This report by the U.S. Geological Survey focuses on describing geomorphic changes in the alluvial segments of the Green River within Echo, Island, and Rainbow Parks of Dinosaur National Monument, between the 1990s and 2019. Substantial channel change occurred within these meandering alluvial segments following the construction and closure of Flaming Gorge Dam in 1962. Geomorphic analyses in the early 1990s documented this change, but variations in dam operations, climate, and the natural sand supply from the Yampa River have since occurred. It was unclear whether channel change within those meandering alluvial segments had continued since the early 1990s; hence, our study provides an update to previous work. This study used three primary methods to quantify the amount and style of channel change that occurred within those alluvial segments of the Green River: (1) digital aerial-photograph analyses, (2) surveys of alluvial topography in the 1990s and 2019 at fixed cross-section locations, and (3) surveys of channel bathymetry in 1998 and 2019. Our analyses show that channel narrowing has continued, with declines in channel width of 4 percent in Echo Park, 16 percent in Island Park, and 15 percent in Rainbow Park from 1993 through 2019. In 11 of the 15 cross sections examined, vertical accretion of sediment on the floodplain and lateral accretion of sediment on the channel margins led to net sediment deposition and a loss of cross-sectional area. Mean changes in bed elevations showed slight erosion; however, bed elevations were considered stable within the bounds of measurement uncertainty and annual variability within the study area.
These results show that channel change has continued to occur in these alluvial segments of the Green River from 1993 through 2019, with the dominant changes including sediment deposition and channel narrowing. Although changes in the operations of Flaming Gorge Dam have occurred, these changes have had little effect on flood peak or duration in the segment of the Green River downstream from its confluence with the Yampa River. Instead, ongoing channel change is likely driven by the amount of sediment supplied from the Yampa River, the duration and magnitude of the combined annual spring snowmelt flood from both the Green River upstream from the Yampa River confluence and the Yampa River, and the capacity of this flood to convey the supplied sediment through these wider alluvial reaches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255078","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Dean, D.J., Grams, P.E., Sartain, S.L., Leonard, C.M., Griffiths, R.E., Unema, J.A., Topping, D.J., and Schmidt, J.C., 2025, Channel and floodplain cross-section and bed-elevation analyses of the Green River in Echo, Island, and Rainbow Parks, Dinosaur National Monument, Colorado and Utah: U.S. Geological Survey Scientific Investigations Report 2025–5078, 36 p., https://doi.org/10.3133/sir20255078.","productDescription":"Report: vii, 36 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-156670","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":497786,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118910.htm"},{"id":496222,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20255075","text":"Scientific Investigations Report 2025-5075","description":"SIR 2025-5075","linkHelpText":"- Controls on sediment transport and storage in the Little Snake, Yampa, and Green Rivers in the vicinities of Dinosaur National Monument and Ouray National Wildlife Refuge, Colorado and Utah, with implications for fish habitat in the middle Green River"},{"id":496221,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13MZCVJ","text":"USGS data release","description":"Dean, D.J., Grams, P.E., Griffiths, R.E., Unema, J.A., Sartain, S.L., and Topping, D.J., 2024, Channel and floodplain cross-section and bed-elevation data for the Green River in Echo, Island, and Rainbow Parks, Dinosaur National Monument, Colorado and Utah: U.S. Geological Survey data release, https://doi.org/10.5066/P13MZCVJ","linkHelpText":"Channel and floodplain cross-section and bed-elevation data for the Green River in Echo, Island, and Rainbow Parks, Dinosaur National Monument, Colorado and Utah"},{"id":496220,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5078/images"},{"id":496219,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5078/sir20255078.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5078 XML"},{"id":496218,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255078/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5078 HTML"},{"id":496217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5078/sir20255078.pdf","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5078 PDF"},{"id":496216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5078/coverthb.jpg"}],"country":"United States","state":"Colorado, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.5,\n 40.75\n ],\n [\n -109.5,\n 40\n ],\n [\n -108,\n 40\n ],\n [\n -108,\n 40.75\n ],\n [\n -109.5,\n 40.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Southwest Biological Science Center
U.S. Geological Survey
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The transport of sand and finer sediment in the Yampa and Green river network is typically in disequilibrium with the local sediment supply because of the partial decoupling of the sources of water and sediment: most of the water is supplied farther upstream than most of the sediment. This decoupling leads to sand being transported in the main-stem rivers as elongating sand waves following sand resupply during tributary floods. Because of the large amount of sand supplied to the Yampa River by the Little Snake River, Yampa River annual floods generate sand waves that migrate downstream in the Green River causing longitudinal patterns in bed-sand grain size that, in turn, lead to large spatial changes in sand transport. These changes in bed-sand grain size dominate over changes in water discharge in regulating sand transport in the sand-bedded reaches of these rivers. Furthermore, at any given discharge, these changes in bed-sand grain size dominate over all other processes in regulating sand transport in both sand- and gravel-bedded reaches of these rivers. Consequently, erosion or deposition of sand, and the associated changes in fish habitat in the Uinta Basin segment of the Green River are only indirectly related to Green River discharge and Flaming Gorge Dam operations. Owing to the longitudinal patterns of bed-sand grain size associated with the downstream migration of sand waves generated by the Yampa River, a multi-year sequence of large, and likely slightly declining, annual floods on the Yampa River is the probable mechanism that increases backwater fish habitat in the Uinta Basin segment of the Green River.
Cross-section resurveys indicate that the Uinta Basin (Jensen to Ouray) segment of the Green River has undergone sand erosion caused by slight channel widening since the 1990s (a channel response in opposition to that observed farther downstream in Canyonlands National Park during this period). These resurveys indicate that sand deposition leads to a decrease in channel complexity whereas sand erosion generally leads to an increase in channel complexity. The backwaters used as native fish nursery habitat consist of deep pools downstream from and adjacent to large bank-attached sandbars; thus, more extensive backwater habitat equates to greater channel complexity. The generation of the sand wave during the first large Yampa River flood in a sequence (that is, the year-1 flood) causes fining of the bed sand near Jensen. The downstream coarsening associated with bed sand that is finer near Jensen than downstream near Ouray causes a downstream decrease in sand transport in the Uinta Basin segment, leading to net sand deposition and decreased channel complexity. Continued downstream migration of this sand wave during the following year’s annual flood (that is, the year-2 flood) then causes downstream fining, leading to erosion of sand and increased channel complexity in this segment.
