{"pageNumber":"5","pageRowStart":"100","pageSize":"25","recordCount":41014,"records":[{"id":70275281,"text":"70275281 - 2026 - Evaluating snow depth measurements from ground-penetrating radar and airborne lidar in boreal forest and tundra environments during the NASA SnowEx 2023 campaign","interactions":[],"lastModifiedDate":"2026-04-27T15:06:55.279093","indexId":"70275281","displayToPublicDate":"2026-04-17T10:04:43","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating snow depth measurements from ground-penetrating radar and airborne lidar in boreal forest and tundra environments during the NASA SnowEx 2023 campaign","docAbstract":"Snow is a vital component of high-latitude terrestrial systems, but environmental factors (e.g., permafrost) and complex vegetation challenge the accurate measurement of key snowpack properties. We evaluated local-scale ground-penetrating radar (GPR) and large-scale airborne lidar retrievals of snow depth collected during the NASA SnowEx 2023 campaign in tundra and boreal forest environments in Alaska along 44 short (3–12 m) transects. Compared to in situ observations, we identified modest biases for GPR snow depths (bias <0.03 m in tundra, +0.06 m in boreal forests) and larger biases for lidar snow depths in the boreal forests (–0.16 m). At the Upper Kuparuk-Toolik tundra site, lidar snow depths exhibited a small bias (–0.02 m), whereas the bias was much larger at the Arctic Coastal Plain tundra site (+0.19 m). For most sites, biases were primarily related to sub-snow vegetation, tussocks, and seasonally dynamic ground. However, we identified vertical alignment issues with the Arctic Coastal Plain lidar snow depth dataset that likely contributed to the higher bias. The complex ground surface and sub-snow vegetation in these environments present a challenge to established snow depth measurement methods, which needs to be considered when evaluating novel remote sensing approaches.","language":"English","publisher":"European Geoscience Union","doi":"10.5194/tc-20-2169-2026","usgsCitation":"Holland-Goon, K., Bonnell, R.R., McGrath, D., Baxter, W.B., Meehan, T., Webb, R., Larsen, C.F., Marshall, H., Mason, M.A., and Vuyovich, C., 2026, Evaluating snow depth measurements from ground-penetrating radar and airborne lidar in boreal forest and tundra environments during the NASA SnowEx 2023 campaign: The Cryosphere, v. 20, no. 4, p. 2169-2179, https://doi.org/10.5194/tc-20-2169-2026.","productDescription":"11 p.","startPage":"2169","endPage":"2179","ipdsId":"IP-188066","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":503766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-20-2169-2026","text":"Publisher Index Page"},{"id":503549,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -152.09112510557452,\n              70.69728866358227\n            ],\n            [\n              -141.42463228102963,\n              70.69728866358227\n            ],\n            [\n              -141.42463228102963,\n              62.185185988357944\n            ],\n            [\n              -152.09112510557452,\n              62.185185988357944\n            ],\n            [\n              -152.09112510557452,\n              70.69728866358227\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"20","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Holland-Goon, Kajsa","contributorId":370419,"corporation":false,"usgs":false,"family":"Holland-Goon","given":"Kajsa","affiliations":[{"id":88022,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":960321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonnell, Randall Ray 0000-0002-8812-351X","orcid":"https://orcid.org/0000-0002-8812-351X","contributorId":365098,"corporation":false,"usgs":true,"family":"Bonnell","given":"Randall","middleInitial":"Ray","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":960322,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGrath, Daniel","contributorId":302518,"corporation":false,"usgs":false,"family":"McGrath","given":"Daniel","affiliations":[{"id":40333,"text":"Department of Geosciences, Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":960323,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baxter, W. Brad","contributorId":370420,"corporation":false,"usgs":false,"family":"Baxter","given":"W.","middleInitial":"Brad","affiliations":[{"id":88023,"text":"Cold Regions Research and Engineering Laboratory, U.S. Army Corps of Engineers, Fairbanks, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":960324,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meehan, Tate","contributorId":330962,"corporation":false,"usgs":false,"family":"Meehan","given":"Tate","email":"","affiliations":[{"id":79079,"text":"Cold Regions Research and Engineering Laboratory, U.S. Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":960325,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Webb, Ryan","contributorId":330966,"corporation":false,"usgs":false,"family":"Webb","given":"Ryan","email":"","affiliations":[{"id":79081,"text":"Department of Civil and Architectural Engineering and Construction Management, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":960326,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larsen, Christopher F.","contributorId":370421,"corporation":false,"usgs":false,"family":"Larsen","given":"Christopher","middleInitial":"F.","affiliations":[{"id":88024,"text":"Geophysical Institute, University of Alaska, Fairbanks, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":960327,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Marshall, Hans-Peter","contributorId":365100,"corporation":false,"usgs":false,"family":"Marshall","given":"Hans-Peter","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":960328,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mason, Megan A.","contributorId":213047,"corporation":false,"usgs":false,"family":"Mason","given":"Megan","email":"","middleInitial":"A.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":960329,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vuyovich, Carrie","contributorId":365104,"corporation":false,"usgs":false,"family":"Vuyovich","given":"Carrie","affiliations":[{"id":40052,"text":"NASA Goddard","active":true,"usgs":false}],"preferred":false,"id":960330,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70275240,"text":"70275240 - 2026 - A Bayesian hierarchical modeling approach for species diversity in ecology","interactions":[],"lastModifiedDate":"2026-04-24T14:13:47.76728","indexId":"70275240","displayToPublicDate":"2026-04-17T09:11:08","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1457,"text":"Ecological Informatics","active":true,"publicationSubtype":{"id":10}},"title":"A Bayesian hierarchical modeling approach for species diversity in ecology","docAbstract":"<p><span>Species diversity is the foundation of many ecological disciplines. This metric is often approximated using species richness and evenness, even though actual richness likely exceeds observations due to imperfect sampling methods. Estimating the “true” species richness, which includes identifying the number of missing species, has intrigued ecologists for decades. We adopted a parametric model that appeared in Fisher et al. (1943), which models the numbers of individuals from different species as random samples from a negative binomial distribution, and developed a Bayesian computational approach to directly estimate the distribution model parameters. The model parameters represent species abundance and evenness, and can be used to derive species richness. We evaluated our parametric approach using (1) a simulation study and (2) three historical data sets. Furthermore, we illustrated the hierarchical modeling approach to combine data from multiple parallel studies using a biannual fishery survey data set. Our parametric model formulation is computationally efficient, and the hierarchical structure facilitates embedding diversity estimation into broader application, such as assessing spatial and temporal trends in species diversity associated with environmental stressors. Additionally, because the two parameters of the negative binomial distribution model represent species abundance and evenness of a community, this parametric approach facilitates a deeper understanding of the ecological systems under study. The negative binomial distribution model works with a wide range of species frequency distribution types. As a result, our emphasis on a parametric model can help us characterize the structure of an ecosystem and provide a greater depth of ecologically meaningful information.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2026.103773","usgsCitation":"Qian, S.S., Dufour, M.R., Jaffe, S., Hilling, C.D., and Hintz, W.D., 2026, A Bayesian hierarchical modeling approach for species diversity in ecology: Ecological Informatics, v. 95, 103773, 9 p., https://doi.org/10.1016/j.ecoinf.2026.103773.","productDescription":"103773, 9 p.","ipdsId":"IP-170631","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":503760,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoinf.2026.103773","text":"Publisher Index Page"},{"id":503510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","noUsgsAuthors":false,"publicationDate":"2026-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Qian, Song S.","contributorId":370365,"corporation":false,"usgs":false,"family":"Qian","given":"Song","middleInitial":"S.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":960214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":960215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaffe, Sabrina","contributorId":333990,"corporation":false,"usgs":false,"family":"Jaffe","given":"Sabrina","email":"","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":960216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":960217,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hintz, William D.","contributorId":370367,"corporation":false,"usgs":false,"family":"Hintz","given":"William","middleInitial":"D.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":960218,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70275178,"text":"70275178 - 2026 - Water volumes, heat flow, and solute discharge from Old Faithful Geyser eruptions, Yellowstone National Park, USA","interactions":[],"lastModifiedDate":"2026-04-21T15:01:47.79332","indexId":"70275178","displayToPublicDate":"2026-04-17T07:49:27","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Water volumes, heat flow, and solute discharge from Old Faithful Geyser eruptions, Yellowstone National Park, USA","docAbstract":"<p><span>The iconic Old Faithful Geyser in Yellowstone National Park, USA, has attracted a significant amount of research because of the relative regularity and impressive size of its eruptions. Numerous studies have included observations, measurements, and analyses that informed models of geyser eruptions. However, fundamental quantities, including the associated mass and heat discharged, remain poorly constrained. In April 2025 we measured the volume of water from 45 Old Faithful Geyser eruptions using a portable flume in an outflow channel and specific conductance measurements in the Firehole River. We used high-speed video to perform velocimetry, measured changes in water chemistry to calculate the volume of water evaporated along the outflow channels, and used thermodynamic calculations to estimate the volume of water erupted as steam and to quantify the geyser's heat output. The calculated average volume of water discharged by Old Faithful Geyser in each eruption is 27.9&nbsp;±&nbsp;9.4&nbsp;m</span><sup>3</sup><span>, with no relation between eruption volume and the length of the preceding eruption interval. Video analysis of the eruption's liquid-dominated phase yields similar volumes of 21–30&nbsp;m</span><sup>3</sup><span>. The calculated heat flow from the geyser is 2.2–2.4&nbsp;MW and the average annual discharge of chloride, fluoride, and arsenic are 63 tons, 3.9 tons, and 241&nbsp;kg, respectively. Average annual silica deposition rate on the geyser cone and along the outflow channels is 7 tons. This study provides a methodology for future studies at geysers worldwide and a baseline for monitoring future activity changes at Old Faithful.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2026.108624","usgsCitation":"Hurwitz, S., McCleskey, R., Rudolph, M.L., Peek, S., Roth, D.A., Schott-Atkins, M., Manga, M., Folz Donahue, K.F., Reed, M.H., and Hungerford, J.D., 2026, Water volumes, heat flow, and solute discharge from Old Faithful Geyser eruptions, Yellowstone National Park, USA: Journal of Volcanology and Geothermal Research, v. 474, 108624, 13 p., https://doi.org/10.1016/j.jvolgeores.2026.108624.","productDescription":"108624, 13 p.","ipdsId":"IP-185069","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":503750,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2026.108624","text":"Publisher Index Page"},{"id":503268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Old Faithful Geyser, Yellowstone National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.05006455962126,\n              45.0129140640631\n            ],\n            [\n              -111.05006455962126,\n              43.754298426623194\n            ],\n            [\n              -109.3525348558879,\n              43.754298426623194\n            ],\n            [\n              -109.3525348558879,\n              45.0129140640631\n            ],\n            [\n              -111.05006455962126,\n              45.0129140640631\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"474","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":216321,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":959885,"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":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":959886,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rudolph, Maxwell L.","contributorId":370157,"corporation":false,"usgs":false,"family":"Rudolph","given":"Maxwell","middleInitial":"L.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":959887,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peek, Sara 0000-0002-9770-6557","orcid":"https://orcid.org/0000-0002-9770-6557","contributorId":209971,"corporation":false,"usgs":true,"family":"Peek","given":"Sara","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":959888,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, David A. 0000-0002-7515-3533 daroth@usgs.gov","orcid":"https://orcid.org/0000-0002-7515-3533","contributorId":202097,"corporation":false,"usgs":true,"family":"Roth","given":"David","email":"daroth@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":959889,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schott-Atkins, Melissa","contributorId":370161,"corporation":false,"usgs":false,"family":"Schott-Atkins","given":"Melissa","affiliations":[{"id":87977,"text":"U. Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":959890,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manga, Michael","contributorId":370162,"corporation":false,"usgs":false,"family":"Manga","given":"Michael","affiliations":[{"id":33781,"text":"U.C. Berkeley","active":true,"usgs":false}],"preferred":false,"id":959891,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Folz Donahue, Kiernan F.","contributorId":370163,"corporation":false,"usgs":false,"family":"Folz Donahue","given":"Kiernan","middleInitial":"F.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":959892,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reed, Mara H.","contributorId":370164,"corporation":false,"usgs":false,"family":"Reed","given":"Mara","middleInitial":"H.","affiliations":[{"id":33781,"text":"U.C. Berkeley","active":true,"usgs":false}],"preferred":false,"id":959893,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hungerford, Jefferson D.G.","contributorId":370165,"corporation":false,"usgs":false,"family":"Hungerford","given":"Jefferson","middleInitial":"D.G.