the concentration of dissolved inorganic nitrogen (DIN; the sum of NH4+ and NO3−) in the effluent was reduced by about 75 percent between May 2018 and May 2022. The BNR upgrade resulted in a reduction in NH4+ concentrations in aquatic habitats immediately below the facility, designated as the “north Delta tidal transition zone” (Bergamaschi and others, 2024), from about 30 μM pre-upgrade to near zero during the 2022 spring survey, whereas effluent NO3− increased from median concentrations of about 7 μM to about 15 μM. Because of the reduced effluent nitrogen loads and variability in Sacramento River nitrogen loads from upstream sources, DIN concentrations in the north Delta tidal transition zone decreased from a median of 53.3 μM in 2018 to 35.3 μM in 2020, 20.7 μM in 2021, and 11.3 μM in 2022 during the spring surveys.
The changes in DIN concentration and form observed in the north Delta tidal transition zone after the EchoWater Facility upgrade extended downstream but were rapidly altered by hydrologic mixing, biogeochemical processes, and other nutrient source inputs. Most of the Delta indicated near-zero concentrations of NH4+ 1 year after the completion of the EchoWater Facility upgrades represented by the 2022 survey. Exceptions to this finding were observed in the San Joaquin River near Stockton and in Suisun Bay, indicating there are NH4+ inputs to these locations from other sources (for example, Stockton Regional Wastewater Control Facility and Central Contra Costs Sanitary District wastewater treatment plants or agricultural and urban runoff).
Although there was an increase in NO3− concentrations in the north Delta tidal transition zone after the upgrade, increases in NO3− in other zones were not apparent, presumably because nitrification of effluent derived ammonium was no longer a source of NO3−. Concentrations of DIN in many Delta zones were lower in 2022 compared to 2018 and 2020, with concentrations near or below what is considered potentially nitrogen limiting conditions for phytoplankton growth in the North Delta tidal transition zone and the Cache Slough complex channel system. Unrelated to the EchoWater Facility upgrade, NO3− and therefore DIN concentrations increased in the San Joaquin River near Stockton and in adjacent water bodies by survey date (likely associated with increasing drought conditions). The Mokelumne River had low DIN concentrations, except in 2018 when the Delta Cross Channel was open, which allowed nutrient-rich Sacramento River water to flow into this section of the river. Data from these surveys also support the hypothesis that nutrient drawdown during phytoplankton blooms may create localized nitrogen limiting conditions.
The BNR upgrade resulted in lower effluent phosphate (PO43−) concentrations, which lowered PO43− concentrations in some zones of the Delta during the four spring surveys; however, PO43− concentrations throughout the Delta remained above 0.3 μM, indicating that primary productivity was not limited by phosphorous availability. DIN and PO43− decreased after the upgrade in many areas of the Delta, and the DIN to dissolved inorganic phosphorus (DIN:DIP) ratio remained similar to pre-upgrade conditions and was often below the Redfield Ratio of 16, indicating nitrogen is more likely to limit phytoplankton growth than phosphorous. Inputs of dissolved organic carbon (DOC) from the EchoWater Facility are a minor source of this constituent to the Delta, so the upgrade had little to no effect on DOC concentrations across the Delta.
Because phytoplankton abundance and species composition in the Delta are shaped by multiple factors other than nutrients (for example, light availability, temperature, salinity, and predation), it is important to consider these factors (as well as long-term monitoring) in addition to the EchoWater Facility upgrade. Although phytoplankton populations were low across much of the Delta during the spring surveys, several localized phytoplankton blooms (defined here as greater than 15 micrograms per liter [μg/L] of chlorophyll) provide insight into conditions that may favor the growth of beneficial and harmful species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255035","collaboration":"Prepared in cooperation with the Delta Regional Monitoring Program","programNote":"Water Resources Mission Area—Water Availability and Use Science Program","usgsCitation":"Richardson, E., Kraus, T., O’Donnell, K., Soto-Perez, J., Sturgeon, C., Stumpner, E., and Bergamaschi, B., 2025, Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the California\nSacramento–San Joaquin Delta at the landscape scale—Comparison of four (2018, 2020, 2021, 2022) spring high-resolution mapping surveys: U.S. Geological Survey Scientific Investigations Report 2025–5035, 78 p.,\nhttps://doi.org/10.3133/sir20255035.","productDescription":"Report: x, 78 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-151343","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":491472,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5035/images"},{"id":491473,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5035/sir20255035.XML"},{"id":491469,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FQEUAL","text":"USGS data release","description":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients and related water quality constituents in the California Sacramento–San Joaquin Delta at the landscape scale—2018 High resolution mapping surveys (ver. 2.0, October 2023)"},{"id":491468,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255035/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5035"},{"id":491467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5035/sir20255035.pdf","text":"Report","size":"41.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5035"},{"id":491466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5035/coverthb.jpg"},{"id":491471,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QULEAT","text":"USGS data release","description":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients, phytoplankton, and related water quality constituents in the California Sacramento–San Joaquin Delta at the landscape scale—2022 High resolution mapping surveys"},{"id":491470,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90VYUBX","text":"USGS data release","description":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the California Sacramento–San Joaquin Delta at the landscape scale—2020–2021 high-resolution mapping surveys"}],"country":"United States","state":"Callifornia","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121,\n 38.5833\n ],\n [\n -122.1667,\n 38.5833\n ],\n [\n -122.1667,\n 37.5\n ],\n [\n -121,\n 37.5\n ],\n [\n -121,\n 38.5833\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Following the recovery of Peregrine Falcons (Falco peregrinus), the US Fish and Wildlife Service began a process to allow “take” (capture) of wild peregrines for falconry in the United States. Recently, that effort involved generating updated estimates of the collective abundance of the three North American peregrine subspecies: F. p. anatum, F. p. tundrius, and F. p. pealei (Peale's Peregrine Falcon). Because of the more limited distribution of F. p. pealei, we conducted an analysis specific to its geographic range. We analyzed data from a long-term banding and resighting program on three beaches on the southern coast of Washington, USA, to estimate the annual abundance of migrating and overwintering F. p. pealei, using the capture histories of 250 Peregrine Falcons, nearly all of which were captured during 1277 vehicle surveys between 1995 and 2024. Because we studied an open population of migratory individuals, we used a zero-inflated Poisson log-normal mark-resight model to estimate annual abundance. For the analyses, we partitioned our survey data into sighting periods, each of which extended from 1 September of one year to 31 May of the next. We anticipated that first-year F. p. pealei would be identified for falconry take, and our annual abundance estimates for first-year birds of this subspecies ranged from a high of 24.8 ± 6.1 (SE) individuals in the 2014–2015 sighting period to a low of 1.9 ± 1.4 individuals in the 2023–2024 sighting period. Peregrine Falcon abundance varied annually and appeared to decline during the last two sighting periods. Our sighting rate of marked peregrines was negatively associated with Bald Eagle (Haliaeetus leucocephalus) encounter rate. There was a lesser relationship to human activity, and we suspect the change in sighting rate was a behavioral response by Peregrine Falcons to the threat of kleptoparasitism by Bald Eagles. We currently lack comprehensive information about the natal origin of the individual peregrines in our study area, which prevented us from assessing the degree to which falconry take from the pool of falcons migrating to or through Washington might potentially impact local or regional abundances. Although a better understanding of natal origins is needed, our data add clarity to the migration and overwinter abundance of F. p. pealei on the Washington coast and may inform decisions about the take of this subspecies for falconry.