Although the year-1 Yampa River flood supplies the sand and deposits the large sandbars required to form backwaters, and thereby makes possible future backwater habitat, these floods cause a temporary reduction in backwater habitat in the Uinta Basin segment because they tend to cause net sand deposition. It is the subsequent out-year Yampa River floods of likely equal or lesser magnitude that maintain or increase backwater habitat because these are the floods that convey sand through or erode sand from this segment. These typically smaller out-year Yampa River floods rework the sandbars deposited during the year-1 annual flood, thereby leading to the increases in both backwater area and volume that have been measured upon recession of these floods. Although artificial floods released from Flaming Gorge Dam might be used to simulate the habitat maintenance achieved by out-year Yampa River floods, the limited sand supply and stage associated with such dam releases precludes their use as a replacement for the sandbar-depositing role of year-1 Yampa River floods that is a prerequisite for backwater formation in the Uinta Basin segment of the Green River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255075","usgsCitation":"Topping, D.J., Griffiths, R.E., Unema, J.A., and Dean, D.J., 2025, Controls on sediment transport and storage in the Little Snake, Yampa, and Green Rivers in the vicinities of Dinosaur National Monument and Ouray National Wildlife Refuge, Colorado and Utah, with implications for fish habitat in the middle Green River: U.S. Geological Survey Scientific Investigations Report 2025–5075, 117 p., https://doi.org/10.3133/sir20255075.","productDescription":"Report: xiii, 117 p.; Data Release","numberOfPages":"117","onlineOnly":"Y","ipdsId":"IP-143069","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":497642,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255075/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5075 HTML"},{"id":496088,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5075/sir20255075.XML","description":"SIR 2025-5075 XML"},{"id":496089,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5075/images"},{"id":496084,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5075/coverthb.jpg"},{"id":496086,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5075/sir20255075.pdf","text":"Report","size":"15.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5075 PDF"},{"id":496223,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20255078","text":"Scientific Investigations Report 2025-5078","description":"Dean, D.J., Grams, P.E., Sartain, S.L., Leonard, C.M., Griffiths, R.E., Unema, J.A., Topping, D.J., and Schmidt, J.C., 2025, Channel and floodplain cross-section and bed-elevation analyses of the Green River in Echo, Island, and Rainbow Parks, Dinosaur National Monument, Colorado and Utah: U.S. Geological Survey Scientific Investigations Report 2025–5078, 36 p., https://doi.org/10.3133/sir20255078.","linkHelpText":"- Channel and floodplain cross-section and bed-elevation analyses of the Green River in Echo, Island, and Rainbow Parks, Dinosaur National Monument, Colorado and Utah"},{"id":496242,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231070","text":"Open-File Report 2023-1070","description":"Griffiths, R.E., Topping, D.J., Leonard, C., and Unema, J.A., 2024, Resurvey of cross sections on the Yampa and Little Snake Rivers in Lily and Deerlodge Parks, Colorado: U.S. Geological Survey Open-File Report 2023–1070, 12 p., https://doi.org/10.3133/ofr20231070.","linkHelpText":"- Resurvey of cross sections on the Yampa and Little Snake Rivers in Lily and Deerlodge Parks, Colorado"},{"id":496241,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.1029/2017JF004534","text":"Journal article","description":"Topping, D.J., Mueller, E.R., Schmidt, J.C., Griffiths, R.E., Dean, D.J., and Grams, P.E., 2018, Long-term evolution of sand transport through a river network—Relative influences of a dam versus natural changes in grain size from sand waves: Journal of Geophysical Research—Earth Surface, v. 123, no. 8, p. 1879–1909, https://doi.org/10.1029/2017JF004534.","linkHelpText":"- Long-term evolution of sand transport through a river network—Relative influences of a dam versus natural changes in grain size from sand waves"},{"id":496090,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ND61HI","text":"USGS data release","description":"Griffiths, R.E., Kohl, K.A., and Unema, J.A., 2023, Surveyed coordinates and elevations in a 2020 resurvey of previously established cross sections on the Green River between Jensen and Ouray, Utah: U.S. Geological Survey data release, https://doi.org/10.5066/P9ND61HI.","linkHelpText":"Surveyed coordinates and elevations in a 2020 resurvey of previously established cross sections on the Green River between Jensen and Ouray, Utah"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Green River, Little Snake River, Yampa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.75,\n 40.75\n ],\n [\n -109.75,\n 40\n ],\n [\n -108,\n 40\n ],\n [\n -108,\n 40.75\n ],\n [\n -109.75,\n 40.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
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The U.S. Geological Survey is working with Federal land management agencies to develop a series of planned structured science syntheses to support environmental effects analyses that agencies conduct under the National Environmental Policy Act (NEPA). This report synthesizes science information relevant to environmental effects analyses concerned with potential increases in the distribution and abundance of invasive annual grasses (IAGs) from proposed vegetation treatments for habitat restoration. The focal environments for this synthesis are rangelands in the intermontane valleys of Montana and the northern Great Plains of Montana, North Dakota, and South Dakota. The synthesis is organized to align with the standard elements of NEPA analyses and provides information on relevant scientific studies, data availability, analysis methods, and mitigation measures. We found that the likelihood of increasing IAGs from vegetation treatments depends on treatment type and environmental context. In sagebrush ecosystems of the focal region, prescribed fire often reduces or does not increase IAGs. Treatments that cause soil disturbances, such as mechanical removals of sagebrush or firebreak constructions, are more likely to increase IAGs than other treatments. Herbicides applied to reduce sagebrush cover have not increased the proportion of IAGs in the plant community. Temperature and precipitation have been strong factors in determining IAG responses to vegetation treatments in sagebrush ecosystems of the focal region, where more precipitation in spring and summer likely provides a competitive edge to native, perennial grasses more than winter annual grasses like Bromus tectorum L. (cheatgrass). In grasslands, prescribed fire often reduces IAGs, but effects depend on the abundance of native species and are often short lived. Mowing can increase or decrease IAGs in grassland ecosystems. Grassland site conditions, such as southeast-facing slopes, sandier or rockier sites, or lower native species cover or richness affect the likelihood of invasion by annual grasses. Maintaining adequate cover of perennial vegetation creates rangelands that are resistant and resilient to annual grass invasions. Managers can minimize invasion potential by focusing on treatment type, placement, and seasonal timing. Herbicides also can provide effective mitigation, especially in combination with other controls such as prescribed fire or grazing. This report can be incorporated by reference in NEPA documentation, included in a project record, or provide a general reference for understanding and identifying literature about increases in IAGs associated with vegetation treatments in rangelands in this focal region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255058","collaboration":"Prepared in cooperation with the Bureau of Land Management and the U.S. Fish and Wildlife Service","usgsCitation":"Johnston, A.N., Wood, D.J.A., Ebenhoch, K.G., Rutherford, T.K., Maxwell, L.M., and Carter, S.K., 2025, Potential risks of vegetation treatments to introduce and increase invasive annual grasses in rangelands of Montana, North Dakota, and South Dakota—A science synthesis to inform National Environmental Policy Act analyses: U.S. Geological Survey Scientific Investigations Report 2025–5058, 36 p., https://doi.org/10.3133/sir20255058.","productDescription":"ix, 36 p.","onlineOnly":"Y","ipdsId":"IP-157949","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":496260,"rank":9,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, Northern Rocky Mountain Science Center
U.S. Geological Survey
2327 University Way, Suite 2
Bozeman, MT 59715
Between 1980 and 1986, the U.S. Geological Survey made a series of 1:2,000-scale topographic contour maps from aerial photographic surveys to monitor the eruption. These maps were made for operational purposes and were not intended for publication. Since then, advances in technology made it possible to digitize the original, highly detailed hardcopy maps and derive new digital data elevation models of the surface of the lava dome. These digital elevation models allow for the visualization of the progression of the eruption and reveal the rubbly, chaotic surface of the lava flows and dome. Additionally, these new data help fill gaps in the long-term record of topographic changes that have occurred at the volcano since the May 18, 1980, eruption.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip262","usgsCitation":"Bard, J.A., Friedle, C.M., Bartee, L., Dierker, B.C., Ganick, J.M., Gregory, N.M., Hill, K.R., Klug, J.G., Kruger, A., Mooney, D.T., Morrison, R.T., Rojas, I.I., Rollo, P., Stanton, S.A., Stewart, B., Stuhlmuller, B.E., Zyla, A.D., 2025, Rebuilding a volcano one lava flow at a time—Visualizing the lava dome-building eruption in the crater of Mount St. Helens, 1982–1986: U.S. Geological Survey General Information Product 262, https://doi.org/10.3133/gip262.","productDescription":"1 p.","onlineOnly":"N","ipdsId":"IP-180615","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":496232,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/262/coverthb.jpg"},{"id":496233,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/262/gip262.pdf","text":"Document","size":"33.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 262"},{"id":497787,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118911.htm"}],"country":"United States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.38015899040656,\n 46.32565684366381\n ],\n [\n -122.38015899040656,\n 46.080226964619754\n ],\n [\n -122.00665168620951,\n 46.080226964619754\n ],\n [\n -122.00665168620951,\n 46.32565684366381\n ],\n [\n -122.38015899040656,\n 46.32565684366381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Science Analytics and Synthesis Program
U.S. Geological Survey
P.O. Box 25046, Mail Stop 302
Denver, CO 80225
This report describes the techniques and methods for computing the mean-channel velocity and discharge using the entropy-based probability concept (probability concept). The method is an alternative to or augments standard streamgaging methods adopted by the U.S. Geological Survey (USGS). Although sensor technology for measuring the mean velocity and discharge has advanced, standard streamgaging and computational methods have remained relatively unchanged since the USGS established its first streamgage at the Rio Grande at Embudo, New Mexico in 1889.
Standard streamgaging methods rely on integrating velocities and depths measured at multiple verticals at a channel cross section (standard cross section) to compute a discharge. The probability concept computes discharge at a single vertical (y-axis) using the ratio of the mean-channel velocity (mean velocity) and maximum velocity, the measured maximum velocity, and the area as a function of stage at the standard cross section. Proper siting and operation and maintenance are required. If siting is conducted appropriately, the probability concept parameters and the y-axis stationing will be similar for different streamflow conditions. The timing of operation and maintenance visits should be based on hydrologic and meteorologic occurrences and seasonality and should capture low, medium, high, and opportunistic streamflow conditions.