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":959894,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70275076,"text":"ofr20261002 - 2026 - Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system","interactions":[{"subject":{"id":70273478,"text":"70273478 - 2026 - Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system","indexId":"70273478","publicationYear":"2026","noYear":false,"title":"Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system"},"predicate":"SUPERSEDED_BY","object":{"id":70275076,"text":"ofr20261002 - 2026 - Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system","indexId":"ofr20261002","publicationYear":"2026","noYear":false,"title":"Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system"},"id":1}],"lastModifiedDate":"2026-04-20T17:44:26.164652","indexId":"ofr20261002","displayToPublicDate":"2026-04-16T14:10:00","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2026-1002","displayTitle":"Computation of Regional Groundwater Budgets for the Virginia Coastal Plain Aquifer System","title":"Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system","docAbstract":"<p>Computation of detailed groundwater flow budgets for subdivisions of the Virginia Coastal Plain aquifer system has enabled quantification and more thorough understanding of groundwater flow within this important water resource. A zone budget analysis based on previously published groundwater models of the Virginia Coastal Plain and Virginia Eastern Shore indicates that groundwater conditions vary substantially throughout the Coastal Plain aquifer system because of local variations in hydrogeology and historical and ongoing variations in groundwater use and management. Decades of substantial groundwater withdrawal from the Coastal Plain aquifer system have altered groundwater flow from predevelopment conditions. Rates of sustainable withdrawal are limited because the downward groundwater flow rate into confined aquifers is a relatively small part of the total groundwater budget for the aquifer system compared to the rate of recharge at the land surface.</p><p>Analyses of groundwater budgets from the Virginia Coastal Plain model indicate that groundwater flow is generally outward from the surficial aquifer to rivers and coastal waterbodies and downward through a series of underlying aquifers and confining units to the Potomac aquifer, which is the deepest aquifer and the source of most groundwater withdrawals. Downward flow into the Potomac aquifer is estimated to be only 7 percent of total net precipitation-derived net recharge at the land surface but makes up about 66 percent of inflow to the aquifer in Virginia, with much of the remaining inflow occurring laterally from outside of defined groundwater budget regions in Virginia. For several decades prior to 2010, high rates of withdrawal from the Potomac aquifer resulted in substantial decline in groundwater storage in the aquifer and in most overlying aquifers and confining units. From 2010 to 2023, rates of withdrawal substantially lower than the historical maximum resulted in small net increases in groundwater storage in the confined aquifer system for most regions of the Virginia Coastal Plain. Nevertheless, for the same period, groundwater storage for the entire model domain continues to incrementally decline, indicating that storage recovery in Virginia is offset by a continued decrease in storage in areas beneath the Chesapeake Bay or adjacent areas of Maryland and North Carolina. Withdrawals from the Potomac aquifer have induced substantial downward flow which is a large part of groundwater budgets for confined aquifers such as the Potomac. For the most recent simulated conditions (2023) downward groundwater flow continues, but because vertical flow rates are a function of the difference between water pressure in the upper surficial systems and lower confined units, rates of downward flow are lower than those in earlier decades as the confined water levels partially recover from larger groundwater withdrawals in the past. Geographically, groundwater flow is generally inward from perimeter regions of the Virginia Coastal Plain toward central regions with the largest withdrawal rates. Groundwater inflow from coastal regions could be contributing to saltwater intrusion, even though that was not measured in this study.</p><p>Analyses of groundwater budgets from the Virginia Eastern Shore peninsula, a geographic region of the Virginia Coastal Plain, indicate that groundwater flow for that isolated aquifer system is generally outward from the surficial aquifer to coastal water bodies and downward into the confined Yorktown-Eastover aquifer system, which is the source of most withdrawals. Downward groundwater flow into the confined Yorktown-Eastover aquifer system is estimated to be less than 2 percent of total recharge and less than 9 percent of net recharge at the water table but makes up more than 93 percent of all inflow to the confined aquifer system. Decades of substantial but relatively consistent groundwater withdrawals have induced greater downward flow rates into the confined aquifer system but also have resulted in loss of groundwater from storage. For the most recent simulated period (2023), estimated storage loss accounts for slightly under 7 percent of withdrawals from the confined aquifer system. The reported withdrawal rate for this period from the confined Yorktown-Eastover system is near the highest reported rate for the Virginia Eastern Shore, which means that the storage depletion is expected to continue, even though groundwater levels appear to be relatively stable. Estimated groundwater flow rates upward from the confining unit underlying the Yorktown-Eastover system and low rates of inflow from coastal water bodies underscore ongoing concerns about up-coning and lateral intrusion of salty groundwater.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20261002","collaboration":"Prepared in cooperation with the Virginia Department of Environmental Quality","usgsCitation":"Pope, J.P., Gordon, A.D., and Frederiks, R.S., 2026, Computation of regional groundwater budgets for the Virginia Coastal Plain aquifer system: U.S. Geological Survey Open-File Report 2026–1002, 48 p., https://doi.org/10.3133/ofr20261002. [Supersedes USGS Preprint https://doi.org/10.31223/X5HB5D.]","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-185679","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":503256,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119369.htm","linkFileType":{"id":5,"text":"html"}},{"id":502777,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13GJEYW","text":"USGS data release","linkHelpText":"Input and output files from the Zonebudget program used with MODFLOW models to compute regional groundwater budgets for the Virginia Coastal Plain aquifer system"},{"id":502776,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2026/1002/images/"},{"id":502775,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2026/1002/ofr20261002.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2026-1002 XML"},{"id":502772,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2026/1002/coverthb.jpg"},{"id":502773,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2026/1002/ofr20261002.pdf","size":"6.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2026-1002 PDF"},{"id":502774,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20261002/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2026-1002 HTML"}],"country":"United States","state":"Virginia","otherGeospatial":"Virginia Coastal Plain","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -77.5,\n              38.5\n            ],\n            [\n              -75,\n              38.5\n            ],\n            [\n              -75,\n              36.55435844550527\n            ],\n            [\n              -77.5,\n              36.55435844550527\n            ],\n            [\n              -77.5,\n              38.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_va@usgs.gov\" data-mce-href=\"mailto:dc_va@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/virginia-and-west-virginia-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/virginia-and-west-virginia-water-science-center\">Virginia and West Virginia Water Science Center</a><br>U.S. Geological Survey<br>1730 East Parham Road<br>Richmond, Virginia 23228</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Description of Study Area</li><li>Application of ZONEBUDGET Software to Groundwater Models</li><li>Groundwater Budgets for the Virginia Coastal Plain</li><li>Summary and Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2026-04-16","noUsgsAuthors":false,"publicationDate":"2026-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Pope, Jason P. 0000-0003-3199-993X jpope@usgs.gov","orcid":"https://orcid.org/0000-0003-3199-993X","contributorId":2044,"corporation":false,"usgs":true,"family":"Pope","given":"Jason","email":"jpope@usgs.gov","middleInitial":"P.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gordon, Alison D. 0000-0002-9502-8633","orcid":"https://orcid.org/0000-0002-9502-8633","contributorId":221457,"corporation":false,"usgs":true,"family":"Gordon","given":"Alison","email":"","middleInitial":"D.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frederiks, Ryan S. 0000-0003-2400-2222","orcid":"https://orcid.org/0000-0003-2400-2222","contributorId":365185,"corporation":false,"usgs":false,"family":"Frederiks","given":"Ryan","middleInitial":"S.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":959379,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70275103,"text":"sir20265003 - 2026 - Historical ice jams and associated environmental conditions on Osoyoos Lake","interactions":[],"lastModifiedDate":"2026-04-20T17:42:39.793478","indexId":"sir20265003","displayToPublicDate":"2026-04-16T12:50:00","publicationYear":"2026","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":"2026-5003","displayTitle":"Historical Ice Jams and Associated Environmental Conditions on Osoyoos Lake","title":"Historical ice jams and associated environmental conditions on Osoyoos Lake","docAbstract":"<p>Ice jams occur regularly at the southern outlet of Osoyoos Lake, which spans the border between the State of Washington and British Columbia, Canada. In recent winters, ice jams caused (1) decreases in downstream discharge that may adversely affect salmon spawning habitat and (2) short-duration lake-level rise that can interfere with lake level management agreements. In response, water managers sought to understand the environmental conditions associated with the historical ice-jam occurrences on Osoyoos Lake. Researchers compiled datasets of discharge, lake level, and air temperature from four meteorological and three hydrologic stations near Oroville, Washington, to determine “ice-jam” or “non-ice-jam” days from 1942 to 2024.</p><p>After confirming known ice jams since 1994 using Landsat 8–9 and Sentinel–2 satellite imagery along with discharge, lake level, and air temperature data, researchers designated ice-jam days. They conducted statistical analyses to examine environmental conditions associated with ice-jam occurrences on Osoyoos Lake. Statistical tests indicated significant differences in wind speed, wind direction, and air temperature between ice-jam and non-ice-jam days. A linear discriminant-analysis model correctly predicted 12 of 13 historical ice-jam days since 1994 and determined that ice jams are more likely under westerly and northwesterly winds near or above 10 kilometers per hour (km/h) and minimum temperatures near or below –9.4 degrees Celsius (°C). An analysis of historical discharge suggests that ice jams have occurred since at least the 1940s, but 13 ice jam days occurred in the past decade (2014–2024), exceeding any previous decade. The daily minimum air temperature in the Osoyoos Lake region has increased at a rate of 0.021 °C per year since the 1940s, but ice jams usually occur in winters with colder average temperatures.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265003","collaboration":"Prepared in cooperation with the International Osoyoos Lake Board of Control","programNote":"Water Availability and Use Science Program","usgsCitation":"Sutfin, N.A., and Breen, S.J., 2026, Historical ice jams and associated environmental conditions on Osoyoos Lake: U.S. Geological Survey Scientific Investigations Report 2026–5003, 38 p., https://doi.org/10.3133/sir20265003.","productDescription":"vii, 38 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-171288","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":503254,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119368.htm","linkFileType":{"id":5,"text":"html"}},{"id":502874,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5003/sir20265003.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5003 XML"},{"id":502873,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265003/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5003 HTML"},{"id":502872,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5003/sir20265003.pdf","size":"49.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5003 PDF"},{"id":502871,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5003/coverthb.jpg"},{"id":502875,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5003/images/"}],"country":"Canada, United States","state":"British Columbia, Washington","otherGeospatial":"Osoyoos Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.67443538186532,\n              49.11443094771283\n            ],\n            [\n              -119.3142803507082,\n              49.11443094771283\n            ],\n            [\n              -119.3142803507082,\n              48.87431047779373\n            ],\n            [\n              -119.67443538186532,\n              48.87431047779373\n            ],\n            [\n              -119.67443538186532,\n              49.11443094771283\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/centers/washington-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/washington-water-science-center\">Washington Water Science Center</a><br>934 Broadway<br>Suite 300<br>Tacoma, WA 98402</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Geographic Setting</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Conclusions</li><li>References Cited</li><li>Appendix 1. Data Source Information</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2026-04-16","noUsgsAuthors":false,"plainLanguageSummary":"<p>Ice jams are accumulations of ice that partially block water from flowing downstream in rivers and lakes. Ice jams form at the shallow outlet of Osoyoos Lake, which drains into the Okanogan River at the border of the United States and Canada. These ice jams can temporarily reduce river flow downstream, which can harm salmon habitat and cause short lived increases in lake levels that complicate international agreements for managing water levels.</p><p>To better understand when and why these ice jams form, researchers from the U.S. Geological Survey examined historical records of river flow, lake level, and air temperature data from stations near Oroville, Washington (located just south of the lake outlet), for the years 1942–2024. Researchers used satellite images and environmental data during 1994–2024 to confirm known ice jams and then identified “ice jam days” for that period.</p><p>The team compared weather conditions on ice jam days and non-ice-jam days. They found that ice jams are more likely to form when winds blow from the west or northwest at speeds of about 10 kilometers per hour or more, and when minimum temperatures drop to –9.4 degrees Celsius or lower. A statistical model based on air temperature, wind speed, and wind direction correctly identified nearly all known ice jam days since 1994. While the statistical model identified some days without ice jams as ice-jam days, no ice-jam days occurred outside of the range of wind and temperature conditions identified.</p><p>Although ice jams have occurred since at least the 1940s, they have become more frequent in recent years: 13 ice jam days occurred during 2014–2024, more than in any previous decade. Even though winter temperatures in the Osoyoos Lake region have risen slightly over time, ice jams tend to occur during colder than average winters.</p><p>Understanding the conditions that lead to ice jams can help decision-makers better anticipate when ice jams may occur and plan for their potential effects on salmon habitat and lake level management.</p>","publicationDate":"2026-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sutfin, Nicholas A. 0000-0003-4429-7814","orcid":"https://orcid.org/0000-0003-4429-7814","contributorId":357883,"corporation":false,"usgs":true,"family":"Sutfin","given":"Nicholas","middleInitial":"A.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breen, Stephen J. 0000-0002-2630-6206","orcid":"https://orcid.org/0000-0002-2630-6206","contributorId":369971,"corporation":false,"usgs":true,"family":"Breen","given":"Stephen","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959462,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70275748,"text":"70275748 - 2026 - Logical data model for hydrographic data based on HY_Features concepts","interactions":[],"lastModifiedDate":"2026-05-18T14:33:53.753123","indexId":"70275748","displayToPublicDate":"2026-04-16T09:24:17","publicationYear":"2026","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":24339,"text":"OCG Public Engineering Report","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"25-045","title":"Logical data model for hydrographic data based on HY_Features concepts","docAbstract":"<p>This report describes background and design of the “hydrofabric data model” which defines logic for implementation of data schemas and software that deals with hydrologic geospatial data. As a “logical” data model, the hydrofabric data model specifies details necessary to support compatibility of data and software that satisfy diverse needs without unnecessarily restricting implementation details. The logic presented in this report is based on concepts defined in WaterML2 Part 3 Surface Hydrology Features Concepts and is designed to serve the needs of a range of hydroscience use cases. </p><p>Development of international community standards applicable to hydrofabrics began, prompted by the World Meteorological Organization Commission for Hydrology, in 2012 [5] . More than 10 years later, this report documents one aspect of a long-term research and development activity that traces its roots back that far. </p><p>This report describes terminology, use cases, and background as context preceding presentation of the logical model and discussion of its design. Three appendices document related data models, an example encoding of the hydrofabric data model, and an artificial schematic and tabular data example. The sections of the report can be accessed in the Clause 5 section.</p>","language":"English","publisher":"Open Geospatial Consortium","usgsCitation":"2026, Logical data model for hydrographic data based on HY_Features concepts: OCG Public Engineering Report 25-045, v, 76 p.","productDescription":"v, 76 p.","ipdsId":"IP-172082","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":504473,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":504462,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"http://www.opengis.net/doc/PER/hydrofabric"}],"noUsgsAuthors":false,"publicationDate":"2026-04-16","publicationStatus":"PW","contributors":{"editors":[{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":961717,"contributorType":{"id":2,"text":"Editors"},"rank":1}]}}
