This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggae353","usgsCitation":"Minson, S.E., 2024, Cross-fade sampling: Extremely efficient Bayesian inversion for a variety of geophysical problems: Geophysical Journal International, v. 239, no. 3, p. 1629-1649, https://doi.org/10.1093/gji/ggae353.","productDescription":"21 p.","startPage":"1629","endPage":"1649","ipdsId":"IP-158117","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":466901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggae353","text":"Publisher Index Page"},{"id":463412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"239","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":917419,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70258746,"text":"sir20245071 - 2024 - Pesticides in surface water downstream of and near agricultural and developed land in Hawai‘i, 2015–19","interactions":[],"lastModifiedDate":"2025-12-29T14:29:03.412249","indexId":"sir20245071","displayToPublicDate":"2024-09-25T13:15:46","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5071","displayTitle":"Pesticides in Surface Water Downstream of and Near Agricultural and Developed Land in Hawai‘i, 2015–19","title":"Pesticides in surface water downstream of and near agricultural and developed land in Hawai‘i, 2015–19","docAbstract":"Pesticides and pesticide degradates (herein referred to as pesticides) in surface water were assessed at 78 sites on 4 Hawaiian Islands (Kauaʻi, Oʻahu, Maui, and Island of Hawaiʻi) during 2015–19. Each site was downstream of or near agricultural land, developed land, or both. Most (58) sites were streams; the remaining sites were canals, ditches, anchialine pools, coastal ponds, and the nearshore ocean. Pesticides in water at each site were assessed by collection of one to four water samples, by a weeks-long deployment of a passive sampler, or both. Passive-sampler extracts and water samples, which consisted of fair-weather samples and storm samples, were analyzed for as many as 253 pesticides that consisted of 129 herbicides, 101 insecticides, and 23 fungicides.
A total of 117 pesticides were detected in water. Of these, 30 pesticides were detected at more than 20 percent of their assessment sites and thus were considered “common” pesticides. The common pesticides included 17 herbicides (ametryn, atrazine, bentazon, bromacil, diuron, hexazinone, metolachlor, propazine, simazine, triclopyr, prometryn, and degradates of atrazine [4], diuron, hexazinone, and prometryn); 8 insecticides (carbaryl, dinotefuran, fipronil, flubendiamide, imidacloprid, methoxyfenozide and 2 degradates of fipronil); and 5 fungicides (azoxystrobin, metalaxyl, propiconazole, a degradate of chlorothalonil, and a degradate of thiophanate-methyl and benomyl). Common pesticides typically were present more frequently in storm samples than in fair-weather samples. A mixture of two or more pesticides was detected in 86 percent of the water samples and in water during every passive-sampler deployment.
About 92 percent of all pesticide detections had concentrations less than 100 nanograms per liter. Pesticide concentrations were less than Federal aquatic-life benchmarks (ALBs) for vertebrates and typically were less than ALBs for invertebrates, nonvascular plants, and vascular plants. Acute ALBs were exceeded by acetochlor, atrazine, carbaryl, chlorpyrifos, cis-permethrin, diazinon, diuron, halosulfuron methyl, and imidacloprid in storm samples at one to four sites. ALBs for invertebrates were exceeded by clothianidin, diazinon, fipronil, and imidacloprid in fair-weather samples, during passive-sampler deployments, or both at 1 to 17 sites. Federal drinking water standards were available for only five pesticides detected in water, and the standard for atrazine was exceeded in one storm sample. Pesticide concentrations did not exceed any Federal human-health benchmarks, but many of the detected pesticides did not have a benchmark.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245071","collaboration":"Prepared in cooperation with the State of Hawai‘i Department of Agriculture","usgsCitation":"Johnson, A.G., Kennedy, J.J., and Alvarez, D.A., 2024, Pesticides in surface water downstream of and near agricultural and developed land in Hawai‘i, 2015–19: U.S. Geological Survey Scientific Investigations Report 2024–5071, 94 p., https://doi.org/10.3133/sir20245071.","productDescription":"Report: ix, 94 p.; 3 Data Releases","numberOfPages":"94","onlineOnly":"Y","ipdsId":"IP-154311","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":462624,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K782ZO","text":"USGS Data Release","description":"Johnson, A.G., and Alvarez, D.A., 2024, Pesticide, organic-contaminant, and wastewater-indicator results for passive samplers deployed in surface water and groundwater in Hawai‘i, 2015–19: U.S. Geological Survey data release, https://doi.org/10.5066/P9K782ZO.","linkHelpText":"Pesticide, organic-contaminant, and wastewater-indicator results for passive samplers deployed in surface water and groundwater in Hawai‘i, 2015–19"},{"id":462625,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95XY6ZY","text":"USGS Data Release","description":"Johnson, A.G., and Kennedy, J.J., 2024, Surface-water sites in Hawai‘i where pesticides were assessed, 2015–19: U.S. Geological Survey data release, https://doi.org/10.5066/P95XY6ZY.","linkHelpText":"Surface-water sites in Hawai‘i where pesticides were assessed, 2015–19"},{"id":462623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WX2RGU","text":"USGS Data Release","description":"Johnson, A.G. 2024, Pesticide concentrations in water samples and quality-control samples collected at surface-water sites in Hawai‘i, 2016–19: U.S. Geological Survey data release, https://doi.org/10.5066/P9WX2RGU.","linkHelpText":"Pesticide concentrations in water samples and quality-control samples collected at surface-water sites in Hawai‘i, 2016–19"},{"id":462622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5071/sir20245071.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5071/covrthb.jpg"},{"id":497954,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117499.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United 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\"}}]}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
National parks and preserves in the South Atlantic-Gulf Region contain valuable coastal habitats such as tidal wetlands and mangrove forests, as well as irreplaceable historic buildings and archeological sites located in low-lying areas. These natural and cultural resources are vulnerable to accelerated sea-level rise and escalating high tide flooding events. Through a Natural Resources Preservation Program-funded project during 2021–23, the U.S. Geological Survey, in collaboration with the National Park Service, estimated the probability of inundation at San Juan National Historic Site, Puerto Rico, and several other parks under various sea-level rise scenarios and contemporary high tide flooding thresholds. The maps produced for this effort can be used to assess potential habitat change and explore how infrastructure and cultural resources within the park may be exposed to future flooding-related hazards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243021","issn":"2327-6932","collaboration":"Prepared in collaboration with the National Park Service","usgsCitation":"Thurman, H.R., Enwright, N.M., Osland, M.J., Passeri, D.L., Day, R.H., and Simons, B.M., 2024, Projected sea-level rise and high tide flooding at San Juan National Historic Site, Puerto Rico: U.S. Geological Survey Fact Sheet 2024–3021, 6 p., https://doi.org/10.3133/fs20243021.