{"pageNumber":"62","pageRowStart":"1525","pageSize":"25","recordCount":46619,"records":[{"id":70270796,"text":"70270796 - 2024 - Assessing habitat use and population dynamics of fisheries resources at Kaloko Fishpond","interactions":[],"lastModifiedDate":"2025-08-26T15:40:07.79183","indexId":"70270796","displayToPublicDate":"2024-07-31T10:28:47","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":22189,"text":"Hawai’i Cooperative Fishery Research Unit Technical Report Series","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"HCFRU-003","title":"Assessing habitat use and population dynamics of fisheries resources at Kaloko Fishpond","docAbstract":"<p><span>Throughout Hawai'i, fishponds are considered by their local communities as important cultural touchstones, a source of local, sustainably produced food, and an important component to the development of community-based management for nearshore fisheries. Within Kaloko-Honokōhau National Historical Park, the restoration of Kaloko Fishpond for traditional aquaculture management is a goal of both the National Park Service (NPS) and Hui Kaloko-Honokōhau, a community-based group of kia'i, i.e., caretakers and native Hawaiian cultural practitioners. However, existing data on the demographics and condition of the fish populations within the pond, and the fish-habitat quality are poor to non-existent. Therefore, the objectives of this study were to: catalog fish species composition and distribution in the pond; estimate the abundance of focal species/taxonomic groups; and evaluate the occupancy patterns of the invasive algae Acanthophora spicifera and Upside-down Jellyfish Cassiopea andromeda. As part of these objectives, a survey protocol and analysis framework were designed and evaluated to ensure that the NPS and community group would be able to refine and implement them to continue their monitoring efforts. We conducted dual-observer shore-based visual surveys multiple times per week during September-October 2020 and April-September 2022. A total of 41 species/taxonomic groups were recorded over the course of the surveys. The largest number of species/taxonomic groups were observed at survey stations located on or near the kuapā, or wall separating the fishpond from the ocean. N-mixture models fitted to the data estimated a total population of 353 – 392 mullets, 134 – 192 flagtails (āholehole), and 189 – 277 Milkfish (Awa) Chanos chanos occurring within the 1.2-ha portion of Kaloko Fishpond that could be surveyed visually from the shoreline. Multi-season occupancy models fitted to the surveyed presence of A. spicifera and Upside-down Jellyfish indicted sites throughout most of the pond exhibited moderate and consistent occupancy (ψ = 0.30 – 0.40) throughout much of the pond, except for the northeast corner of the pond (Kaloko Iki) where colonization rates were lower and extinction rates higher than other areas within Kaloko. The visual survey method developed for this study provides a low-cost and effective starting point for the development of methodology that can be used both by NPS personnel and volunteers from the community group. However, we were only able to estimate fish populations for approximately 24% of the area of Kaloko Fishpond with this method. Given that the deeper areas of Kaloko Fishpond are completely inaccessible to the visual survey method used, generating population estimates for the entire pond based on the parameters estimated in the current study is not recommended without further investigation into fish movement and habitat use. Various means to refine this protocol to better meet the needs and abilities of the NPS and community group are proposed.</span></p>","language":"English","publisher":"University of Hawai'i","usgsCitation":"Grabowski, T.B., Tabandera, R., Greenwald, N., and Larson, A., 2024, Assessing habitat use and population dynamics of fisheries resources at Kaloko Fishpond: Hawai’i Cooperative Fishery Research Unit Technical Report Series HCFRU-003, 80 p.","productDescription":"80 p.","ipdsId":"IP-154335","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":494691,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hdl.handle.net/10790/43639","linkFileType":{"id":5,"text":"html"}},{"id":494905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kalako Fishpond","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -156.03516036623958,\n              19.68986132494203\n            ],\n            [\n              -156.03516036623958,\n              19.686419593600434\n            ],\n            [\n              -156.0305731643826,\n              19.686419593600434\n            ],\n            [\n              -156.0305731643826,\n              19.68986132494203\n            ],\n            [\n              -156.03516036623958,\n              19.68986132494203\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationDate":"2024-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Grabowski, Timothy B. 0000-0001-9763-8948 tgrabowski@usgs.gov","orcid":"https://orcid.org/0000-0001-9763-8948","contributorId":4178,"corporation":false,"usgs":true,"family":"Grabowski","given":"Timothy","email":"tgrabowski@usgs.gov","middleInitial":"B.