,{"id":70275149,"text":"70275149 - 2026 - Characterizing changes in postfire debris-flow hazard as burned areas recover","interactions":[],"lastModifiedDate":"2026-06-02T15:36:59.528242","indexId":"70275149","displayToPublicDate":"2026-04-16T08:18:14","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing changes in postfire debris-flow hazard as burned areas recover","docAbstract":"<p><span>Emergency assessments of postfire debris-flow hazards that are performed by the U.S. Geological Survey (USGS) provide estimates of debris-flow likelihood and rainfall triggering conditions that are used for evaluating and managing runoff-generated debris-flow hazards in recently burned areas throughout the western United States. Although the immediate postfire period, within roughly one year after fire, is typically the most susceptible to runoff-generated debris flows, the hazard evolves in time and space as the burned area recovers. The recovery trajectory a given burned area will take depends on local climate and weather and can be difficult to predict. Some burned areas recover quickly, whereas others experience debris flows for multiple years after fire. As a result, extending our ability to update debris-flow likelihood estimates and rainfall thresholds based on observed recovery of the burned area would be beneficial. We present a method for multi-year runoff-generated debris-flow hazard assessment that leverages the USGS “M1” debris-flow likelihood model and integrates updated, satellite-derived, normalized burn ratio data to estimate vegetation recovery. We predict recovery-aware rainfall thresholds and validate them against a multi-year debris-flow hazard prediction and could be adapted for use with other debris-flow models that incorporate burn severity data.</span></p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02936.1","usgsCitation":"Graber, A.P., Thomas, M.A., Kean, J.W., King, J., and Kostelnik, J., 2026, Characterizing changes in postfire debris-flow hazard as burned areas recover: Geosphere, v. 22, no. 3, p. 494-515, https://doi.org/10.1130/GES02936.1.","productDescription":"22 p.","startPage":"494","endPage":"515","ipdsId":"IP-164736","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":503207,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":503429,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02936.1","text":"Publisher Index Page"}],"country":"United States","state":"Arizona, California, Colorado, New Mexico, Washington","otherGeospatial":"western United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -125.36424169921565,\n              48.80629133966778\n            ],\n            [\n              -125.66472868317365,\n              39.31207129554762\n            ],\n            [\n              -120.57415228729428,\n              32.73149552158408\n            ],\n            [\n              -102.99484796823218,\n              31.342076734545344\n            ],\n            [\n              -103.00675719934162,\n              36.96546857037297\n            ],\n            [\n              -102.11928173488546,\n              36.875567234398886\n            ],\n            [\n              -102.08687031215692,\n              40.90392441449369\n            ],\n            [\n              -117.07043377025515,\n              40.85240779137647\n            ],\n            [\n              -116.74859098099466,\n              48.97374450456343\n            ],\n            [\n              -125.36424169921565,\n              48.80629133966778\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"22","issue":"3","noUsgsAuthors":false,"publicationDate":"2026-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Graber, Andrew Paul 0000-0003-4179-0291","orcid":"https://orcid.org/0000-0003-4179-0291","contributorId":304628,"corporation":false,"usgs":true,"family":"Graber","given":"Andrew","email":"","middleInitial":"Paul","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":959656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":959657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":959658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Jonathan Michael 0000-0003-0834-2200","orcid":"https://orcid.org/0000-0003-0834-2200","contributorId":350805,"corporation":false,"usgs":true,"family":"King","given":"Jonathan Michael","affiliations":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"preferred":true,"id":959659,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kostelnik, Jaime 0000-0002-1817-5461","orcid":"https://orcid.org/0000-0002-1817-5461","contributorId":300717,"corporation":false,"usgs":true,"family":"Kostelnik","given":"Jaime","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":959660,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70273879,"text":"70273879 - 2026 - The global proliferation of aquatic, benthic </i>Microcoleus</i>: Taxonomy, distribution, toxin production, ecology, and future directions","interactions":[],"lastModifiedDate":"2026-02-11T15:20:31.144819","indexId":"70273879","displayToPublicDate":"2026-04-15T08:14:14","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"The global proliferation of aquatic, benthic </i>Microcoleus</i>: Taxonomy, distribution, toxin production, ecology, and future directions","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>There have been sporadic reports of aquatic, benthic&nbsp;</span><i>Microcoleus</i><span>&nbsp;proliferations in freshwater rivers, lakes, and reservoirs for four decades, with reports increasing in frequency over the last twenty years, suggesting a possible rise in their global distribution, frequency, and intensity.&nbsp;</span><i>Microcoleus</i><span>&nbsp;can produce anatoxins which are neurotoxic, and ingestion of toxic mats has caused hundreds of dog fatalities and raised serious human and ecological health concerns. This review synthesizes and evaluates current knowledge on&nbsp;</span><i>Microcoleus</i><span>&nbsp;distribution, taxonomy, toxin production, toxicity, ecology, environmental drivers, and biotic interactions. Toxin-producing&nbsp;</span><i>Microcoleus</i><span>&nbsp;have been reported in at least 18 countries, though many regions have not conducted toxin testing, suggesting a broader but under-reported distribution. Proliferations occur across diverse habitats, including cobble-bedded streams, large sandy rivers, reservoirs, and lakes.&nbsp;</span><i>Microcoleus</i><span>&nbsp;proliferations also occur on macrophytes, both in lakes and rivers. Genomic analyses currently classify anatoxin-producing&nbsp;</span><i>Microcoleus</i><span>&nbsp;into distinct species, with all known anatoxin-producers isolated from freshwater ecosystems. Anatoxin concentrations vary widely over space and time, within and among waterbodies. While studies on environmental drivers remain limited, research in cobble-bedded rivers suggests that moderate enrichment of dissolved inorganic nitrogen and low dissolved reactive phosphorus concentrations in the water column promote proliferation. Metagenomic approaches have revealed unique nutrient acquisition and storage strategies used by&nbsp;</span><i>Microcoleus</i><span>. Key knowledge gaps remain around the environmental and ecological triggers of proliferation, toxin production, genomic diversity and microbial interactions. Addressing these gaps through coordinated, global studies using robust datasets and consistent methods is critical to improve prediction, monitoring, and mitigation of this increasingly widespread public and ecological health threat.</span></span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2026.125441","usgsCitation":"Kelly, L.T., Beach, D.G., Blaszczak, J.R., Bouma-Gregson, K., Brown, S.M., Cheng, H., Davidson, J.L., Fastner, J., Francis, M., Garcia Jimenez, A., Genzoli, L., Goel, R., Gonzalez, D., Handley, K.M., Hilt, S., Humbert, J., Jamieson, R., Johnston, L., Junier, P., Lawrence, J., McCarron, P., Meissner, S., Mormando, J., Puddick, J., Quiblier, C., Rajpirathap, N., Schampera, C., Selwood, A., Shearer, K., Sohrab, A., Stancheva, R., Valadez-Cano, C., Zebrecky, J.M., and Wood, S.A., 2026, The global proliferation of aquatic, benthic </i>Microcoleus</i>: Taxonomy, distribution, toxin production, ecology, and future directions: Water Research, v. 294, 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,{"id":70276348,"text":"70276348 - 2026 - Predicted range shifts of non‐native grasses in response to climate change are influenced by photosynthetic pathway: A case study in the Hawaiian Islands","interactions":[],"lastModifiedDate":"2026-06-01T14:08:47.436304","indexId":"70276348","displayToPublicDate":"2026-04-14T09:03:35","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Predicted range shifts of non‐native grasses in response to climate change are influenced by photosynthetic pathway: A case study in the Hawaiian Islands","docAbstract":"<h3 id=\"ddi70190-sec-0001-title\" class=\"article-section__sub-title section1\">Aim</h3><p>Grasses comprise three main photosynthetic pathway variants (C<sub>3</sub>-BOP, C<sub>3</sub>-PACMAD and C<sub>4</sub>-PACMAD hereafter referred to as C<sub>4</sub>). We sought to confirm climate niche differences among these photosynthetic pathway variants and assessed whether predicted non-native grass range shift patterns with climate change differ among photosynthetic pathway variants.</p><h3 id=\"ddi70190-sec-0002-title\" class=\"article-section__sub-title section1\">Location</h3><p>Hawaiian Islands.</p><h3 id=\"ddi70190-sec-0003-title\" class=\"article-section__sub-title section1\">Methods</h3><p>We used a species distribution modelling (SDM) approach that uses global occurrence records to inform local SDM based on local (Hawaiian Islands) occurrences. We compared climate niches and projected climate-driven range shifts, assuming moderate climate change (RCP 4.5, end of century), among 22 non-native grasses representing C<sub>3</sub>-BOP, C<sub>3</sub>-PACMAD and C<sub>4</sub><span>&nbsp;</span>photosynthetic pathway variants.</p><h3 id=\"ddi70190-sec-0004-title\" class=\"article-section__sub-title section1\">Results</h3><p>C<sub>4</sub><span>&nbsp;</span>grasses exhibited the warmest temperature niches on average, but did not differ substantially in rainfall niche versus C<sub>3</sub>-BOP grasses. C<sub>3</sub>-PACMAD species averaged high suitability across a broad range of temperatures and rainfall conditions, except extreme aridity. In response to projected climate change, C<sub>4</sub><span>&nbsp;</span>grasses had projected range increases. C<sub>3</sub>-BOP grasses typically responded with net range decreases, while C<sub>3</sub>-PACMAD grasses had variable range responses. However, patterns were contingent on elevation: for instance, the projected expansion of C<sub>4</sub><span>&nbsp;</span>grasses was generally limited to elevations below 2000 m, with the largest increases in areas up to ~750 m. Areas of greatest reduction for C<sub>3</sub>-BOP and C<sub>3</sub>-PACMAD were projected at 750–1900 m and 100–1100 m elevation, respectively. Above 2000 m, range increases were projected for both C<sub>3</sub><span>&nbsp;</span>grass variants.</p><h3 id=\"ddi70190-sec-0005-title\" class=\"article-section__sub-title section1\">Main Conclusions</h3><p>Our projections suggest that non-native C<sub>4</sub><span>&nbsp;</span>grasses pose the greatest risk for increasing spread and impacts under RCP 4.5, while certain C<sub>3</sub>-PACMAD grasses may endanger valuable high-elevation habitats. Photosynthetic pathway may be a useful component of weed risk assessment to evaluate how species may respond to climate change as similar range response patterns may be expected for other non-native grasses in other tropical and subtropical regions.</p>","language":"English","publisher":"Wiley","doi":"10.1111/ddi.70190","usgsCitation":"Daehler, C., Faccenda, K., Aquino Peterson, E., Brock, K., and Fortini, L., 2026, Predicted range shifts of non‐native grasses in response to climate change are influenced by photosynthetic pathway: A case study in the Hawaiian Islands: Diversity and Distributions, v. 32, no. 4, e70190, 16 p., https://doi.org/10.1111/ddi.70190.","productDescription":"e70190, 16 p.","ipdsId":"IP-177967","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":505041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.70190","text":"Publisher Index 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 \"}}]}","volume":"32","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Daehler, Curtis","contributorId":346962,"corporation":false,"usgs":false,"family":"Daehler","given":"Curtis","email":"","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":962183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Faccenda, Kevin","contributorId":371622,"corporation":false,"usgs":false,"family":"Faccenda","given":"Kevin","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":962184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aquino Peterson, Elizabeth","contributorId":371623,"corporation":false,"usgs":false,"family":"Aquino Peterson","given":"Elizabeth","affiliations":[{"id":40951,"text":"University of Hawai‘i - Mānoa","active":true,"usgs":false}],"preferred":false,"id":962185,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brock, Kelsey C.","contributorId":354589,"corporation":false,"usgs":false,"family":"Brock","given":"Kelsey C.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":962186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fortini, Lucas B. 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":202074,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas B.