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-156844","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}],"links":[{"id":497956,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117503.htm","linkFileType":{"id":5,"text":"html"}},{"id":462351,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20243008","text":"USGS Fact Sheet 2024-3008","description":"FS 2024-3008","linkHelpText":"- Projected Sea-Level Rise and High Tide Flooding at Timucuan Ecological and Historic Preserve, Florida"},{"id":462349,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20243023","text":"USGS Fact Sheet 2024-3023","description":"FS 2024-3023","linkHelpText":"- Projected Sea-Level Rise and High Tide Flooding at Dry Tortugas National Park, Florida"},{"id":462141,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3021/fs20243021.pdf","size":"3.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3021"},{"id":462142,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GKQT13","text":"USGS Data Release","linkHelpText":"Sea-level rise and high tide flooding inundation probability and depth statistics at San Juan National Historic Site, Puerto Rico"},{"id":462140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3021/coverthb.jpg"},{"id":462350,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20243024","text":"USGS Fact Sheet 2024-3024","description":"FS2024-3024","linkHelpText":"- Projected Sea-Level Rise and High Tide Flooding at Biscayne National Park, Florida"},{"id":462352,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20243022","text":"USGS Fact Sheet 2024-3022","description":"FS 2024-3022","linkHelpText":"- Projected Sea-Level Rise and High Tide Flooding at Big Cypress National Preserve, Florida"},{"id":462353,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20243016","text":"USGS Fact Sheet 2024-3016","description":"FS 2024-3016","linkHelpText":"- Projected Sea-Level Rise and High Tide Flooding at De Soto National Memorial, Florida"}],"country":"United States","otherGeospatial":"San Juan National Historic Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.14822077462958,\n 18.477902946276473\n ],\n [\n -66.14822077462958,\n 18.454711725978513\n ],\n [\n -66.10248459843638,\n 18.454711725978513\n ],\n [\n -66.10248459843638,\n 18.477902946276473\n ],\n [\n -66.14822077462958,\n 18.477902946276473\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506–3152
Contact Us- USGS Publications Warehouse
","tableOfContents":"Spatial and temporal distribution data provide critical information for invasive species management. For example, distribution data can help managers with early detections and guiding other response actions. Environmental DNA (eDNA)-based sampling exists as one tool for monitoring invasive species. As part of bigheaded carp Hypophthalmichthys spp. monitoring efforts in the Illinois River, USA, we compared eDNA-based sampling results at multiple habitats across an invasion gradient in 2015. Greater densities of carp occurred downriver in the Illinois River and lower densities occurred upriver. We sampled from five locations along this gradient and from three habitat types (backwater, main channel, and shoreline) within each location. We sampled each location in April and June. A priori, we hypothesized that more eDNA detections would occur downriver, where higher densities of carp occur; that more eDNA detections would occur in backwater habitats compared to areas of the river with more fish movement; and that more eDNA detections would occur in April, because bigheaded carps are thought to use our sampling areas more during the spring. We compared the proportion of samples positive across this gradient, the habitat type, and the two sampling time periods. The most downriver location had the highest proportion of samples with eDNA detections, the backwater habitats had the highest proportion of samples with eDNA detections, and April had more positive detections than June. Our results highlight the importance of sampling across multiple habitat types and across time to gain a clear understanding of distribution when using eDNA-based sampling. Thus, being cognizant of the interactions between seasonal habitat use and eDNA-based detections is important for managers who rely upon eDNA-based monitoring.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-23-038","usgsCitation":"Peterman, L.L., Tuttle-Lau, M.T., DeHaan, P.W., Coulter, D.P., Spear, S.F., and Erickson, R.A., 2024, Spatial variation of eDNA detection across an invasion gradient for invasive species monitoring programs: Journal of Fish and Wildlife Management, v. 15, no. 2, p. 350-360, https://doi.org/10.3996/JFWM-23-038.","productDescription":"11 p.","startPage":"350","endPage":"360","ipdsId":"IP-138781","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":466902,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-038","text":"Publisher Index Page"},{"id":462743,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.97996788684303,\n 41.96009875147479\n ],\n [\n -89.36825568969475,\n 41.96009875147479\n ],\n [\n -89.36825568969475,\n 40.943421835218516\n ],\n [\n -87.97996788684303,\n 40.943421835218516\n ],\n [\n -87.97996788684303,\n 41.96009875147479\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterman, Laura Lynne 0000-0001-6976-4138","orcid":"https://orcid.org/0000-0001-6976-4138","contributorId":328472,"corporation":false,"usgs":true,"family":"Peterman","given":"Laura","email":"","middleInitial":"Lynne","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":915401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tuttle-Lau, Maren T.","contributorId":146196,"corporation":false,"usgs":false,"family":"Tuttle-Lau","given":"Maren","email":"","middleInitial":"T.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":915402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeHaan, Patrick W.","contributorId":145918,"corporation":false,"usgs":false,"family":"DeHaan","given":"Patrick","email":"","middleInitial":"W.","affiliations":[{"id":16297,"text":"USFWS Abernathy Fish Technology Center, Longview, WA 98632","active":true,"usgs":false}],"preferred":false,"id":915403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coulter, David P.","contributorId":205629,"corporation":false,"usgs":false,"family":"Coulter","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":915404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spear, Stephen Frank 0000-0001-8351-9382","orcid":"https://orcid.org/0000-0001-8351-9382","contributorId":293162,"corporation":false,"usgs":true,"family":"Spear","given":"Stephen","email":"","middleInitial":"Frank","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":915405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":915406,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259609,"text":"70259609 - 2024 - Groundwater-Surface water interactions research: Past trends and future directions","interactions":[],"lastModifiedDate":"2024-10-17T11:58:22.178363","indexId":"70259609","displayToPublicDate":"2024-09-25T06:55:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater-Surface water interactions research: Past trends and future directions","docAbstract":"The U.S. Geological Survey (USGS) in partnership with the International Joint Commission installed and operated continuous water-quality monitors at three sites on the Souris River from May 2019 to October 2023. Continuously recorded data included dissolved oxygen (DO), water temperature, and specific conductance at the Souris River near Sherwood, North Dakota (USGS station 05114000), Souris River above Minot, N. Dak. (USGS station 05117500), and Souris River near Westhope, N. Dak (USGS station 05124000). The three sites on the Souris River were chosen for additional DO monitoring because they provided the best opportunity to capture potential effects on DO in areas downstream from major flow control structures and because identifying the connection of streamflow to DO at the international border is a focus of the International Souris River Board (ISRB).