Advantages of the probability concept are the capacity to (1) compute discharge time series immediately after streamgage siting, (2) compute discharge for complex streamflow conditions that cannot be quantified by stage-discharge methods, (3) augment time-series data where gaps exist, and (4) integrate with surface velocity sensors such as Doppler velocity radars and cameras, which are not subject to damage caused by ice, debris, and flood flows. Potential sources of bias in discharge derived from the probability concept include (1) rain, (2) wind, and (3) geomorphologic and hydraulic instabilities. Recommendations to address these biases are provided.
This report guides users through the steps to parameterize the probability concept, process field data, and compute the mean velocity and discharge using the probability concept.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm3A26","usgsCitation":"Fulton, J.W., Engel, F.L., Eggleston, J.R., and Chiu, C.-L., 2025, Computing discharge using the entropy-based probability concept: U.S. Geological Survey Techniques and Methods book 3, chap. A26, 66 p., https://doi.org/10.3133/tm3A26.","productDescription":"Report: viii, 66 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-138301","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":496208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/03/a26/tm3a26.pdf","text":"Report","size":"6.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2-A26"},{"id":496210,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/03/a26/Appendix_3_Wind_Bias.csv","text":"Appendix 3","size":"8.0 KB","linkFileType":{"id":7,"text":"csv"},"description":"T and M 2-A26 Appendix 3","linkHelpText":"Correction for Wind Bias"},{"id":496207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/03/a26/coverthb.jpg"}],"contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
This report describes the steps and the theory to compute the speed and flow of water in streams using the probability concept.
","publicationDate":"2025-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engel, Frank L. 0000-0002-4253-2625","orcid":"https://orcid.org/0000-0002-4253-2625","contributorId":218208,"corporation":false,"usgs":true,"family":"Engel","given":"Frank","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eggleston, Jack R. 0000-0001-6633-3041","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":204628,"corporation":false,"usgs":true,"family":"Eggleston","given":"Jack R.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":949520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chiu, Chao-Lin","contributorId":361821,"corporation":false,"usgs":false,"family":"Chiu","given":"Chao-Lin","affiliations":[{"id":86362,"text":"Emeritus - University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":949521,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272101,"text":"70272101 - 2025 - Developing empirical fragility functions for lava flow building damage","interactions":[],"lastModifiedDate":"2026-02-10T13:33:34.995824","indexId":"70272101","displayToPublicDate":"2025-09-29T09:58:07","publicationYear":"2025","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":"Developing empirical fragility functions for lava flow building damage","docAbstract":"Fragility functions are vital tools in volcanic risk assessments to evaluate the probability of damage to structures at given hazard intensities. Traditionally, lava flow damage is assumed to be binary, whereby in contact with lava results in complete destruction and not in contact with lava remains undamaged. However, past studies present examples of structures exhibiting resistance to lava and not destruction. Developing empirical fragility functions requires damage data. We collected data from field campaigns and aerial imagery to assess damage across three case studies: 2021 Cumbre Vieja lava flows, La Palma, 2018 lower East Rift Zone lava flows, Kīlauea, Hawaiʻi, and 2014–2015 Fogo lava flows, Cabo Verde. This involved manually digitising 4545 structure footprints and assigning types and damage state categories to 10,439 structures. Of the impacted structures, 6 % were classified as damaged (not destroyed). Using this dataset, we developed the first empirical fragility functions from multiple eruptions for assessing lava flow damage, for masonry, metal, and timber building types. The functions reflect the probability of a structure sustaining any of six levels of damage severity given final lava flow thickness. Lava flows thicker than 6 m generally destroy structures, but some structures, particularly masonry buildings or those with a circular shape, can resist flows thinner than 6 m. The fragility functions reflect that lava flow impacts are not binary, and that structure types and shape are important. These empirical fragility functions can differentiate between structural attributes, thereby enhancing damage, risk, and impact assessments for lava flows, for places with similar building types.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2025.105844","usgsCitation":"Meredith, E.S., Jenkins, S.F., Hayes, J.L., Chee, D.J., Lallemant, D., Deligne, N.I., Meletlidis, S., and Felpeto, A., 2025, Developing empirical fragility functions for lava flow building damage: International Journal of Disaster Risk Reduction, no. 130, 105844, 19 p., https://doi.org/10.1016/j.ijdrr.2025.105844.","productDescription":"105844, 19 p.","ipdsId":"IP-178627","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":496718,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2025.105844","text":"Publisher Index Page"},{"id":496503,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"130","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meredith, Elinor S. 0000-0002-3869-1180","orcid":"https://orcid.org/0000-0002-3869-1180","contributorId":270269,"corporation":false,"usgs":false,"family":"Meredith","given":"Elinor","email":"","middleInitial":"S.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":950066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Susanna F. 0000-0002-7523-1423","orcid":"https://orcid.org/0000-0002-7523-1423","contributorId":270268,"corporation":false,"usgs":false,"family":"Jenkins","given":"Susanna","email":"","middleInitial":"F.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":950067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Josh L. 0000-0001-7099-1063","orcid":"https://orcid.org/0000-0001-7099-1063","contributorId":270275,"corporation":false,"usgs":false,"family":"Hayes","given":"Josh","email":"","middleInitial":"L.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":950068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chee, Denny J.","contributorId":362125,"corporation":false,"usgs":false,"family":"Chee","given":"Denny","middleInitial":"J.","affiliations":[{"id":16631,"text":"Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":950069,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lallemant, David","contributorId":334346,"corporation":false,"usgs":false,"family":"Lallemant","given":"David","affiliations":[{"id":16631,"text":"Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":950070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deligne, Natalia I. 0000-0001-9221-8581","orcid":"https://orcid.org/0000-0001-9221-8581","contributorId":257389,"corporation":false,"usgs":true,"family":"Deligne","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":950071,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meletlidis, Stravos 0000-0002-4629-0344","orcid":"https://orcid.org/0000-0002-4629-0344","contributorId":362128,"corporation":false,"usgs":false,"family":"Meletlidis","given":"Stravos","affiliations":[{"id":86475,"text":"Centro Geofísico de Canarias, Instituto Geográfico Nacional, Spain","active":true,"usgs":false}],"preferred":false,"id":950072,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Felpeto, Alicia 0000-0002-8152-7394","orcid":"https://orcid.org/0000-0002-8152-7394","contributorId":362129,"corporation":false,"usgs":false,"family":"Felpeto","given":"Alicia","affiliations":[{"id":86477,"text":"Observatorio Geofísico Central, Instituto Geográfico Nacional, Spain","active":true,"usgs":false}],"preferred":false,"id":950073,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70271933,"text":"sir20255083 - 2025 - Regional hydraulic geometry characteristics of stream channels in the Boston Mountains in Arkansas","interactions":[],"lastModifiedDate":"2026-02-03T16:07:29.146923","indexId":"sir20255083","displayToPublicDate":"2025-09-29T08:02:22","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5083","displayTitle":"Regional Hydraulic Geometry Characteristics of Stream Channels in the Boston Mountains in Arkansas","title":"Regional hydraulic geometry characteristics of stream channels in the Boston Mountains in Arkansas","docAbstract":"Many stream-channel infrastructure, habitat enhancement, and restoration projects are undertaken on streams throughout Arkansas by Federal, State, and local agencies as well as by private organizations and businesses with limited data on local geomorphology and streamflow conditions. Equations that relate drainage area above stable stream reaches to the basin characteristics, bankfull streamflow, and the associated channel dimensions can be used to estimate stream conditions. These equations, along with streambed material particle information, provide information that can be used to improve stream-channel projects. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, Little Rock District, completed a study to develop these equations for streams in the Boston Mountains in Arkansas.
