We investigate the shallow plumbing system of the Deccan Traps Large Igneous Province using rock and mineral data from Giant Plagioclase Basalt (GPB) lava flows from around the entire province, but with a focus on the Saurashtra Peninsula, the Malwa Plateau, and the base and top of the Western Ghats (WG) lava pile. GPB lavas in the WG typically occur at the transition between chemically distinct basalt formations. Most GPB samples are evolved basalts, with high Fe and Ti contents, and show major and trace elements and Sr-Nd-Pb isotopic compositions generally similar to those of previously studied Deccan basalts. Major element modeling suggests that high-Fe, evolved melts typical of GPB basalts may derive from less evolved Deccan basalts by low-pressure fractional crystallization in a generally dry magmatic plumbing system. The basalts are strongly porphyritic, with 6–25% of mm- to cm-sized plagioclase megacrysts, frequently occurring as crystal clots, plus relatively rare olivine and clinopyroxene. The plagioclase crystals are mostly labradoritic, but some show bytownitic cores (general range of anorthite mol%: 78–55). A common feature is a strong Fe enrichment at the plagioclase rims, indicating interaction with an Fe-rich melt similar to that represented by the matrix compositions (FeOt up to 16–17 wt%). Plagioclase minor and trace elements and Sr isotopic compositions analyzed by laser ablation inductively coupled plasma mass spectrometry show evidence of a hybrid and magma mixing origin. In particular, several plagioclase crystals show variable 87Sr/86Sri, which only partially overlaps with the 87Sr/86Sri of the surrounding matrix. Diffusion modeling suggests residence times of decades to centuries for most plagioclase megacrysts. Notably, some plagioclase crystal clots show textural evidence of deformation as recorded by electron back-scatter diffraction analyses and chemical maps, which suggest that the plagioclase megacrysts were deformed in a crystal-rich environment in the presence of melt. We interpret the plagioclase megacrysts as remnants of a crystal mush originally formed in the shallow plumbing system of the Deccan basalts. In this environment, plagioclase acquired a zoned composition due to the arrival of chemically distinct basaltic magmas. Prior to eruption, a rapidly rising but dense Fe-rich magma was capable of disrupting the shallow level crystal mush, remobilizing part of it and carrying a cargo of buoyant plagioclase megacrysts. Our findings suggest that basaltic magmas from the Deccan Traps, and possibly from LIPs in general, are produced within complex transcrustal magmatic plumbing systems with widespread crystal mushes developed in the shallow crust.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/petrology/egac075","usgsCitation":"Marzoli, A., Renne, P.R., Andreasen, R., Spiess, R., Chiaradia, M., Ruth, D.C., Tholt, A., Pande, K., and Costa, F.J., 2022, The Shallow Magmatic Plumbing System of the Deccan Traps, Evidence from Plagioclase Megacrysts and Their Host Lavas: Journal of Petrology, v. 63, no. 9, egac075, https://doi.org/10.1093/petrology/egac075.","productDescription":"egac075","ipdsId":"IP-137938","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":489006,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/28790e6d-08a1-4f02-b893-195df971663e","text":"External Repository"},{"id":463238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Marzoli, A.","contributorId":167328,"corporation":false,"usgs":false,"family":"Marzoli","given":"A.","email":"","affiliations":[{"id":24687,"text":"Universitá Degli Studi di Padova, Padova, Italy","active":true,"usgs":false}],"preferred":false,"id":916902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renne, Paul R. 0000-0003-1769-5235","orcid":"https://orcid.org/0000-0003-1769-5235","contributorId":229577,"corporation":false,"usgs":false,"family":"Renne","given":"Paul","email":"","middleInitial":"R.","affiliations":[{"id":37390,"text":"Department of Earth and Planetary Science, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":916903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andreasen, R","contributorId":345557,"corporation":false,"usgs":false,"family":"Andreasen","given":"R","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":916904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spiess, R","contributorId":345558,"corporation":false,"usgs":false,"family":"Spiess","given":"R","email":"","affiliations":[{"id":82629,"text":"Universita di Padova","active":true,"usgs":false}],"preferred":false,"id":916905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chiaradia, M","contributorId":345561,"corporation":false,"usgs":false,"family":"Chiaradia","given":"M","email":"","affiliations":[{"id":82630,"text":"Universite de Geneve","active":true,"usgs":false}],"preferred":false,"id":916906,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruth, Dawn Catherine Sweeney 0000-0001-9369-9364","orcid":"https://orcid.org/0000-0001-9369-9364","contributorId":334908,"corporation":false,"usgs":true,"family":"Ruth","given":"Dawn","email":"","middleInitial":"Catherine Sweeney","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916907,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tholt, A.J.","contributorId":345562,"corporation":false,"usgs":false,"family":"Tholt","given":"A.J.","email":"","affiliations":[{"id":38176,"text":"Berkeley Geochronology Center","active":true,"usgs":false}],"preferred":false,"id":916908,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pande, K","contributorId":345563,"corporation":false,"usgs":false,"family":"Pande","given":"K","email":"","affiliations":[{"id":7210,"text":"Indian Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916909,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Costa, Fabio J. 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Spillover of pathogens into humans and domestic animals may negatively impact public health and the economy, requiring increased proactive management actions. The North American Wildlife Management Model provides the philosophical basis for managing wildlife and underpins all management options. Diseases in wildlife populations may be managed by manipulation of the environment, manipulation of the host, manipulation of the agent, and modification of human behavior. Important considerations include setting management goals, and metrics to assess success. Future strategies include using systems and One Health approaches to develop interventions that optimize outcomes for humans, animals, and the environment.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Fowler's zoo and wild animal medicine current therapy, volume 10","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","usgsCitation":"Drew, M.L., and Sleeman, J.M., 2022, Management of diseases in free-ranging wildlife populations, chap. 9 of Fowler's zoo and wild animal medicine current therapy, volume 10, p. 47-53.","productDescription":"7 p.","startPage":"47","endPage":"53","ipdsId":"IP-124038","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":404653,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":404632,"type":{"id":15,"text":"Index Page"},"url":"https://www.elsevier.com/books/fowler's-zoo-and-wild-animal-medicine-current-therapyvolume-10/978-0-323-82852-9"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Miller, Eric","contributorId":294470,"corporation":false,"usgs":false,"family":"Miller","given":"Eric","affiliations":[],"preferred":false,"id":848064,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Lamberski, Nadine","contributorId":259228,"corporation":false,"usgs":false,"family":"Lamberski","given":"Nadine","affiliations":[{"id":38792,"text":"San Diego Zoo Global","active":true,"usgs":false}],"preferred":false,"id":848065,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Calle, Paul","contributorId":294471,"corporation":false,"usgs":false,"family":"Calle","given":"Paul","email":"","affiliations":[{"id":47877,"text":"Wildlife Conservation Society, Bronx, NY, USA","active":true,"usgs":false}],"preferred":false,"id":848066,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Drew, Mark L.","contributorId":169527,"corporation":false,"usgs":false,"family":"Drew","given":"Mark","email":"","middleInitial":"L.","affiliations":[{"id":25555,"text":"Idaho Dept. of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":847980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":847981,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70239760,"text":"70239760 - 2022 - Predation thresholds for reintroduction of native avifauna following suppression of invasive brown treesnakes on Guam","interactions":[],"lastModifiedDate":"2023-01-18T14:30:11.981529","indexId":"70239760","displayToPublicDate":"2022-08-02T08:26:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Predation thresholds for reintroduction of native avifauna following suppression of invasive brown treesnakes on Guam","docAbstract":"The brown treesnake (BTS) (Boiga irregularis) invasion on Guåhan (in English, Guam) led to the extirpation of nearly all native forest birds. In recent years, methods have been developed to reduce BTS abundance on a landscape scale. To help assess the prospects for the successful reintroduction of native birds to Guåhan following BTS suppression, we modeled bird population persistence based on their life history characteristics and relative sensitivity to BTS predation. We constructed individual-based models and simulated BTS predation in hypothetical founding populations for each of seven candidate bird species. We represented BTS predation risk in two steps: risk of being encountered and risk of mortality if encountered. We link encounter risk from the bird's perspective to snake contact rates at camera traps with live animal lures, the most direct practical means of estimating BTS predation risk. Our simulations support the well-documented fact that Guåhan's birds cannot persist with an uncontrolled population of BTS but do indicate that bird persistence in Guåhan's forests is possible with suppression short of total eradication. We estimate threshold BTS contact rates would need to be below 0.0002–0.0006 snake contacts per bird per night for these birds to persist on the landscape, which translates to an annual encounter probability of 0.07–0.20. We simulated the effects of snake-proof nest boxes for Sihek (Todiramphus cinnamominus) and Såli (Aplonis opaca), but the benefits were small relative to the overall variation in contact rate thresholds among species. This variation among focal bird species in sustainable predation levels can be used to prioritize species for reintroduction in a BTS-suppressed landscape, but variation among these species is narrow relative to the required reduction from current BTS levels, which may be four orders of magnitude higher (>0.18). Our modeling indicates