The continuous water-quality monitoring at three sites on the Souris River from May 16, 2019, to October 1, 2023, indicated different patterns in DO among the three sites, and the different patterns indicate different factors affect DO concentrations among the sites. DO concentrations near Sherwood indicated the strong effect of algal dynamics at lower streamflow conditions with large diurnal fluctuations in DO concentration and indicated that streamflow does seem to affect DO concentrations when the streamflow is greater than about 100 cubic feet per second. DO concentrations were also frequently less than the water-quality objective (WQO) of 5 milligrams per liter in the summer and winter months, particularly during relatively low streamflow conditions in 2020 and 2021. DO concentrations above Minot had a different pattern with considerably fewer diurnal fluctuations than near Sherwood, high DO concentrations most winters except for the winter of 2021–22, and fewer instances when the DO was less than the WQO compared to Sherwood. The pattern of DO concentrations near Westhope seemed to be mainly influenced by the water chemistry coming out of J. Clark Salyer Pool 357 rather than streamflow and channel conditions at the site. The Westhope site also had the most days with daily minimum DO concentrations less than the WQO among the three sites, mainly in the winter when concentrations were consistently at or near 0 milligrams per liter for most of the winter months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241043","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Galloway, J.M., 2024, Dissolved oxygen monitoring on the Souris River, 2019–23: U.S. Geological Survey Open-File Report 2024–1043, 13 p., https://doi.org/10.3133/ofr20241043.","productDescription":"Report: iv, 13 p.; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167787","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":497955,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117500.htm","linkFileType":{"id":5,"text":"html"}},{"id":462183,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241043/full"},{"id":462182,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":462181,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1043/images/"},{"id":462180,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1043/ofr20241043.XML"},{"id":462179,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1043/ofr20241043.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1043"},{"id":462178,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1043/coverthb.jpg"}],"country":"Canada, United States","state":"North Dakota","otherGeospatial":"Souris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.03302625208204,\n 50.79350866915286\n ],\n [\n -105.03302625208204,\n 47.533599370512235\n ],\n [\n -98.11163953333218,\n 47.533599370512235\n ],\n [\n -98.11163953333218,\n 50.79350866915286\n ],\n [\n -105.03302625208204,\n 50.79350866915286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
The U.S. Geological Survey, in cooperation with Colorado Springs Utilities and Colorado Springs Stormwater Enterprise, synthesized previous studies and evaluated recent monitoring data to understand the distribution of fish and invertebrates in the Fountain Creek Basin and documented response to streamflow, water temperature, and water quality. The goal was to identify opportunities for aligning data collection to help maximize information gained from additional monitoring. Fifty-two publications were compiled from the literature that were completed within the study area between 1964 and 2022. Of these publications, 19 were fish and invertebrate focused. Overall, the distribution of fish and invertebrates in the Fountain Creek Basin has changed since the early 1900s. The occurrence of several fish species has increased or decreased since 2003, and a few species have not been collected in more than 100 years. Several mayfly, stonefly, and caddisfly taxa once common at several locations before 2000 are now rarely encountered, and those that now occur more frequently are associated with warmer-water streams. Decreasing invertebrate multimetric index values were noted at six locations, and the invasive Potamopyrgus antipodarum (New Zealand mud snail) is now established at two locations and occurs at several others, but in low numbers. Various streamflow characteristics were frequently noted to affect spatial and temporal patterns in fish and invertebrate communities, including early development and recruitment of Platygobio gracilis (flathead chub). Water quality and temperature contributed to patterns in aquatic communities, but less is known about the direct effects as these data were inconsistently available. Reach-scale habitat also contributed to patterns in aquatic communities, especially measures associated with the streambank, stream channel, and composition of streambed substrate. Moving forward, aligning consistent streamflow, water temperature, water quality, and geomorphic data collection at fish-, invertebrate-, and habitat-monitoring locations could maximize information gained from monitoring efforts and potentially inform evolving management activities and interests within the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245074","collaboration":"Prepared in cooperation with Colorado Springs Utilities and Colorado Springs Stormwater Enterprise","usgsCitation":"Zuellig, R.E., Wahl, C.F., Hennessy, E.K., Jouney, A., and Foutz, P., 2024, Evaluation and review of ecology-focused stream studies to support cooperative monitoring, Fountain Creek Basin, Colorado: U.S. Geological Survey Scientific Investigations Report 2024–5074, 30 p., https://doi.org/10.3133/sir20245074.","productDescription":"Report: vii, 30 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-158983","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":497958,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117504.htm","linkFileType":{"id":5,"text":"html"}},{"id":462209,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245074/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5074"},{"id":462156,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5074/images"},{"id":462112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91QQ6GT","text":"USGS data release","linkHelpText":"Datasets for Evaluation and Review of Ecology-Focused Stream Studies, Fountain Creek Basin, Colorado"},{"id":462111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5074/sir20245074.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5074"},{"id":462110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5074/coverthb.jpg"},{"id":462157,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5074/sir20245074.xml"}],"country":"United States","state":"Colorado","otherGeospatial":"Fountain Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.45322123672925,\n 39.26331261795599\n ],\n [\n -105.45322123672925,\n 38.0924698636114\n ],\n [\n -104.3145085834951,\n 38.0924698636114\n ],\n [\n -104.3145085834951,\n 39.26331261795599\n ],\n [\n -105.45322123672925,\n 39.26331261795599\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
The cisco (Coregonus artedi) population in Crystal Lake, Pennsylvania, is of great scientific interest as it either originated from Lake Erie or Lake Ontario. Cisco in Lake Erie once supported the largest freshwater fishery in the world, but populations were extirpated by 1960. We conducted a morphological analysis of Crystal Lake cisco to determine whether it was consistent with a distinctive Lake Erie form (albus), which was also historically documented, albeit rarely, in western Lake Ontario. Using principal component analysis, we compared eight morphometric ratios and one meristic from our Crystal Lake cisco collection with historical and contemporary collections of cisco from Lakes Erie and Ontario. Maximum likelihood ellipse overlaps between Crystal Lake cisco and presumed albus (the dominant Lake Erie form prior to extirpation) collections averaged 54%. For all groups, the greatest morphological overlap (73.9%) occurred between Crystal Lake and 1957 Lake Erie cisco, which only differed from Crystal Lake cisco in dorsal fin length. Alternatively, overlap between Crystal Lake cisco and all other Lake Ontario collections averaged 3.2%. Our results demonstrate that Crystal Lake cisco are likely an albus form; furthermore, historical documentation and our morphological results suggest a Lake Erie origin. Substantial overlap between Crystal Lake cisco and Lake Ontario albus collected in 1917 is likely explained by continuous entrainment of Lake Erie larvae into Lake Ontario. We suspect this created an albus metapopulation spanning Lakes Erie and Ontario, yet albus are no longer observed in either lake today.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102407","usgsCitation":"Schmitt, J., Fischer, D., Kao, Y., Frey, A., Chalupnicki, M., McKenna, J.E., Phillips, K., Dufour, M.R., Kraus, R., and Eshenroder, R.L., 2024, Historical and morphological evidence for a remnant population of Lake Erie cisco Coregonus artedi (albus) in Crystal Lake, Pennsylvania: Journal of Great Lakes Research, v. 50, no. 5, 102407, 10 p., https://doi.org/10.1016/j.jglr.2024.102407.","productDescription":"102407, 10 p.","ipdsId":"IP-157966","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":489856,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.jglr.2024.102407","text":"Publisher Index Page"},{"id":481509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Crystal Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.5439783458529,\n 41.64664146749939\n ],\n [\n -75.5439783458529,\n 41.632376138207945\n ],\n [\n -75.52974900812394,\n 41.632376138207945\n ],\n [\n -75.52974900812394,\n 41.64664146749939\n ],\n [\n -75.5439783458529,\n 41.64664146749939\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":925745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fischer, Douglas P.","contributorId":342445,"corporation":false,"usgs":false,"family":"Fischer","given":"Douglas P.","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":925746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kao, Yu-Chun","contributorId":35626,"corporation":false,"usgs":false,"family":"Kao","given":"Yu-Chun","affiliations":[{"id":6649,"text":"University of Michigan, School of Natural Resources and Environment","active":true,"usgs":false}],"preferred":false,"id":925747,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frey, Aaron","contributorId":350337,"corporation":false,"usgs":false,"family":"Frey","given":"Aaron","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":925748,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chalupnicki, Marc 0000-0002-3792-9345","orcid":"https://orcid.org/0000-0002-3792-9345","contributorId":242991,"corporation":false,"usgs":true,"family":"Chalupnicki","given":"Marc","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":925749,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":195894,"corporation":false,"usgs":true,"family":"McKenna","given":"James","suffix":"Jr.","email":"jemckenna@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":925750,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Phillips, Kristy 0000-0001-8378-0660","orcid":"https://orcid.org/0000-0001-8378-0660","contributorId":204292,"corporation":false,"usgs":true,"family":"Phillips","given":"Kristy","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":925751,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":925754,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":925752,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Eshenroder, Randy L.","contributorId":177867,"corporation":false,"usgs":false,"family":"Eshenroder","given":"Randy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":925753,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70258644,"text":"ofr20241058 - 2024 - ECCOE Landsat quarterly Calibration and Validation report—Quarter 1, 2024","interactions":[],"lastModifiedDate":"2026-06-11T17:18:08.212028","indexId":"ofr20241058","displayToPublicDate":"2024-09-24T09:48:42","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1058","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 1, 2024","title":"ECCOE Landsat quarterly Calibration and Validation report—Quarter 1, 2024","docAbstract":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 1 (January–March), 2024. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website:
https://earthexplorer.usgs.gov.
This quarterly report is the third to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data. Additional information about Landsat 9 prelaunch, commissioning, and early on-orbit imaging performance is available at https://www.mdpi.com/journal/remotesensing/special_issues/15B4V2K92K.