","language":"English","publisher":"BioOne","doi":"10.3356/jrr2482","usgsCitation":"Daniel E. Varland, Joseph B. Buchanan, Guthrie S. Zimmerman, Bauder, J.M., Tracy L. Fleming, Brian A. Millsap, 2025, Estimated annual abundance of migratory Peale's Peregrine Falcons in coastal Washington, USA: Journal of Raptor Research, v. 59, no. 3, p. 1-16, https://doi.org/10.3356/jrr2482.","productDescription":"16 p.","startPage":"1","endPage":"16","ipdsId":"IP-177337","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr2482","text":"Publisher Index Page"},{"id":500426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.72822793496042,\n 47.20172472210811\n ],\n [\n -124.72822793496042,\n 46.15365987938665\n ],\n [\n -123.42708843918685,\n 46.15365987938665\n ],\n [\n -123.42708843918685,\n 47.20172472210811\n ],\n [\n -124.72822793496042,\n 47.20172472210811\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Daniel E. Varland","contributorId":366594,"corporation":false,"usgs":false,"family":"Daniel E. Varland","affiliations":[{"id":37634,"text":"Coastal Raptors","active":true,"usgs":false}],"preferred":false,"id":956075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Joseph B. Buchanan","contributorId":366595,"corporation":false,"usgs":false,"family":"Joseph B. Buchanan","affiliations":[{"id":7113,"text":"private citizen","active":true,"usgs":false}],"preferred":false,"id":956076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guthrie S. Zimmerman","contributorId":366596,"corporation":false,"usgs":false,"family":"Guthrie S. Zimmerman","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":956077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tracy L. Fleming","contributorId":366597,"corporation":false,"usgs":false,"family":"Tracy L. Fleming","affiliations":[{"id":7113,"text":"private citizen","active":true,"usgs":false}],"preferred":false,"id":956079,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brian A. Millsap","contributorId":366598,"corporation":false,"usgs":false,"family":"Brian A. Millsap","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":956080,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70271321,"text":"70271321 - 2025 - Living with wildfire in West Vail, Eagle County, Colorado: 2022 data report","interactions":[],"lastModifiedDate":"2025-09-08T14:49:06.641038","indexId":"70271321","displayToPublicDate":"2025-07-01T09:41:25","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":72,"text":"Research Note","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"RMRS-RN-106","title":"Living with wildfire in West Vail, Eagle County, Colorado: 2022 data report","docAbstract":"Community wildfire readiness includes homeowner wildfire risk mitigation and wildfire evacuation preparedness. This report presents results from a household survey distributed to 725 households in West Vail, Colorado in 2022. Results indicate that West Vail survey respondents may not fully understand the risk of damage or property loss due to a wildfire and may benefit from further information about wildfire risk contributors - in general and specific to their property - as well as evacuation preparedness information. Among common sources of wildfire risk information, respondents rated Vail Fire & Emergency Services as the most useful source. Most residents support wildfire risk reduction strategies on nearby public lands.
","language":"English","publisher":"USDA Forest Service Rocky Mountain Research Station","doi":"10.2737/RMRS-RN-106","usgsCitation":"Donovan, C., Champ, P.A., Wittenbrink, S., Cada, P., Brenkert-Smith, H., Meldrum, J., Barth, C.M., Wagner, C., Forrester, C., and Goolsby, J., 2025, Living with wildfire in West Vail, Eagle County, Colorado: 2022 data report: Research Note RMRS-RN-106, vi, 36 p., https://doi.org/10.2737/RMRS-RN-106.","productDescription":"vi, 36 p.","ipdsId":"IP-172452","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":495215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"West Vail","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.39497622711538,\n 39.65046974893238\n ],\n [\n -106.44217903630162,\n 39.65046974893238\n ],\n [\n -106.44217903630162,\n 39.61038105443487\n ],\n [\n -106.39497622711538,\n 39.61038105443487\n ],\n [\n -106.39497622711538,\n 39.65046974893238\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Donovan, Colleen","contributorId":240586,"corporation":false,"usgs":false,"family":"Donovan","given":"Colleen","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":948031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Champ, Patricia A. 0000-0003-1917-883X","orcid":"https://orcid.org/0000-0003-1917-883X","contributorId":360956,"corporation":false,"usgs":false,"family":"Champ","given":"Patricia","middleInitial":"A.","affiliations":[{"id":86128,"text":"U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":948032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wittenbrink, Suzanne","contributorId":333353,"corporation":false,"usgs":false,"family":"Wittenbrink","given":"Suzanne","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":948033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cada, Paul","contributorId":296736,"corporation":false,"usgs":false,"family":"Cada","given":"Paul","email":"","affiliations":[{"id":64155,"text":"Vail Fire and Emergency Services","active":true,"usgs":false}],"preferred":false,"id":948034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brenkert-Smith, Hannah 0000-0001-6117-8863","orcid":"https://orcid.org/0000-0001-6117-8863","contributorId":195485,"corporation":false,"usgs":false,"family":"Brenkert-Smith","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":948035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":948036,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barth, Christopher M.","contributorId":360969,"corporation":false,"usgs":false,"family":"Barth","given":"Christopher","middleInitial":"M.","affiliations":[{"id":86132,"text":"U.S. Department of Agriculture, Forest Service, Washington Office","active":true,"usgs":false}],"preferred":false,"id":948037,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wagner, Carolyn","contributorId":240587,"corporation":false,"usgs":false,"family":"Wagner","given":"Carolyn","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":948038,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Forrester, Chiara","contributorId":328660,"corporation":false,"usgs":false,"family":"Forrester","given":"Chiara","email":"","affiliations":[{"id":48103,"text":"Wildfire Research (WiRē) Center","active":true,"usgs":false}],"preferred":false,"id":948039,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Goolsby, Julia 0000-0002-2229-5685","orcid":"https://orcid.org/0000-0002-2229-5685","contributorId":295471,"corporation":false,"usgs":false,"family":"Goolsby","given":"Julia","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":948040,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70270114,"text":"70270114 - 2025 - Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses","interactions":[],"lastModifiedDate":"2025-08-11T15:22:04.615437","indexId":"70270114","displayToPublicDate":"2025-07-01T08:15:23","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}},"title":"Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses","docAbstract":"Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-06613-6","usgsCitation":"Gholizadeh, H., Clement, T., Green, C., Tick, G.R., Plattner, A., and