that the required predation thresholds may need to be lower than have yet been demonstrated with current BTS management. Our predation threshold metric provides an important management tool to help estimate target BTS suppression levels that can be used to determine when bird reintroduction campaigns might begin and serves as a model for other systems to match predator control with reintroduction efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2716","usgsCitation":"McElderry, R., Paxton, E.H., Nguyen, A.V., and Siers, S.R., 2022, Predation thresholds for reintroduction of native avifauna following suppression of invasive brown treesnakes on Guam: Ecological Applications, v. 32, no. 8, e2716, 19 p., https://doi.org/10.1002/eap.2716.","productDescription":"e2716, 19 p.","ipdsId":"IP-130719","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":446952,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2716","text":"Publisher Index Page"},{"id":412025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.97077826134404,\n 13.59091065759955\n ],\n [\n 144.85095546721982,\n 13.663691679762252\n ],\n [\n 144.77392652814024,\n 13.509785599826927\n ],\n [\n 144.6134495717218,\n 13.445281650900142\n ],\n [\n 144.6284274209881,\n 13.332878728890421\n ],\n [\n 144.6797800470419,\n 13.235003984055268\n ],\n [\n 144.726853287589,\n 13.224589457355279\n ],\n [\n 144.78890437740444,\n 13.272492591536036\n ],\n [\n 144.7931837629091,\n 13.401575642368869\n ],\n [\n 144.93226379180425,\n 13.509785472844925\n ],\n [\n 144.97077826134404,\n 13.59091065759955\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McElderry, Robert","contributorId":265185,"corporation":false,"usgs":false,"family":"McElderry","given":"Robert","email":"","affiliations":[{"id":54632,"text":"Research Corporation of the University of Guam","active":true,"usgs":false}],"preferred":false,"id":861792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":861793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nguyen, Andre Van 0000-0001-5917-0360","orcid":"https://orcid.org/0000-0001-5917-0360","contributorId":301028,"corporation":false,"usgs":true,"family":"Nguyen","given":"Andre","email":"","middleInitial":"Van","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":861794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":861795,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237209,"text":"70237209 - 2022 - Chapter 1: General conceptual model for climate change in the Upper San Francisco Estuary","interactions":[],"lastModifiedDate":"2022-10-05T20:04:24.970765","indexId":"70237209","displayToPublicDate":"2022-08-01T11:35:25","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":12617,"text":"IEP Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"99","chapter":"1","title":"Chapter 1: General conceptual model for climate change in the Upper San Francisco Estuary","docAbstract":"This report is a collaboration by many state and federal agencies working in the Upper San Francisco Estuary to analyze the potential impacts of climate change to different ecosystems found here. Management stategies for ecological values in the face of climate change require reliable and focused information. In this technical report, our focus is on the Upper San Francisco Estuary (SFE), which contains the Sacramento-San Joaquin Delta and Suisun Bay. This area is home to three interconnected ecosystems: open water, floodplain, and tidal marsh. For this geographical area, we have decades of in-depth monitoring information and scientific investigations that have been successfully used to address a number of management needs. In 2019, the Interagency Ecological Program established a diverse work team to improve our ability to anticipate and respond to climate change impacts. The charge to the group was to:
• synthesize science relevant to climate change,
• determine important knowledge gaps, and
• identify ecosystem metrics for climate change.
We focus our analyses on the likely impacts of climate change on interconnected aquatic habitats. We illustrate how changes in habitats are likely to affect diverse species.
In this report we describe ecological trends attributable to climate change and likely future impacts. We address four principal questions:
1. How have the habitats and biotic communities changed due to climatic trends and events?
2. How are estuarine habitats, flora, and fauna likely to change as climate change trends continue?
3. What are key metrics to document ecosystem change as a result of climate change?
4. How should our monitoring change to improve information value?
Our work builds on the similar work of the San Francisco Baylands Goals Project (Goals Project 2015), which addressed climate change impacts to wetlands downstream of the confluence of the Sacramento and San Joaquin Rivers. We aim to contribute to an integrated baseline understanding of climate change impacts for the entire San Francisco Estuary.
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Davis","active":true,"usgs":false}],"preferred":false,"id":853911,"contributorType":{"id":2,"text":"Editors"},"rank":14},{"text":"Matthias, Bryan G.","contributorId":240763,"corporation":false,"usgs":false,"family":"Matthias","given":"Bryan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":853638,"contributorType":{"id":2,"text":"Editors"},"rank":15},{"text":"Pien, Catarina","contributorId":297193,"corporation":false,"usgs":false,"family":"Pien","given":"Catarina","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":853639,"contributorType":{"id":2,"text":"Editors"},"rank":16},{"text":"Wulff, Marissa L. 0000-0003-0121-9066","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":229534,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853640,"contributorType":{"id":2,"text":"Editors"},"rank":17},{"text":"Malinich, Timothy D.","contributorId":7583,"corporation":false,"usgs":true,"family":"Malinich","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":853912,"contributorType":{"id":2,"text":"Editors"},"rank":17},{"text":"McKenzie, Ryan","contributorId":297366,"corporation":false,"usgs":false,"family":"McKenzie","given":"Ryan","email":"","affiliations":[],"preferred":false,"id":853913,"contributorType":{"id":2,"text":"Editors"},"rank":17}],"authors":[{"text":"Bush, Eva","contributorId":297190,"corporation":false,"usgs":false,"family":"Bush","given":"Eva","email":"","affiliations":[{"id":64315,"text":"Delta Stewardship Council Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":853632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herbold, Bruce","contributorId":51223,"corporation":false,"usgs":false,"family":"Herbold","given":"Bruce","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":853633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853634,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237658,"text":"70237658 - 2022 - Examining industry vulnerability: A focus on mineral commodities used in the automotive and electronics industries","interactions":[],"lastModifiedDate":"2022-10-18T15:51:22.56241","indexId":"70237658","displayToPublicDate":"2022-08-01T10:45:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3266,"text":"Resources Policy","active":true,"publicationSubtype":{"id":10}},"title":"Examining industry vulnerability: A focus on mineral commodities used in the automotive and electronics industries","docAbstract":"Automotive manufacturing is material-intensive and dependent on a broad range of mineral commodities. Moreover, the automotive manufacturing industries are reliant on complex and sometimes opaque multi-tiered global supply chains. Among the many industries on which automotive supply chains depend are the electronics and semiconductor industries, which are themselves material-intensive and reliant on opaque global supply chains. A linear programming model built on mineral end-use data and input-output tables provides a tool for investigating inter-industry relationships between the two sets of industry sectors and industrial vulnerability to mineral commodity supply disruptions. Supply disruptions in aluminum, magnesium metal, and zinc—metals used in the body-in-white, wheels and other parts—have significant potential to disrupt the automotive industries. On the other hand, supply disruptions in gallium, tellurium, and indium for example—semiconductor elements used in power electronics, screen coatings and other parts—have significant potential to impact the electronics and computer industries. More interestingly, case studies of the automotive and electronics industries show how supply disruptions in mineral commodities that are generally considered semiconductor materials, such as gallium, can significantly impact the automotive sector.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.resourpol.2022.102894","usgsCitation":"Manley, R., Alonso, E., and Nassar, N.T., 2022, Examining industry vulnerability: A focus on mineral commodities used in the automotive and electronics industries: Resources Policy, v. 78, 102894, 8 p., https://doi.org/10.1016/j.resourpol.2022.102894.","productDescription":"102894, 8 p.","ipdsId":"IP-135107","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":487793,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resourpol.2022.102894","text":"Publisher Index Page"},{"id":408492,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manley, Ross 0000-0002-3341-4766","orcid":"https://orcid.org/0000-0002-3341-4766","contributorId":223012,"corporation":false,"usgs":true,"family":"Manley","given":"Ross","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alonso, Elisa 0000-0002-0090-8284","orcid":"https://orcid.org/0000-0002-0090-8284","contributorId":223015,"corporation":false,"usgs":true,"family":"Alonso","given":"Elisa","email":"","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854895,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":854896,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70234118,"text":"70234118 - 2022 - Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs","interactions":[],"lastModifiedDate":"2022-08-01T14:36:06.939046","indexId":"70234118","displayToPublicDate":"2022-08-01T09:25:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1333,"text":"Continental Shelf Research","active":true,"publicationSubtype":{"id":10}},"title":"Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs","docAbstract":"Structure from motion (SfM) photogrammetry is an increasingly common technique for measuring landscape change over time by deriving 3D point clouds and surface models from overlapping photographs. Traditional change detection approaches require photos that are geotagged with a differential GPS (DGPS) location, which requires expensive equipment that can limit the ability of communities and researchers to perform frequent (i.e. daily, weekly, and/or monthly) surveys. Crowd-sourced photos can lower the barrier