This quarterly report is the first to not include analysis results for Landsat 7 because Enhanced Thematic Mapper Plus imaging was suspended on January 19, 2024, after the satellite transitioned into full sunlight. The satellite has been drifting since early 2022 after being lowered from the nominal orbit altitude, and the transition into full sunlight is a result of the satellite operating in its extended science mission. Additional information about the imaging suspension is available at https://www.usgs.gov/landsat-missions/news/landsat-7-imaging-suspended. Additional information about the Landsat 7 extended science mission is available at https://www.usgs.gov/landsat-missions/landsat-7-extended-science-mission.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241058","usgsCitation":"Haque, M.O., Hasan, M.N., Shrestha, A., Rengarajan, R., Lubke, M., Shaw, J.L., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Clauson, J., Thome, K., Barsi, J., Kaita, E., Levy, R., Miller, J., and Ding, L., 2024, ECCOE Landsat\nquarterly Calibration and Validation report—Quarter 1, 2024 (ver. 1.2, June 2026): U.S. Geological Survey Open-File Report 2024–1058, 57 p., https://doi.org/10.3133/ofr20241058.","productDescription":"Report: viii, 57 p.; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-165326","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":505306,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1058/ofr20241058.pdf","text":"Report","size":"5.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1058 PDF"},{"id":505311,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241058/full"},{"id":505307,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1058/versionHist.txt","text":"Version History","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":439142,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"- EarthExplorer"},{"id":439140,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1058/images/"},{"id":439137,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1058/coverthb3.jpg"},{"id":439139,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1058/ofr20241058.XML","description":"OFR 2024–1058 XML"}],"edition":"Version 1.0: September 24, 2024; Version 1.1: December 11, 2024; Version 1.2: June 11, 2026","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
To better understand the primary drivers of the thermal regime in a Great Lakes estuary, and their implications for local biota, water temperature variations in the Milwaukee Estuary of Lake Michigan were studied between July and October of 2019 using a network of 25 sensors at 18 locations. Like Lake Michigan, the estuary was thermally stratified July to October, and historically dredged channels with water depths greater than 6 m allowed for the free movement of cold lake water into the estuary. However, temperatures in the estuary fluctuated rapidly both spatially and temporally, reflecting lake temperature fluctuations driven by changing meteorological conditions. Lake-driven upwelling and downwelling events influenced water temperature more than tributary contributions. Periodic upwelling and downwelling events caused temperature changes by up to 15 °C in less than 24 h. Nearshore upwelling events occasionally allowed cold, hypolimnetic lake water to reach tributary portions of the estuary beyond dredged areas, while downwelling events disrupted thermal stratification and caused the deep, dredged portions of the estuary to exceed 20 °C. Thermal impacts from these events were especially noticeable in transition zones between dredged and not dredged channels less than 2 m deep. The warming effects from downwelling persisted longer inside the estuary – up to two weeks – than cooling effects from upwelling, which typically lasted a few days. The predominantly lake-driven, rapid summer water temperature fluctuations observed in the Milwaukee Estuary serve as an important consideration in habitat restoration activities happening in Great Lakes estuaries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102416","usgsCitation":"Stefaniak, O.M., Fitzpatrick, F., Dow, B.A., Blount, J.D., Sullivan, D.J., and Reneau, P., 2024, Influences of meteorological conditions, runoff, and bathymetry on summer thermal regime of a Great Lakes estuary: Journal of Great Lakes Research, v. 50, no. 5, 102416, 14 p., https://doi.org/10.1016/j.jglr.2024.102416.","productDescription":"102416, 14 p.","ipdsId":"IP-141345","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":466904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102416","text":"Publisher Index Page"},{"id":462484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Milwaukee Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.9583,\n 43.0667\n ],\n [\n -87.9583,\n 43\n ],\n [\n -87.89167,\n 43\n ],\n [\n -87.89167,\n 43.0667\n ],\n [\n -87.9583,\n 43.0667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stefaniak, Owen M. 0000-0001-5394-8338 ostefaniak@usgs.gov","orcid":"https://orcid.org/0000-0001-5394-8338","contributorId":271143,"corporation":false,"usgs":true,"family":"Stefaniak","given":"Owen","email":"ostefaniak@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209540,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","email":"","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dow, Brennan A.","contributorId":344687,"corporation":false,"usgs":false,"family":"Dow","given":"Brennan","email":"","middleInitial":"A.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":914521,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blount, James D. 0000-0002-0006-3947 jblount@usgs.gov","orcid":"https://orcid.org/0000-0002-0006-3947","contributorId":200231,"corporation":false,"usgs":true,"family":"Blount","given":"James","email":"jblount@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sullivan, Daniel J. 0000-0003-2705-3738","orcid":"https://orcid.org/0000-0003-2705-3738","contributorId":204322,"corporation":false,"usgs":true,"family":"Sullivan","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914523,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reneau, Paul 0000-0002-1335-7573","orcid":"https://orcid.org/0000-0002-1335-7573","contributorId":217293,"corporation":false,"usgs":true,"family":"Reneau","given":"Paul","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914524,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259283,"text":"70259283 - 2024 - Long-term distributed temperature sensing monitoring for near-wellbore gas migration and gas hydrate formation","interactions":[],"lastModifiedDate":"2024-11-25T14:17:16.586869","indexId":"70259283","displayToPublicDate":"2024-09-24T09:27:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3325,"text":"SPE Journal","active":true,"publicationSubtype":{"id":10}},"title":"Long-term distributed temperature sensing monitoring for near-wellbore gas migration and gas hydrate formation","docAbstract":"Well integrity monitoring has always been a critical component of subsurface oil and gas operations. Distributed fiber-optic sensing is an emerging technology that shows great promise for monitoring processes, both in boreholes and in other settings. In this study, we present a case study of using distributed temperature sensing (DTS) technology to monitor a cemented and plugged well in the Alaska North Slope (ANS). The well was drilled as part of a long-term gas hydrate study, and the downhole DTS data were recorded over a period of approximately 2 years. By applying a temporal gradient and removing instrument instability noise, we reveal subtle (<0.001°C/h) thermal anomalies, which are characterized by brief warming periods followed by longer cooling periods at discrete depths along the borehole. The observed coherent events show an upward trajectory from deeper formations into the overlying permafrost interval, with the thermal anomalies concentrated in relatively coarse-grained sandstone layers. We also observe that the upward migration rate of the DTS anomalies varies with formation lithology and that there is a spatial and temporal correlation between the subsurface events and measured wellhead annular pressures. We interpret that the observed warming events represent the exothermic process of gas hydrate formation that is occurring in association with the upward migration of gas outside the well casing, and this interpretation is confirmed by numerical simulations. These observations demonstrate the ability of suitably processed DTS data to detect subtle processes and highlight the value of DTS technologies for wellbore integrity monitoring.