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":962187,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70275093,"text":"70275093 - 2026 - Morphometric properties of the CP-21 landing site on the Moon at Mons Gruithuisen Gamma","interactions":[],"lastModifiedDate":"2026-04-16T13:34:29.776864","indexId":"70275093","displayToPublicDate":"2026-04-14T08:35:56","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17061,"text":"Planetary Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Morphometric properties of the CP-21 landing site on the Moon at Mons Gruithuisen Gamma","docAbstract":"<p><span>Characterizing terrain surface properties is an essential step in assessing the feasibility of landing successfully at a location on a planetary surface. Slopes and terrain ruggedness index (TRI) values derived from high-resolution (2 m pixel</span><sup>−1</sup><span>) digital terrain models provided important constraints in selecting the landing site for the upcoming Payloads and Research Investigations on the Surface of the Moon program as part of the Commercial Lunar Payload Services task order CP-21 mission. The selected landing site needed to balance safety requirements with the ability to achieve the science and exploration goals of the Lunar Vulkan Imaging and Spectroscopy Explorer payload. In this study, we compare several morphometric parameters in the context of the CP-21 landing site on Mons Gruithuisen Gamma, or the Gamma dome, and quantify the information they convey about lunar surface properties to assess their utility for future landing site evaluation. TRI was found to be a useful metric for assessing landing site safety. Metrics that better decouple slope and surface roughness, the vector ruggedness measure and the standard deviation of slope, provided additional information about surface characteristics and textures such as the degree to which roughness is isotropic.</span></p>","language":"English","publisher":"American Astronomical Society","doi":"10.3847/PSJ/ae523b","usgsCitation":"Williams, J., Valencia, S., Bennett, K.A., Landis, M., Donaldson Hanna, K.L., Dove, A.T., O'Brien, P., Denevi, B.W., Hagerty, J., Hardgrove, C., Hayne, P.O., LaMee, A., Prettyman, T.H., Shirley, K.A., Siegler, M.A., and Sunshine, J.M., 2026, Morphometric properties of the CP-21 landing site on the Moon at Mons Gruithuisen Gamma: Planetary Science Journal, v. 7, no. 4, 78, 9 p., https://doi.org/10.3847/PSJ/ae523b.","productDescription":"78, 9 p.","ipdsId":"IP-177339","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":502977,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3847/psj/ae523b","text":"Publisher Index Page"},{"id":502820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mons Gruithuisen Gamma, Moon","volume":"7","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Jean-Pierre","contributorId":291741,"corporation":false,"usgs":false,"family":"Williams","given":"Jean-Pierre","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":959432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valencia, Sarah","contributorId":369959,"corporation":false,"usgs":false,"family":"Valencia","given":"Sarah","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":959433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennett, Kristen A. 0000-0001-8105-7129","orcid":"https://orcid.org/0000-0001-8105-7129","contributorId":237068,"corporation":false,"usgs":true,"family":"Bennett","given":"Kristen","email":"","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":959434,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landis, Margaret E.","contributorId":176713,"corporation":false,"usgs":false,"family":"Landis","given":"Margaret E.","affiliations":[{"id":25655,"text":"Lunar and Planetary Laboratory, 1629 E. University Blvd., The University of Arizona, Tucson, AZ 85721, United States","active":true,"usgs":false}],"preferred":false,"id":959435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donaldson Hanna, Kerri L.","contributorId":237920,"corporation":false,"usgs":false,"family":"Donaldson Hanna","given":"Kerri","middleInitial":"L.","affiliations":[{"id":24567,"text":"UCF","active":true,"usgs":false}],"preferred":false,"id":959436,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dove, Addison T.","contributorId":337563,"corporation":false,"usgs":false,"family":"Dove","given":"Addison","email":"","middleInitial":"T.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":959437,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O'Brien, Patrick 0000-0002-8956-2741","orcid":"https://orcid.org/0000-0002-8956-2741","contributorId":361059,"corporation":false,"usgs":false,"family":"O'Brien","given":"Patrick","affiliations":[{"id":86177,"text":"School of the Environment, Trent University, Peterborough, ON, K9L 0G2, Canada","active":true,"usgs":false}],"preferred":false,"id":959438,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Denevi, Brett W.","contributorId":210563,"corporation":false,"usgs":false,"family":"Denevi","given":"Brett","email":"","middleInitial":"W.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":959439,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hagerty, Justin 0000-0003-3800-7948 jhagerty@usgs.gov","orcid":"https://orcid.org/0000-0003-3800-7948","contributorId":911,"corporation":false,"usgs":true,"family":"Hagerty","given":"Justin","email":"jhagerty@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":959440,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hardgrove, Craig","contributorId":13546,"corporation":false,"usgs":false,"family":"Hardgrove","given":"Craig","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":959441,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hayne, Paul O.","contributorId":331019,"corporation":false,"usgs":false,"family":"Hayne","given":"Paul","middleInitial":"O.","affiliations":[{"id":79091,"text":"Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":959442,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"LaMee, Adam","contributorId":369963,"corporation":false,"usgs":false,"family":"LaMee","given":"Adam","affiliations":[{"id":18879,"text":"University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":959443,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Prettyman, Thomas H.","contributorId":267902,"corporation":false,"usgs":false,"family":"Prettyman","given":"Thomas","middleInitial":"H.","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":959444,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Shirley, Katherine A.","contributorId":369965,"corporation":false,"usgs":false,"family":"Shirley","given":"Katherine","middleInitial":"A.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":959445,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Siegler, Matthew A.","contributorId":237898,"corporation":false,"usgs":false,"family":"Siegler","given":"Matthew","middleInitial":"A.","affiliations":[{"id":24584,"text":"PSI","active":true,"usgs":false}],"preferred":false,"id":959446,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Sunshine, Jessica M.","contributorId":149244,"corporation":false,"usgs":false,"family":"Sunshine","given":"Jessica","middleInitial":"M.","affiliations":[{"id":17688,"text":"Univ. Maryland","active":true,"usgs":false}],"preferred":false,"id":959447,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70276537,"text":"70276537 - 2026 - Biological and environmental drivers of early life fawn survival in a declining pronghorn population","interactions":[],"lastModifiedDate":"2026-06-10T15:13:17.306331","indexId":"70276537","displayToPublicDate":"2026-04-13T10:13:57","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Biological and environmental drivers of early life fawn survival in a declining pronghorn population","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Pronghorn&nbsp;</span><i>Antilocapra americana</i><span>&nbsp;occupy only a portion of their historical range and in Oklahoma occur at the eastern edge of the species' contemporary distribution. Monitoring has suggested pronghorn populations in Oklahoma have declined in recent years. We captured and collared 125 adult females across two winters, monitored them for signs of parturition during each subsequent spring, and then captured and radio-collared 70 fawns ≤ 4 days old. We assessed cause-specific mortality, estimated proportional survival, and visualized survival of fawns through 60 days of life with Kaplan–Meier curves. Nearly 87% of fawn mortalities were attributed to predation, with &gt; 77% of predations attributed to coyotes&nbsp;</span><i>Canis latrans</i><span>. Our results indicated that fawn survival was lowest during the first 15 days of life, with 33% of fawns surviving to 15 days and 12% surviving to 60 days. We used known-fate models to evaluate the influence of biological factors (i.e. sex, mass, birth timing), environmental factors (i.e. ambient temperature, precipitation, vegetation), and temporal variation on the probability of early life (i.e. the first 15 days) survival. For each adult female with a collared fawn, we used female space-use patterns for 30 days before and 15 days after parturition to collect environmental covariates. Early life probability of survival was lower for larger fawns, those born earlier in the parturition period (i.e. earlier in the year relative to the range of parturition days), and those with higher pre-parturition temperatures; daily probability of survival decreased with time-since-parturition within the first 15 days of life. Our results indicate poor fawn survival, highlight a potential limitation of population growth, and can inform population management by identifying factors influencing early life fawn survival.</span></span></p>","language":"English","publisher":"Nordic Society Oikos","doi":"10.1002/wlb3.01577","usgsCitation":"Hahn, D.P., Lonsinger, R.C., Chitwood, M.C., Moeller, A.K., Turnley, M.T., Dart, M.M., Heffelfinger, L.J., Tanner, E.P., Cherry, M.J., Wang, H.G., and Fairbanks, W.S., 2026, Biological and environmental drivers of early life fawn survival in a declining pronghorn population: Wildlife Biology, v. 2026, no. 3, e01577, 15 p., https://doi.org/10.1002/wlb3.01577.","productDescription":"e01577, 15 p.","ipdsId":"IP-180721","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":505495,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wlb3.01577","text":"Publisher Index 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Colter","contributorId":371964,"corporation":false,"usgs":false,"family":"Chitwood","given":"M.","middleInitial":"Colter","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":962614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moeller, Anna K.","contributorId":371965,"corporation":false,"usgs":false,"family":"Moeller","given":"Anna","middleInitial":"K.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":962615,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Turnley, Matthew T.","contributorId":371966,"corporation":false,"usgs":false,"family":"Turnley","given":"Matthew","middleInitial":"T.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":962616,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dart, Marlin M.","contributorId":371967,"corporation":false,"usgs":false,"family":"Dart","given":"Marlin","middleInitial":"M.","affiliations":[{"id":88240,"text":"Texas A&M University– Kingsville","active":true,"usgs":false}],"preferred":false,"id":962617,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heffelfinger, Levi J.","contributorId":371968,"corporation":false,"usgs":false,"family":"Heffelfinger","given":"Levi","middleInitial":"J.","affiliations":[{"id":88241,"text":"Texas A&M University–Kingsville","active":true,"usgs":false}],"preferred":false,"id":962618,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tanner, Evan P.","contributorId":371969,"corporation":false,"usgs":false,"family":"Tanner","given":"Evan","middleInitial":"P.","affiliations":[{"id":88240,"text":"Texas A&M University– Kingsville","active":true,"usgs":false}],"preferred":false,"id":962619,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cherry, Michael J.","contributorId":371970,"corporation":false,"usgs":false,"family":"Cherry","given":"Michael","middleInitial":"J.","affiliations":[{"id":88241,"text":"Texas A&M University–Kingsville","active":true,"usgs":false}],"preferred":false,"id":962620,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wang, H. George","contributorId":371971,"corporation":false,"usgs":false,"family":"Wang","given":"H.","middleInitial":"George","affiliations":[{"id":85772,"text":"East Central University","active":true,"usgs":false}],"preferred":false,"id":962621,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Fairbanks, W. Sue","contributorId":371972,"corporation":false,"usgs":false,"family":"Fairbanks","given":"W.","middleInitial":"Sue","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":962622,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70275553,"text":"70275553 - 2026 - Two-stage approach to automatic detection with machine learning for improved surveillance of the invasive Cuban treefrog","interactions":[],"lastModifiedDate":"2026-05-04T15:33:56.987572","indexId":"70275553","displayToPublicDate":"2026-04-13T08:27:25","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1457,"text":"Ecological Informatics","active":true,"publicationSubtype":{"id":10}},"title":"Two-stage approach to automatic detection with machine learning for improved surveillance of the invasive Cuban treefrog","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>The Cuban treefrog (</span><i>Osteopilus septentrionalis</i><span>), as an invasive species in the southern United States, presents a need for effective surveillance. Automated detection expedites processing of audio data for large-scale surveillance and monitoring programs. However, current available methods commonly used for anuran species have not been sufficient to detect Cuban treefrogs. Here, we present results from a two-stage method for automated detection that employs both cross-correlation template matching and secondary supervised learning classifiers. In the first stage, audio data are screened for initial detections using template matching, in which the detections contain both true and false positives. In the second stage, the false positives are screened out using classifier algorithms. We used this method to process 139,985 audio recordings, consisting of 596,046 total minutes, collected at 13 locations in Louisiana and Florida from 2014 to 2022. From the stage 1 template matching, we detected 83,191 Cuban treefrog signals across recordings. The stage 2 machine learning model was able to identify stage 1 false positive detections with a testing accuracy of 98.46% and a testing false positive rate of 1.116%. After pruning false positive detections, a total of 20,271 individual Cuban treefrog detections remained, distributed mainly across 3 sites in an area with known presence. Locations with presumed absence had an easily verifiable number of false positive detections (</span><i>n</i><span>&nbsp;=&nbsp;109 across all other sites). The two-stage methodology utilizing both template matching and machine learning algorithms can be integrated into wildlife surveillance or monitoring programs for species with distinctive, conserved calls as an effective way to achieve sensitive species detection with a low incidence of false positives.</span></span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2026.103764","usgsCitation":"Huber, K., Waddle, J., Glorioso, B.M., and Donovan, T.M., 2026, Two-stage approach to automatic detection with machine learning for improved surveillance of the invasive Cuban treefrog: Ecological Informatics, v. 95, 103764, 10 p., https://doi.org/10.1016/j.ecoinf.2026.103764.","productDescription":"103764, 10 p.","ipdsId":"IP-179486","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":504174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoinf.2026.103764","text":"Publisher Index Page"},{"id":503938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Louisiana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -91.59682098903093,\n              30.32840235701677\n            ],\n            [\n              -91.37974643263087,\n              29.071575029507784\n            ],\n            [\n              -88.87546703451804,\n              28.886613103681952\n            ],\n            [\n              -88.62225814238174,\n              29.917322662392543\n            ],\n            [\n              -86.8107919373446,\n              30.348932080727437\n            ],\n            [\n              -86.86083569259117,\n              30.577875739656974\n            ],\n            [\n              -89.18432013663814,\n              30.169644003954026\n            ],\n            [\n              -91.59682098903093,\n              30.32840235701677\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"95","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Huber, Kaitlin","contributorId":336969,"corporation":false,"usgs":false,"family":"Huber","given":"Kaitlin","affiliations":[{"id":80934,"text":"Vermont Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":960868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waddle, J. Hardin 0000-0003-1940-2133","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":222187,"corporation":false,"usgs":true,"family":"Waddle","given":"J. Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":960869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glorioso, Brad M. 0000-0002-5400-7414","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":219360,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":960870,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":960871,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70275192,"text":"70275192 - 2026 - Initial condition uncertainty exerts a large and persistent influence on model simulations of ecosystem carbon dynamics in California","interactions":[],"lastModifiedDate":"2026-04-22T14:41:53.267617","indexId":"70275192","displayToPublicDate":"2026-04-13T07:37:24","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22185,"text":"Environmental Research: Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Initial condition uncertainty exerts a large and persistent influence on model simulations of ecosystem carbon dynamics in California","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Uncertainties in terrestrial ecosystem models limit their predictive power. Efforts to reduce projection error have rarely focused on constraining uncertainty in the initial state of the ecosystem, however, despite evidence that matching model initial conditions to real-world observations reduces overall model bias. Here we use an integrated land change and carbon gain-loss model to evaluate the influence of initial condition uncertainty on simulations of California wildland ecosystems during the years 1985–2020. We generated 36 initial conditions scenarios by varying the source data used to initialize state variables and then ran simulations based on each of these scenarios under a constant set of historical conditions. We found that discrepancies in initial forest extent and initial forest age among scenarios generated wide uncertainty ranges in model estimates of terrestrial ecosystem carbon stocks and flux rates at the outset of the simulation period, but differences in initial forest composition had no impact. Over time, forest age became more homogeneous across model scenarios leading to exponential rates of decline in the uncertainty ranges of live biomass and dead wood carbon but little to no impact on uncertainties in litter and soil organic carbon. Uncertainties in individual carbon flux rates were consistent with uncertainties in their source pools. In contrast, model estimates of ecosystem carbon balance demonstrated a shift in system behavior not apparent in trends for individual carbon stocks and fluxes. Specifically, estimates of ecosystem carbon balance converged across scenarios for the first 20 years of the simulation period but then began to diverge at an accelerating rate, possibly due to weakened resilience to the increased frequency and severity of climate-driven disturbances. Our results demonstrate that uncertainty in the initial state of the system can have large and persistent impacts on the predictability of ecosystem carbon dynamics, and that ongoing shifts in external forcing by climate and climate-driven disturbances can exacerbate these impacts.</span></span></p>","language":"English","publisher":"IOP Publishing","doi":"10.1088/2752-664X/ae565f","usgsCitation":"Selmants, P.C., Sleeter, B., and Daniel, C.J., 2026, Initial condition uncertainty exerts a large and persistent influence on model simulations of ecosystem carbon dynamics in California: Environmental Research: Ecology, v. 5, no. 2, 025001, 20 p., https://doi.org/10.1088/2752-664X/ae565f.","productDescription":"025001, 20 p.","ipdsId":"IP-182837","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":504058,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P137ZDHO","text":"USGS data 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,{"id":70276412,"text":"70276412 - 2026 - Channel morphology and large wood control postfire debris-flow erosion and deposition","interactions":[],"lastModifiedDate":"2026-06-04T15:21:04.88419","indexId":"70276412","displayToPublicDate":"2026-04-12T08:10:59","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Channel morphology and large wood control postfire debris-flow erosion and deposition","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Runoff-generated debris flows are a known response to wildfire, and accurately predicting the volume of these debris flows is important for estimating the magnitude of downstream hazards. Prior data collection efforts have focused on debris-flow volume measurements at catchment outlets, but few studies have considered how erosion and deposition modulate the volume of sediment arriving at catchment outlets. This study takes advantage of a high-resolution dataset to document the factors that control the total debris-flow volume reaching the catchment outlet during a fatal postfire debris flow. Using pre- and post-event airborne lidar, satellite imagery and field mapping, we found that a postfire debris flow in the Black Hollow catchment in northern Colorado eroded 136,000 ± 30,000 m</span><sup>3</sup><span>&nbsp;and redeposited 27,000 ± 7,500 m</span><sup>3</sup><span>&nbsp;in the main channel. Most of the in-channel deposition (52% by volume) occurred where a confined channel reach transitioned to an unconfined channel reach downstream, allowing the flow to widen and deposit material. Wood jams played multiple roles in the debris-flow dynamics, both nucleating deposition (25% of the deposit volume was stored behind wood jams) and exacerbating erosion (50% of the total erosion occurred downstream from a wood dam break). The remaining deposition occurred due to spatial changes in channel slope as well as deposition observed at newly formed channel bars. Using these data in this study, we identified topographic and vegetation metrics that can be used (pre-event) to anticipate where deposition may occur in channels prior to a debris flow.</span></span></p>","language":"English","publisher":"Wiley","doi":"10.1002/esp.70287","usgsCitation":"Rengers, F.K., Stoker, J.M., Kostelnik, J., Kean, J.W., Wohl, E.E., Barnhart, K.R., and Guido, L.E., 2026, Channel morphology and large wood control postfire debris-flow erosion and deposition: Earth Surface Processes and Landforms, v. 51, no. 4, e70287, 13 p., https://doi.org/10.1002/esp.70287.","productDescription":"e70287, 13 p.","ipdsId":"IP-170524","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":505058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.70287","text":"Publisher Index Page"},{"id":504998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Black Hollow","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -104.8998964,\n              40.6376787\n            ],\n            [\n              -104.8998964,\n              40.6376787\n            ],\n            [\n              -104.8998964,\n              40.6376787\n            ],\n            [\n              -104.8998964,\n              40.6376787\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -104.89825097286936,\n              40.63651931644344\n            ],\n            [\n              -104.8713368174039,\n              40.63651931644344\n            ],\n            [\n              -104.8713368174039,\n              40.622069349338375\n            ],\n            [\n              -104.89825097286936,\n              40.622069349338375\n            ],\n            [\n              -104.89825097286936,\n              40.63651931644344\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"51","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-04-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":962354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":962355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kostelnik, Jaime 0000-0002-1817-5461","orcid":"https://orcid.org/0000-0002-1817-5461","contributorId":300717,"corporation":false,"usgs":true,"family":"Kostelnik","given":"Jaime","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":962356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":962392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wohl, Ellen E.","contributorId":313566,"corporation":false,"usgs":false,"family":"Wohl","given":"Ellen","middleInitial":"E.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":962358,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":962359,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Guido, Lauren Elizabeth 0000-0003-4449-560X","orcid":"https://orcid.org/0000-0003-4449-560X","contributorId":371791,"corporation":false,"usgs":true,"family":"Guido","given":"Lauren","middleInitial":"Elizabeth","affiliations":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"preferred":true,"id":962360,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70275364,"text":"70275364 - 2026 - The Climate Hazards Center Infrared Precipitation with Stations, version 3","interactions":[],"lastModifiedDate":"2026-05-19T15:42:18.853167","indexId":"70275364","displayToPublicDate":"2026-04-11T08:47:50","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"The Climate Hazards Center Infrared Precipitation with Stations, version 3","docAbstract":"<p><span>The Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) data stream combines: (1) a high-resolution climatology, (2) thermal infrared (TIR) geostationary satellite observations, and (3) station observations. In the past, CHIRPS version 2 (CHIRPS2) has proven to be valuable for drought monitoring, hydrologic modeling, scientific studies and agricultural decision making. Version 3 (CHIRPS3) improves each of these components. The new version, CHIRPS3 extends to 60°S/N, adopts an improved variance-preserving TIR-to-precipitation estimation method, uses many more stations and station sources than the original CHIRPS2 product, and implements gauge-undercatch correction. In this paper, we evaluate the performance of satellite-only CHIRP3, CHIRP2, IMERG, PERSIANN- CCS, and GPI using high quality interpolated data in twelve regions with dense station coverage. CHIRP3 represents both the observed mean and variance more accurately than CHIRP2. A usage section in Morocco shows that CHIRPS3 better captures the observed rainfall variability when compared to CHIRPS2. This section also demonstrates how station data should be gauge-undercatch-corrected when validating CHIRPS3.</span></p>","language":"English","publisher":"Nature","doi":"10.1038/s41597-026-07096-4","usgsCitation":"Funk, C., Peterson, P., Harrison, L., Saldivar, R., Landsfeld, M., Pedreros, D., Shukla, S., Fink, A.H., Davenport, F., Peterson, S.H., Turner, W., Sonnier, A., Budde, M., Tabor, K., Verdin, J., Hauzaree, D., Naim, M., Alaso, D., and Husak, G., 2026, The Climate Hazards Center Infrared Precipitation with Stations, version 3: Scientific Data, v. 13, 78, 24 p., https://doi.org/10.1038/s41597-026-07096-4.","productDescription":"78, 24 p.","ipdsId":"IP-182849","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":504155,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-026-07096-4","text":"Publisher Index Page"},{"id":503879,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2026-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Funk, Chris","contributorId":302160,"corporation":false,"usgs":false,"family":"Funk","given":"Chris","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":960716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Pete","contributorId":337013,"corporation":false,"usgs":false,"family":"Peterson","given":"Pete","affiliations":[{"id":80950,"text":"UCSB Climate Hazards Center","active":true,"usgs":false}],"preferred":false,"id":960717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, Laura","contributorId":192382,"corporation":false,"usgs":false,"family":"Harrison","given":"Laura","email":"","affiliations":[],"preferred":false,"id":960718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saldivar, Robert","contributorId":370741,"corporation":false,"usgs":false,"family":"Saldivar","given":"Robert","affiliations":[{"id":80950,"text":"UCSB Climate Hazards Center","active":true,"usgs":false}],"preferred":false,"id":960719,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":960720,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pedreros, Diego 0000-0001-9943-7373 pedreros@usgs.gov","orcid":"https://orcid.org/0000-0001-9943-7373","contributorId":4195,"corporation":false,"usgs":true,"family":"Pedreros","given":"Diego","email":"pedreros@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":960721,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":960722,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fink, Andreas H.","contributorId":370744,"corporation":false,"usgs":false,"family":"Fink","given":"Andreas","middleInitial":"H.","affiliations":[{"id":39624,"text":"Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":960723,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davenport, Frank","contributorId":145816,"corporation":false,"usgs":false,"family":"Davenport","given":"Frank","email":"","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":960724,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Peterson, Seth H.","contributorId":139568,"corporation":false,"usgs":false,"family":"Peterson","given":"Seth","email":"","middleInitial":"H.","affiliations":[{"id":12804,"text":"Univ. of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":960725,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Turner, William","contributorId":370745,"corporation":false,"usgs":false,"family":"Turner","given":"William","affiliations":[{"id":80950,"text":"UCSB Climate Hazards Center","active":true,"usgs":false}],"preferred":false,"id":960726,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sonnier, Austin","contributorId":370746,"corporation":false,"usgs":false,"family":"Sonnier","given":"Austin","affiliations":[{"id":80950,"text":"UCSB Climate Hazards Center","active":true,"usgs":false}],"preferred":false,"id":960727,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":960728,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tabor, Karyn","contributorId":192412,"corporation":false,"usgs":false,"family":"Tabor","given":"Karyn","email":"","affiliations":[],"preferred":false,"id":960729,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Verdin, James","contributorId":337042,"corporation":false,"usgs":false,"family":"Verdin","given":"James","affiliations":[{"id":48664,"text":"USAID","active":true,"usgs":false}],"preferred":false,"id":960730,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Hauzaree, Disha","contributorId":370753,"corporation":false,"usgs":false,"family":"Hauzaree","given":"Disha","affiliations":[{"id":80950,"text":"UCSB Climate Hazards Center","active":true,"usgs":false}],"preferred":false,"id":960731,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Naim, Mohamed","contributorId":370754,"corporation":false,"usgs":false,"family":"Naim","given":"Mohamed","affiliations":[{"id":88068,"text":"IUSS Pavia & Univ. of Messina, Italy","active":true,"usgs":false}],"preferred":false,"id":960732,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Alaso, Daniella","contributorId":370755,"corporation":false,"usgs":false,"family":"Alaso","given":"Daniella","affiliations":[{"id":80950,"text":"UCSB Climate Hazards Center","active":true,"usgs":false}],"preferred":false,"id":960733,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Husak, Gregory","contributorId":145811,"corporation":false,"usgs":false,"family":"Husak","given":"Gregory","affiliations":[{"id":16236,"text":"UCSB Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":960734,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}}