Fourteen U.S. Geological Survey streamgages and stream reaches located in the Boston Mountains were selected for analysis. Geomorphic parameters of streams, including the mean bankfull channel dimensions (cross-sectional area, top width, mean depth, and streamflow), and the contributing drainage areas were investigated. Streambed materials were collected at eight of these sites to develop descriptive statistics of the streambed particle-size distributions and percentages of substrate type. Stream reaches at each study site were classified to Rosgen level II stream type based on the averages of stream-channel metrics collected from site cross sections and profiles. Of the 14 selected Boston Mountain stream reaches, 7 were classified as B-type streams, and 7 were classified as C-type streams. For these streams, the significant differences in measured parameters between stream types were that the B-type streams had greater depth, hydraulic radii, and bar D50 and D85 particle sizes, while C-type streams had greater watershed slopes. Streambed material particle size decreased with mean drainage basin elevation and decreased with increasing entrenchment ratios. Bar sediment size exhibited decreasing size with increasing sinuosity. Regional hydraulic geometry curves were constructed for the streams in the Boston Mountains by plotting measured bankfull geometry dimensions from stable reaches and the associated bankfull streamflow against the contributing drainage area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255083","issn":"2328-0328","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Little Rock District","usgsCitation":"Kroes, D.E., Ruhl-Whittle, L., Pieri, A.C., and Pugh, A.L., 2025, Regional hydraulic geometry characteristics of stream channels in the Boston Mountains in Arkansas: U.S. Geological Survey Scientific Investigations Report 2025–5083, 28 p., https://doi.org/10.3133/sir20255083.","productDescription":"Report: vii, 28 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-166842","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":497784,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118903.htm"},{"id":496007,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XARR7X","text":"USGS Data Release","linkHelpText":"- Hydraulic geometry of stream channels in the Boston Mountains of Arkansas"},{"id":496006,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255083/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5083 HTML"},{"id":496005,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5083/sir20255083.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2025-5083 XML"},{"id":496004,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5083/sir20255083.pdf","size":"2.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5083"},{"id":496003,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5083/images"},{"id":496002,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5083/coverthb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Boston Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95,\n 36.25\n ],\n [\n -95,\n 35.5\n ],\n [\n -91.5,\n 35.5\n ],\n [\n -91.5,\n 36.25\n ],\n [\n -95,\n 36.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Komatiitic volcanic rocks are important hosts of Ni sulfide mineralization and record early Earth evolution; however, those in the well-studied Archean Wyoming Province have received little attention. Here, we elucidate the timing and petrogenesis of the Bradley Peak komatiitic volcanic rocks using field and textural observations, geochronology, and geochemistry. Detrital and igneous zircon U-Pb ages for two samples from previously undated units support published age determinations, placing the eruption age at 2.72 Ga. Stratigraphy of the volcanic flows was mapped and 36 samples including cumulates, greenschists, and spinifex-textured rocks were collected. Whole-rock geochemistry was used to classify the spinifex-textured samples as Al-undepleted komatiitic basalts (11–17 wt% MgO). Platinum-group element concentrations (n = 25) are like those in global Al-undepleted komatiitic basalts, and PGE/Ti ratios do not indicate the volcanic flows likely host sulfide mineralization. Initial εNd values of −0.5 to +4.7 (n = 16), indicate that these lavas were derived from a depleted mantle source and have negligible evolved crust contamination. The primary magma to the komatiitic basalt flows is estimated to have had 19 wt% MgO and be derived from ∼15 to 25 % mantle partial melting at 3–4 GPa. Trace element chemistry and thermodynamic modeling suggest the primary melt assimilated local banded iron formation. Although the Bradley Peak komatiitic basalts do not contain positive evidence of magmatic sulfide deposits, depleted Au in the flows suggests they could be source rocks for nearby orogenic gold deposits.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2025.107929","usgsCitation":"Zieman, L.J., Jenkins, M., and Poletti, J.E., 2025, Petrogenesis and mineralization potential of spinifex komatiitic basalts in the Bradley Peak greenstone terrane, Wyoming Province: Precambrian Research, v. 430, 107929, 16 p., https://doi.org/10.1016/j.precamres.2025.107929.","productDescription":"107929, 16 p.","ipdsId":"IP-180102","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":496332,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.precamres.2025.107929","text":"Publisher Index Page"},{"id":496269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bradley Peak greenstone terrane","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.0833,\n 42.25\n ],\n [\n -107.0833,\n 42.125\n ],\n [\n -106.9167,\n 42.125\n ],\n [\n -106.9167,\n 42.25\n ],\n [\n -107.0833,\n 42.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"430","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zieman, Lisa Joanne 0000-0002-0065-2565","orcid":"https://orcid.org/0000-0002-0065-2565","contributorId":345932,"corporation":false,"usgs":true,"family":"Zieman","given":"Lisa","email":"","middleInitial":"Joanne","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":949679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Michael 0000-0002-4261-409X mjenkins@usgs.gov","orcid":"https://orcid.org/0000-0002-4261-409X","contributorId":172433,"corporation":false,"usgs":true,"family":"Jenkins","given":"Michael","email":"mjenkins@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":949680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poletti, Jacob Evan 0000-0002-3091-1249","orcid":"https://orcid.org/0000-0002-3091-1249","contributorId":345933,"corporation":false,"usgs":true,"family":"Poletti","given":"Jacob","email":"","middleInitial":"Evan","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":949681,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272205,"text":"70272205 - 2025 - An expert elicitation to inform coastal management decision-making for mitigating future hazards","interactions":[],"lastModifiedDate":"2025-11-19T16:09:27.810179","indexId":"70272205","displayToPublicDate":"2025-09-27T10:00:26","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"An expert elicitation to inform coastal management decision-making for mitigating future hazards","docAbstract":"A scientific expert elicitation was conducted to address the feasibility of restoring coastal environments in response to future hazards to best meet management objectives. Subject matter experts produced probabilistic estimates of coastal change metrics used to evaluate decision objectives and alternatives informed by a stakeholder advisory group. Changes in salt marsh extents, storm surge flooding and barrier island morphology by the year 2050 were estimated for three scenarios of management actions (no action, interior headland restoration, beach and dune nourishment), while also considering the effects of future sea level rise (SLR). Collectively the participants were confident in their expectations of increased storm surge flooding with SLR, regardless of management interventions. Estimates of marsh response had large uncertainty, but experts generally hypothesized that marsh area would decrease with increasing SLR if no action was taken, especially in areas already experiencing marsh deterioration. There was agreement that dune heights and barrier island widths would decrease with SLR if no action was taken. Experts felt that beach and dune nourishment may reduce the amount of erosion under future SLR. All experts recognized the dynamic effects of SLR and feedback between bio-geo-physical processes that govern coastal systems. Participants agreed that size and location of management actions were important factors for influencing the coastal response. Expert elicitation is novel in the context of coastal management decision making and can be a useful tool for informing future scientific needs and providing rapid results to end users to inform reallocation of resources surrounding research and application.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2025.127447","usgsCitation":"Passeri, D., Richardson, M., Martin, J., Yurek, S., Alizad, K., Bilskie, M.V., Flocks, J., Frank-Gilchrist, D.P., Jenkins, R., Mickey, R.C., Palmsten, M.L., Smith, C.F., Smith, K., and Zeigler, S., 2025, An expert elicitation to inform coastal management decision-making for mitigating future hazards: Journal of Environmental Management, v. 394, 127447, 15 p., https://doi.org/10.1016/j.jenvman.2025.127447.","productDescription":"127447, 15 p.","ipdsId":"IP-180296","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":496748,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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stops","interactions":[],"lastModifiedDate":"2025-11-14T16:55:04.367089","indexId":"70272068","displayToPublicDate":"2025-09-27T09:51:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5408,"text":"Urban Climate","active":true,"publicationSubtype":{"id":10}},"title":"Hot stops, cool looks: Aesthetic solutions for thermal comfort at transit stops","docAbstract":"Increased urban heat intensifies thermal discomfort, particularly in critical public spaces such as transit stops. This study investigated the predictors of transit users' thermal perceptions in Denver, Colorado—a semi-arid city. Sixty bus stops spanning a gradient of land cover compositions were selected for study. Micrometeorological data, including thermal comfort indices, were collected alongside survey responses from 77 users at 31 unique stops. Survey responses captured thermal sensation votes (TSV) and thermal comfort votes (TCV) as well as aesthetic preference votes (APV) of bus stop structure. Ordinal forest analysis revealed that for both TSV and TCV, aesthetic preferences and thermal comfort indices were the most influential predictors of transit user thermal perception. Multiple ordered logistic regression further demonstrated that, for TSV, higher APV was associated with lower odds of rating a thermal environment as hot (OR = 0.664, p < 0.002) while increased Physiological Equivalent Temperature (PET) raised these odds (OR = 1.101, p < 0.006). An interaction analysis demonstrated that APV significantly moderated the effect of PET on TCV (interaction OR = 1.040, p < 0.041), suggesting that aesthetic preferences are significantly correlated with an alleviation of thermal discomfort under high heat stress. Bivariate analyses further indicated that bus stops with greater tree canopy cover (OR = 1.032, p < 0.025) and higher visible vegetation view factors (OR = 10.350, p < 0.022) were more likely to be rated as aesthetically pleasing. These findings underscore the importance of aesthetic preferences in transit stop planning for urban heat resiliency.