","language":"English","publisher":"Society of Petroleum Engineers","doi":"10.2118/223111-PA","usgsCitation":"Garcia-Ceballos, A., Jin, G., Collett, T., Merey, S., and Haines, S.S., 2024, Long-term distributed temperature sensing monitoring for near-wellbore gas migration and gas hydrate formation: SPE Journal, v. 29, no. 11, p. 5804-5819, https://doi.org/10.2118/223111-PA.","productDescription":"16 p.","startPage":"5804","endPage":"5819","ipdsId":"IP-162957","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":498024,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2118/223111-pa","text":"Publisher Index Page"},{"id":464441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"North Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151,\n 71\n ],\n [\n -151,\n 70\n ],\n [\n -148,\n 70\n ],\n [\n -148,\n 71\n ],\n [\n -151,\n 71\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia-Ceballos, Ana","contributorId":333715,"corporation":false,"usgs":false,"family":"Garcia-Ceballos","given":"Ana","email":"","affiliations":[],"preferred":false,"id":914769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Ge","contributorId":333716,"corporation":false,"usgs":false,"family":"Jin","given":"Ge","email":"","affiliations":[],"preferred":false,"id":914770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220812,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":914771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merey, Sukru","contributorId":344807,"corporation":false,"usgs":false,"family":"Merey","given":"Sukru","email":"","affiliations":[{"id":82412,"text":"Batman University","active":true,"usgs":false}],"preferred":false,"id":914772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":914773,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258727,"text":"70258727 - 2024 - Parasite abundance-occupancy relationships across biogeographic regions: Joint effects of niche breadth, host availability and climate","interactions":[],"lastModifiedDate":"2024-12-26T16:46:26.036627","indexId":"70258727","displayToPublicDate":"2024-09-24T06:55:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Parasite abundance-occupancy relationships across biogeographic regions: Joint effects of niche breadth, host availability and climate","docAbstract":"Changing biodiversity and environmental conditions may allow multi-host pathogens to spread among host species and affect prevalence. There are several widely acknowledged theories about mechanisms that may influence variation in pathogen prevalence, including the controversially debated dilution effect and abundance-occupancy relationship hypotheses. Here, we explore such abundance-occupancy relationships for unique lineages of three vector-borne avian blood parasite genera (the avian malaria parasite Plasmodium and the related haemosporidian parasites Parahaemoproteus and Leucocytozoon) across biogeographical regions.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.15015","usgsCitation":"Wells, K., Bell, J.A., Fecchio, A., Drovetski, S.V., Galen, S.C., Hackett, S., Lutz, H.L., Skeen, H., Voelker, G., Wamiti, W., Weckstein, J.D., and Clark, N.J., 2024, Parasite abundance-occupancy relationships across biogeographic regions: Joint effects of niche breadth, host availability and climate: Journal of Biogeography, v. 52, no. 1, p. 55-65, https://doi.org/10.1111/jbi.15015.","productDescription":"11 p.","startPage":"55","endPage":"65","ipdsId":"IP-167631","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466905,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.15015","text":"Publisher Index Page"},{"id":462239,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Konstans","contributorId":265392,"corporation":false,"usgs":false,"family":"Wells","given":"Konstans","email":"","affiliations":[{"id":54671,"text":"Department of Biosciences, Swansea University, Swansea, SA2 8PP UK","active":true,"usgs":false}],"preferred":false,"id":913896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bell, Jeffrey A","contributorId":265373,"corporation":false,"usgs":false,"family":"Bell","given":"Jeffrey","email":"","middleInitial":"A","affiliations":[{"id":52695,"text":"Department of Biology, University of North Dakota, Grand Forks, ND 58201, USA","active":true,"usgs":false}],"preferred":false,"id":913897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fecchio, Alan 0000-0002-7319-0234","orcid":"https://orcid.org/0000-0002-7319-0234","contributorId":265372,"corporation":false,"usgs":false,"family":"Fecchio","given":"Alan","email":"","affiliations":[{"id":54651,"text":"Programa de Pós-Graduação em Ecologia e Conservação da Biodiversidade, Universidade Federal de Mato Grosso, Avenida Fernando Corrêa da Costa 2367, Cuiabá, MT, 78060900, Brazil","active":true,"usgs":false}],"preferred":false,"id":913898,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":913899,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galen, Spencer C","contributorId":229671,"corporation":false,"usgs":false,"family":"Galen","given":"Spencer","email":"","middleInitial":"C","affiliations":[],"preferred":false,"id":913900,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hackett, Shannon","contributorId":265389,"corporation":false,"usgs":false,"family":"Hackett","given":"Shannon","email":"","affiliations":[{"id":54668,"text":"The Richard and Jill Chaifetz Associate Curator of Birds, Life Sciences and Pritzker Lab Field Museum of Natural History, 1400 South Lake Shore Drive, Chicago, IL 60605, USA","active":true,"usgs":false}],"preferred":false,"id":913901,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lutz, Holly L","contributorId":265375,"corporation":false,"usgs":false,"family":"Lutz","given":"Holly","email":"","middleInitial":"L","affiliations":[{"id":54653,"text":"Department of Surgery, University of Chicago, 5812 S. Ellis Ave., Chicago, IL 60637 and Integrative Research Center, Field Museum of Natural History, 1400 South Lake Shore Drive, Chicago, IL 60605 USA","active":true,"usgs":false}],"preferred":false,"id":913902,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Skeen, Heather","contributorId":265374,"corporation":false,"usgs":false,"family":"Skeen","given":"Heather","email":"","affiliations":[{"id":54652,"text":"Committee on Evolutionary Biology, University of Chicago, Chicago, IL, 6063 and Negaunee Integrative Research Center, The Field Museum, Chicago, IL, 60605 USA","active":true,"usgs":false}],"preferred":false,"id":913903,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Voelker, Gary","contributorId":229521,"corporation":false,"usgs":false,"family":"Voelker","given":"Gary","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":913904,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wamiti, Wanyoike","contributorId":265381,"corporation":false,"usgs":false,"family":"Wamiti","given":"Wanyoike","email":"","affiliations":[{"id":54660,"text":"Zoology Department, National Museums of Kenya, P.O. Box 40658-00100, Nairobi, Kenya","active":true,"usgs":false}],"preferred":false,"id":913905,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Weckstein, Jason D","contributorId":265391,"corporation":false,"usgs":false,"family":"Weckstein","given":"Jason","email":"","middleInitial":"D","affiliations":[{"id":54670,"text":"Department of Ornithology, Academy of Natural Sciences of Drexel University, Philadelphia, PA 19103, USA and Department of Biodiversity, Earth, and Environmental Sciences, Drexel University, Philadelphia, PA 19103, USA","active":true,"usgs":false}],"preferred":false,"id":913906,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Clark, Nicholas J.","contributorId":204867,"corporation":false,"usgs":false,"family":"Clark","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":16755,"text":"University of Queensland, Australia","active":true,"usgs":false}],"preferred":false,"id":913907,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70258081,"text":"70258081 - 2024 - The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning","interactions":[],"lastModifiedDate":"2024-11-22T16:08:59.048285","indexId":"70258081","displayToPublicDate":"2024-09-23T17:17:05","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning","docAbstract":"The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national-scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland-specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non-wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.
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\"}}]}","volume":"49","issue":"14","noUsgsAuthors":false,"publicationDate":"2024-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":912039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":912040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Britt Windsor 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The streamgages provide information on river height and streamflow, typically at 15-minute intervals. This information is then made available to everyone, most of it delivered nearly in realtime on the USGS National Water Dashboard.