Zhang, Y., 2025, Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses: Scientific Reports, v. 15, 21699, 18 p., https://doi.org/10.1038/s41598-025-06613-6.","productDescription":"21699, 18 p.","ipdsId":"IP-175082","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":494188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-06613-6","text":"Publisher Index Page"},{"id":493932,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","county":"Baldwin County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.91513509957319,\n 30.59435431566419\n ],\n [\n -87.91513509957319,\n 30.204167726022206\n ],\n [\n -87.32356119618261,\n 30.204167726022206\n ],\n [\n -87.32356119618261,\n 30.59435431566419\n ],\n [\n -87.91513509957319,\n 30.59435431566419\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gholizadeh, Hossein","contributorId":352234,"corporation":false,"usgs":false,"family":"Gholizadeh","given":"Hossein","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":945513,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clement, T. 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Prabhakar","affiliations":[{"id":85819,"text":"Department of Civil, Construction, and Environmental Engineering, University of Alabama","active":true,"usgs":false}],"preferred":false,"id":945514,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Christopher 0000-0002-6480-8194","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":201642,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":945515,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tick, Geoffrey R.","contributorId":359462,"corporation":false,"usgs":false,"family":"Tick","given":"Geoffrey","middleInitial":"R.","affiliations":[{"id":85820,"text":"Santa Clara Valley Water District, Groundwater Management Unit, San Jose, CA","active":true,"usgs":false}],"preferred":false,"id":945516,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Plattner, Alain","contributorId":359463,"corporation":false,"usgs":false,"family":"Plattner","given":"Alain","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":945517,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Yong","contributorId":352236,"corporation":false,"usgs":false,"family":"Zhang","given":"Yong","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":945519,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70274677,"text":"70274677 - 2025 - A molecular specimen bank for contemporary and future study captures landscape-scale biodiversity baselines before Klamath River dam removal","interactions":[],"lastModifiedDate":"2026-04-03T16:11:35.913599","indexId":"70274677","displayToPublicDate":"2025-07-01T00:00:00","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}},"title":"A molecular specimen bank for contemporary and future study captures landscape-scale biodiversity baselines before Klamath River dam removal","docAbstract":"Global restoration and conservation of freshwater biodiversity are represented in practice by works such as the Klamath River Renewal Project (KRRP), the largest dam removal and river restoration in the United States, which has reconnected 640 river kilometers. With dam removals, many biological outcomes remain understudied due to a lack of pre-impact data and complex ecosystem recovery timeframes. To avoid this, we created the KRRP molecular library, an environmental specimen bank, for long-term curation of environmental nucleic acids collected from the restoration project. We used these initial samples, environmental DNA metabarcoding, and generalized linear mixed-effects models to evaluate patterns of pre-dam removal fish richness and diversity. Demonstrating the suitability to resolve biological differences, the baseline shows that tributary and mainstem streams had greater native fish diversity and 2.3–10.7 times greater native fish species richness than reservoirs. These and future sampling efforts should, at a minimum, allow tracking of fish community response to ecosystem restoration. Anticipating the acceleration of omics innovation, we preserved samples for long-term storage and identified requisite phases for sustained function and adaptation of the molecular library: securing a physical storage facility for genetic material, establishing a governance structure, and confirming support for archive management.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-07042-1","usgsCitation":"Keel, D.J., Karpenko, K., Blankenship, S.M., Schumer, G., O’Rourke, O., Ostberg, C.O., Chase, D.A., and Duda, J.J., 2025, A molecular specimen bank for contemporary and future study captures landscape-scale biodiversity baselines before Klamath River dam removal: Scientific Reports, v. 15, 20679, 16 p., https://doi.org/10.1038/s41598-025-07042-1.","productDescription":"20679, 16 p.","ipdsId":"IP-173262","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":502171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.187198933266,\n 42.294953678476304\n ],\n [\n -122.187198933266,\n 41.842920701832696\n ],\n [\n -121.52967806160167,\n 41.842920701832696\n ],\n [\n -121.52967806160167,\n 42.294953678476304\n ],\n [\n -122.187198933266,\n 42.294953678476304\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Keel, Dylan J. 0009-0006-8445-7033","orcid":"https://orcid.org/0009-0006-8445-7033","contributorId":369238,"corporation":false,"usgs":false,"family":"Keel","given":"Dylan","middleInitial":"J.","affiliations":[{"id":87745,"text":"Resource Environmental Solutions, LLC. Sacramento, CA","active":true,"usgs":false}],"preferred":false,"id":958667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karpenko, Katie","contributorId":337249,"corporation":false,"usgs":false,"family":"Karpenko","given":"Katie","email":"","affiliations":[],"preferred":false,"id":958668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blankenship, Scott M.","contributorId":369239,"corporation":false,"usgs":false,"family":"Blankenship","given":"Scott","middleInitial":"M.","affiliations":[{"id":87746,"text":"Genidaqs laboratory of Cramer Fish Sciences, West Sacramento, CA","active":true,"usgs":false}],"preferred":false,"id":958669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schumer, Gregg","contributorId":337251,"corporation":false,"usgs":false,"family":"Schumer","given":"Gregg","email":"","affiliations":[],"preferred":false,"id":958670,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O’Rourke, Oshun","contributorId":369240,"corporation":false,"usgs":false,"family":"O’Rourke","given":"Oshun","affiliations":[{"id":87747,"text":"Yurok Tribal Fisheries Department, Klamath, CA","active":true,"usgs":false}],"preferred":false,"id":958671,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ostberg, Carl O. 0000-0003-1479-8458 costberg@usgs.gov","orcid":"https://orcid.org/0000-0003-1479-8458","contributorId":217721,"corporation":false,"usgs":true,"family":"Ostberg","given":"Carl","email":"costberg@usgs.gov","middleInitial":"O.