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This gap is partially due to the lack of nationally consistent data on tsunamigenic sources and associated return periods. This study addresses this issue and provides estimates of average annualized losses (AAL) for potential residential fatalities and capital stock losses associated with building damage (i.e., structural, non-structural, contents, and inventory damage) in the U.S. by curating tsunami-hazard information based on deterministic scenarios and probabilistic approaches, calculating potential losses, and estimating return periods where necessary. This assessment was done for the U.S. West Coast, Alaska, Hawaii, U.S. Pacific Territories, and U.S. Atlantic Territories. We estimate that earthquake-generated tsunamis that could affect these states and territories collectively represent $1 billion in potential AAL with 79 % of losses due to residential fatalities and 21 % of losses due to capital stock losses from building damage. We identify AAL variations based on county and county equivalents, states and territories, geographic regions, return periods, and departure-delay assumptions for evacuating residents. Results include high AAL values for potential fatalities in Puerto Rico and the U.S. Pacific Northwest region, high AAL values for potential building-related damage in Hawaii and California, and high building- and population-loss ratios for county equivalents in Alaska and U.S. territories.
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Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-5081","displayTitle":"Multidecadal Change in Pesticide Concentrations Relative to Human Health Benchmarks in the Nation’s Groundwater","title":"Multidecadal change in pesticide concentrations relative to human health benchmarks in the Nation’s groundwater","docAbstract":"Groundwater-quality trend assessments identify aquifers that are responding to changes in pesticide use and the compounds that may pose a threat to water availability. The U.S. Geological Survey has been monitoring pesticide concentrations in groundwater for 25 principal aquifers across the conterminous United States since 1993. The groundwater well locations represent a range of soils, climate, and landforms. The wells are used to monitor groundwater underlying selected agricultural and urban settings and groundwater used for domestic supply. This study examined changes in relative concentrations, defined here as the percentage of wells with pesticide concentrations exceeding a human health benchmark (HHB). HHBs used in this report are legally enforceable drinking-water standards and nonenforceable drinking water levels. Relative pesticide concentration increases may lead to decreased water availability, as restrictions may be put in place for groundwater used as a drinking-water source.
This study focused on concentration changes in 22 pesticides that were included in laboratory analysis from 1993 to 2023. The analysis and interpretation of these pesticide concentrations in groundwater have been separated into approximate decadal intervals (decade 1 (1993–2001), decade 2 (2002–12), and decade 3 (2013–22). For one pesticide, 1,2-dibromo-3-chloropropane (DBCP), concentration data were also collected in decade 4 (2023–onward).
Atrazine, deethylatrazine, alachlor, prometon, and simazine were 5 pesticides detected at moderate concentrations (greater than 10 percent of the HHB but less than or equal to the HHB). The percentage of wells that had groundwater pesticide concentrations in the moderate concentration category decreased from 7 percent in decade 1 to 2 percent in decade 3. The agricultural networks had the highest percentages of wells with moderate concentrations, and these percentages decreased from 13 percent in decade 1 to 4 percent in decade 3. Moderate concentrations in the urban networks decreased between decades 1 and 2 from 4 percent to 0 percent. No moderate concentrations occurred in the urban networks in decade 3. The percentage of wells with moderate concentrations in the domestic supply networks (1 percent) was the lowest of all the network types and did not change across the three decades. Moderate atrazine or deethylatrazine concentrations occurred across all three decades in aggregated ecoregions representing similar soils, climate, and landforms in the Semiarid West, Midcontinent, and Northeastern United States. Moderate concentrations of prometon, alachlor, and simazine also occurred in the Midcontinent, Arid West, Northeast, South Atlantic Gulf, and Semiarid West regions, but the moderate concentrations did not persist across all three decades.
DBCP was the only pesticide that exceeded its respective HHB, and the exceedances occurred across all four decades. In this report, the DBCP analysis was limited to one well network in the Central Valley, California. Agricultural use of DBCP was suspended in 1977. Forty-five years after being banned, DBCP concentrations were greater than the maximum contaminant level of 2 micrograms per liter (μg/L), but the number of exceedances decreased from 50 percent to 15 percent of the samples between 1993 and 2023.
This assessment of decadal groundwater pesticide concentrations provides a characterization of changes in water availability because of pesticide contamination in areas where groundwater is used as a drinking-water source. The results highlight the importance of continued long-term monitoring and assessment of groundwater pesticides to identify locations and specific compounds that may pose a potential risk to human health.
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Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, Virginia 20192
Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Florida’s Freshwater Fisheries Long-Term Monitoring Program was implemented in 2006 to track changes in freshwater fish populations and communities. As part of an evaluation of the program, this study used a simulation framework to assess trend detection for fish abundance and biomass indices and how sampling intensity (number of samples per year) and frequency (number of years) can influence detection of these trends.
Using count and weight data from fall electrofishing samples collected between 2006 and 2021 from 21 lakes, trends were simulated for annual mean count and weight over a 10-year period that ranged from −70% to +200%. In all, simulations were performed for seven game fish species and three management-relevant groups (large nongame, nonnative, and prey species). For sampling intensity, data were simulated with a range of sample sizes, from 10 to 40 electrofishing transects or the maximum number available for a given lake. For sampling frequency, data were simulated for different sampling schedules that included sampling 1 year followed by 1- or 2-year breaks (4–5 years of sampling in a 10-year period), sampling two consecutive years followed by 1- or 2-year breaks (6–7 years of sampling in a 10-year period), and sampling the first 5 years only.
Simulations based on weight and count data yielded similar results, but the effect of sampling frequency and sampling schedule varied by species, management group, and lake. Trend detection was lower and more variable when mean counts and weights of fish in electrofishing samples were low. Overall, at least a 60% increase or 40% decrease over a 10-year period was typically needed for trends in mean weight and count to be detected at least 80% of the time in at least half of the lakes. Increasing sampling intensity did not substantially improve trend detection for lower-magnitude changes, but reducing sample intensity to a minimum of 10 electrofishing transects per year would have a large negative effect on trend detection in almost all lakes. Detection of trends improved as the number of years sampled increased, but ideally, sampling should be spaced throughout the entire 10-year period to capture the full magnitude of change. Sampling every year generally resulted in better trend detection and for many species and groups was the only sampling schedule that resulted in all study lakes achieving the 80% target detection level. Of the alternative schedules considered, those involving 2 years of consecutive sampling outperformed those with only 1 year of sampling followed by a 1- or 2-year break.
Relatively large changes in mean count and weight were required to detect trends over a 10-year period, but there was no clear advantage of using count or weight data for monitoring purposes. Further, study results support the current sampling intensity, but trend detection is optimized at higher mean catch and weight values. Although sampling every year is ideal, an alternative schedule involving sampling two consecutive years with 1- or 2-year breaks could be considered in certain situations. These results will be important for informing future decisions regarding Florida’s Freshwater Fisheries Long-Term Monitoring Program and other monitoring initiatives.