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Observing Systems Division
Water Mission Area
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, Georgia 30093
Wet slab avalanches are poorly understood and often difficult to forecast. Yet, wet slab avalanches can be destructive and may become more common in a changing climate. As the onset of wet avalanches moves earlier in the winter season due to climate change, understanding snowpack and meteorological characteristics of wet slab avalanches will become increasingly important. In this study, we examined two recent late-January wet slab cycles triggered by warming and solar input in the Rocky Mountains of Idaho and Montana, United States. We posed two questions to help us understand these potentially increasingly frequent, mid-winter wet slab cycles: i) what are the weather and snowpack patterns of two mid-winter wet slab cycles and ii) how did these non-rain-on-snow, mid-winter warming events compare to the historical climate normal? In both locations, a short-lived ridge of high pressure strengthened over the region from late January to early February 2024. Temperatures rapidly increased to 6° C at middle and upper-elevation locations and remained above freezing for 48 to 72 hours. Wet slab avalanches (Montana: n=68, Idaho: n=11) released on a layer of facets resting on a crust formed in late December and slab depths averaged 40 to 150 cm. Avalanches primarily occurred on southeast, south, and southwest aspects at middle and upper elevations. Additionally, positive January monthly temperature trends exist in both locations from 1990 to 2020, and these two cycles from 2024 highlight impacts of midwinter warming events with unstable snowpack conditions. These cycles also highlight how a relatively uncommon mid-winter warming event with low radiative input can produce very large destructive wet slab avalanches. Understanding mid-winter warming events and associated wet slab avalanche cycles will help us prepare and forecast for potentially more common scenarios like this in the future.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: International Snow Science Workshop 2024, Tromsø, Norway","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Montana State University","usgsCitation":"Peitzsch, E.H., Johnson, C., Savage, S., Miller, Z., and Hoppinen, Z., 2024, A case study and comparison of mid-winter warming and solar driven wet slab avalanche cycles, in Proceedings: International Snow Science Workshop 2024, Tromsø, Norway, p. 620-628.","productDescription":"9 p.","startPage":"620","endPage":"628","ipdsId":"IP-169607","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":433687,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item/3200","linkFileType":{"id":5,"text":"html"}},{"id":503271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.88112358398492,\n 48.92833882805152\n ],\n [\n -109.31628352458347,\n 48.92833882805152\n ],\n [\n -109.31628352458347,\n 43.82396648591791\n ],\n [\n -116.88112358398492,\n 43.82396648591791\n ],\n [\n -116.88112358398492,\n 48.92833882805152\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Cameron","contributorId":344132,"corporation":false,"usgs":false,"family":"Johnson","given":"Cameron","affiliations":[{"id":82294,"text":"USDA Forest Service Flathead Avalanche Center","active":true,"usgs":false}],"preferred":false,"id":912903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Savage, Scott","contributorId":209938,"corporation":false,"usgs":false,"family":"Savage","given":"Scott","email":"","affiliations":[{"id":38032,"text":"U.S.D.A. Forest Service Sawtooth National Forest Avalanche Center, Ketchum, Idaho, USA","active":true,"usgs":false}],"preferred":false,"id":912904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoppinen, Zachary","contributorId":344133,"corporation":false,"usgs":false,"family":"Hoppinen","given":"Zachary","affiliations":[{"id":82296,"text":"Boise State University, Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":912906,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258323,"text":"70258323 - 2024 - Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools","interactions":[],"lastModifiedDate":"2026-04-21T15:42:50.874132","indexId":"70258323","displayToPublicDate":"2024-09-23T10:35:37","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools","docAbstract":"Large-magnitude avalanches often alter vegetation composition, avalanche path dimensions, and subsequent avalanche return periods. Understanding temporal changes in individual avalanche path trimlines, runout zones, and geomorphic characteristics helps forecasters, planners, and engineers estimate potential avalanche destructive size and impact on infrastructure or settlements in the runout zone. Understanding these changes on a large scale also provides information on post-cycle avalanche distribution. Here, we use remote sensing platforms and change detection techniques to examine vegetation change in avalanche paths in Montana and Colorado. In northwest Montana, we implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. In Colorado, we investigated changes in avalanche path vegetation characteristics prior to and after a widespread large-magnitude avalanche cycle. The highest frequency of avalanche return periods was broadly characterized by grassland and shrubland, but topography greatly influences vegetation classes and return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. We used optical sensors from satellite imagery to analyze changes in Normalized Difference Vegetation Index (NDVI) to calculate ground cover change over time. NDVI, a measure of near-infrared and red bands within the imagery, allowed us to distinguish between green vegetation (e.g., trees and shrubs) and non-vegetated ground cover (e.g., dead and downed trees, rocks, and dirt) within avalanche paths. For this study, we calculated changes in NDVI values by comparing imagery from 2018 to imagery from 2019 after a widespread large magnitude avalanche cycle occurred in March 2019 in Colorado, United States. We applied a filtering process to reduce error, classified NDVI change based on the value distribution, and then calculated area change of all areas within each avalanche path. We completed this process for 1633 avalanche paths throughout Colorado. We found that using NDVI difference values pre- and post-avalanche cycle allowed us to identify ground cover change in avalanche paths throughout Colorado. These changes span from a slight expansion of existing avalanche paths to substantial landscape disturbance. For example, a size D5 avalanche caused severe ground cover change in 18% of one single path near Aspen, Colorado. This suggests that large magnitude avalanches can redefine avalanche path dimensions and could impact subsequent avalanche size and frequency. Using NDVI from satellite imagery is a simple way to detect ground cover changes in avalanche paths on a large scale or in remote areas. In general, remote sensing products to detect and examine vegetation and ground cover change in avalanche paths can help inform avalanche distribution and benefit planning efforts.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: International Snow Science Workshop 2024, Tromsø, Norway","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Montana State University","usgsCitation":"Peitzsch, E.H., Miller, Z., Simonhois, R., and Greene, E.M., 2024, Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools, in Proceedings: International Snow Science Workshop 2024, Tromsø, Norway, p. 595-602.","productDescription":"8 p.","startPage":"595","endPage":"602","ipdsId":"IP-169616","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":503270,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":433686,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item/3195"}],"country":"United States","state":"Montana","otherGeospatial":"southern Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.5623583,\n 48.2436818\n ],\n [\n -113.4828952,\n 48.2409445\n ],\n [\n -113.214365,\n 48.4312921\n ],\n [\n -113.3650708,\n 48.6118719\n ],\n [\n -113.9377526,\n 48.5375401\n ],\n [\n -113.5623583,\n 48.2436818\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peitzsch, Erich H. 0000-0001-7624-0455 epeitzsch@usgs.gov","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":3786,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","email":"epeitzsch@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":912899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simonhois, Ron","contributorId":344129,"corporation":false,"usgs":false,"family":"Simonhois","given":"Ron","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":912900,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greene, Ethan M.","contributorId":330958,"corporation":false,"usgs":false,"family":"Greene","given":"Ethan","middleInitial":"M.","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":912901,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70258161,"text":"cir1525 - 2024 - Remote sensing for monitoring mine lands and recovery efforts","interactions":[],"lastModifiedDate":"2024-09-23T14:10:34.799856","indexId":"cir1525","displayToPublicDate":"2024-09-23T09:35:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1525","displayTitle":"Remote Sensing for Monitoring Mine Lands and Recovery Efforts","title":"Remote sensing for monitoring mine lands and recovery efforts","docAbstract":"Under the Bipartisan Infrastructure Law Ecosystem Restoration Program, the U.S. Department of the Interior has invested in assessing and recovering degraded ecosystems to promote healthy human communities and wildlife habitats. One priority established by the program is the need to address degraded ecosystems associated with mine lands, including active, inactive, and abandoned mines. Mine lands occur in every State of the United States and present a range of environmental hazards and safety risks to human communities and wildlife habitats. However, limited information compiled across the United States exists on the whereabouts of mining activities and their potential environmental effects on landscapes. Remote sensing is the process of acquiring information about the landscape from ground platforms, aircraft, or satellites to assess surface characteristics such as topography, vegetation, and soil properties and can therefore provide important cost-effective methods for identifying mining sites and assessing their environmental effects. Based on a literature review of remote sensing applications that assessed land health conditions and monitoring of mine lands, this report highlights important approaches, capabilities, considerations, and case studies using a breadth of techniques. The report identifies considerations for setting appropriate vegetation recovery targets and demonstrates how remote sensing can inform the prioritization, recovery design, and long-term assessments of recovery to help support decision makers and land managers. Applications of remote sensing to mine recovery will be most effective when recovery targets are clearly defined and quantifiable from data collected before mining activity, per the Surface Mining Control and Reclamation Act of 1977 and similar laws. Additionally, the collected data would need to be accessible and maintained in databases for baseline references used to establish these recovery targets.