,{"id":70276605,"text":"70276605 - 2026 - Quality and quantity of terrestrial landscape connectivity in the US Great Lakes Coastal Zone","interactions":[],"lastModifiedDate":"2026-06-11T14:19:11.254488","indexId":"70276605","displayToPublicDate":"2026-04-10T09:12:29","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Quality and quantity of terrestrial landscape connectivity in the US Great Lakes Coastal Zone","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Context</h3><p>The Great Lakes Coastal Zone (GLCZ), the world’s longest continuous freshwater coastline, consists of interspersed natural and developed landcover and is inhabited by millions of people. Anthropogenic change fragments the GLCZ, decreasing landscape connectivity.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Objectives</h3><p>We evaluated functional landscape connectivity of the U.S. GLCZ, assessing regional connectivity, protected areas’ effects on connectivity, and identified pinchpoints where connectivity is most vulnerable.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Methods</h3><p>We modeled landscape connectivity to describe corridors and redundant pathways for two scenarios: (1) continuous terrestrial habitat patches and (2) protected areas. We compared these two scenarios by total corridor area and quality at two spatial scales (coastal zone and ecoregion). We used random forest analyses to examine how ecological factors influenced the connectivity rank and pinchpoint values. A principal components analysis identified how land cover influenced connectivity among ecoregions.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>The GLCZ was 69% connected. Although 91% of the GLCZ’s high-quality habitat was within corridors, less than 50% of the corridor area was high-quality (low resistance to movement). While the northernmost region retained high-quality connectivity, connectivity loss and degradation increased with development further south. Corridors between protected areas provided less connectivity, of worse quality, in areas of development and agriculture. Lower connectivity was associated with higher impervious surface cover.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusions</h3><p>Protected area placement affected both the quantity and quality of connectivity. Improving and maintaining functional connectivity could be accomplished by strategically restoring and protecting habitats most vulnerable to connectivity loss, predominantly in urban and agricultural regions. Our connectivity improvement index is a useful tool to identify areas where connectivity may be improved.</p>","language":"English","publisher":"Springer","doi":"10.1007/s10980-025-02208-8","usgsCitation":"Hunt, L., Pavlovic, N., and Grundel, R., 2026, Quality and quantity of terrestrial landscape connectivity in the US Great Lakes Coastal Zone: Landscape Ecology, v. 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,{"id":70274762,"text":"sir20265138 - 2026 - Arsenic and isotope concentrations in the lower Platte River valley of eastern Nebraska, early 1970s to 2023","interactions":[],"lastModifiedDate":"2026-04-16T17:22:01.249485","indexId":"sir20265138","displayToPublicDate":"2026-04-09T15:06:02","publicationYear":"2026","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":"2026-5138","displayTitle":"Arsenic and Isotope Concentrations in the Lower Platte River Valley of Eastern Nebraska, early 1970s to 2023.","title":"Arsenic and isotope concentrations in the lower Platte River valley of eastern Nebraska, early 1970s to 2023","docAbstract":"<p>The City of Lincoln, Nebraska, has been monitoring concentrations of arsenic in their source water and evaluating their options for treatment and removal since at least 2002. In 2022, the City of Lincoln, Nebr., with funding assistance from the Nebraska Water Sustainability Fund, began cooperating with the U.S. Geological Survey to examine arsenic concentrations in surface water and groundwater in the lower Platte River valley and the area around City of Lincoln Water System (LWS) well field. Arsenic data collected from the Platte River since 1974 were examined using the “weighted regression on time, discharge, and season” model, which compared the streamflow (also referred to as “discharge”), time of year, and season to estimate concentrations of arsenic. Annual mean arsenic concentrations modeled for more than 49 years at the Platte River at Louisville, Nebr., U.S. Geological Survey streamgage (station 06805500), indicated a significant increasing trend. Arsenic concentrations in the Platte River were seasonal, with the highest concentrations being observed during mid- to late summer. When seasonal patterns and streamflow were combined with arsenic concentrations in the Platte River during low streamflow conditions, groundwater contributions, which can have higher arsenic concentrations, make up a larger portion of the streamflow. Arsenic samples were collected from upstream rivers in 2022 and 2023 and were paired to analyze the arsenic contributions at the U.S. Geological Survey streamgage on the Platte River near Ashland, Nebr. (station 06801000), near the City of Lincoln well field. The arsenic concentrations from the streamgage on the Platte River near Ashland, Nebr., location, were higher than the U.S. Geological Survey streamgage on the Elkhorn River at Waterloo, Nebr. (station 06800500), and significantly lower than at the U.S. Geological Survey streamgage on the Platte River near Leshara, Nebr.(station 06796500), indicating that the Platte River usually contributes a higher concentration of arsenic than does the Elkhorn River as they join near Ashland, Nebr. During 1991–2023, six groundwater monitoring wells were analyzed to identify trends in arsenic concentrations. Two of the six wells had a positive trend during the 33-year period. One monitoring well did not reveal a long-term trend during this period but showed a trend during 2019–23, correlating to a period when the island in the middle of the Platte River was connected to the east bank of the river when manganese reducing conditions were present and groundwater levels were declining in the well. Across all wells the oxidation and reduction (redox) condition during the time of sampling was assessed. Mixed anoxic and (or) oxic redox condition was the most common redox process and the highest sampled arsenic concentrations in monitoring wells were observed in anoxic conditions driven by manganese reduction. Groundwater arsenic concentrations had seasonal variation around the City of Lincoln well field, with higher arsenic concentrations tending to be further south in comparison to samples collected further north. Isotope samples were collected and analyzed in surface water and groundwater around the LWS well field. The samples indicate that the proportion of surface water present in the LWS production wells can be higher in the spring and lower in the summer. With higher arsenic concentrations observed in the stream water during the summer period, the LWS source water can be affected by these elevated arsenic concentrations even though the proportion of surface water is lower.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265138","collaboration":"Prepared in cooperation with City of Lincoln, Nebraska","usgsCitation":"Moser, M.T., Cherry, M.L., and Hall, B.M., 2026, Arsenic and isotope concentrations in the lower Platte River valley of eastern Nebraska, early 1970s to 2023: U.S. Geological Survey Scientific Investigations Report 2026–5138, 23 p., https://doi.org/10.3133/sir20265138.","productDescription":"Report: vii; 23 p.; Data Release; Dataset","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161400","costCenters":[{"id":84311,"text":"Central Plains Water Science Center","active":true,"usgs":true}],"links":[{"id":502306,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release"},{"id":502305,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5138/images"},{"id":502304,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265138/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5138 HTML"},{"id":502303,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5138/sir20265138.pdf","text":"Report","size":"5.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5138"},{"id":502302,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5138/coverthb.jpg"},{"id":502715,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119359.htm","linkFileType":{"id":5,"text":"html"}},{"id":502308,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5138/sir20265138.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5138 XML"},{"id":502307,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/mission-areas/water-resources/science/usgs-national-water-quality-network","text":"USGS National Water Quality Network"}],"country":"United States","state":"Nebraska","otherGeospatial":"lower Platte River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -95.95,\n              41.667\n            ],\n            [\n              -97,\n              41.667\n            ],\n            [\n              -97,\n              40.667\n            ],\n            [\n              -95.95,\n              40.667\n            ],\n            [\n              -95.95,\n              41.667\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/central-plains-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/central-plains-water-science-center\">Central Plains Water Science Center</a><br>U.S. Geological Survey<br>1217 Biltmore Drive Lawrence, KS 66049<br>5231 South 19th Street Lincoln, NE 68512</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Sample Collection and Analysis Methods<br></li><li>Arsenic Assessments in the Lower Platte River</li><li>Arsenic Concentrations in Groundwater around the Lincoln Well Field</li><li>Using Stable Isotopes to Estimate Recharge Sources</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2026-04-09","noUsgsAuthors":false,"publicationDate":"2026-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Moser, Matthew T. 0000-0002-4891-3381","orcid":"https://orcid.org/0000-0002-4891-3381","contributorId":94994,"corporation":false,"usgs":true,"family":"Moser","given":"Matthew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":958963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cherry, Mikaela L. 0000-0003-1081-0296 mcherry@usgs.gov","orcid":"https://orcid.org/0000-0003-1081-0296","contributorId":303279,"corporation":false,"usgs":true,"family":"Cherry","given":"Mikaela","email":"mcherry@usgs.gov","middleInitial":"L.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hall, Brent M. 0000-0003-3815-5158 bhall@usgs.gov","orcid":"https://orcid.org/0000-0003-3815-5158","contributorId":4547,"corporation":false,"usgs":true,"family":"Hall","given":"Brent","email":"bhall@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958965,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70275001,"text":"70275001 - 2026 - Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund Site, Rockingham County, New Hampshire","interactions":[{"subject":{"id":70275001,"text":"70275001 - 2026 - Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund Site, Rockingham County, New Hampshire","indexId":"70275001","publicationYear":"2026","noYear":false,"title":"Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund Site, Rockingham County, New Hampshire"},"predicate":"SUPERSEDED_BY","object":{"id":70275642,"text":"sir20265008 - 2026 - Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund site, Rockingham County, New Hampshire","indexId":"sir20265008","publicationYear":"2026","noYear":false,"title":"Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund site, Rockingham County, New Hampshire"},"id":1}],"supersededBy":{"id":70275642,"text":"sir20265008 - 2026 - Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund site, Rockingham County, New Hampshire","indexId":"sir20265008","publicationYear":"2026","noYear":false,"title":"Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund site, Rockingham County, New Hampshire"},"lastModifiedDate":"2026-05-11T16:04:09.565032","indexId":"70275001","displayToPublicDate":"2026-04-09T09:40:28","publicationYear":"2026","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund Site, Rockingham County, New Hampshire","docAbstract":"<p><span>Per- and polyfluoroalkyl substances (PFAS), including perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS), have been detected at combined concentrations above 2,000 nanograms per liter (ng/L) at groundwater seep locations near the Coakley Landfill Superfund site, in North Hampton, New Hampshire. The landfill was active from 1972 to 1985. An impermeable cap was placed on the landfill in 1998. The adjacent area to the Coakley Landfill has many water supply wells, and transport of PFAS compounds to the wells is a concern. Fracture anisotropy in the underlying bedrock aquifer complicates the understanding of PFAS transport because groundwater preferentially travels along fractures that may not align with the prevailing groundwater flow direction. In 2018, the U.S Environmental Protection Agency and the U.S. Geological Survey began an investigation of the groundwater flow from the Coakley Landfill site. This report describes the modification of a numerical groundwater-flow model for the local area around the Coakley Landfill and summarizes findings of the investigation. In addition, this report includes a brief description of PFOA and PFOS occurrence, a discussion of model construction, evaluation of model performance through calibration, and discussion of simulation results for two periods (before and after capping). Limitations are also discussed. Results show that simulated groundwater flow moves from the Coakley Landfill to the west and north. Advective transport modeling using particle tracking shows that groundwater from the landfill discharges primarily to streams to the west and north, and a small amount is transported to distal wells. Dilution of contaminants through advection and dispersion likely plays a role in whether PFAS compounds from the landfill will be detected above laboratory reporting levels at distal wells.</span></p>","language":"English","publisher":"EarthArXiv","doi":"10.31223/X53761","usgsCitation":"Harte, P., and Collins, A.L., 2026, Simulation of groundwater flow to evaluate hydrogeologic controls on a PFAS plume, Coakley Landfill Superfund Site, Rockingham County, New Hampshire: EarthArXiv, preprint posted April 09, 2026, https://doi.org/10.31223/X53761.","productDescription":"72 p.","ipdsId":"IP-188248","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":502680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2026-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Harte, Phil 0000-0002-7718-1204","orcid":"https://orcid.org/0000-0002-7718-1204","contributorId":369789,"corporation":false,"usgs":false,"family":"Harte","given":"Phil","affiliations":[{"id":63928,"text":"Former USGS (ret.)","active":true,"usgs":false}],"preferred":false,"id":959179,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collins, Andrew L. 0000-0003-4751-7333","orcid":"https://orcid.org/0000-0003-4751-7333","contributorId":332093,"corporation":false,"usgs":true,"family":"Collins","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959180,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70275062,"text":"70275062 - 2026 - Incorporating data sets with multiple sources of uncertainty in integrated species distribution models","interactions":[],"lastModifiedDate":"2026-04-14T16:29:45.038418","indexId":"70275062","displayToPublicDate":"2026-04-09T09:24:19","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating data sets with multiple sources of uncertainty in integrated species distribution models","docAbstract":"<p><span>Data integration methods aim to improve species distribution estimates by incorporating multiple sources of uncertainty across datasets. Two major sources of uncertainty are: (1) variation in sampling effort across space and within datasets, and (2) variation in reliability associated with data collection protocols or timing among datasets. Our goal was to evaluate how different approaches to address these uncertainties influence predictive performance of integrated models. We modeled distributions of four bird species using three datasets that differed in sampling design. We examined three strategies to reduce uncertainty: (1) filtering data, (2) incorporating functions that account for uncertainty in observation models, and (3) varying how datasets are integrated into a single estimate. We first examine methods to account for variable effort in observations, focusing on both spatial differences in sampling intensity and effort given to a single observation record. We then examine approaches to account for data sets with differing reliability. Sampling effort was best addressed through conservative filtering, including spatial thinning and excluding observations with highly variable effort. Next, we considered how to account for potential false positive detections—due to either misidentification or changes in distributions. We found that treating less reliable data as a covariate, an approach previously suggested for data integration that can greatly speed up model fitting, performed well. Other effective approaches included directly modeling false positive rates and complete exclusion of less reliable data sets. Our results provide insights into best practices in integrated modeling for handling uncertainty in integrated models. We demonstrate the flexible options available when using integrated models to address uncertainty.