to entry and substantially increase the frequency of surveys, although such photos often lack accurate location information and can vary in quality. This paper presents a SfM approach for monitoring environmental change in high relief coastal environments that does not require all photos have DGPS location information and does not require field survey data. A 1.5 km section of coastal bluffs near the Elwha River Delta (Washington state) is used to demonstrate the efficacy of this approach. Photos of the bluff were collected with a digital SLR camera or phone camera while either on foot along the beach or from a boat as part of monitoring following removal of two large dams along the Elwha River during 2011–2013. Only 33% of photos had DGPS location information, whereas most photos had no location information or locations that were accurate to a couple of meters. All photos were processed using 3D, 4D, and fixed-floating (FF) SfM alignment methods and the resulting dense point clouds are used to compare the different alignment approaches with crowd-sourced photo sets. Results demonstrate that 4D and FF approaches are more likely to reconstruct and are more accurate than the 3D approach. While the 4D and FF have comparable accuracies, the FF approach is several orders of magnitude more efficient, as this method can leverage camera location information from relatively few photos to improve the accuracy of all aligned and derived products. Effectively utilizing crowd-sourced photos in SfM change detection can improve the frequency of surveying a landscape in a more cost-effective approach that also has potential for citizen-science engagement and communication. This is especially important for data-poor environments such as high-relief coastal cliffs and bluffs, where near-nadir imagery and LIDAR may fail to accurately capture near-vertical cliffs or bluff faces. Based on the analysis of different photo alignment and filtering approaches, we present suggested best practices for engaging citizen scientists in coastal cliff and bluff monitoring efforts through collecting photos amenable for SfM reconstruction.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.csr.2022.104799","usgsCitation":"Wernette, P., Miller, I.M., Ritchie, A.C., and Warrick, J.A., 2022, Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs: Continental Shelf Research, v. 245, 104799, 12 p., https://doi.org/10.1016/j.csr.2022.104799.","productDescription":"104799, 12 p.","ipdsId":"IP-129225","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446968,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5061/dryad.63xsj3v4s","text":"External Repository"},{"id":404570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.54572296142578,\n 48.146846885734256\n ],\n [\n -123.52460861206055,\n 48.12805945422104\n ],\n [\n -123.51877212524414,\n 48.13413175409871\n ],\n [\n -123.52632522583006,\n 48.13963057588326\n ],\n [\n -123.53284835815428,\n 48.146846885734256\n ],\n [\n -123.54537963867186,\n 48.150970035875766\n ],\n [\n -123.54572296142578,\n 48.146846885734256\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"245","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wernette, Phillipe Alan 0000-0002-8902-5575","orcid":"https://orcid.org/0000-0002-8902-5575","contributorId":259274,"corporation":false,"usgs":true,"family":"Wernette","given":"Phillipe Alan","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Ian M. 0000-0002-3289-6337","orcid":"https://orcid.org/0000-0002-3289-6337","contributorId":41951,"corporation":false,"usgs":false,"family":"Miller","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":847870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ritchie, Andrew C. aritchie@usgs.gov","contributorId":4984,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew","email":"aritchie@usgs.gov","middleInitial":"C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847872,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237091,"text":"70237091 - 2022 - Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018","interactions":[],"lastModifiedDate":"2026-01-12T16:42:05.865776","indexId":"70237091","displayToPublicDate":"2022-08-01T09:21:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018","docAbstract":"This remote sensing section is based on Nagler et al. (in preparation for the journal Hydrological Processes) and is a summary of the USGS preliminary findings to date.
This report documents the changes in green foliage density (greenness) as measured by satellite vegetation index (VI) data and corresponding evapotranspiration (ET) in the riparian corridor of the Colorado River delta associated with the Minutes 319 and 323 environmental water deliveries using time-series data from 2013 through 2018. The report focuses on what happened only within the riparian corridor’s seven reaches since the 2014 flows, and despite being a continuation of measuring greenness and ET after the 2017 end of Minute 319, this study continued the tracking of these two variables, greenness and ET, in these original riparian corridor focal areas. Two spatial scales are used here: (1) Landsat satellite imagery at 30 m pixels and (2) the EOS-1 satellite sensor the Moderate Resolution Imaging Spectrometer (MODIS) with a resolution of 250 m pixels. The focal period includes 2013 (prepulse flow) and the years 2014-2018, with a focus on imagery collected from the Summer growing seasons 2014 through 2018 (one-year, pre-pulse and several post-pulse years, respectively).
This report re-creates the 2013-2017 Landsat-based results from Jarchow et al. (2017a, b) by using the same region of interest (ROI). The report now provides revised and re-created results using all new imagery acquisition and processing techniques, as well as extraction code, created by the Vegetation Index and Phenology (VIP) Lab of the Biosystems Engineering Department of the University of Arizona (UofA). In 2018, methods employed by the VIP lab (and not ArcGIS) were used. ArcGIS was only used in the newly processed data to display the final difference maps. The entire spatial tile data from NASA was downloaded and processed at the VIP Lab using satellite imagery at two resolutions: 250 m MODIS and 30 m Landsat using three sensors, Landsat 5, Landsat 7 ETM+ and Landsat 8 Operational Land Imager (OLI), with added scenes for each year based on new clear atmosphere requirements. The VIP lab clipped the river boundary and seven riparian reaches from the previously existing ROI used in Jarchow et al. (2017 a, b) for the analyses done under Minute 319. The NASA image datasets for this riparian corridor ROI in seven reaches were re-processed to produce additional vegetation index (VI) information for years 2013 to 2018 for this report. At the same time, the report acquired and processed imagery from 2000- 2018 (data outside the scope of this report and data not shown here). The additional VIs (NDVI, scaled NDVI, EVI, EVI2) were analyzed so that new assessments of greenness and ET could be produced from the imagery datasets following methods in Nagler et al. (2013). These VI choices were based on previous performance comparisons between biophysical ground-based data and radiometric satellite-based data collected from this riparian ecosystem (Nagler et al., 2001) as well as performance related to ET estimation (Nagler et al., 2005a, b) and current advancements in VIs such as EVI2.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Minute 323: Colorado River limitrophe and delta environmental flows monitoring interim report for 2018","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"International Boundary and Water Commission United States and Mexico","usgsCitation":"Nagler, P.L., Barreto-Munoz, A., Jarchow, C., and Didan, K., 2022, Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018, 10 p.","productDescription":"10 p.","startPage":"39","endPage":"48","ipdsId":"IP-114755","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":407594,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.17517089843749,\n 31.587894464070395\n ],\n [\n -114.3621826171875,\n 31.587894464070395\n ],\n [\n -114.3621826171875,\n 32.99484290420988\n ],\n [\n -115.17517089843749,\n 32.99484290420988\n ],\n [\n -115.17517089843749,\n 31.587894464070395\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Munoz, Armando","contributorId":131000,"corporation":false,"usgs":false,"family":"Barreto-Munoz","given":"Armando","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":853314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":853315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":853316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234354,"text":"70234354 - 2022 - Rural turtles: Estimating the occupancy of Northwestern Pond Turtles and non-native red-eared sliders in agricultural habitats in California's Sacramento Valley and Sacramento-San Joaquin River Delta","interactions":[],"lastModifiedDate":"2022-08-09T12:19:57.100101","indexId":"70234354","displayToPublicDate":"2022-08-01T07:16:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2901,"text":"Northwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Rural turtles: Estimating the occupancy of Northwestern Pond Turtles and non-native red-eared sliders in agricultural habitats in California's Sacramento Valley and Sacramento-San Joaquin River Delta","docAbstract":"The Northwestern Pond Turtle (Actinemys marmorata; WPT) was once widespread throughout the Sacramento Valley and the Sacramento-San Joaquin River Delta. Much of its historical range has been converted into agricultural land, reducing and altering aquatic habitat and surrounding uplands. Red-eared Sliders (Trachemys scripta elegans; RES) have been introduced throughout much of the existing WPT range, particularly near urban centers, potentially competing with WPT for resources. Previous surveys for turtles in central California have primarily focused on rivers, lakes, and protected wetlands. Little is known about where WPT and RES occur in the vast expanses of agricultural land across the Sacramento Valley and Sacramento-San Joaquin River Delta. Using aquatic hoop nets, we surveyed 142 locations (102 irrigation canal sites, 39 wetlands, 1 tidally influenced slough) across 8 counties during the summers of 2018 and 2019. Both species were detected in agricultural habitats. Using occupancy modeling, we estimated that WPT occur at 44 (95% CRI = 38–53) of our trapping sites and RES occur at 51 (41–66) sampled sites. Co-occurrence of these 2 species was rare; the species were found together at only 6 sites. RES were primarily found in restored wetlands near major roads and the Sacramento metropolitan area, whereas WPT were more commonly found farther from urban areas in wider canals. Our work provides a picture of how WPT and RES occupy this modified agroecosystem that can inform future conservation efforts.