Wind erosion and sediment transport continue to increase in many parts of the world, leading to decreased soil quality, accelerated snow-melt, respiratory diseases, and traffic accidents. The processes that control sediment transport are well understood at small scales of mm to m but are less well understood at larger scales of km to hundreds of km. Here we test four approaches aimed at improving the variance explained in sediment transport measured in a network of 52 horizontal sediment flux collecting devices located on the Colorado Plateau, USA. First, switching from a regression tree to random forest statistical analysis increased the variance in sediment transport explained from 58% to 91%. Soil moisture as a single variable explained 52% of variation in sediment flux, but had a negligible effect on a random forest model with season (Winter, Spring, Summer), wind speed, and seasonal total precipitation. Similarly, adding four years of new data to an existing five-year dataset or adding measurements of soil roughness and grazing failed to improve variance explained. By explaining 91% of the variance in sediment transport, our model provides baseline model for understanding sediment transport on the landscape scale. Dust flux networks in new regions would likely need to collect at least 300-500 samples to describe variation in sediment transport values using random forest analyses of the effects of season, wind speed, seasonal rain and vegetation type.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0333166","usgsCitation":"Kulmatiski, A., Ozturk, M., Bladen, K.K., Brahney, J., and Duniway, M.C., 2025, Season, wind speed, and seasonal rain are major drivers of a regional aeolian sediment transport model: PLoS ONE, v. 20, no. 9, e0333166, 15 p., https://doi.org/10.1371/journal.pone.0333166.","productDescription":"e0333166, 15 p.","ipdsId":"IP-175885","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498292,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0333166","text":"Publisher Index Page"},{"id":498100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah","volume":"20","issue":"9","noUsgsAuthors":false,"publicationDate":"2025-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Kulmatiski, Andrew","contributorId":210408,"corporation":false,"usgs":false,"family":"Kulmatiski","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":952968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ozturk, Mehmet mozturk@usgs.gov","contributorId":196300,"corporation":false,"usgs":false,"family":"Ozturk","given":"Mehmet","email":"mozturk@usgs.gov","affiliations":[],"preferred":false,"id":952969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bladen, Kelvyn K.","contributorId":364634,"corporation":false,"usgs":false,"family":"Bladen","given":"Kelvyn","middleInitial":"K.","affiliations":[{"id":86880,"text":"Department of Mathematics and Statistics, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":952970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brahney, Janice","contributorId":269810,"corporation":false,"usgs":false,"family":"Brahney","given":"Janice","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":952971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":952972,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272627,"text":"70272627 - 2025 - Tapwater-contaminant mixtures and risk in a biofuel-facility impacted private-well community","interactions":[],"lastModifiedDate":"2025-11-26T14:21:13.043317","indexId":"70272627","displayToPublicDate":"2025-09-26T08:14:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13794,"text":"Environmental Science: Water Research and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Tapwater-contaminant mixtures and risk in a biofuel-facility impacted private-well community","docAbstract":"We assessed private-well drinking water (DW) at the point of use (i.e., tapwater, TW) within a rural Nebraska community around a state-closed biofuel facility, which used pesticide-treated corn seed as feedstock for ethanol production. Organic (485), inorganic (34), and microbial (13) analytes were assessed at 15 locations in June 2022, to evaluate the relative contribution of facility-consistent pesticides (seed-treatment fungicides and insecticides) to overall TW-contaminant exposures and predicted human-health risks. Thirty-three organics (12 pesticides) and 28 inorganics were detected, the former including the fungicide sedaxane, insecticide chlorantraniliprole, and multiple neonicotinoid insecticides/degradates, all consistent with seed treatment and respective biofuel-facility waste. Assessment of pesticides only at extant point-of-use (POU) treatment taps at three sites demonstrated complete elimination of all TW-pesticide detections. Based on detection of maximum pesticide concentrations in a home located downstream along a creek capturing facility runoff, pesticides only were assessed in January 2023 again at this home and at three adjacent locations, confirming results at the former and documenting decreasing TW-pesticide concentrations, including neonicotinoids, with increasing distance from the creek. Human-health DW benchmarks are not available for many detected pesticides, including the detected fungicide and insecticides, but precautionary screening levels were exceeded frequently due to multiple inorganics. The results indicate that exposures to multiple (median: 4.5; range: 1–7) co-occurring TW contaminants of potential human-health concern are common, warranting consideration of point-of-entry or POU treatment(s) throughout the community to reduce or eliminate unrecognized exposures to TW contaminants, including facility-associated pesticides in down-gradient locations. More broadly, results emphasize the importance of continued characterization of private-TW exposures, employing a environmentally informative analytical scope, to identify and mitigate risks of unrecognized exposures in private-well-dependent rural communities.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/d5ew00490j","usgsCitation":"Bradley, P., Meppelink, S.M., Romanok, K., Schreiner, M., Smalling, K., Bartelt-Hunt, S.L., Densmore, B., Gordon, S.E., Loftin, K., McCleskey, R., Rogan, E.G., Rus, D., and Snow, D.D., 2025, Tapwater-contaminant mixtures and risk in a biofuel-facility impacted private-well community: Environmental Science: Water Research and Technology, v. 11, p. 2572-2594, https://doi.org/10.1039/d5ew00490j.","productDescription":"23 p.","startPage":"2572","endPage":"2594","ipdsId":"IP-178170","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":496935,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1039/d5ew00490j","text":"Publisher Index Page"},{"id":496898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","county":"Saunders County","city":"Mead","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.667,\n 41.25\n ],\n [\n -96.667,\n 41\n ],\n [\n -96.333,\n 41\n ],\n [\n -96.333,\n 41.25\n ],\n [\n -96.667,\n 41.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":205668,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":951024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meppelink, Shannon M. 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Nonetheless, predictions of future GHG fluxes under SLR remain uncertain. Unlike prior studies limited to controlled or single-site settings, we deploy cross-latitude “marsh-organ” designs in China to access GHG fluxes in mangroves and neighboring mudflats. Our findings show that SLR-stimulated CH4 emissions in mangroves could increase by 10% under RCP 4.5 and by 22% under RCP 8.5, relative to current sea level by 2100. Conversely, SLR decreases ecosystem respiration and N2O emissions by 35%–51% and 28%–36%, respectively, while net ecosystem productivity decreases by 12%–28% as SLR increases. Overall, our results forecast a 17%–30% decline in mangroves’ climate mitigation efficiency. We recommend focusing on non-CO2 GHG emissions from mangroves, as they may significantly offset climate mitigation capacity under climate change.
","language":"English","publisher":"CellPress","doi":"10.1016/j.crsus.2025.100520","usgsCitation":"Qiao, P., Chen, L., Krauss, K.W., Guo, X., Xu, L., Gu, X., and Dong, Y., 2025, Rising sea level reduces carbon sequestration and CO2 and N2O fluxes while promoting CH4 flux from mangroves: Cell Reports Sustainability, v. 2, no. 9, 100520, 14 p., https://doi.org/10.1016/j.crsus.2025.100520.","productDescription":"100520, 14 p.","ipdsId":"IP-161820","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":496740,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.crsus.2025.100520","text":"Publisher Index Page"},{"id":496627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 80.57015756499158,\n 48.27012766647769\n ],\n [\n 75.42230729961719,\n 38.90640011346567\n ],\n [\n 81.13428892410838,\n 29.300749868715243\n ],\n [\n 97.2975108439603,\n 27.85568804464836\n ],\n [\n 96.34901460778869,\n 24.676885661757105\n ],\n [\n 100.11949002967802,\n 22.993015680009208\n ],\n [\n 114.32150013829988,\n 19.304266946071124\n ],\n [\n 122.53523908803231,\n 28.098180971448855\n ],\n [\n 126.06169784553734,\n 39.5634605005496\n ],\n [\n 134.67921396551895,\n 48.27720376673415\n ],\n [\n 129.04027802119387,\n 48.62328251469675\n ],\n [\n 125.11529031342536,\n 53.24939424723944\n ],\n [\n 118.16530899949154,\n 50.60211817144479\n ],\n [\n 116.5584041747958,\n 47.43180361776413\n ],\n [\n 80.57015756499158,\n 48.27012766647769\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Qiao, Peiyang","contributorId":303861,"corporation":false,"usgs":false,"family":"Qiao","given":"Peiyang","email":"","affiliations":[{"id":47617,"text":"Xiamen University, China","active":true,"usgs":false}],"preferred":false,"id":950482,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Luzhen","contributorId":194706,"corporation":false,"usgs":false,"family":"Chen","given":"Luzhen","email":"","affiliations":[],"preferred":false,"id":950483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken W. 