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.73185","usgsCitation":"Lunt, F., Scher, C.L., Mummah, R.O., and Miller, D.A., 2026, Incorporating data sets with multiple sources of uncertainty in integrated species distribution models: Ecology and Evolution, v. 16, no. 4, e73185, 11 p., https://doi.org/10.1002/ece3.73185.","productDescription":"e73185, 11 p.","ipdsId":"IP-180463","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":503008,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.73185","text":"Publisher Index Page"},{"id":502788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationDate":"2026-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Lunt, Fiona","contributorId":369894,"corporation":false,"usgs":false,"family":"Lunt","given":"Fiona","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":959344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scher, C. Lane","contributorId":369895,"corporation":false,"usgs":false,"family":"Scher","given":"C.","middleInitial":"Lane","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":959345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mummah, Riley Olivia 0000-0002-4542-3483","orcid":"https://orcid.org/0000-0002-4542-3483","contributorId":342242,"corporation":false,"usgs":true,"family":"Mummah","given":"Riley","email":"","middleInitial":"Olivia","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":959346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, David A.W.","contributorId":367856,"corporation":false,"usgs":false,"family":"Miller","given":"David","middleInitial":"A.W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":959347,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70275071,"text":"70275071 - 2026 - Semantic segmentation of light-toned veins in multimodal ChemCam data","interactions":[],"lastModifiedDate":"2026-04-14T15:24:12.421568","indexId":"70275071","displayToPublicDate":"2026-04-09T08:12:35","publicationYear":"2026","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":"Semantic segmentation of light-toned veins in multimodal ChemCam data","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Since the Mars Science Laboratory landed in 2012, the ChemCam instrument aboard the rover has collected in-situ laser-induced breakdown spectroscopy (LIBS) data and context images along more than 35 km of the Gale Crater traverse, providing valuable observations including diagenetic features such as light-toned veins. These veins are of particular scientific interest because they are interpreted as indicators of past fluid circulation on Mars and provide insights into the evolution of habitability on Mars. Their identification, however, currently relies on manual visual inspection of Remote Micro Imager&nbsp;(RMI) images, a process that is time-consuming and sensitive to differences in human interpretation. To address this issue, in this paper we introduce a novel pixel-level labeled, multimodal dataset of ChemCam observations specifically tailored for vein detection, along with customized U-Net models to integrate both textural&nbsp;(RMI) and chemical&nbsp;(LIBS) modalities. To further ensure trustworthy scientific use, we incorporate the Learn-Then-Test&nbsp;(LTT) framework to provide statistical control of the false discovery rate without requiring model retraining. The experimental results demonstrate that the proposed customized U-Net models trained on the developed dataset, combined with risk-controlled prediction, increases the efficiency of pixel-level vein identification through automation and produces statistically reliable predictions for multimodal ChemCam data.</span></span></p>","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-026-47207-0","usgsCitation":"Lomashvili, A., Rammelkamp, K., Bhattacharjee, P., Gasnault, O., Clavé, E., Egerland, C.H., Schröder, S., Gabriel, T.S., Essunfeld, A., Le Mouélic, S., and Demir, B., 2026, Semantic segmentation of light-toned veins in multimodal ChemCam data: Scientific Reports, v. 16, 12052, 15 p., https://doi.org/10.1038/s41598-026-47207-0.","productDescription":"12052, 15 p.","ipdsId":"IP-187556","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":503006,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-026-47207-0","text":"Publisher Index Page"},{"id":502785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"16","noUsgsAuthors":false,"publicationDate":"2026-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Lomashvili, Ana","contributorId":369911,"corporation":false,"usgs":false,"family":"Lomashvili","given":"Ana","affiliations":[{"id":64112,"text":"German Aerospace Center","active":true,"usgs":false}],"preferred":false,"id":959359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rammelkamp, Kristin","contributorId":289781,"corporation":false,"usgs":false,"family":"Rammelkamp","given":"Kristin","affiliations":[{"id":62247,"text":"Institut de Recherche en Astrophysique et Planetologie","active":true,"usgs":false}],"preferred":false,"id":959360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bhattacharjee, Protim","contributorId":369912,"corporation":false,"usgs":false,"family":"Bhattacharjee","given":"Protim","affiliations":[{"id":64112,"text":"German Aerospace Center","active":true,"usgs":false}],"preferred":false,"id":959361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gasnault, Olivier","contributorId":181928,"corporation":false,"usgs":false,"family":"Gasnault","given":"Olivier","affiliations":[],"preferred":false,"id":959362,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clavé, Elise","contributorId":296842,"corporation":false,"usgs":false,"family":"Clavé","given":"Elise","affiliations":[{"id":64188,"text":"Planetary Exploration Team, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":959363,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Egerland, Christoph H.","contributorId":369913,"corporation":false,"usgs":false,"family":"Egerland","given":"Christoph","middleInitial":"H.","affiliations":[{"id":64112,"text":"German Aerospace Center","active":true,"usgs":false}],"preferred":false,"id":959364,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schröder, Susanne","contributorId":351652,"corporation":false,"usgs":false,"family":"Schröder","given":"Susanne","affiliations":[{"id":47627,"text":"DLR","active":true,"usgs":false}],"preferred":false,"id":959365,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gabriel, Travis S.J. 0000-0002-9767-4153","orcid":"https://orcid.org/0000-0002-9767-4153","contributorId":267903,"corporation":false,"usgs":true,"family":"Gabriel","given":"Travis","middleInitial":"S.J.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":959366,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Essunfeld, Ari","contributorId":369917,"corporation":false,"usgs":false,"family":"Essunfeld","given":"Ari","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":959367,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Le Mouélic, Stéphane","contributorId":92786,"corporation":false,"usgs":false,"family":"Le Mouélic","given":"Stéphane","affiliations":[],"preferred":false,"id":959368,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Demir, Begüm","contributorId":369925,"corporation":false,"usgs":false,"family":"Demir","given":"Begüm","affiliations":[{"id":87885,"text":"BIFOLD and Tu Berlin","active":true,"usgs":false}],"preferred":false,"id":959369,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70275750,"text":"70275750 - 2026 - Describing the seasonal abundance and growth rates of larval fishes across productivity gradients in Lake Huron in 2017","interactions":[],"lastModifiedDate":"2026-05-18T15:07:07.260384","indexId":"70275750","displayToPublicDate":"2026-04-09T07:57:51","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Describing the seasonal abundance and growth rates of larval fishes across productivity gradients in Lake Huron in 2017","docAbstract":"<p><span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span>Several of the Laurentian Great Lakes, including Lake Huron, have undergone oligotrophication in the past decades and prey fish biomass has concomitantly declined. One potential mechanism to explain declines in prey fish is slower growth and lower survival at the larval stage. To determine whether reduced productivity affects the growth of larval fish, we sampled larval fishes and their environment approximately monthly from May through August 2017 at nine nearshore to offshore transects across Lake Huron that included North Channel, Georgian Bay, and the main basin. North Channel transects had the highest chlorophyll&nbsp;</span><i>a</i><span>&nbsp;concentrations and zooplankton densities. Rainbow smelt (</span><i>Osmerus mordax</i><span>), burbot (</span><i>Lota lota</i><span>), bloater (</span><i>Coregonus hoyi</i><span>), and shiners (</span><i>Notropis</i><span>&nbsp;spp.) were the most abundant larval fish taxa, peaking in June and July. We aged rainbow smelt and bloater using otoliths, and estimates of growth rate revealed rainbow smelt always grew faster. For both species, we explained variation in total length by comparing 16 candidate linear mixed-effects models, with age, chlorophyll&nbsp;</span><i>a</i><span>, zooplankton, water temperature, larval fish density, and interactions with age as predictor variables. For rainbow smelt, the full model was best; zooplankton had the greatest effect, but it was negative and opposite from our hypothesis. For bloater, four candidate models were most parsimonious; water temperature had the greatest effect, and it was positive as predicted from our hypothesis. To more effectively evaluate whether zooplankton can limit larval fish growth and survival, we recommend that future designs conduct more frequently sampling within a year even at the expense of fewer transects.</span></span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2026.102817","usgsCitation":"Bunnell, D.B., Eaton, L.A., Dieter, P.M., Collingsworth, P., Hoffman, J.C., Rowe, M.D., Stott, W., Ackiss, A.S., and Rutherford, E.S., 2026, Describing the seasonal abundance and growth rates of larval fishes across productivity gradients in Lake Huron in 2017: Journal of Great Lakes Research, 102817, 15 p., https://doi.org/10.1016/j.jglr.2026.102817.","productDescription":"102817, 15 p.","ipdsId":"IP-182655","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":504477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Huron","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -84.58185200845517,\n              46.519644985424634\n            ],\n            [\n              -84.58185200845517,\n              43.082049264308324\n            ],\n            [\n              -79.6331783866257,\n              43.082049264308324\n            ],\n            [\n              -79.6331783866257,\n              46.519644985424634\n            ],\n            [\n              -84.58185200845517,\n              46.519644985424634\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bunnell, David B. 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":216545,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":961635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eaton, Lauren A.","contributorId":211815,"corporation":false,"usgs":false,"family":"Eaton","given":"Lauren","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":961636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dieter, Patricia M. 0000-0003-1686-2679","orcid":"https://orcid.org/0000-0003-1686-2679","contributorId":217345,"corporation":false,"usgs":true,"family":"Dieter","given":"Patricia","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":961637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collingsworth, Paris D.","contributorId":354643,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris D.","affiliations":[{"id":84645,"text":"Illinois-Indiana SeaGrant","active":true,"usgs":false}],"preferred":false,"id":961638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoffman, Joel C.","contributorId":361653,"corporation":false,"usgs":false,"family":"Hoffman","given":"Joel","middleInitial":"C.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":961639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowe, Mark D.","contributorId":208536,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark","middleInitial":"D.","affiliations":[],"preferred":false,"id":961640,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stott, Wendylee 0000-0002-5252-4901","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":242990,"corporation":false,"usgs":false,"family":"Stott","given":"Wendylee","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":961641,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ackiss, Amanda Susanne 0000-0002-8726-7423","orcid":"https://orcid.org/0000-0002-8726-7423","contributorId":272165,"corporation":false,"usgs":true,"family":"Ackiss","given":"Amanda","email":"","middleInitial":"Susanne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":961642,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rutherford, Edward S.","contributorId":175426,"corporation":false,"usgs":false,"family":"Rutherford","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12789,"text":"NOAA Great Lakes Environmental Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":961643,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70274705,"text":"sir20265001 - 2026 - Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","interactions":[{"subject":{"id":70267521,"text":"70267521 - 2025 - Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","indexId":"70267521","publicationYear":"2025","noYear":false,"title":"Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020"},"predicate":"SUPERSEDED_BY","object":{"id":70274705,"text":"sir20265001 - 2026 - Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","indexId":"sir20265001","publicationYear":"2026","noYear":false,"title":"Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020"},"id":1}],"lastModifiedDate":"2026-04-10T18:20:31.25097","indexId":"sir20265001","displayToPublicDate":"2026-04-08T06:49:00","publicationYear":"2026","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":"2026-5001","displayTitle":"Simulated Seasonal Loads of Total Nitrogen and Total Phosphorus by Major Source from Watersheds Draining to Washington Waters of the Salish Sea, 2005 through 2020","title":"Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","docAbstract":"<p>The U.S. Geological Survey and the Washington State Department of Ecology (Ecology) have developed watershed models of seasonal load estimates of total nitrogen (TN) and total phosphorus (TP) discharging into the Washington State waters of the Salish Sea from 2005 through 2020. The modeling approach used was dynamic SPARROW (SPAtially Referenced Regressions On Watershed attributes), a statistical-physical watershed modeling technique, initially applied at large spatial scales to represent long-term average stream loads throughout a stream network, refined here to estimate seasonal TN and TP loads across watersheds.</p><p>Upstream contributing sources included permitted treated wastewater facilities, crop fertilizer, animal feeding operations, septic systems, urban land and stormwater, atmospheric deposition (TN only), nitrogen fixation by Alnus rubra Bong. (red alder) trees (TN only), and background geologic material (TP only). Instream load magnitudes and their source compositions varied across watersheds, and even within each watershed, yet the largest loads typically occurred in the large rivers during winter and fall when streamflow was highest. Likewise, instream loads were typically lowest in summer during low streamflow, yet the relative instream aquatic decay was highest. The seasonal storage lag component of all nonpoint sources was estimated to contribute a quarter of the seasonal instream load during winter and fall high streamflow and sometimes half of the instream load during summer low streamflow.</p><p>Simulated seasonal loads carried by streams to a few hundred river mouth marine discharge points ranged by several orders-of-magnitude for TN and TP due to the spatial and seasonal differences in hydrologic flows, magnitude and timing of contributing sources, and instream decay. The Snohomish and Skagit Rivers discharged the largest TN and TP loads, yet the Samish River was shown to have some of the highest TN and TP yields and concentrations. Additionally, a reference scenario estimate developed of the pre-industrial local and regional TN loads suggests that red alder tree density has increased in lower riparian areas and that treated wastewater is the dominant source in some watersheds that has led to increases in TN loading to marine waters.