The locations of many faults in and near the Rialto-Colton groundwater subbasin are not precisely known because the spatial density of existing lithologic and hydrologic data used to infer the locations of faults can be sparse. The U.S. Geological Survey, in cooperation with the San Bernardino Valley Municipal Water District, analyzed structural control of groundwater flow in and near the Rialto-Colton groundwater subbasin using Interferometric Synthetic Aperture Radar (InSAR) methods. Faults commonly are barriers to groundwater flow, and the high spatial resolution of InSAR imagery can be used to infer the locations of buried faults where groundwater pumping occurs. InSAR results have revealed three areas in and near the Rialto-Colton groundwater subbasin where buried faults are interpreted as groundwater-flow barriers: the northwestern area about 3 miles northwest of the City of Rialto, the San Jacinto fault area west of the City of San Bernardino, and the southeastern area about 2 miles southeast of the City of Colton. The InSAR results were combined with knowledge gained from previous studies to better define the location and extent of faults acting as groundwater-flow barriers. New data about faults acting as groundwater-flow barriers can be incorporated into future conceptual and hydrologic models of the Rialto-Colton groundwater subbasin and provide water managers information to help effectively manage groundwater resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221030","collaboration":"Prepared in cooperation with the San Bernardino Valley Municipal Water District","programNote":"Water Availability and Use Science Program","usgsCitation":"Brandt, J.T., 2022, Mapping structural control through analysis of land-surface deformation for the Rialto-Colton groundwater subbasin, San Bernardino County, California, 1992–2010: U.S. Geological Survey Open-File Report 2022–1030, 11 p., https://doi.org/10.3133/ofr20221030.","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-084965","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501769,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113347.htm","linkFileType":{"id":5,"text":"html"}},{"id":403230,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1030/images"},{"id":403228,"rank":1,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1030/ofr20221030.xml"},{"id":403229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1030/ofr20221030.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":403232,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"Data release","description":"U.S. Geological Survey, 2014, Web interface: U.S. Geological Survey National Water Information System web page, accessed June 11, 2014, at https://doi.org/10.5066/F7P55KJN.","linkHelpText":"Web interface: U.S. Geological Survey National Water Information System web page"},{"id":404520,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1030/covrthb.jpg"},{"id":404546,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221030/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1030"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Rialto-Colton groundwater subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.51319885253905,\n 34.01851844336969\n ],\n [\n -117.2138214111328,\n 34.01851844336969\n ],\n [\n -117.2138214111328,\n 34.19362958613085\n ],\n [\n -117.51319885253905,\n 34.19362958613085\n ],\n [\n -117.51319885253905,\n 34.01851844336969\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The electrification of volcanic plumes has been described intermittently since at least the time of Pliny the Younger and the 79 AD eruption of Vesuvius. Although sometimes disregarded in the past as secondary effects, recent work suggests that the electrical properties of volcanic plumes reveal intrinsic and otherwise inaccessible parameters of explosive eruptions. An increasing number of volcanic lightning studies across the last decade have shown that electrification is ubiquitous in volcanic plumes. Technological advances in engineering and numerical modelling, paired with close observation of recent eruptions and dedicated laboratory studies (shock-tube and current impulse experiments), show that charge generation and electrical activity are related to the physical, chemical, and dynamic processes underpinning the eruption itself. Refining our understanding of volcanic plume electrification will continue advancing the fundamental understanding of eruptive processes to improve volcano monitoring. Realizing this goal, however, requires an interdisciplinary approach at the intersection of volcanology, atmospheric science, atmospheric electricity, and engineering. Our paper summarizes the rapid and steady progress achieved in recent volcanic lightning research and provides a vision for future developments in this growing field.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01591-3","usgsCitation":"Cimarelli, C., Behnke, S., Genareau, K., Méndez Harper, J., and Van Eaton, A.R., 2022, Volcanic electrification: Recent advances and future perspectives: Bulletin of Volcanology, v. 84, 78, 10 p., https://doi.org/10.1007/s00445-022-01591-3.","productDescription":"78, 10 p.","ipdsId":"IP-139794","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-022-01591-3","text":"Publisher Index Page"},{"id":466638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"84","noUsgsAuthors":false,"publicationDate":"2022-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cimarelli, Corrado","contributorId":257017,"corporation":false,"usgs":false,"family":"Cimarelli","given":"Corrado","affiliations":[{"id":47800,"text":"Ludwig Maximilian University of Munich","active":true,"usgs":false}],"preferred":false,"id":924031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Behnke, Sonja A","contributorId":184085,"corporation":false,"usgs":false,"family":"Behnke","given":"Sonja A","affiliations":[],"preferred":false,"id":924032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Genareau, Kimberly","contributorId":345648,"corporation":false,"usgs":false,"family":"Genareau","given":"Kimberly","affiliations":[{"id":82675,"text":"The University of Alabama, Tuscaloosa, AL","active":true,"usgs":false}],"preferred":false,"id":924033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Méndez Harper, Joshua","contributorId":349123,"corporation":false,"usgs":false,"family":"Méndez Harper","given":"Joshua","affiliations":[{"id":83438,"text":"University of Oregon, Eugene, USA","active":true,"usgs":false}],"preferred":false,"id":924034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924035,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70234181,"text":"70234181 - 2022 - Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle","interactions":[],"lastModifiedDate":"2022-08-03T12:09:42.114227","indexId":"70234181","displayToPublicDate":"2022-07-29T07:05:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle","docAbstract":"Avian influenza viruses can pose serious risks to agricultural production, human health, and wildlife. An understanding of viruses in wild reservoir species across time and space is important to informing surveillance programs, risk models, and potential population impacts for vulnerable species. Although it is recognized that influenza A virus prevalence peaks in reservoir waterfowl in late summer through autumn, temporal and spatial variation across species has not been fully characterized. We combined two large influenza databases for North America and applied spatiotemporal models to explore patterns in prevalence throughout the annual cycle and across the continental United States for 30 waterfowl species. Peaks in prevalence in late summer through autumn were pronounced for dabbling ducks in the genera Anas and Spatula, but not Mareca. Spatially, areas of high prevalence appeared to be related to regional duck density, with highest predicted prevalence found across the upper Midwest during early fall, though further study is needed. We documented elevated prevalence in late winter and early spring, particularly in the Mississippi Alluvial Valley. Our results suggest that spatiotemporal variation in prevalence outside autumn staging areas may also represent a dynamic parameter to be considered in IAV ecology and associated risks.