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":947091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tabandera, Ricky","contributorId":360473,"corporation":false,"usgs":false,"family":"Tabandera","given":"Ricky","affiliations":[{"id":64379,"text":"University of Hawai'i at Hilo","active":true,"usgs":false}],"preferred":false,"id":947092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greenwald, Nathaniel","contributorId":360476,"corporation":false,"usgs":false,"family":"Greenwald","given":"Nathaniel","affiliations":[{"id":64379,"text":"University of Hawai'i at Hilo","active":true,"usgs":false}],"preferred":false,"id":947093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, Annie","contributorId":360479,"corporation":false,"usgs":false,"family":"Larson","given":"Annie","affiliations":[{"id":64379,"text":"University of Hawai'i at Hilo","active":true,"usgs":false}],"preferred":false,"id":947094,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70259309,"text":"70259309 - 2024 - Toward a set of essential biodiversity variables for assessing change in mountains globally","interactions":[],"lastModifiedDate":"2024-10-03T15:23:00.803017","indexId":"70259309","displayToPublicDate":"2024-07-31T10:14:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":997,"text":"BioScience","active":true,"publicationSubtype":{"id":10}},"title":"Toward a set of essential biodiversity variables for assessing change in mountains globally","docAbstract":"<p><span>Mountain regions harbor unique and rich biodiversity, forming an important part of our global life support system. This rich biodiversity underpins the ecological intactness and functioning of mountain ecosystems, which are imperative for the provision of key ecosystem services. A considerable amount of data are required to assess ecological intactness and ecosystem functioning and, given the profound anthropogenic pressures many mountain regions are being subjected to, are urgently needed. However, data on mountain biodiversity remain lacking. The essential biodiversity variables (EBVs) framework can help focus efforts related to detecting, investigating, predicting, and managing global biodiversity change, but has not yet been considered in the context of mountains. Here, we review key biological processes and physical phenomena that strongly influence mountain biodiversity and ecosystems and elucidate their associations with potential mountain EBVs. We identify seven EBVs of highest relevance for tracking and understanding the most critical drivers and responses of mountain biodiversity change. If they are implemented, the selected EBVs will contribute useful information to inform management and policy interventions seeking to halt mountain biodiversity loss and maintain functional mountain ecosystems.</span></p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biae052","usgsCitation":"Schmeller, D., Thornton, J., Urbach, D., Alexander, J., Jetz, W., Kulonen, A., Mills, R., Notornicola, C., Pallazi, E., Pauli, H., Randin, C., Rosbakh, S., Sayre, R., Tehrani, N., Verbiest, W., Walker, T., Wipf, S., and Adler, C., 2024, Toward a set of essential biodiversity variables for assessing change in mountains globally: BioScience, v. 74, no. 8, p. 539-551, https://doi.org/10.1093/biosci/biae052.","productDescription":"13 p.","startPage":"539","endPage":"551","ipdsId":"IP-161747","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":466973,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1093/biosci/biae052","text":"External Repository"},{"id":462543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"74","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmeller, Dirk","contributorId":344844,"corporation":false,"usgs":false,"family":"Schmeller","given":"Dirk","affiliations":[{"id":82423,"text":"Toulouse University","active":true,"usgs":false}],"preferred":false,"id":914866,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thornton, James","contributorId":344845,"corporation":false,"usgs":false,"family":"Thornton","given":"James","affiliations":[{"id":82424,"text":"Mountain Research Initiative, University of Bern","active":true,"usgs":false}],"preferred":false,"id":914867,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Urbach, Davnah","contributorId":344846,"corporation":false,"usgs":false,"family":"Urbach","given":"Davnah","affiliations":[{"id":82427,"text":"Global Mountain Biodiversity Assessment, University of Bern","active":true,"usgs":false}],"preferred":false,"id":914868,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Jake","contributorId":344847,"corporation":false,"usgs":false,"family":"Alexander","given":"Jake","affiliations":[{"id":27579,"text":"Swiss Federal Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":914869,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jetz, Walter","contributorId":344848,"corporation":false,"usgs":false,"family":"Jetz","given":"Walter","affiliations":[{"id":37550,"text":"Yale University","active":true,"usgs":false}],"preferred":false,"id":914870,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kulonen, Aino","contributorId":344849,"corporation":false,"usgs":false,"family":"Kulonen","given":"Aino","affiliations":[{"id":82424,"text":"Mountain Research Initiative, University of Bern","active":true,"usgs":false}],"preferred":false,"id":914871,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mills, Robert","contributorId":344850,"corporation":false,"usgs":false,"family":"Mills","given":"Robert","email":"","affiliations":[{"id":35536,"text":"University of