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20265001","collaboration":"Prepared in cooperation with Washington State Department of Ecology","programNote":"Water Availability and Use Science Program","usgsCitation":"Schmadel, N.M., Figueroa-Kaminsky, C., Wise, D.R., Wasielewski, J.K., Johnson, Z.C., and Black, R.W., 2026, Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020: U.S. Geological Survey Scientific Investigations Report 2026–5001, 66 p., https://doi.org/10.3133/sir20265001. [Supersedes preprint https://doi.org/10.22541/essoar.173878059.92247480/v1.]","productDescription":"Report: x; 66 p.; Data Release","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-171269","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":502711,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_119356.htm","linkFileType":{"id":5,"text":"html"}},{"id":502222,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LY1PQF","text":"USGS data release","linkHelpText":"Model application and calibration load data for seasonally dynamic total nitrogen and total phosphorus SPARROW models developed for watersheds draining to Washington waters of the Salish Sea, 2005 through 2020"},{"id":502221,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2026/5001/images"},{"id":502220,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2026/5001/sir20265001.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2026-5001 XML"},{"id":502218,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2026/5001/sir20265001.pdf","text":"Report","size":"45.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2026-5001 PDF"},{"id":502217,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2026/5001/coverthb.jpg"},{"id":502219,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20265001/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2026-5001 HTML"}],"country":"Canada, United States","state":"British Columbia, Washington","otherGeospatial":"Salish Sea","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121,\n              49.5\n            ],\n            [\n              -125,\n              49.5\n            ],\n            [\n              -125,\n              46\n            ],\n            [\n              -121,\n              46\n            ],\n            [\n              -121,\n              49.5\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/or-water\" href=\"https://www.usgs.gov/centers/or-water\" target=\"_blank\" rel=\"noopener\">Oregon Water Science Center</a><br><a data-mce-href=\"https://www.usgs.gov/\" href=\"https://www.usgs.gov/\" target=\"_blank\" rel=\"noopener\">U.S. Geological Survey</a><br>601 SW 2nd Avenue, Suite 1950<br>Portland, Oregon 97204<br></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Simulated Seasonal Total Nitrogen and Total Phosphorus Load Results</li><li>Historical Red Alder and Wetland Reference Scenario</li><li>Discussion</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Additional Model Inputs</li><li>Appendix 2. Results Summary</li></ul>","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2026-04-08","noUsgsAuthors":false,"publicationDate":"2026-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah M. 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":958752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Figueroa-Kaminsky, Cristiana","contributorId":350514,"corporation":false,"usgs":false,"family":"Figueroa-Kaminsky","given":"Cristiana","affiliations":[{"id":25353,"text":"Washington State Department of Ecology","active":true,"usgs":false}],"preferred":false,"id":958753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wise, Daniel R. 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wasielewski, Jamie K. 0009-0005-7497-3344","orcid":"https://orcid.org/0009-0005-7497-3344","contributorId":344993,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Jamie K.","affiliations":[{"id":82458,"text":"Washington Dept. of Ecology","active":true,"usgs":false}],"preferred":false,"id":958755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":958756,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Black, Robert W. 0000-0002-4748-8213 rwblack@usgs.gov","orcid":"https://orcid.org/0000-0002-4748-8213","contributorId":1820,"corporation":false,"usgs":true,"family":"Black","given":"Robert","email":"rwblack@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":958757,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70275014,"text":"70275014 - 2026 - Influence of modeling assumptions on pedestrian evacuation success for non-eruptive lahar hazards at Mount Rainier, Washington","interactions":[],"lastModifiedDate":"2026-04-10T15:17:52.010543","indexId":"70275014","displayToPublicDate":"2026-04-07T10:10:31","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"Influence of modeling assumptions on pedestrian evacuation success for non-eruptive lahar hazards at Mount Rainier, Washington","docAbstract":"<p><span>Previous efforts to characterize lahar threats posed to communities downstream of volcanoes have focused primarily on delineating hazard zones that lack information on lahar-arrival times and exposure estimates that implicitly treat threats to be the same regardless of distance from the volcano. Estimated lahar-arrival times, travel times for individuals to leave hazard zones, and possible evacuation delays related to event identification, warning dissemination, and evacuee behavior are important, but often overlooked, aspects of understanding the societal threats posed by lahars. These temporal considerations are important for unexpected lahars that could occur due to slope failure in the absence of precursory volcanic unrest or eruption. This case study examines the role of time in lahar evacuations by quantifying population exposure and evacuation potential for non-eruptive lahar hazards associated with Mount Rainier, Washington. Lahars could directly affect tens of thousands of residents and employees, thousands of students at primary and secondary schools, and hundreds of individuals at long-term residential care facilities. Geospatial path-distance modeling quantified evacuation potential for 736 scenarios that represent combinations of lahar sources, evacuation destinations, pedestrian travel speeds, and a range of departure-delay assumptions. Depending on location, some communities may have substantial loss of life in tens of minutes after lahar initiation, whereas other communities may be managing large-scale evacuations over several hours. Estimates of evacuation success based on a range of scenarios provide individuals in hazard zones and risk-reduction agencies with insights on how their actions may increase or decrease the number of people that survive future lahars.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2026.106132","usgsCitation":"Wood, N.J., and Peters, J., 2026, Influence of modeling assumptions on pedestrian evacuation success for non-eruptive lahar hazards at Mount Rainier, Washington: International Journal of Disaster Risk Reduction, v. 139, 106132, 16 p., https://doi.org/10.1016/j.ijdrr.2026.106132.","productDescription":"106132, 16 p.","ipdsId":"IP-186816","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":502990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2026.106132","text":"Publisher Index Page"},{"id":502692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.5,\n              47.25\n            ],\n            [\n              -122.5,\n              46.7\n            ],\n            [\n              -121.75,\n              46.7\n            ],\n            [\n              -121.75,\n              47.25\n            ],\n            [\n              -122.5,\n              47.25\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"139","noUsgsAuthors":false,"publicationDate":"2026-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":959203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peters, Jeff 0000-0003-4312-0590 jpeters@usgs.gov","orcid":"https://orcid.org/0000-0003-4312-0590","contributorId":4711,"corporation":false,"usgs":true,"family":"Peters","given":"Jeff","email":"jpeters@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":959204,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70275375,"text":"70275375 - 2026 - Drought resistance is greater in montane conifers compared to coastal conifers in northern California","interactions":[],"lastModifiedDate":"2026-05-01T14:37:40.638757","indexId":"70275375","displayToPublicDate":"2026-04-05T09:34:03","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Drought resistance is greater in montane conifers compared to coastal conifers in northern California","docAbstract":"<p><span>Frequent and intense droughts are rapidly altering stand dynamics in western North American forests. The ecological and geographical diversity of northern California provides a unique opportunity to measure these responses across species, habitat types, and levels of competitive pressure. This study used dendrochronological techniques and linear mixed-effects models to assess growth responses to drought in four montane and two coastal conifer species across 54 study sites (nine sites per species, 526 trees total) in northern California. Montane species included&nbsp;</span><i>Abies magnifica</i><span>&nbsp;var.&nbsp;</span><i>shastensis</i><span>,&nbsp;</span><i>Picea breweriana</i><span>,&nbsp;</span><i>Pinus lambertiana</i><span>, and&nbsp;</span><i>Pinus monticola</i><span>; coastal species included&nbsp;</span><i>Picea sitchensis</i><span>&nbsp;and&nbsp;</span><i>Tsuga heterophylla</i><span>. Growth was evaluated from 2002 to 2018 and the drought period was from 2013 to 2015. There were significant differences among species and environments (coastal vs montane) in growth, drought resistance and resilience, and annual latewood proportion. Growth in montane species was generally positively related to moisture availability (Palmer Drought Severity Index) and negatively related to tree competition. The four montane species maintained relatively stable drought resistance, resilience, and latewood proportion across the study period. In contrast, growth in the two coastal species was influenced more by tree size and crown ratio than moisture availability or competition. As the 2013–2015 drought proceeded, coastal species showed marked reductions in drought resistance and resilience and increases in latewood proportion. Across the six conifer species, mean reductions in growth during and after the drought were generally less than 20% and never exceeded 40%. Compared to montane species, the lower resistance measured in coastal species suggests greater risk for increased stress and mortality in the event of more severe, prolonged, and/or frequent droughts.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2026.123744","usgsCitation":"Roletti, G.J., Sherriff, R.L., Kerhoulas, L.P., Robinson, W., Beckmann, J.J., and van Mantgem, P., 2026, Drought resistance is greater in montane conifers compared to coastal conifers in northern California: Forest Ecology and Management, v. 612, 123744, 16 p., https://doi.org/10.1016/j.foreco.2026.123744.","productDescription":"123744, 16 p.","ipdsId":"IP-174027","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":504161,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2026.123744","text":"Publisher Index Page"},{"id":503885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -124.8764513358662,\n              42.01734731000886\n            ],\n            [\n              -120.00375565084616,\n              42.01734731000886\n            ],\n            [\n              -120.00375565084616,\n              38.8177753545599\n            ],\n            [\n              -124.8764513358662,\n              38.8177753545599\n            ],\n            [\n              -124.8764513358662,\n              42.01734731000886\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"612","noUsgsAuthors":false,"publicationDate":"2026-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Roletti, Gabriel J.","contributorId":370786,"corporation":false,"usgs":false,"family":"Roletti","given":"Gabriel","middleInitial":"J.","affiliations":[{"id":37071,"text":"California State Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":960772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherriff, Rosemary L.","contributorId":370787,"corporation":false,"usgs":false,"family":"Sherriff","given":"Rosemary","middleInitial":"L.","affiliations":[{"id":37071,"text":"California State Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":960773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kerhoulas, Lucy P.","contributorId":370788,"corporation":false,"usgs":false,"family":"Kerhoulas","given":"Lucy","middleInitial":"P.","affiliations":[{"id":37071,"text":"California State Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":960774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robinson, Wallis","contributorId":303695,"corporation":false,"usgs":false,"family":"Robinson","given":"Wallis","email":"","affiliations":[{"id":65879,"text":"California State Polytechnic University, Humboldt","active":true,"usgs":false}],"preferred":false,"id":960775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beckmann, Jill J.","contributorId":370791,"corporation":false,"usgs":false,"family":"Beckmann","given":"Jill","middleInitial":"J.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":960776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":960777,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70274706,"text":"70274706 - 2026 - Long-term monotonic trends in water budget components in the contiguous United States: Insights from two hydrologic models","interactions":[],"lastModifiedDate":"2026-04-08T13:47:35.960955","indexId":"70274706","displayToPublicDate":"2026-04-04T09:25:05","publicationYear":"2026","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Long-term monotonic trends in water budget components in the contiguous United States: Insights from two hydrologic models","docAbstract":"<p><span>Characterizing changes to water availability for domestic, industrial, agricultural, and other uses is essential to support water management. To better quantify these changes, the U.S. Geological Survey and National Science Foundation National Center for Atmospheric Research produced two hydrologic models simulating water budget components from 1980 to 2021 over the contiguous United States (CONUS). Both hydrologic models were driven by a common atmospheric forcing dataset and aggregated to common spatial and temporal scales, which enables a novel evaluation of congruency between the models. We present annual and seasonal trends in six water budget components (precipitation, evapotranspiration, streamflow, groundwater recharge, soil saturation, and snow water equivalent) based on the Mann–Kendall test for monotonic trend and Theil-Sen slope estimate for the water year 1983–2021 period for ~86,000 catchments in CONUS. Additional components and metrics from our analysis pipeline are available in an associated published dataset, which contains more than 46 million trend results. The water budget trends showed broad agreement with prior observational and modeling studies that indicate increasing trends in the northeast and decreasing trends in southwestern CONUS. We found the seasonal variability in water budget trends was greatest in the southern, central, and northwest CONUS. These findings support integrated trend assessments when coupled with trends in water quality and use.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.70109","usgsCitation":"Goodling, P.J., Foks, S., and Ayers, J., 2026, Long-term monotonic trends in water budget components in the contiguous United States: Insights from two hydrologic models: Journal of the American Water Resources Association, v. 62, no. 2, e70109, 10 p., https://doi.org/10.1111/1752-1688.70109.","productDescription":"e70109, 10 p.","ipdsId":"IP-179372","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":502480,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.70109","text":"Publisher Index Page"},{"id":502232,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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