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States\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
We analyze aftershocks of the 2019 M 7.1 Ridgecrest mainshock and isolated M 5–6 mainshocks in southern California to test predictions made by the rate‐and‐state friction model of Dieterich (1994). Rate‐and‐state friction predicts that the seismicity rate after a stress step follows Omori decay, where the Omori c‐value, which is the saturation in aftershock rate observed at small times, is larger for smaller stress steps. Put in the context of an aftershock sequence, this predicts that the Omori c‐value will be systematically larger at greater distances from the mainshock. To our knowledge, this predicted effect has not been observed. In part this may be because the Omori c‐value is difficult to measure because it often reflects short‐term catalog incompleteness rather than a true saturation in aftershock rate.
We explore the dependence of the Omori c‐value on the distance to the mainshock by applying the “a‐positive” method (van der Elst and Page, 2023). This method is insensitive to short‐term aftershock incompleteness and allows resolution of the true aftershock rate deep into the mainshock coda. For aftershocks of the Ridgecrest mainshock and stacked M 5–6 mainshocks, we observe systematic differences in early aftershock rates, relative to mainshock distance, consistent with the predictions of rate‐and‐state friction. Furthermore, for the larger Ridgecrest dataset, we observe that aftershocks nearer to the mainshock start earlier, and we resolve a flattening of the Omori curve consistent with a larger Omori c‐value for the farthest aftershocks, as predicted by Dieterich (1994).
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240179","usgsCitation":"Page, M.T., van der Elst, N., and Hainzl, S., 2024, Testing rate‐and‐state predictions of aftershock decay with distance: Seismological Research Letters, v. 95, no. 6, p. 3376-3386, https://doi.org/10.1785/0220240179.","productDescription":"11 p.","startPage":"3376","endPage":"3386","ipdsId":"IP-165789","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":502578,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://gfzpublic.gfz-potsdam.de/pubman/item/item_5029404","text":"External Repository"},{"id":483131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":930197,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van der Elst, Nicholas 0000-0002-3812-1153 nvanderelst@usgs.gov","orcid":"https://orcid.org/0000-0002-3812-1153","contributorId":147858,"corporation":false,"usgs":true,"family":"van der Elst","given":"Nicholas","email":"nvanderelst@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":930198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hainzl, Sebastian","contributorId":352178,"corporation":false,"usgs":false,"family":"Hainzl","given":"Sebastian","affiliations":[{"id":84127,"text":"Univ. of Potsdam","active":true,"usgs":false}],"preferred":false,"id":930199,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70258516,"text":"sir20245046 - 2024 - Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington","interactions":[],"lastModifiedDate":"2025-12-23T22:15:55.273826","indexId":"sir20245046","displayToPublicDate":"2024-09-23T08:38:10","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5046","displayTitle":"Groundwater and Surface-Water Interactions in the Lower Duwamish Waterway, Seattle, Washington","title":"Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the Washington State Department of Ecology (Ecology), conducted a study to describe the current understanding of the regional groundwater system of the lower Duwamish River valley and groundwater and surface-water interactions in the lower Duwamish Waterway. The lower Duwamish Waterway is the final 5-mile (mi) reach of the Duwamish River before it empties into Elliott Bay in Puget Sound near Seattle, Washington. A nearshore site (hereinafter referred to as “Nearshore Site” to distinguish the particular site from general discussions of nearshore areas) along the western shoreline of the Duwamish River, about 1.5 mi upstream from the river mouth, was selected for focused groundwater data collection by USGS. Data loggers were deployed in seven groundwater wells and one stilling well in the Duwamish River to measure specific conductance, temperature, and depth at 15-minute intervals for a period of about 2 years.
At the Nearshore Site during 2020–22, water levels in the shallow wells were 3–8 feet (ft) higher than water levels in the deep wells, providing evidence for a low-permeability layer between the shallow and deep aquifers in this area. The shallow wells had a pronounced seasonal variability, with high water levels in winter and low water levels in summer. Data from the deep wells showed far less seasonal variability, with slight increases in winter and a near-constant water level from spring to autumn. The deep wells had a strong hydraulic connection to the Duwamish River, as evidenced by the synchronous water-level variability during the tidal cycle, whereas the shallow wells had minimal to no tidal response. The potentiometric maps developed for the Nearshore Site and surrounding areas indicate large differences in groundwater-flow directions for the shallow and deep aquifers at low and high tides. For the shallow aquifer, flow is toward the lower Duwamish Waterway near the Nearshore Site, regardless of the tidal condition. For the deep aquifer, a potentiometric trough forms parallel to the shoreline during high tide, indicating that groundwater flow converges from the uplands to the west and the Duwamish River to the east. The geometry of the potentiometric surfaces between the nearshore-most well and the shoreline is complex and is further confounded by intermittent shoreline armoring and other buried infrastructure, which could serve as either a barrier or a conduit to flow.
Groundwater and surface-water interactions in the lower Duwamish Waterway are inherently complex as a result of three overarching factors. First, water levels in the lower reaches of the Duwamish River vary daily by 11–16 ft because of tides from Puget Sound, which create large swings in the hydraulic gradient in the nearshore groundwater system. Second, the density and chemical composition of water in the Duwamish River change daily with the tides and seasonally, which constrains how river water entering the nearshore sediments interacts with discharging groundwater. Third, the nearshore subsurface and shoreline conditions are heterogenous because of extensive shoreline armoring over the past century, which governs the flow of groundwater and infiltrating river water. These unique features of groundwater and surface-water interactions in the lower Duwamish Waterway thus govern the transport of terrestrial contaminants to the lower Duwamish Waterway. Furthermore, the heterogenous aquifer properties in the lower Duwamish Waterway contribute to spatially and temporally dynamic contaminant-transport processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245046","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Mitchell, J.N., and Conn, K.E., 2024, Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5046, 43 p., https://doi.org/10.3133/sir20245046.","productDescription":"Report: vi, 43 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-158073","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":497961,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117497.htm","linkFileType":{"id":5,"text":"html"}},{"id":434877,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.XML"},{"id":434876,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5046/images"},{"id":434875,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1F7QX42","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of groundwater- and surface-water-interactions report— Groundwater and surface-water interactions in the Lower Duwamish Waterway, Seattle, Washington"},{"id":434874,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245046/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5046"},{"id":434873,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.pdf","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5046"},{"id":434872,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5046/sir20245046.jpg"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Lower Duwamish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.45687695827122,\n 47.691936104689205\n ],\n [\n -122.45687695827122,\n 47.28\n ],\n [\n -122,\n 47.28\n ],\n [\n -122,\n 47.691936104689205\n ],\n [\n -122.45687695827122,\n 47.691936104689205\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Growth of the toxic alga Prymnesium parvum is hormetically stimulated with environmentally relevant concentrations of glyphosate. The mechanisms of glyphosate hormesis in this species, however, are unknown. We evaluated the transcriptomic response of P. parvum to glyphosate at concentrations that stimulate maximum growth and where growth is not different from control values, the zero-equivalent point (ZEP). Maximum growth occurred at 0.1 mg l−1 and the ZEP was 2 mg l−1. At 0.1 mg l−1, upregulated transcripts outnumbered downregulated transcripts by one order of magnitude. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that the upregulated transcriptome is primarily associated with metabolism and biosynthesis. Transcripts encoding heat shock proteins and co-chaperones were among the most strongly upregulated, and several others were associated with translation, Redox homeostasis, cell replication, and photosynthesis. Although most of the same transcripts were also upregulated at concentrations ≥ZEP, the proportion of downregulated transcripts greatly increased as glyphosate concentrations increased. At the ZEP, downregulated transcripts were associated with photosynthesis, cell replication, and anion transport, indicating that specific interference with these processes is responsible for the nullification of hormetic growth. Transcripts encoding the herbicidal target of glyphosate, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), were upregulated at concentrations ≥ZEP but not at 0.1 mg l−1, indicating that disruption of EPSPS activity occurred at high concentrations and that nullification of hormetic growth involves the direct interaction of glyphosate with this enzyme. Results of this study may contribute to a better understanding of glyphosate hormesis and of anthropogenic factors that influence P. parvum biogeography and bloom formation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.176451","usgsCitation":"Chávez Montesa, R., Mary, M.A., Rashel, R.H., Fokar, M., Herrera-Estrella, L., Lopez-Arredondo, D., and Patino, R., 2024, Hormetic and transcriptomic responses of the toxic alga Prymnesium parvum to glyphosate: Science of the Total Environment, v. 954, 176451, 12 p., https://doi.org/10.1016/j.scitotenv.2024.176451.","productDescription":"176451, 12 p.","ipdsId":"IP-167518","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":484505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"954","noUsgsAuthors":false,"publicationDate":"2024-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Chávez Montesa, Ricardo A.","contributorId":353196,"corporation":false,"usgs":false,"family":"Chávez Montesa","given":"Ricardo A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":933155,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mary, Mousumi A.","contributorId":341256,"corporation":false,"usgs":false,"family":"Mary","given":"Mousumi","email":"","middleInitial":"A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":933156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rashel, Rakib H.","contributorId":200015,"corporation":false,"usgs":false,"family":"Rashel","given":"Rakib","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":933157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fokar, Mohamed","contributorId":353199,"corporation":false,"usgs":false,"family":"Fokar","given":"Mohamed","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":933158,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herrera-Estrella, Luis","contributorId":353202,"corporation":false,"usgs":false,"family":"Herrera-Estrella","given":"Luis","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":933159,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lopez-Arredondo, Damar","contributorId":353205,"corporation":false,"usgs":false,"family":"Lopez-Arredondo","given":"Damar","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":933160,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":933161,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70259520,"text":"70259520 - 2024 - Using environmental DNA to assess the response of steelhead/Rainbow Trout and Coastrange Sculpin populations to postfire debris flows in coastal streams of Big Sur, California","interactions":[],"lastModifiedDate":"2025-01-13T16:14:12.955347","indexId":"70259520","displayToPublicDate":"2024-09-22T07:07:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Using environmental DNA to assess the response of steelhead/Rainbow Trout and Coastrange Sculpin populations to postfire debris flows in coastal streams of Big Sur, California","docAbstract":"Debris flows are among the most extreme disturbances to streams and are predicted to become more frequent under climate change. We assessed the response of steelhead Oncorhynchus mykiss (anadromous Rainbow Trout)/Rainbow Trout (hereafter, collectively referred to as O. mykiss) and Coastrange Sculpin Cottus aleuticus populations to major postfire debris flows in two small coastal basins of California using noninvasive environmental DNA (eDNA) sampling.
We analyzed water samples from disturbed reaches for eDNA in the first two summers after debris flows. In Big Creek, all Coastrange Sculpin habitat was disturbed by debris flows, but a major tributary occupied by O. mykiss was not affected. In Mill Creek, all available habitat for both species was disturbed. We also sampled two unburned basins as undisturbed reference streams.
In Big Creek, O. mykiss eDNA was detected in all water samples during both years, and concentrations in some samples approached the lowest concentrations in the reference streams. Coastrange Sculpin eDNA was detected only in a single water sample in the first year and was not detected in the second year. In Mill Creek, neither species was detected in the first year, but during the second year, O. mykiss eDNA was detected at very low concentrations in 40% of samples and Coastrange Sculpin eDNA was detected in a single sample.
Our results indicate that O. mykiss and Coastrange Sculpins either survived in or quickly reoccupied the disturbed reaches from within the basins. However, the detection rates for cases in which all habitat for a species in a basin was affected by debris flows indicate that abundances were very low, suggesting that persistence may be uncertain unless there is successful reproduction or immigration. Finally, eDNA appears to be effective for monitoring sensitive fish populations after a major disturbance; however, detection rates in individual samples may be low and require appropriate sampling designs to achieve the desired detection probability.
Although Mars today does not have a core dynamo, magnetizations in the Martian crust and in meteorites suggest a magnetic field was present prior to 3.7 billion years (Ga) ago. However, the lack of ancient, oriented Martian bedrock samples available on Earth has prevented accurate estimates of the dynamo's intensity, lifetime, and direction. Constraining the nature and lifetime of the dynamo are vital to understanding the evolution of the Martian interior and the potential habitability of the planet. The Perseverance rover, which is exploring Jezero crater, is providing an unprecedented opportunity to address this gap by acquiring absolutely oriented bedrock samples with estimated ages from ∼2.3 to >4.1 Ga. As a first step in establishing whether these samples could contain records of Martian paleomagnetism, it is important to determine their ferromagnetic mineralogy, the grain sizes of the phases, and the forms of any natural remanent magnetization. Here, we synthesize data from various Perseverance instruments to achieve those goals and discuss the implications for future laboratory paleomagnetic analyses. Using the rover's instrument payload, we find that cored samples likely contain iron oxides enriched in Cr and Ti. The relative proportions of Fe, Ti, and Cr indicate that the phases may be titanomagnetite or Fe-Ti-Cr spinels that are ferromagnetic at room temperature, but we cannot rule out the presence of non-ferromagnetic ulvöspinel, ilmenite, and chromite due to signal mixing. Importantly, the inferred abundance of iron oxides in the samples suggests that even <1 mm-sized samples will be easily measurable by present-day magnetometers.
Constraining stresses in the Earth's crust in volcanic regions is critical for understanding many mechanical processes related to eruptive activity. Dike pathways, in particular, are shaped by the orientation of principal stress axes. Therefore, accurate models of dike trajectories and future vent locations rely on accurate estimates of stresses in the subsurface. This work presents a framework for probabilistic constraint of the stress state of calderas by combining three-dimensional physics-based dike pathway models with observed past vent locations using a Monte Carlo approach. The retrieved stress state is then used to produce probability maps of future vent opening across a caldera. We test our stress inversion and vent forecast approach on synthetic scenarios, and find it successful depending on the distribution of the available vents and the complexity of the volcano's structural history. We explore the potential and limitations of the approach, show how its performance is sensitive to the assumptions in the models and available prior information, and discuss how it may be applied to real calderas.
Ecologists, biologists, and conservation scientists are increasingly interested in the use of environmental DNA (eDNA) data for research and potentially decision-making. While commercial DNA extraction kits are typically user-friendly and accessible, they may fail to deliver the desired results with inherently complex eDNA samples, necessitating protocol optimization or educated selection of alternative approaches. To this end, knowledge of the basic steps and principles of DNA extractions is essential, but traditional education tracks in ecology, conservation, and environmental management typically do not include in-depth training in molecular methods. The primary objective of this paper is to enable scientists with an ecological background and limited molecular training to understand the four key steps of eDNA isolations, and to use this expertise to their advantage. We describe the purpose of commonly used reagents and chemicals, point out alternatives for each key step, explain the impact of certain choices regarding isolation approaches on DNA integrity and purity, and highlight the possibility of a tailor-made “mix and match” approach. We anticipate that this paper will enable field ecologists to develop a deeper understanding of the mechanisms and chemistry underlying eDNA extractions, thus allowing them to make informed decisions regarding the best eDNA extraction method for their research goals. Our intention is not to provide comprehensive, step-by-step protocols, but to offer guiding principles while highlighting alternative solutions. Finally, we hope that this paper will act as a useful resource to support knowledge transfer and teaching.