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958672,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chase, Daniel A.","contributorId":369241,"corporation":false,"usgs":false,"family":"Chase","given":"Daniel","middleInitial":"A.","affiliations":[{"id":87745,"text":"Resource Environmental Solutions, LLC. Sacramento, CA","active":true,"usgs":false}],"preferred":false,"id":958673,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":958674,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70268679,"text":"sir20255053 - 2025 - Assessing the potential for evaluation of wildland fire models using remotely sensed data—Summary proceedings from a U.S. Geological Survey workshop in 2024","interactions":[],"lastModifiedDate":"2026-01-26T19:29:20.8728","indexId":"sir20255053","displayToPublicDate":"2025-06-30T13:30:00","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":"2025-5053","displayTitle":"Assessing the Potential for Evaluation of Wildland Fire Models Using Remotely Sensed Data—Summary Proceedings from a U.S. Geological Survey Workshop in 2024","title":"Assessing the potential for evaluation of wildland fire models using remotely sensed data—Summary proceedings from a U.S. Geological Survey workshop in 2024","docAbstract":"On September 19, 2024, the U.S. Geological Survey (USGS) held a virtual workshop titled “Potential for Evaluation of Fire Models with Remote Sensing Data Workshop” to assess the feasibility of using remotely sensed datasets to evaluate next-generation wildland fire behavior models. Remote sensing and fire modelling experts gathered to: (1) assess the suitability of a variety of classified, commercial, and publicly available remotely sensed datasets for advancing fire model evaluation; (2) develop ideas on how to integrate remotely sensed data products with fire model inputs and outputs; and (3) identify any barriers and limitations to performing an evaluation of next-generation fire models. The USGS National Civil Applications Center, USGS Earth Resources Observation and Science Center, and USGS Fort Collins Ecosystem Science Center presented information on remote sensing datasets for three Arizona wildfire case studies. The development teams of the Fire Dynamics Simulator and QUIC-Fire fire behavior models presented their models and current evaluation methodologies. Interspersed with these presentations were discussions regarding how to expand current wildfire remote sensing data collection efforts beyond operational needs to assist in future fire modeling.
Workshop participants agreed that several of the remote sensing datasets have potential for wildfire model evaluation. However, participants also identified several barriers and complications to performing a model evaluation including key gaps in wildfire datasets; uncertainties related to model fire-atmosphere reinitiation; lack of ground truthing and atmospheric correction of remotely sensed datasets; and differences in spatial, geolocation, radiometric, and temporal resolutions between the datasets and models. Further, the absence of standardized methodologies for image interpretation, poor understanding of sensor capabilities and limitations, and a lack of automation also hinder model evaluation efforts. Based on feedback from this workshop, USGS fire modelers are considering a project to address the uncertainties related to fire model reinitiation and encouraging fire practitioners to collaborate with remote sensing experts on wildland fires to improve data collection for a broader community of practice. Additionally, multiagency efforts are in development for a comprehensive cross-sensor validation and ground-truth campaign to test spatial, spectral, and geolocation sensor capabilities, determine limitations, and identify observational gaps for future sensor development and acquisition.
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\"}}]}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Seawater intrusion (SWI) affects coastal landscapes worldwide. Here we describe the hydrologic pathways through which SWI occurs - over land via storm surge or tidal flooding, under land via groundwater transport, and through watersheds via natural and artificial surface water channels—and how human modifications to those pathways alter patterns of SWI. We present an approach to advance understanding of spatiotemporal patterns of salinization that integrates these hydrologic pathways, their interactions, and how humans modify them. We use examples across the East Coast of the United States that exemplify mechanisms of salinization that have been reported around the planet to illustrate how hydrologic connectivity and human modifications alter patterns of SWI. Finally, we suggest a path for advancing SWI science that includes (a) deploying standardized and well-distributed sensor networks at local to global scales that intentionally track SWI fronts, (b) employing remote sensing and geospatial imaging techniques targeted at integrating above and belowground patterns of SWI, and (c) continuing to develop data analysis and model-data fusion techniques to measure the extent, understand the effects, and predict the future of coastal salinization.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038720","usgsCitation":"Helton, A., Dennedy-Frank, J., Emanuel, R., Neubauer, S.C., Adams, K., Ardon, M., Band, L., Befus, K.A., Borstlap, H., Duberstein, J., Gold, A., Kominoski John, Manda, A., Michael, H.A., Moysey, S., Myers-Pigg, A., Neville, J.A., Noe, G.E., Panthi, J., Pezeshki, E., Sirianni, M., and Ward.Nicolas, 2025, Over, under, and through: Hydrologic connectivity and the future of coastal landscape salinization: Water Resources Research, v. 61, no. 7, e2024WR038720, 8 p., https://doi.org/10.1029/2024WR038720.","productDescription":"e2024WR038720, 8 p.","ipdsId":"IP-167925","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":492056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Ryan","contributorId":333342,"corporation":false,"usgs":false,"family":"Emanuel","given":"Ryan","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":941664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Neubauer, Scott C","contributorId":169723,"corporation":false,"usgs":false,"family":"Neubauer","given":"Scott","email":"","middleInitial":"C","affiliations":[{"id":25575,"text":"Dept. of Biology, Virginia Commonwealth University, Richmond, VA","active":true,"usgs":false}],"preferred":false,"id":941665,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Kyra","contributorId":357529,"corporation":false,"usgs":false,"family":"Adams","given":"Kyra","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":941666,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ardon, 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gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941679,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Panthi, Jeeban","contributorId":357534,"corporation":false,"usgs":false,"family":"Panthi","given":"Jeeban","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":941680,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Pezeshki, Elnaz","contributorId":357535,"corporation":false,"usgs":false,"family":"Pezeshki","given":"Elnaz","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":941681,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Sirianni, Matthew","contributorId":357536,"corporation":false,"usgs":false,"family":"Sirianni","given":"Matthew","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":941682,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Ward.Nicolas","contributorId":357537,"corporation":false,"usgs":false,"family":"Ward.Nicolas","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":941683,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70273107,"text":"70273107 - 2025 - Spatiotemporal variations in strain release and seismic rupture in multifault systems: An example from Panamint Valley, southeastern California","interactions":[],"lastModifiedDate":"2025-12-16T15:46:46.130415","indexId":"70273107","displayToPublicDate":"2025-06-27T09:40:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2626,"text":"Lithosphere","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal variations in strain release and seismic rupture in multifault systems: An example from Panamint Valley, southeastern California","docAbstract":"Geometrically complex, multifault ruptures have been observed in recent, damaging earthquakes in southeastern California, sparking renewed efforts to identify physical conditions that promote or inhibit fault discontinuity-spanning coseismic ruptures. The likelihood of ruptures propagating across fault discontinuities is thought to be partly controlled by fault geometries, rupture direction, and the history of strain release. However, these parameters vary in space and time over multiple earthquake cycles, making it difficult to forecast the likelihood that an earthquake on one fault will trigger rupture on a nearby fault. Here we use tectono-geomorphic mapping of a geometrically complex fault zone in Panamint Valley, southeastern California, to assess spatiotemporal variations of paleo-rupture patterns and geometries of fault discontinuities over multiple earthquake cycles. First, we identify ten generations of late Pleistocene to Holocene alluvium using geomorphic parameters and luminescence dating to constrain ages of alluvium and bracket late Holocene earthquake timing. Then, we quantify slip kinematics using high-resolution structure from motion digital surface models. We find the Panamint Valley transtensional relay (PVTR) hosted four late Holocene earthquakes, bracketed to ~5.8–3.4 ka, ~3.8–2.2 ka, ~2.4–0.6 ka, and ~0.64–0.16 ka, with ~0.6–1.1 m of slip per event, correlative to Mw ≈ 6.7–6.9 earthquakes. Additionally, we find similarities in earthquake timing on the Ash Hill, PVTR, and Panamint Valley faults and similarities in the slip magnitude and slip kinematics between the Ash Hill and PVTR faults, implying that the PVTR may co-rupture with nearby faults. Paleo-rupture patterns indicate that seismogenic strain transfer may occur through the PVTR, along different combinations of fault segments and jump distances, over multiple earthquake cycles. These data highlight the utility of tectono-geomorphic mapping in evaluating paleo-rupture patterns and suggest that the PVTR may act to propagate and/or arrest rupture between the Ash Hill and Panamint Valley faults.
","language":"English","publisher":"Geological Society of America","doi":"10.2113/2024/lithosphere_2024_187","usgsCitation":"LaPlante, A., Regalla, C., Sethanant, I., Mahan, S.A., and Gray, H., 2025, Spatiotemporal variations in strain release and seismic rupture in multifault systems: An example from Panamint Valley, southeastern California: Lithosphere, v. 2024, no. Special 15, lithosphere_2024_187, 38 p., https://doi.org/10.2113/2024/lithosphere_2024_187.","productDescription":"lithosphere_2024_187, 38 p.","ipdsId":"IP-167806","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":497727,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2113/2024/lithosphere_2024_187","text":"Publisher Index Page"},{"id":497572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Panamint Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.5,\n 36.333\n ],\n [\n -117.5,\n 35.75\n ],\n [\n -117,\n 35.75\n ],\n [\n -117,\n 36.333\n ],\n [\n -117.5,\n 36.333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2024","issue":"Special 15","noUsgsAuthors":false,"publicationDate":"2025-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"LaPlante, Aubrey 0000-0003-4770-2619","orcid":"https://orcid.org/0000-0003-4770-2619","contributorId":331133,"corporation":false,"usgs":false,"family":"LaPlante","given":"Aubrey","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":952351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regalla, Christine 0000-0003-2975-8336","orcid":"https://orcid.org/0000-0003-2975-8336","contributorId":254361,"corporation":false,"usgs":false,"family":"Regalla","given":"Christine","email":"","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":952352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sethanant, Israporn","contributorId":364204,"corporation":false,"usgs":false,"family":"Sethanant","given":"Israporn","affiliations":[{"id":86768,"text":"University of Melbourne (Australia)","active":true,"usgs":false}],"preferred":false,"id":952353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":952354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, Harrison J. 0000-0002-4555-7473","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":207019,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":952355,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268445,"text":"sir20245134 - 2025 - Assessment and validation of depressions in digital elevation models from multiple elevation data sources and delineation of depressions, sinking streams, and their watersheds in Tennessee and parts of Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi","interactions":[],"lastModifiedDate":"2025-08-14T19:40:56.797048","indexId":"sir20245134","displayToPublicDate":"2025-06-26T13:45:32","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":"2024-5134","displayTitle":"Assessment and Validation of Depressions in Digital Elevation Models From Multiple Elevation Data Sources and Delineation of Depressions, Sinking Streams, and Their Watersheds in Tennessee and Parts of Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi","title":"Assessment and validation of depressions in digital elevation models from multiple elevation data sources and delineation of depressions, sinking streams, and their watersheds in Tennessee and parts of Kentucky, Virginia, North Carolina, Georgia, Alabama, and Mississippi","docAbstract":"Closed depressions and sinking streams in karst landscapes pose difficulties for water-resources management, in the construction of roads and other public works, and in hydrologic and hydrogeomorphic analyses. Digital elevation models (DEMs) can be used to identify the location and determine the size and shape of closed depressions, but separating artificial depressions due to error from real depressions in DEMs can be difficult. Artificial depressions in the DEMs can result from errors that were inherited from limitations in the source data, the interpolation of the elevation data into a grid of values, or horizontal and vertical accuracy of the elevation data. Because the source dataset used to derive DEMs is only a model of the true landscape, field verification is necessary to separate artificial depressions from real ones in DEMs. DEM analysis alone can only be used to determine whether a depression is likely or unlikely to exist in the landscape.