We examine the real‐time earthquake detection and alerting behavior of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm and compare PLUM’s performance with the real‐time performance of the current source‐characterization‐based ShakeAlert System. In the United States (U.S.), PLUM uses a two‐station approach to detect earthquakes. Once a detection is confirmed, observed modified Mercalli intensity (MMI) distributions are forecast onto a regular grid, in which the preferred alert regions are grid cells with MMI 4.0+ forecasts. Although locations of dense station coverage allow PLUM to detect small (M < 4.5) earthquakes typically not considered for EEW in the U.S., a PLUM detection on a small earthquake does not always generate an alert. This is because PLUM alerts are determined by current shaking distributions. If the MMI 4.0+ shaking subsides prior to detection confirmation by shaking at a second neighboring station, the prior MMI 4.0+ information will not be in the alert forecasts. Of the 432 M 3.0+ U.S. West Coast earthquakes in 2021, 33 produced ground motions large enough to be detected by PLUM. Twenty‐four generated MMI 4.0+ PLUM alerts, whereas ShakeAlert issued public EEW alerts for 13 of these earthquakes. We compare PLUM and ShakeAlert alert regions with ShakeMap and “Did You Feel It?” intensity distributions. Because PLUM alert regions surround stations observed to have strong ground motions (regardless of earthquake magnitude), PLUM alerts reliably include locations that experience significant shaking. This is not necessarily the case for ShakeAlert alert regions when there are large errors in magnitude or epicenter estimates. For two of the largest earthquakes in our real‐time dataset, the M 6.0 Antelope Valley and M 5.1 Petrolia earthquakes, the inclusion of PLUM would have improved real‐time ShakeAlert performance. Our results indicate that incorporation of PLUM into ShakeAlert will improve the robustness of the EEW system.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220022","usgsCitation":"Saunders, J.K., Minson, S.E., Baltay Sundstrom, A.S., Bunn, J.J., Cochran, E.S., Kilb, D.L., O’Rourke, C.T., Hoshiba, M., and Kodera, Y., 2022, Real-time earthquake detection and alerting behavior of PLUM ground-motion-based early warning in the United States: Bulletin of the Seismological Society of America, v. 112, no. 5, p. 2668-2688, https://doi.org/10.1785/0120220022.","productDescription":"21 p.","startPage":"2668","endPage":"2688","ipdsId":"IP-135990","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, 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\"}}]}","volume":"112","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Saunders, Jessie Kate 0000-0001-5340-6715","orcid":"https://orcid.org/0000-0001-5340-6715","contributorId":290634,"corporation":false,"usgs":true,"family":"Saunders","given":"Jessie","email":"","middleInitial":"Kate","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":853347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":853348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunn, Julian J 0000-0002-3798-298X","orcid":"https://orcid.org/0000-0002-3798-298X","contributorId":297107,"corporation":false,"usgs":false,"family":"Bunn","given":"Julian","email":"","middleInitial":"J","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":853349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853350,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kilb, Deborah L.","contributorId":216380,"corporation":false,"usgs":false,"family":"Kilb","given":"Deborah","email":"","middleInitial":"L.","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":853351,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Rourke, Colin T 0000-0001-5403-4685","orcid":"https://orcid.org/0000-0001-5403-4685","contributorId":290635,"corporation":false,"usgs":true,"family":"O’Rourke","given":"Colin","email":"","middleInitial":"T","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853352,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoshiba, Mitsuyuki","contributorId":216382,"corporation":false,"usgs":false,"family":"Hoshiba","given":"Mitsuyuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":853353,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kodera, Yuki","contributorId":290636,"corporation":false,"usgs":false,"family":"Kodera","given":"Yuki","email":"","affiliations":[{"id":39398,"text":"JMA","active":true,"usgs":false}],"preferred":false,"id":853354,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236053,"text":"70236053 - 2022 - Comparisons of the NGA-Subduction ground motion models","interactions":[],"lastModifiedDate":"2022-10-17T16:04:02.593176","indexId":"70236053","displayToPublicDate":"2022-07-28T06:53:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Comparisons of the NGA-Subduction ground motion models","docAbstract":"In this article, ground-motion models (GMMs) for subduction earthquakes recently developed as part of the Next Generation Attenuation-Subduction (NGA-Sub) project are compared. The four models presented in this comparison study are documented in their respective articles submitted along with this article. Each of these four models is based on the analysis of the large NGA-Sub database. Three of the four current models are developed for a global version as well as separate regionalized models. The fourth model was developed based on earthquakes only from Japan, and as such is applicable only for Japan. As part of this comparison study, a general discussion on the parameterization of the four models and the regionalization of the three models is provided. The specific strengths and or weaknesses or the technical decisions and justifications of any one model are not part of this comparison. A selected suite of deterministic attenuation curves and spectra are presented for the models along with a selected suite of currently used subduction models. A limited number of comparisons are presented in this article with a larger number of comparisons and the digital values provided in the electronic attachment. In addition to these scenario calculation comparisons, the results from a standard probabilistic seismic hazard analysis (PSHA) for two sites located in the Pacific Northwest Region in the state of Washington are presented. These calculations highlight the potential impact of using the new GMMs. Based on the comparisons presented here, a general understanding of these new GMMs can be obtained with the expectation that the implementation of a specific seismic hazard study should incorporate similar and additional comparisons and sensitivity studies pertinent to the site of interest.
The southern Rocky Mountains of northern New Mexico and southern Colorado preserve the Oligocene to Pleistocene record of North American continental arc to rift volcanism. The 35–23 million year old (Ma) southern Rocky Mountain volcanic field (SRMVF), spectacularly preserved in the San Juan Mountains of southern Colorado, records the evolution of large andesitic stratovolcanoes to complex caldera clusters, from which at least 22 major ignimbrite sheets (each 150–5,000 cubic kilometers) were erupted. Outflow deposits of the SRMVF preserved along the broadly uplifted northwest flank of the northern Rio Grande rift basin (the San Luis Valley) provide critical structural and temporal constraints on the inception of crustal extension. Coincident with waning stages of SRMVF caldera-forming volcanism (~25.4 Ma), extensional tectonism was accompanied by a transition from bimodal early Miocene to intermediate-composition late Miocene and dominantly basaltic Pliocene rift volcanism of the Taos Plateau in the southern San Luis Basin. Concomitant rift volcanism in the Española Basin and bordering Jemez Mountains of northern New Mexico records a similar Miocene eruptive history dominated by intermediate-composition volcanism that transitioned locally to Pliocene rift-related basaltic volcanism of the Cerros del Rio volcanic field and culminated in eruptions of the iconic rhyolitic Pleistocene Bandelier Tuff and formation of the Valles Caldera along the northwestern rift-basin margin.
This 6-day, 7-night field trip will focus, in broadly equal proportions, on rift-related extensional volcanism of the Jemez Mountains and Taos Plateau regions during the first half of the trip, and on caldera-forming volcanism of the southern Rocky Mountain volcanic field during the second half of the trip. The 35-million-year volcanic history of the region highlighted by new geologic mapping, high-resolution geochronology, petrologic, geochemical, and geophysical data facilitates discussion of (1) the magmatic response to the tectonic transition from subducted-slab arc to continental-rift volcanism; (2) the nature and temporal evolution of rift magmas; (3) fault controls on the spatial evolution of rift magmatism; (4) the diversity of continental-arc ignimbrite volcanism and associated lavas; (5) ignimbrite caldera structure and associated intrusions in three-dimension; (6) the role of recycled crystal mush and magmatic cumulates during growth of Cordilleran batholiths; and (7) high-precision geochronologic contributions to interpretation of relations between regional tectonic and volcanic processes. Most stops will be along roads, but there will be moderate hikes on trails of less than 1-hour duration covering 1–2 kilometers (0.6–1.2 miles) with modest elevation gain of <150 meters (<492 feet).
The route will progress in reverse stratigraphic order, starting in the Jemez Mountains of New Mexico and proceed northward to San Luis Basin and San Luis Hills before turning west to the southeast and central San Juan Mountains. Our last full day takes us to the little-visited and only recently mapped, Bonanza caldera of the northeastern San Juan Mountains and on the final day, we leave the San Luis Valley to briefly explore the Tertiary subvolcanic plutons of the Collegiate Range along the west side of the Arkansas Valley rift valley, en route to Denver.