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":205144,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":950484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guo, Xudong","contributorId":362441,"corporation":false,"usgs":false,"family":"Guo","given":"Xudong","affiliations":[{"id":63579,"text":"Xiamen University","active":true,"usgs":false}],"preferred":false,"id":950485,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Lian","contributorId":210946,"corporation":false,"usgs":false,"family":"Xu","given":"Lian","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":950486,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gu, Xiaoxuan","contributorId":296950,"corporation":false,"usgs":false,"family":"Gu","given":"Xiaoxuan","email":"","affiliations":[{"id":64251,"text":"College of the Environment and Ecology, Xiamen University","active":true,"usgs":false}],"preferred":false,"id":950487,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dong, Ying","contributorId":362442,"corporation":false,"usgs":false,"family":"Dong","given":"Ying","affiliations":[{"id":63579,"text":"Xiamen University","active":true,"usgs":false}],"preferred":false,"id":950488,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70271978,"text":"70271978 - 2025 - Validation of gridded precipitation datasets for flood-typing in select conterminous U.S. basins","interactions":[],"lastModifiedDate":"2025-09-29T15:03:21.41738","indexId":"70271978","displayToPublicDate":"2025-09-26T07:58:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Validation of gridded precipitation datasets for flood-typing in select conterminous U.S. basins","docAbstract":"Gridded precipitation datasets are required for flood-typing historical annual peak streamflow events in basins across the Conterminous United States. Selected gridded precipitation datasets were validated over the period 1981–2013 through comparisons with gage data from the NOAA Global Historical Climatology Network daily (GHCNd). The ability of each gridded dataset to capture the spatiotemporal characteristics of daily precipitation, including multi-day extremes over six selected regions, was assessed using the Kling-Gupta Efficiency metric and its component statistics. Overall, the Parameter-elevation Regression on Independent Slopes Model and Livneh-unsplit were found to best match the spatiotemporal variability of the GHCNd precipitation data, including extremes. The Analysis of Record for Calibration was found to be the third best-performing dataset in most regions except in the western U.S. The performance of reanalysis datasets evaluated appears to be poor compared to gage-based datasets. The reanalysis datasets might not be able to skillfully capture precipitation amounts at the correct location and time. Gage- and radar-based datasets were found to have relatively small biases (within +/-10% on an annual basis), while reanalysis datasets were found to have larger positive apparent biases, especially in winter and spring in most regions. It is possible that the apparent overestimation of winter and spring precipitation in the reanalysis datasets might reflect snow undercatch at gages especially in the central U.S. An overall deterioration of performance for correlation and/or variability was also observed for the summer season compared to other seasons in the reanalysis datasets. Various precipitation datasets might need to be used for flood-typing during different periods from the late 19th century to present. Datasets from different sources have different biases and errors and might have to be homogenized using downscaling and bias-adjustment methods. 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Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying landscape-level biodiversity change in an island ecosystem: A 50-year assessment of shifts in the Hawaiian avian community","docAbstract":"Hawaii has experienced profound declines in native avifauna alongside the introduction of numerous bird species. While site-specific population studies are common, landscape-level analyses of avian population dynamics are rare, particularly in island ecosystems. To address this gap, we used a density surface model to create a spatio-temporal projection of population densities and distributions across the Island of Hawai‘i, spanning nearly five decades (1976–2023). We incorporated environmental covariates of habitat, precipitation, and elevation, to further refine our projections. Our analysis encompassed nine native and six non-native bird species, inhabiting a range of ecological niches. We found five out of nine native species have declined in density and range size while four were stable. For non-native species, two were stable, one was decreasing, and three were increasing in density and range size. Our landscape projections can inform management by suggesting areas critical for habitat preservation and land acquisition for conservation, identifying where range fragmentation is occurring, and pinpointing locations of multi-species declines that are likely driven by a common cause. Our study demonstrates how long-term, landscape-level monitoring and analyses can advance understanding and addressing biodiversity loss, particularly in vulnerable tropical island ecosystems.
","language":"English","publisher":"Nordic Society Oikos","doi":"10.1002/ecog.07907","usgsCitation":"Bak, T., Fortini, L., Hunt, N., Banko, P.C., Schnell, L., and Camp, R.J., 2025, Quantifying landscape-level biodiversity change in an island ecosystem: A 50-year assessment of shifts in the Hawaiian avian community: Ecography, v. 2025, no. 11, e07907, 18 p., https://doi.org/10.1002/ecog.07907.","productDescription":"e07907, 18 p.","ipdsId":"IP-177022","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":496739,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecog.07907","text":"Publisher Index Page"},{"id":496624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.31156357630775,\n 20.24770610218596\n ],\n [\n -156.31156357630775,\n 18.872624778542928\n ],\n [\n -154.63317186924985,\n 18.872624778542928\n ],\n [\n -154.63317186924985,\n 20.24770610218596\n ],\n [\n -156.31156357630775,\n 20.24770610218596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2025","issue":"11","noUsgsAuthors":false,"publicationDate":"2025-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Bak, Trevor","contributorId":292157,"corporation":false,"usgs":false,"family":"Bak","given":"Trevor","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":950519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":950520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Noah","contributorId":355564,"corporation":false,"usgs":false,"family":"Hunt","given":"Noah","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":950521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":950522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schnell, Lena","contributorId":362454,"corporation":false,"usgs":false,"family":"Schnell","given":"Lena","affiliations":[{"id":86531,"text":"Center for the Environmental Management of Military Lands, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":950523,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":950524,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70272023,"text":"70272023 - 2025 - Unveiling coseismic deformation from differenced legacy aerial photography and modern lidar topography: The 1983 M6.9 Borah Peak earthquake, Idaho, USA","interactions":[],"lastModifiedDate":"2025-11-13T16:55:22.485744","indexId":"70272023","displayToPublicDate":"2025-09-25T10:48:59","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Unveiling coseismic deformation from differenced legacy aerial photography and modern lidar topography: The 1983 M6.9 Borah Peak earthquake, Idaho, USA","docAbstract":"The 1983 M6.9 Borah Peak, Idaho, earthquake is one of the largest historical normal fault earthquakes in the western United States. We quantified meter-scale vertical change along the 35 km-long rupture using topographic differencing of 1966 aerial imagery and 2019 lidar-derived data. The initial differencing results are largely obscured by horizontal and vertical georeferencing errors and flight-line stripes. Our error corrections are designed to be insensitive to the coseismic deformation and reduced error by 50%. We calculated vertical separation and resolved a maximum of 2.02 ± 0.46 m at Doublespring Pass. Our vertical separation measurements are generally consistent with those from prior studies using field data and post-earthquake topographic data. However, the differencing measurements are a few decimeters lower than these prior measurements, indicating that differencing can isolate historical from prehistoric earthquake deformation. Our study demonstrates that revisiting historical earthquakes can provide new insights into the magnitude and patterns of coseismic deformation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL115882","usgsCitation":"Scott, C.P., Reitman, N.G., and Bello, S., 2025, Unveiling coseismic deformation from differenced legacy aerial photography and modern lidar topography: The 1983 M6.9 Borah Peak earthquake, Idaho, USA: Geophysical Research Letters, v. 52, no. 18, e2025GL115882, 12 p., https://doi.org/10.1029/2025GL115882.","productDescription":"e2025GL115882, 12 p.","ipdsId":"IP-177417","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":496426,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl115882","text":"Publisher Index Page"},{"id":496410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Borah Peak","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.1667,\n 44.25\n ],\n [\n -114.1667,\n 44\n ],\n [\n -113.667,\n 44\n ],\n [\n -113.667,\n 44.25\n ],\n [\n -114.1667,\n 44.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"18","noUsgsAuthors":false,"publicationDate":"2025-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Chelsea P 0000-0002-3884-4693","orcid":"https://orcid.org/0000-0002-3884-4693","contributorId":248847,"corporation":false,"usgs":false,"family":"Scott","given":"Chelsea","email":"","middleInitial":"P","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":949752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":949753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bello, Simone","contributorId":360174,"corporation":false,"usgs":false,"family":"Bello","given":"Simone","affiliations":[{"id":85980,"text":"3Department of Sciences, University G. d’Annunzio Chieti-Pescara, 66100, Chieti, Italy","active":true,"usgs":false}],"preferred":false,"id":949754,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70272020,"text":"70272020 - 2025 - Fiber-imaged supershear dynamics in the 2024 Mw 7 Mendocino Fault earthquake","interactions":[],"lastModifiedDate":"2025-11-13T16:44:30.12897","indexId":"70272020","displayToPublicDate":"2025-09-25T10:38:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Fiber-imaged supershear dynamics in the 2024 Mw 7 Mendocino Fault earthquake","docAbstract":"Fault structure and rupture physics are deeply intertwined, and observations of this coupling are critical for understanding earthquake behavior. Rupture propagation is observable at fine scales using dense seismic networks. Fiber-optic sensing allows for long-term deployments of ultradense arrays that enable high-resolution measurements of infrequent, large earthquakes. We recorded the 2024 moment magnitude (Mw) 7 Mendocino Fault earthquake with a nearby fiber-optic array and imaged its behavior with seismic beamforming. The rupture propagated to the east at subshear velocity; stagnated near the Mendocino Triple Junction, a zone of structural complexity; and subsequently transitioned to supershear velocity. The correlation between source physics and structure shows how lithospheric heterogeneity affects first-order characteristics of earthquake ruptures. Our results also demonstrate the potential for fiber-optic sensing to improve real-time estimation of key parameters for early warning.