The U.S. Geological Survey has applied methods to delineate depressions, sinking streams, and their watersheds by using DEMs derived from two sources of elevation data within karst areas of Tennessee and parts of surrounding States. Preliminary depressions, which include all depressions before separating the likely depressions from the unlikely depressions, were delineated from the DEMs with 30- by 30-foot cells derived from each elevation data source. The characteristics of these preliminary depressions were compared to occurrence probabilities for depressions derived from numerical error propagation tests in 10 test areas across the study area and to topographic-contour source data within a 17,739-square-mile test area in middle Tennessee and northern Alabama. The comparison was conducted to determine depression characteristics that, when combined with depression-proximity filters, could be used to separate unlikely from likely depressions. Preliminary depressions were examined in the field at 91 sites in Tennessee, and field observations were compared to digital determinations of unlikely and likely depressions.
The density and size of depressions derived from each elevation dataset were compared within eight karst regions in the study area. Depressions and their watersheds were compiled from each elevation dataset. Sinking streams derived from the National Hydrography Dataset and their watersheds also were compiled for the study area.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"The National Fish Habitat Partnership (NFHP) is a science based, non-regulatory, partnership-driven effort to conserve fish habitat across the USA. The NFHP was developed in the early to mid-2000s in response to the noted declines to fish populations and their associated habitats across the USA with the effort led by the Association of Fish and Wildlife Agencies and supported by a wide range of federal and state fisheries agencies along with conservation organizations. Since 2006, 20 Fish Habitat Partnerships have been organized around specific habitats, fish species, or geographic areas. These partnerships have implemented 1,613 projects resulting in 9,720 habitat enhancements or parcels of habitat protected, including 7,962 riverine or coastline miles and 98,255 acres of marine, lake, impoundment, and reservoir fish habitat along with 1,499 science and data products using US$60,613,842 of direct NFHP investment matched or leveraged by $332,841,072. The NFHP was codified by the U.S. Congress in 2020 and reauthorized in 2024, building on the strong initial foundation and ensuring success into the future. Follow-up publications to this overview paper are planned by each Fish Habitat Partnership to fully describe the depth and breadth of this key U.S. habitat program.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/fshmag/vuaf053","usgsCitation":"Whelan, G., McOwen, A., and Wieferich, D.J., 2025, The National Fish Habitat Partnership – A unique path to conserving fish habitat: Fisheries, v. 50, no. 11, p. 512-520, https://doi.org/10.1093/fshmag/vuaf053.","productDescription":"9 p.","startPage":"512","endPage":"520","ipdsId":"IP-175799","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":497681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"50","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Whelan, Gary","contributorId":146115,"corporation":false,"usgs":false,"family":"Whelan","given":"Gary","email":"","affiliations":[{"id":16584,"text":"Fisheries Division, Michigan Department of Natural Resources, P.O. Box 30446, Lansing, MI 48909","active":true,"usgs":false}],"preferred":false,"id":952594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McOwen, Alexandra","contributorId":364367,"corporation":false,"usgs":false,"family":"McOwen","given":"Alexandra","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":952595,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wieferich, Daniel J. 0000-0003-1554-7992 dwieferich@usgs.gov","orcid":"https://orcid.org/0000-0003-1554-7992","contributorId":176205,"corporation":false,"usgs":true,"family":"Wieferich","given":"Daniel","email":"dwieferich@usgs.gov","middleInitial":"J.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":5069,"text":"Office of the AD Core Science Systems","active":true,"usgs":true}],"preferred":true,"id":952596,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70268817,"text":"70268817 - 2025 - In situ, modeled, and earth observation monitoring of surface water availability in West African rangelands","interactions":[],"lastModifiedDate":"2025-07-08T15:28:17.235284","indexId":"70268817","displayToPublicDate":"2025-06-26T10:21:12","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"In situ, modeled, and earth observation monitoring of surface water availability in West African rangelands","docAbstract":"Introduction: Rangeland ponds are vital to the livelihoods of pastoral and agropastoral communities in Africa, providing an important source of water for livestock. However, sparse instrumentation across much of Africa makes it extremely challenging to monitor surface water availability in these areas. Model estimates of surface water, for example, as used by the Famine Early Warning Systems Network (FEWS NET) Water Point Viewer, are one of the few operational tools available to monitor surface water stress across pastoral areas of the Sahel and East Africa.
Methods: Water availability data from these models are difficult to validate. New methods using satellite data to classify surface water provide an opportunity to assess the performance of these tools. This study compares water availability estimates derived from Landsat and Sentinel 1 satellite imagery to in situ observations and model simulations of water availability in 22 ephemeral ponds located in the Ferlo region of Senegal.