The authors of all daily contributions acknowledge the helpful reviews by Amy Gilmer and Joe Colgan and thank Christine Chan and Jeremy Havens for assistance with figures, tables, and guidebook text.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175022R","usgsCitation":"Thompson, R.A., Turner, K.J., Lipman, P.W., Wolff, J.A., and Dungan, M.A., 2022, Field-trip guide to continental arc to rift volcanism of the southern Rocky Mountains—Southern Rocky Mountain, Taos Plateau, and Jemez Mountains volcanic fields of southern Colorado and northern New Mexico: U.S. Geological Survey Scientific Investigations Report 2017-5022-R, 346 p., https://doi.org/10.3133/sir20175022R.","productDescription":"Report: xix, 346 p.; Data Release","numberOfPages":"346","onlineOnly":"Y","ipdsId":"IP-103158","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":501942,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113348.htm","linkFileType":{"id":5,"text":"html"}},{"id":404505,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5022/r/sir20175022r.pdf","text":"Report","size":"51 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":404504,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5022/r/covrthb.jpg"},{"id":404506,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BKL3ZE","text":"Data Release","description":"Turner, K.J., Thompson, R.A., and Cosca, M.A., 2022, Data release for geochronology and geochemistry of volcanic rocks in the Southern Rocky Mountains and Taos Plateau volcanic fields and other Oligocene to Pleistocene volcanic rocks within the southern San Luis Basin and San Juan Mountains, southern Colorado and northern New Mexico: U.S. Geological Survey data release, https://doi.org/10.5066/P9BKL3ZE.","linkHelpText":"Data release for geochronology and geochemistry of volcanic rocks in the Southern Rocky Mountains and Taos Plateau volcanic fields and other Oligocene to Pleistocene volcanic rocks within the southern San Luis Basin and San Juan Mountains, southern Colorado and northern New Mexico"}],"country":"United States","state":"Colorado, New Mexico","otherGeospatial":"Southern Rocky Mountain, Taos Plateau, and Jemez Mountains Volcanic Fields","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.52319335937497,\n 35.51434313431818\n ],\n [\n -105.10620117187499,\n 35.51434313431818\n ],\n [\n -105.10620117187499,\n 37.76202988573206\n ],\n [\n -107.52319335937497,\n 37.76202988573206\n ],\n [\n -107.52319335937497,\n 35.51434313431818\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
The morphology of the summit of Nevado del Ruiz volcano (Colombia) and its active Arenas crater is the product of complex interactions between effusive and explosive eruptions, and the dynamics of the summit glacier. Here, we document the morphologic evolution of the summit of Nevado del Ruiz, and the growth of its dome, from a variety of methods: monitoring data (2010 to 2021), photogrammetry, remote sensing, and quantitative modeling. The present morphology of Arenas crater, with small terraces limited by the walls of the crater, various vents of ash emission, and zones of fumarolic activity, has been shaped by the activity following the eruptions of 1845, 1985, 1989, 2012 and the volcanic unrest of the last 10 years. The latest emplacement of a lava dome at the bottom of the main crater began in 2015. The dome grew, with fluctuations in its extrusion rate between ~0.19 m3/s (November 2015) and 0.02 m3/s (February 2018), until December 2019, reaching a diameter of ~130 m, a maximum height of ~60 m, and a volume of 1.7 ± 0.2 × 106 m3.
Geological reservoir characterization is essential for accurate evaluation of gas production performance from gas hydrate reservoirs. Particularly, the understanding of reservoir architecture and heterogeneity is of great importance since these are considered as major controls on fluid hydrodynamic and thermodynamic conditions. This study deals with well log and three-dimensional (3-D) vertical seismic profile (VSP) data acquired from the Hydrate-01 Stratigraphic Test Well within the 7-11-12 prospect, Prudhoe Bay Unit, Alaska North Slope and reports on the results of geological/geophysical evaluation related to the geological structure and reservoir properties of the 7-11-12 prospect. The structural trends of the target reservoirs, based on well correlations, are mostly consistent with the predrill prediction using the surface seismic data, and infer the existence of subseismic faults cutting through the Hydrate-01 well. The 3-D VSP data confirm a down-to-the-east normal fault that offsets the reservoir units across the Hydrate-01 well, which is concordant with the well identification of the same fault, and indicate a northeast-dipping relay structure associated with the overstepping normal faults. The edge enhancement attribute associated with discontinuity generated from the 3-D VSP data shows small faults/fractures, possibly as part of a complex fault network within the imaged normal fault system. These results reveal that the 3-D VSP data provide detailed structural information that is not present from the surface seismic data. The Hydrate-01 well log data confirm the occurrence of gas hydrate at high saturation in the two targeted sand units (B1 and D1 sands), and the comparison to a nearby pre-existing well (7-11-12 well) shows the same general trend in gas hydrate saturation as a map of seismic impedance generated from surface seismic data. The well log data also suggest that the base of gas hydrate occurrence in the Hydrate-01 and 7-11-12 wells is almost aligned at the same depth in both of the targeted B1 and D1 sand reservoirs. Especially for the D1 sand in the Hydrate-01 well, the resistivity logs show a sharp transition from high gas hydrate saturation to fully water-saturated within the D1 sand, suggesting a common gas hydrate/water contact. The results of this study will be used to construct the geological models needed for reservoir simulation studies and they can provide important insights into the geological factors that control the occurrence of gas hydrate on the Alaska North Slope.
Municipal water supply for Albuquerque, New Mexico, is provided, in part, through diversion of surface water from the Rio Grande by way of the San Juan-Chama Drinking Water Project diversion structure. Changes in surface-water quality along the Rio Grande and its tributaries upstream from the San Juan-Chama Drinking Water Project diversion structure are not well characterized. This study describes the methods and results of an analysis of surface-water-quality trends for selected constituents in the Rio Grande upstream from Albuquerque. Trends were evaluated for differing time periods ranging from 2004 to 2019 by using the Seasonal Kendall Tau (SKT) test and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model.
Water-quality data at three long-term sites were used for the trend analyses in this study, with the Cochiti and Alameda sites along the Rio Grande and the Jemez Canyon Dam site along the Jemez River, a tributary of the Rio Grande. The proximity of the Cochiti and Jemez Canyon Dam sites to dams is a drawback to the analysis because it is difficult to differentiate between the influence of dam management and the influence of streamflow on water-quality trends. The data used also did not fully meet desired levels of seasonal sampling density and had shorter periods of record than typically used for trend analysis, and this should be considered in the interpretation of these results.
Study results indicate that concentrations, and thereby fluxes, are influenced by changes in streamflow at the Alameda site. Most trends from the WRTDS results, obtained by using flow-normalization, were downward for constituents at the Alameda site. Most constituents that were analyzed for trends by using SKT did not have a significant trend at any of the sites included in this study, indicating either that the water quality in the Middle Rio Grande Basin has been stable during the study period or that not enough samples were collected during different seasons to characterize the range of concentration variability with streamflow. The SKT test results indicate upward trends in concentrations of the following constituents: aluminum and antimony at the Alameda site, nitrate and nitrate plus nitrite at the Cochiti site, and potassium and antimony during the spring season at Jemez Canyon Dam. The SKT test results indicate a downward trend in cobalt at the Cochiti site that is subject to bias in the cobalt concentrations. SKT test results also indicate small, downward trends in Kjeldahl nitrogen at the Alameda and Cochiti sites.
Concentrations of water-quality constituents were also compared to Federal and State water-quality standards to provide context and relevance to the results. No concentrations were above the national primary or secondary drinking water standards at the Alameda and Cochiti sites, but the Jemez Canyon Dam site did have concentrations above the U.S. Environmental Protection Agency primary drinking water standard for arsenic and above the national secondary drinking water standards for dissolved solids and aluminum. The Alameda and Cochiti sites are on reaches of the Rio Grande that are listed as impaired for gross alpha particles and the Alameda site is on a reach of the Rio Grande that is listed as impaired for Escherichia coli, but there were no consistent changes in concentrations of these constituents at the impaired locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225062","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Flickinger, A.K., and Shephard, Z.M., 2022, Water-quality trends in surface waters of the Jemez River and Middle Rio Grande Basin from Cochiti to Albuquerque, New Mexico, 2004–19: U.S. Geological Survey Scientific Investigations Report 2022–5062, 33 p., https://doi.org/10.3133/sir20225062.","productDescription":"Report: vi, 33 p.; 4 Appendixes; Dataset","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-125261","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":404243,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5062/coverthb.jpg"},{"id":404250,"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":404245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062.pdf","text":"Report","size":"4.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5062"},{"id":404246,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062_appendixes.xlsx","text":"Appendixes 1–4","size":"59.7 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022–5062, appendixes 1–4"},{"id":404248,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062_appendixes.zip","text":"Appendixes 1–4","size":"17.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022–5062, appendixes 1–4"},{"id":404251,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5062/sir20225062.XML"},{"id":404252,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5062/images"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.24829101562499,\n 33.8430453147447\n ],\n [\n -105.16113281249999,\n 33.8430453147447\n ],\n [\n -105.16113281249999,\n 36.589068371399115\n ],\n [\n -108.24829101562499,\n 36.589068371399115\n ],\n [\n -108.24829101562499,\n 33.8430453147447\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