","language":"English","publisher":"AAAS","doi":"10.1126/science.adx6858","usgsCitation":"Atterholt, J.W., McGuire, J.J., Barbour, A.J., Stewart, C., and Moschetti, M.P., 2025, Fiber-imaged supershear dynamics in the 2024 Mw 7 Mendocino Fault earthquake: Science, v. 389, no. 6767, p. 1361-1365, https://doi.org/10.1126/science.adx6858.","productDescription":"5 p.","startPage":"1361","endPage":"1365","ipdsId":"IP-178222","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":496407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mendocino Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.75,\n 41.15\n ],\n [\n -126,\n 41.15\n ],\n [\n -126,\n 39.75\n ],\n [\n -123.75,\n 39.75\n ],\n [\n -123.75,\n 41.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"389","issue":"6767","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Atterholt, James William 0000-0003-1603-5518","orcid":"https://orcid.org/0000-0003-1603-5518","contributorId":361969,"corporation":false,"usgs":true,"family":"Atterholt","given":"James","middleInitial":"William","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":949742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":220939,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":949743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barbour, Andrew J. 0000-0002-6890-2452","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":215339,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":949744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Connie","contributorId":361970,"corporation":false,"usgs":false,"family":"Stewart","given":"Connie","affiliations":[{"id":65879,"text":"California State Polytechnic University, Humboldt","active":true,"usgs":false}],"preferred":false,"id":949745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":949746,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271972,"text":"70271972 - 2025 - Gas emissions from the Sulphur Bank Mercury Mine hydrothermal system, Clear Lake volcanic field, California","interactions":[],"lastModifiedDate":"2025-09-29T15:02:51.73324","indexId":"70271972","displayToPublicDate":"2025-09-25T09:57:50","publicationYear":"2025","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":"Gas emissions from the Sulphur Bank Mercury Mine hydrothermal system, Clear Lake volcanic field, California","docAbstract":"The Sulphur Bank Mercury Mine (SBMM) hydrothermal system offers insights into active degassing processes in the Clear Lake volcanic field (CLVF), a high-threat region based on its record of Holocene eruptions and proximity to populated areas. Here we present chemical and isotopic analyses of gas samples collected between 2015 and 2023, along with the first comprehensive CO2 flux survey of the SBMM area conducted in 2023. Sampled gases are CO2- and CH4-rich (≥84 and 6 mol% in dry gas, respectively) with high mantle-derived helium contributions (3He/4He = 6.54–7.86 RC/RA). Carbon isotopic compositions of CO2 (δ13C = −10.0 to −9.5 ‰) and CH4 (δ13C = −35.8 ‰) indicate mixed sources, with significant contributions from metamorphism of organic-rich Franciscan Complex rocks hosting the hydrothermal system. Modeling of gas compositions shows that scrubbing by interaction with air-saturated groundwater strongly influences observed compositional variability. From our CO₂ flux measurements, we estimate the deeply derived CO2 emission rate from the SBMM hydrothermal area (0.2 km2) at 240 t d−1, comparable to many quiescently degassing volcanoes worldwide. We also provide a first-order estimate of CH4 emissions at approximately 0.5 t d−1. Our findings establish crucial baseline data for future volcanic monitoring efforts, enhancing detection capabilities for potential changes in this active hydrothermal system. This work contributes to the broader understanding of volatile contributions from volcanic and metamorphic sources to the global carbon budget, while highlighting the strong influence of bedrock geology on gas compositions in the CLVF.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2025.108453","usgsCitation":"Lewicki, J.L., Peek, S., Clor, L., and Hunt, A.G., 2025, Gas emissions from the Sulphur Bank Mercury Mine hydrothermal system, Clear Lake volcanic field, California: Journal of Volcanology and Geothermal Research, v. 468, 108453, 11 p., https://doi.org/10.1016/j.jvolgeores.2025.108453.","productDescription":"108453, 11 p.","ipdsId":"IP-177603","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":496226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake Volcanic Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.93378541824276,\n 39.13626270834021\n ],\n [\n -122.93378541824276,\n 38.65\n ],\n [\n -122.25,\n 38.65\n ],\n [\n -122.25,\n 39.13626270834021\n ],\n [\n -122.93378541824276,\n 39.13626270834021\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"468","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lewicki, Jennifer L. 0000-0003-1994-9104 jlewicki@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-9104","contributorId":5071,"corporation":false,"usgs":true,"family":"Lewicki","given":"Jennifer","email":"jlewicki@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":949539,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":949540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clor, Laura E. 0000-0003-2633-5100","orcid":"https://orcid.org/0000-0003-2633-5100","contributorId":209969,"corporation":false,"usgs":true,"family":"Clor","given":"Laura E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":949541,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunt, Andrew G. 0000-0002-3810-8610 ahunt@usgs.gov","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":174135,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew","email":"ahunt@usgs.gov","middleInitial":"G.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":949542,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70272174,"text":"70272174 - 2025 - Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain","interactions":[],"lastModifiedDate":"2025-11-18T15:48:07.770771","indexId":"70272174","displayToPublicDate":"2025-09-25T09:44:05","publicationYear":"2025","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":"Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain","docAbstract":"Floodplain forests are being transformed by multiple pressures, prompting widespread management and restoration efforts. It is uncertain how disturbances, including hydrologic change, and management actions will interact to influence the ecology of these threatened forests.
This study examined the effects of alternative management and hydrologic regimes on forest succession at an Upper Mississippi River floodplain site with a restoration project in planning.
We used the spatially explicit forest landscape model, LANDIS-II, to simulate forest succession for 100 years under four hydrogeomorphic management scenarios, three forest management scenarios, and two scenarios of future hydrologic conditions. We evaluated changes in forest biomass and composition over time and assessed the relative importance of management actions and hydrologic change on succession.
Forest aboveground biomass decreased in all management-hydrology scenarios, especially in the wetter hydrological scenario. Intensified hydrogeomorphic and forest management scenarios reduced the magnitude and extent of biomass declines; however, they were unable to prevent overall declines in biomass or cause large shifts in tree species composition. Silver maple (Acer saccharinum) was projected to decrease in biomass, while increases in biomass were projected for several late-successional species including swamp white oak (Quercus bicolor). Among the factors influencing variation in biomass, forest management had the largest influence in the first 50 years of our simulations, but hydrological regime became the most important factor by the end of the century.
Our simulations indicate that management actions could play an important role in the conservation of floodplain forests, but their effectiveness will likely be limited if recent upward trends in flooding conditions in this system continue in the future. Thus, our results highlight both the potential benefits and limitations of management actions in the face of hydrologic change.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-025-02144-7","usgsCitation":"Trumper, M., De Jager, N.R., Van Appledorn, M., and Meier, A.R., 2025, Future forest conditions under alternative management and hydrological scenarios in the Upper Mississippi River floodplain: Landscape Ecology, v. 40, 186, 21 p., https://doi.org/10.1007/s10980-025-02144-7.","productDescription":"186, 21 p.","ipdsId":"IP-173189","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":496735,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-025-02144-7","text":"Publisher Index Page"},{"id":496589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","otherGeospatial":"Reno Bottoms study area, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.39940329445804,\n 43.68624998546463\n ],\n [\n -91.39940329445804,\n 43.44291861394157\n ],\n [\n -91.13016274447915,\n 43.44291861394157\n ],\n [\n -91.13016274447915,\n 43.68624998546463\n ],\n [\n -91.39940329445804,\n 43.68624998546463\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2025-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Trumper, Matthew L. 0000-0002-9881-7742","orcid":"https://orcid.org/0000-0002-9881-7742","contributorId":357508,"corporation":false,"usgs":true,"family":"Trumper","given":"Matthew","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":950315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meier, Andrew R.","contributorId":362320,"corporation":false,"usgs":false,"family":"Meier","given":"Andrew","middleInitial":"R.","affiliations":[{"id":16919,"text":"U.S. Army Corps of Engineers, St. Paul District","active":true,"usgs":false}],"preferred":false,"id":950316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}