Results and discussion: The Active-Passive Water Classification (APWC) algorithm detected surface water at each location. Over 2022 and 2023, water was detected in pond locations annually at a frequency of 68.2% for all ponds and at a frequency of 43.8% for ponds with a surface area <10,000 square meters (m2). The APWC results outperform global and continental surface water datasets in the Ferlo region. Seasonal water availability was captured in 12 ponds over the 2022 and 2023 seasons. The 12 locations can function as sentinel ponds to monitor local water availability. Study results demonstrate the viability of satellite methods to assess water availability in the region, as well as the challenges to using satellite-based methods to estimate water availability in small ponds.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frwa.2025.1320010","usgsCitation":"Slinski, K., Senay, G.B., Adoum, A., Shukla, S., McNally, A., Rowland, J., Fillol, E., Yatheendradas, S., Funk, C., Hoell, A., and Jasinski, M., 2025, In situ, modeled, and earth observation monitoring of surface water availability in West African rangelands: Frontiers in Water, v. 7, 1320010, 17 p., https://doi.org/10.3389/frwa.2025.1320010.","productDescription":"1320010, 17 p.","ipdsId":"IP-162900","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":492054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frwa.2025.1320010","text":"Publisher Index Page"},{"id":491802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Senegal","otherGeospatial":"Ferlo Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -16.125,\n 16.5\n ],\n [\n -16.125,\n 14.6667\n ],\n [\n -14,\n 14.6667\n ],\n [\n -14,\n 16.5\n ],\n [\n -16.125,\n 16.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationDate":"2025-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Slinski, Kimberly","contributorId":337030,"corporation":false,"usgs":false,"family":"Slinski","given":"Kimberly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":942089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":942090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adoum, Alkhalil","contributorId":357639,"corporation":false,"usgs":false,"family":"Adoum","given":"Alkhalil","affiliations":[{"id":85483,"text":"University of California, Climate Hazards Center, Santa Barbara, CA, USA","active":true,"usgs":false}],"preferred":false,"id":942091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shukla, Shraddhanand","contributorId":224784,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":13549,"text":"UC Santa Barbara Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":942092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McNally, Amy","contributorId":331306,"corporation":false,"usgs":false,"family":"McNally","given":"Amy","affiliations":[{"id":79185,"text":"NASA Goddard Space Flight Center/SAIC","active":true,"usgs":false}],"preferred":false,"id":942093,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":942094,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fillol, Erwan","contributorId":357640,"corporation":false,"usgs":false,"family":"Fillol","given":"Erwan","affiliations":[{"id":85484,"text":"Action Contre la Faim, Regional Office for West & Central Africa, Dakar, Senegal","active":true,"usgs":false}],"preferred":false,"id":942095,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yatheendradas, Soni","contributorId":217737,"corporation":false,"usgs":false,"family":"Yatheendradas","given":"Soni","email":"","affiliations":[{"id":39690,"text":"University of Maryland; 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Public participatory geographic information systems (PPGIS) are tools that have been used to help quantify and map the public’s diverse values for a landscape. This work describes the first known Office of Management and Budget–approved use of PPGIS by a Department of the Interior bureau. The U.S. Geological Survey developed an internet-based application to aid in gathering PPGIS data, called Values Mapping for Planning in Regional Ecosystems (VaMPIRE). Further, this work describes the first pilot of the VaMPIRE application in coordination with the Bureau of Land Management to collect spatial data and other survey data regarding the public’s uses of and values for locations within Mojave Trails National Monument. We emailed the link to the VaMPIRE application to an interested party email list in 2024 with 207 valid emails and received 74 responses; we also received 47 responses from members of an off-roading social media group. Of the list of 16 value options, recreation was the most popular value for the monument, followed by wilderness and inspirational. Over 1,000 points were placed throughout the monument, indicating locations people use or value, with the locations spread throughout the entire monument. Additionally, most survey respondents stated their ability to receive benefits in locations they mapped would not change in response to a hypothetical scenario related to recreational facility development. This report describes exploratory results from the first use of the VaMPIRE tool in Mojave Trails National Monument and includes reflections on how the process went and considerations for future use of VaMPIRE.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255037","collaboration":"Prepared in cooperation with the Bureau of Land Management","programNote":"Land Management Research Program","usgsCitation":"Wilkins, E.J., Lindley, S.M., Rogers, K., Schuster, R., Hannon, M.T., Rowland, P.T., and Runnels, M.J., 2025, Using public participatory geographic information systems (PPGIS) to explore uses and values for Mojave Trails National Monument, California: U.S. Geological Survey Scientific Investigations Report 2025–5037, 27 p., https://doi.org/10.3133/sir20255037.","productDescription":"Report: vi, 27 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-168071","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":491391,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5037/sir20255037.xml"},{"id":491390,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5037/images"},{"id":491394,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255037/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5037"},{"id":491306,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1423FLS","text":"USGS data release","linkHelpText":"Values Mapping for Planning in Regional Ecosystems: Mojave Trails National Monument, California, 2024"},{"id":491303,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5037/coverthb.jpg"},{"id":491304,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5037/sir20255037.pdf","text":"Report","size":"5.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5037"}],"country":"United States","state":"California","otherGeospatial":"Mojave Trails National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.37203217608662,\n 35.235920982774275\n ],\n [\n -116.63385757611397,\n 35.235920982774275\n ],\n [\n -116.63385757611397,\n 34.05652997012804\n ],\n [\n -114.37203217608662,\n 34.05652997012804\n ],\n [\n -114.37203217608662,\n 35.235920982774275\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Detecting and cataloging seismic events are among the most fundamental tasks in seismology. Many standardized tools for these tasks exist, including the open‐source package repeating earthquake detector in Python (REDPy). REDPy generates an organized catalog of seismic events from continuous waveform data, in which events are automatically separated into groups (“families”) by their waveform similarity through cross‐correlation. REDPy also automatically generates various outputs that allow a user to visualize important trends in the catalog, which may be used in real time or in retrospective analyses to allow rapid identification of interesting features. The code was designed for near‐real‐time volcano monitoring but is applicable across a broad range of use cases in seismology and seismoacoustics. In this article, the utility and performance of REDPy are demonstrated on two highly seismogenic volcanic eruption sequences: the onset of the dome‐building eruption of Mount St. Helens, Washington, from 2004 to 2005, and the entirety of the summit caldera collapse sequence of Kīlauea, Hawai‘i, in 2018. This article is meant to be a companion to the documentation of the code; in addition to detailing the basic required inputs, script functionality, and resulting outputs, the reasonings behind several important design decisions are also discussed.