New lithologic and detrital zircon (DZ) U-Pb data from Devonian–Triassic strata on St. Lawrence Island in the Bering Sea and from the western Brooks Range of Alaska suggest affinities between these two areas. The Brooks Range constitutes part of the Arctic Alaska–Chukotka microplate, but the tectonic and paleogeographic affinities of St. Lawrence Island are unknown or at best speculative. Strata on St. Lawrence Island form a Devonian–Triassic carbonate succession and a Mississippian(?)–Triassic clastic succession that are subdivided according to three distinctive DZ age distributions. The Devonian–Triassic carbonate succession has Mississippian-age quartz arenite beds with Silurian, Cambrian, Neoproterozoic, and Mesoproterozoic DZ age modes, and it exhibits similar age distributions and lithologic and biostratigraphic characteristics as Mississippian-age Utukok Formation strata in the Kelly River allochthon of the western Brooks Range. Consistent late Neoproterozoic, Cambrian, and Silurian ages in each of the Mississippian-age units suggest efficient mixing of the DZ prior to deposition, and derivation from strata exposed by the pre-Mississippian unconformity and/or Endicott Group strata that postdate the unconformity. The Mississippian(?)–Triassic clastic succession is subdivided into feldspathic and graywacke subunits. The feldspathic subunit has a unimodal DZ age mode at 2.06 Ga, identical to Nuka Formation strata in the Nuka Ridge allochthon of the western Brooks Range, and it records a distinctive depositional episode related to late Paleozoic juxtaposition of a Paleoproterozoic terrane along the most distal parts of the Arctic Alaska–Chukotka microplate. The graywacke subunit has Triassic maximum depositional ages and abundant late Paleozoic grains, likely sourced from fringing arcs and/or continent-scale paleorivers draining Eurasia, and it has similar age distributions to Triassic strata from the Lisburne Peninsula (northwestern Alaska), Chukotka and Wrangel Island (eastern Russia), and the northern Sverdrup Basin (Canadian Arctic), but, unlike the Devonian–Triassic carbonate succession and feldspathic subunit of the Mississippian(?)–Triassic clastic succession, it has no obvious analogue in the western Brooks Range allochthon stack. These correlations establish St. Lawrence Island as conclusively belonging to the Arctic Alaska–Chukotka microplate, thus enhancing our understanding of the circum-Arctic region in late Paleozoic–Triassic time.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02490.1","usgsCitation":"Amato, J.M., Dumoulin, J.A., Gottlieb, E.S., and Moore, T.E., 2022, Detrital zircon ages from upper Paleozoic–Triassic clastic strata on St. Lawrence Island, Alaska: An enigmatic component of the Arctic Alaska–Chukotka microplate: Geosphere, v. 18, no. 5, p. 1492-1523, https://doi.org/10.1130/GES02490.1.","productDescription":"32 p.","startPage":"1492","endPage":"1523","ipdsId":"IP-134575","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":447026,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02490.1","text":"Publisher Index Page"},{"id":435756,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PILIK","text":"USGS data release","linkHelpText":"Location Data for Petrographic Samples and Isotopic and Age Data from Detrital Zircon Grains from Selected Rock Samples from St. Lawrence Island and the Western Brooks Range, Alaska"},{"id":408489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"St. Lawrence Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.968994140625,\n 62.88520467163244\n ],\n [\n -168.50830078125,\n 62.88520467163244\n ],\n [\n -168.50830078125,\n 63.86487567533106\n ],\n [\n -171.968994140625,\n 63.86487567533106\n ],\n [\n -171.968994140625,\n 62.88520467163244\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Amato, Jeffrey M.","contributorId":247883,"corporation":false,"usgs":false,"family":"Amato","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[{"id":49682,"text":"Dept of Geolgical Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":854897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":854898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gottlieb, Eric S. 0000-0002-4904-9492","orcid":"https://orcid.org/0000-0002-4904-9492","contributorId":291239,"corporation":false,"usgs":false,"family":"Gottlieb","given":"Eric","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":854899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Thomas E. 0000-0002-0878-0457 tmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-0878-0457","contributorId":127538,"corporation":false,"usgs":true,"family":"Moore","given":"Thomas","email":"tmoore@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":854900,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235715,"text":"70235715 - 2022 - Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life","interactions":[],"lastModifiedDate":"2022-08-16T11:42:49.081347","indexId":"70235715","displayToPublicDate":"2022-07-25T06:41:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Defining an epidemiological landscape that connects movement ecology to pathogen transmission and pace-of-life","docAbstract":"Pathogen transmission depends on host density, mobility and contact. These components emerge from host and pathogen movements that themselves arise through interactions with the surrounding environment. The environment, the emergent host and pathogen movements, and the subsequent patterns of density, mobility and contact form an ‘epidemiological landscape’ connecting the environment to specific locations where transmissions occur. Conventionally, the epidemiological landscape has been described in terms of the geographical coordinates where hosts or pathogens are located. We advocate for an alternative approach that relates those locations to attributes of the local environment. Environmental descriptions can strengthen epidemiological forecasts by allowing for predictions even when local geographical data are not available. Environmental predictions are more accessible than ever thanks to new tools from movement ecology, and we introduce a ‘movement-pathogen pace of life’ heuristic to help identify aspects of movement that have the most influence on spatial epidemiology. By linking pathogen transmission directly to the environment, the epidemiological landscape offers an efficient path for using environmental information to inform models describing when and where transmission will occur.
Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra (Equus quagga) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-022-00331-8","usgsCitation":"Dougherty, E., Seidel, D., Blackburn, J., Turner, W.C., and Getz, W., 2022, A framework for integrating inferred movement behavior into disease risk models: Movement Ecology, v. 10, 31, 15 p., https://doi.org/10.1186/s40462-022-00331-8.","productDescription":"31, 15 p.","ipdsId":"IP-138565","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-022-00331-8","text":"Publisher Index Page"},{"id":480820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Namibia","otherGeospatial":"Etosha National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 15.655851549884602,\n -18.75382547428198\n ],\n [\n 15.655851549884602,\n -19.249215511439346\n ],\n [\n 16.38844485420617,\n -19.249215511439346\n ],\n [\n 16.38844485420617,\n -18.75382547428198\n ],\n [\n 15.655851549884602,\n -18.75382547428198\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dougherty, Eric R.","contributorId":349723,"corporation":false,"usgs":false,"family":"Dougherty","given":"Eric R.","affiliations":[],"preferred":false,"id":924662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seidel, Dana P.","contributorId":349724,"corporation":false,"usgs":false,"family":"Seidel","given":"Dana P.","affiliations":[],"preferred":false,"id":924663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blackburn, Jason K.","contributorId":349725,"corporation":false,"usgs":false,"family":"Blackburn","given":"Jason K.","affiliations":[],"preferred":false,"id":924664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Getz, Wayne M.","contributorId":287152,"corporation":false,"usgs":false,"family":"Getz","given":"Wayne M.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":924665,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235705,"text":"70235705 - 2022 - Early treatment of white-nose syndrome is necessary to stop population decline","interactions":[],"lastModifiedDate":"2022-10-17T15:49:29.799347","indexId":"70235705","displayToPublicDate":"2022-07-22T07:02:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Early treatment of white-nose syndrome is necessary to stop population decline","docAbstract":"Magnetotelluric (MT) imaging results from mineral provinces in Australia and in the United States show an apparent spatial relationship between crustal-scale electrical conductivity anomalies and major magmatic-hydrothermal iron oxide-apatite/iron oxide-copper-gold (IOA-IOCG) deposits. Although these observations have driven substantial interest in the use of MT data to image ancient fluid pathways, the exact cause of these anomalies has been unclear. Here, we interpret the conductors to be the result of graphite precipitation from CO2-rich magmatic fluids during cooling. These fluids would have exsolved from mafic magmas at mid- to lower-crustal depths; saline magmatic fluids that could drive mineralization were likely derived from related, more evolved intrusions at shallower crustal levels. In our model, the conductivity anomalies then mark zones that once were the deep roots of ancient magmatic-hydrothermal mineral systems.