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240373","usgsCitation":"Hotovec-Ellis, A.J., 2025, REDPy: A Python tool for automated repeating earthquake detection and visualization: Seismological Research Letters, v. 96, no. 6, p. 3849-3865, https://doi.org/10.1785/0220240373.","productDescription":"17 p.","startPage":"3849","endPage":"3865","ipdsId":"IP-178723","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":498209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hotovec-Ellis, Alicia J. 0000-0003-1917-0205","orcid":"https://orcid.org/0000-0003-1917-0205","contributorId":211785,"corporation":false,"usgs":true,"family":"Hotovec-Ellis","given":"Alicia","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":953024,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70268396,"text":"sir20255042 - 2025 - Characterization of the hydrogeologic framework, groundwater-flow system, geochemistry, and aquifer hydraulic properties of the shallow groundwater system in the Wilcox and Lorraine process areas of the Wilcox Oil Company Superfund site near Bristow, Oklahoma, 2022","interactions":[],"lastModifiedDate":"2026-01-26T19:22:45.974234","indexId":"sir20255042","displayToPublicDate":"2025-06-26T09:28:11","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":"2025-5042","displayTitle":"Characterization of the Hydrogeologic Framework, Groundwater-Flow System, Geochemistry, and Aquifer Hydraulic Properties of the Shallow Groundwater System in the Wilcox and Lorraine Process Areas of the Wilcox Oil Company Superfund Site Near Bristow, Oklahoma, 2022","title":"Characterization of the hydrogeologic framework, groundwater-flow system, geochemistry, and aquifer hydraulic properties of the shallow groundwater system in the Wilcox and Lorraine process areas of the Wilcox Oil Company Superfund site near Bristow, Oklahoma, 2022","docAbstract":"The Wilcox Oil Company Superfund site (hereinafter referred to as “the site”) was formerly an oil refinery northeast of Bristow in Creek County, Oklahoma. Historical refinery operations contaminated the soil, surface water, streambed sediments, alluvium, and groundwater with refined and stored products at the site. The Wilcox and Lorraine process areas are where the highest concentrations of volatile organic compounds, semivolatile organic compounds, polycyclic aromatic hydrocarbons, and trace elements (including metals) (collectively hereinafter referred to as “contaminants”) were measured in a local shallow perched groundwater system within the alluvium (hereinafter referred to as the “alluvial aquifer”) at the site during previous site assessments. In order to understand the potential migration of contaminants through the soil and groundwater in these areas, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, investigated aquifer characteristics of the alluvial aquifer in the Wilcox and Lorraine process areas of the site to (1) document hydraulic conductivity and other aquifer characteristics of the alluvial aquifer that govern contaminant fate and transport, (2) describe the geospatial extent and concentration of the contaminants in the alluvial aquifer in the Wilcox and Lorraine process areas, and (3) describe the geochemical controls pertaining to oxidation and reduction governing the fate and transport and the degradation potential of contaminants in the groundwater. Various data were compiled and collected to evaluate the aquifer characteristics at the site including the hydrogeologic framework, groundwater-flow system, geochemistry, and hydraulic properties of the aquifer. A total of 20 new (2022) groundwater monitoring wells were installed at the site to collect data used to supplement groundwater-level altitude and groundwater-quality data collected from older, existing groundwater monitoring wells and piezometers. Data compiled and collected for the study were used to evaluate the characteristics of the alluvial aquifer at the site. These aquifer characteristics are defined by the hydrogeologic framework, groundwater-flow system, geochemistry, and hydraulic properties of the aquifer.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"As the northern high-latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forest and Arctic/subarctic tundra regions impacts permafrost stability through the combustion of insulating organic matter, vegetation, and post-fire changes in albedo. Efforts to synthesis the impacts of wildfire on permafrost are limited and are typically reliant on antecedent pre-fire conditions. To address this, we created the FireALT dataset by soliciting data contributions that included thaw depth measurements, site conditions, and fire event details with paired measurements at environmentally comparable burned and unburned sites. The solicitation resulted in 52 466 thaw depth measurements from 18 contributors across North America and Russia. Because thaw depths were taken at various times throughout the thawing season, we also estimated end-of-season active layer thickness (ALT) for each measurement using a modified version of the Stefan equation. Here, we describe our methods for collecting and quality-checking the data, estimating ALT, the data structure, strengths and limitations, and future research opportunities. The final dataset includes 48 669 ALT estimates with 32 attributes across 9446 plots and 157 burned–unburned pairs spanning Canada, Russia, and the United States. The data span fire events from 1900 to 2022 with measurements collected from 2001 to 2023. The time since fire ranges from 0 to 114 years. The FireALT dataset addresses a key challenge: the ability to assess impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end-of-season maximum. This dataset can be used to address understudied research areas, particularly algorithm development, calibration, and validation for evolving process-based models as well as extrapolating across space and time, which could elucidate permafrost–wildfire interactions under accelerated warming across the high-northern-latitude permafrost zone. The FireALT dataset is available through the Arctic Data Center (https://doi.org/10.18739/A2RN3092P, Talucci et al., 2024).
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-17-2887-2025","usgsCitation":"Talucci, A., Loranty, M., Holloway, J., Rogers, B.M., Alexander, H.D., Baillargeon, N., Baltzer, J.L., Berner, L., Breen, A., Brodt, L., Buma, B., Dean, J., Delcourt, C., Diaz, L., Dieleman, C., Douglas, T.A., Frost, G., Gaglioti, B., Hewitt, R.E., Hollingsworth, T., Jorenson, M., Lara, M.J., Loehman, R.A., Mack, M.C., Manies, K.L., Minions, C., Natali, S., O’Donnell, J.A., Olefeldt, D., Paulson, A., Rocha, A., Saperstein, L., Shestakova, T., Sistla, S., Sizov, O., Soromotin, A., Turetksy, M., Veraverbeke, S., and Walvoord, M.A., 2025, Permafrost–wildfire interactions: active layer thickness estimates for paired burned and unburned sites in northern high latitudes: Earth System Science Data, no. 17, p. 2887-2909, https://doi.org/10.5194/essd-17-2887-2025.","productDescription":"23 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We used a new dataset on the presence of seeds in the alimentary canal or feces to identify flowering plant species whose seeds are ingested by North American ducks or geese. These data are a proxy for dispersal interactions because an important fraction of ingested seeds survives gut passage and is dispersed by endozoochory. We compared the plant traits of species whose seeds were ingested with those of species on the U.S. Department of Agriculture National Wetland Plants List (NWPL). Using a global dataset on plant form and function and chi-squared tests, we compared four categorical traits (moisture requirements, growth form, plant height, and seed mass) between species whose seeds are ingested by North American ducks and geese with the NWPL. Our analyses identified significant differences between the trait distributions of plants whose seeds were ingested by waterfowl guilds and those of the NWPL. Geese and ducks (except whistling ducks) ingested more aquatic and semiaquatic plant species than expected from the NWPL. All guilds except sea ducks ingested more herbaceous graminoids and fewer shrubs or trees than expected. Diving ducks interacted with fewer of the taller plants (>5 m) than expected, but otherwise plant height distributions did not differ from those expected. All waterfowl guilds ingested more species of intermediate seed size (1–10 mg) and fewer species of the smallest (<0.1 mg) or largest (>100 mg) size categories than expected. These results help to explain the role of the long-distance dispersal of seeds by migratory waterfowl in plant biogeography and how plant distributions are likely to respond to global change.
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