Mountain pine beetle (MPB) is a native disturbance agent across most pine forests in the western US. Climate changes will directly and indirectly impact frequencies and severities of MPB outbreaks, which can then alter fuel characteristics and wildland fire dynamics via changes in stand structure and composition. To investigate the importance of MPB to past and future landscape dynamics, we used the mechanistic, spatially explicit ecosystem process model FireBGCv2 to quantify interactions among climate, MPB, wildfire, fire suppression, and fuel management under historical and projected future climates for three western US landscapes. We compared simulated FireBGCv2 output from three MPB modules (none, simple empirical, and complex mechanistic) using three focus variables and six exploratory variables to evaluate the importance of MPB to landscape dynamics.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-022-00137-4","usgsCitation":"Keane, R., Bentz, B., Holsinger, L.M., Saab, V., and Loehman, R.A., 2022, Modeled interactions of mountain pine beetle and wildland fire under future climate and management scenarios for three western US landscapes: Fire Ecology, v. 18, 12, 18 p., https://doi.org/10.1186/s42408-022-00137-4.","productDescription":"12, 18 p.","ipdsId":"IP-139683","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":447042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-022-00137-4","text":"Publisher Index Page"},{"id":412113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.98363968463241,\n 49.37174471222207\n ],\n [\n -124.98363968463241,\n 41.36819847856913\n ],\n [\n -109.9167960885596,\n 41.36819847856913\n ],\n [\n -109.9167960885596,\n 49.37174471222207\n ],\n [\n -124.98363968463241,\n 49.37174471222207\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2022-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Keane, Robert","contributorId":187606,"corporation":false,"usgs":false,"family":"Keane","given":"Robert","affiliations":[],"preferred":false,"id":861970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bentz, Barbara","contributorId":146954,"corporation":false,"usgs":false,"family":"Bentz","given":"Barbara","affiliations":[],"preferred":false,"id":861971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holsinger, Lisa M.","contributorId":187607,"corporation":false,"usgs":false,"family":"Holsinger","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":861972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Saab, Victoria","contributorId":301089,"corporation":false,"usgs":false,"family":"Saab","given":"Victoria","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":861973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":861974,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70233493,"text":"sir20225056 - 2022 - Light attenuation and erosion characteristics of fine sediments in a highly turbid, shallow, Great Basin Lake—Malheur Lake, Oregon, 2017–18","interactions":[],"lastModifiedDate":"2022-09-28T14:04:55.75131","indexId":"sir20225056","displayToPublicDate":"2022-07-21T13:47:09","publicationYear":"2022","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":"2022-5056","displayTitle":"Light Attenuation and Erosion Characteristics of Fine Sediments in a Highly Turbid, Shallow, Great Basin Lake—Malheur Lake, Oregon, 2017–18","title":"Light attenuation and erosion characteristics of fine sediments in a highly turbid, shallow, Great Basin Lake—Malheur Lake, Oregon, 2017–18","docAbstract":"Malheur Lake is a large, shallow, turbid lake in southeastern Oregon that fluctuates widely in surface area in response to yearly precipitation and climatic cycles. High suspended-sediment concentrations (SSCs) likely are negatively affecting the survival of aquatic plants by reducing the intensity of solar radiation reaching the plants, thus inhibiting photosynthesis. This study was designed to determine the types of suspended material, the erodibility of the lakebed, the attenuation of photosynthetically active radiation (PAR) through the water column, and the effects of wind and precipitation on SSC.
Two sites in the lake were monitored for approximately 5 months during the summer growing season each year (2017–19). At these sites, turbidity, chlorophyll a fluorescence (a surrogate for concentration), and underwater PAR measurements were collected continuously, and discrete samples were collected every 2 weeks and analyzed for SSC, loss on ignition, and chlorophyll a concentration. Underwater PAR profile measurements were collected during site visits, and a nearby meteorological station recorded terrestrial PAR and wind speeds.
About 18 percent of suspended material in the water was organic and mostly detrital. Nearly 100 percent of all suspended material was fine material (less than 63 micrometers), and more than 90 percent of the surficial lakebed material was fine material. The high concentrations of fine material in the water column can be expected to strongly attenuate light.
SSC was significantly higher at both sites in 2018 compared to 2017 and 2019; the interannual differences were mostly due to the lower amount of precipitation in 2018, which resulted in shallower lake depths. Three years of SSC values multiplied by water depth showed a seasonal pattern: concentrations were often highest in early spring, lowest in summer, and intermediate in autumn.
Episodic wind events with speeds of 5–10 meters per second caused rapid increases in turbidity above background that lasted for a few days. However, a baseline SSC value multiplied by water depth (estimated to be 0.11 kilograms per square meter) was present between wind events and even under ice, suggesting a persistent suspension of very fine, highly erodible material. Terrestrial and underwater PAR measurements were used to develop a relation between PAR attenuation and turbidity that can be used in modeling restoration scenarios. Calculated bottom shear stress caused by wind-generated waves ranged from 0 to 0.4 pascals (Pa). Erosion experiments indicated variability in the bottom sediments from the two lake sites, but much of the lakebed is highly erodible at a threshold of 0.05 to 0.1 Pa.
Restoration actions may target the persistent turbidity (for example, the use of flocculation) or transient turbidity (for example, construction of wave-reduction barriers), with a goal of attaining approximately 36 micromoles photons per square meter per second of PAR at the lakebed to promote emergence of sago pondweed and other desirable plants. Currently, that threshold often is reached from 4 to 34 centimeters (cm) below the water surface in 1 meter water depth, depending on wind conditions, but halving persistent turbidity would increase the upper end of the range to 55 cm. Additional studies regarding the effects of (1) sediment drying on resuspension and (2) nutrient inputs and internal cycling on phytoplankton populations would help determine the most appropriate restoration strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225056","usgsCitation":"Wood, T.M., and Smith, C.D., 2022, Light attenuation and erosion characteristics of fine sediments in a highly turbid, shallow, Great Basin Lake—Malheur Lake, Oregon, 2017–18: U.S. Geological Survey Scientific Investigations Report 2022–5056, 51 p., https://doi.org/10.3133/sir20225056.","productDescription":"Report: x, 51 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-123319","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":404300,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95FMN8F","text":"USGS data release","description":"USGS data release","linkHelpText":"Photosynthetically active radiation measurements collected at Malheur Lake, Oregon, 2017-18"},{"id":404299,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92ZBWJ5","text":"USGS data release","description":"USGS data release","linkHelpText":"Phytoplankton data for Malheur Lake, Oregon, 2018-20"},{"id":404302,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5056/sir20225056.XML"},{"id":404301,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5056/images"},{"id":404297,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5056/sir20225056.pdf","text":"Report","size":"4.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5056"},{"id":404296,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5056/coverthb.jpg"},{"id":404298,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5056"}],"country":"United States","state":"Oregon","otherGeospatial":"Malheur Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.08218383789062,\n 43.113014204188914\n ],\n [\n -118.42300415039062,\n 43.113014204188914\n ],\n [\n -118.42300415039062,\n 43.49178653083377\n ],\n [\n -119.08218383789062,\n 43.49178653083377\n ],\n [\n -119.08218383789062,\n 43.113014204188914\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 92701
The Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM) was developed through an interagency effort between the U.S. Geological Survey and the Bureau of Reclamation to provide a tool for analyzing the hydrologic system response to the historical evolution of water use and potential changes in water supplies and demands in the Hatch Valley (also known as Rincon Valley in the study area) and Mesilla Basin, New Mexico and Texas, United States, and northern Chihuahua, Mexico. Reclamation operates the Rio Grande Project (RGP) to store and deliver surface water for irrigation and municipal use within the study area and in the El Paso Valley south of the El Paso Narrows.
Biases in the RGTIHM’s simulation of streamflow and aquifer storage depletion and the availability of new estimates of historical agricultural consumptive use in the study area initiated an update and recalibration of the RGTIHM. In addition to the new estimates of historical agricultural consumptive use, updates were made to more accurately represent the natural system and included adjustments to the initial groundwater levels; streamflow rating tables; Rio Grande, canal, and drain streambed elevations; tributary streambed elevations; surface-water inflows and diversions; RGP surface-water deliveries and canal waste; on-farm efficiency; the routing of surface-water runoff within the MODFLOW Farm Process; and general head boundaries used to simulate interbasin groundwater flow. Model settings, including the assignment of hydraulic conductivity and storage properties to model layers and the MODFLOW solver package, were adjusted to improve numerical stability, and the model was recalibrated to better simulate the natural system. The updated and recalibrated RGTIHM demonstrates a robust ability to simulate the spatially and temporally variable measurements, estimates, or reports of hydraulic head, surface-water flows, agricultural pumping, RGP surface-water deliveries and canal waste, and decadal aquifer storage changes, with improvements over the previous version of the model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225045","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Ritchie, A.B., Galanter, A.E., Flickinger, A.K., Shephard, Z.M., and Ferguson, I.M., 2022, Update and recalibration of the Rio Grande Transboundary Integrated Hydrologic Model, New Mexico and Texas, United States, and northern Chihuahua, Mexico: U.S. Geological Survey Scientific Investigations Report 2022–5045, 28 p., https://doi.org/10.3133/sir20225045.","productDescription":"Report: vi, 28 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-132740","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":404022,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PLDXV","text":"USGS data release","linkHelpText":"MODFLOW One-Water Hydrologic Flow Model (MF-OWHM) used to simulate conjunctive use in the Hatch Valley and Mesilla Basin, New Mexico and Texas, United States, and northern Chihuahua, Mexico"},{"id":404024,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5045/sir20225045.xml"},{"id":404023,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5045/images"},{"id":404021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5045/sir20225045.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5045"},{"id":404020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5045/coverthb.jpg"},{"id":502400,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113311.htm","linkFileType":{"id":5,"text":"html"}}],"country":"Mexico, United States","state":"Chihuahua, New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108,\n 31.5\n ],\n [\n -106,\n 31.5\n ],\n [\n -106,\n 33.25\n ],\n [\n -108,\n 33.25\n ],\n [\n -108,\n 31.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113