York","active":true,"usgs":false}],"preferred":false,"id":914872,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Notornicola, Claudia","contributorId":344851,"corporation":false,"usgs":false,"family":"Notornicola","given":"Claudia","email":"","affiliations":[{"id":38842,"text":"EURAC Research","active":true,"usgs":false}],"preferred":false,"id":914873,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pallazi, Elisa","contributorId":344852,"corporation":false,"usgs":false,"family":"Pallazi","given":"Elisa","email":"","affiliations":[{"id":64513,"text":"University of Turin","active":true,"usgs":false}],"preferred":false,"id":914874,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pauli, Harald","contributorId":344853,"corporation":false,"usgs":false,"family":"Pauli","given":"Harald","email":"","affiliations":[{"id":82428,"text":"Austrian Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":914875,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Randin, Christophe","contributorId":344854,"corporation":false,"usgs":false,"family":"Randin","given":"Christophe","email":"","affiliations":[{"id":35541,"text":"University of Lausanne","active":true,"usgs":false}],"preferred":false,"id":914876,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rosbakh, Sergey","contributorId":344855,"corporation":false,"usgs":false,"family":"Rosbakh","given":"Sergey","email":"","affiliations":[{"id":12672,"text":"University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":914877,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":245011,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":914878,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tehrani, Nasrin","contributorId":344856,"corporation":false,"usgs":false,"family":"Tehrani","given":"Nasrin","email":"","affiliations":[{"id":35541,"text":"University of Lausanne","active":true,"usgs":false}],"preferred":false,"id":914879,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Verbiest, William","contributorId":344857,"corporation":false,"usgs":false,"family":"Verbiest","given":"William","email":"","affiliations":[{"id":27567,"text":"Ghent University","active":true,"usgs":false}],"preferred":false,"id":914880,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Walker, Tom","contributorId":344858,"corporation":false,"usgs":false,"family":"Walker","given":"Tom","email":"","affiliations":[{"id":49105,"text":"University of Neuchatel","active":true,"usgs":false}],"preferred":false,"id":914881,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Wipf, Sonja","contributorId":344859,"corporation":false,"usgs":false,"family":"Wipf","given":"Sonja","email":"","affiliations":[{"id":82429,"text":"Institute for Snow and Avalanche Research","active":true,"usgs":false}],"preferred":false,"id":914882,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Adler, Carolina","contributorId":344860,"corporation":false,"usgs":false,"family":"Adler","given":"Carolina","affiliations":[{"id":82424,"text":"Mountain Research Initiative, University of Bern","active":true,"usgs":false}],"preferred":false,"id":914883,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}}
,{"id":70268897,"text":"70268897 - 2024 - Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire","interactions":[],"lastModifiedDate":"2025-07-10T14:01:02.446404","indexId":"70268897","displayToPublicDate":"2024-07-31T08:53:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Background</h3><p>Movement plays a key role in allowing animal species to adapt to sudden environmental shifts. Anthropogenic climate and land use change have accelerated the frequency of some of these extreme disturbances, including megafire. These megafires dramatically alter ecosystems and challenge the capacity of several species to adjust to a rapidly changing landscape. Ungulates and their movement behaviors play a central role in the ecosystem functions of fire-prone ecosystems around the world. Previous work has shown behavioral plasticity is an important mechanism underlying whether large ungulates are able to adjust to recent changes in their environments effectively. Ungulates may respond to the immediate effects of megafire by adjusting their movement and behavior, but how these responses persist or change over time following disturbance is poorly understood.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Methods</h3><p>We examined how an ecologically dominant ungulate with strong site fidelity, Columbian black-tailed deer (<i>Odocoileus hemionus columbianus</i>), adjusted its movement and behavior in response to an altered landscape following a megafire. To do so, we collected GPS data from 21 individual female deer over the course of a year to compare changes in home range size over time and used resource selection functions (RSFs) and hidden Markov movement models (HMMs) to assess changes in behavior and habitat selection.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>We found compelling evidence of adaptive capacity across individual deer in response to megafire. Deer avoided exposed and severely burned areas that lack forage and could be riskier for predation immediately following megafire, but they later altered these behaviors to select areas that burned at higher severities, potentially to take advantage of enhanced forage.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusions</h3><p>These results suggest that despite their high site fidelity, deer can navigate altered landscapes to track rapid shifts in encounter risk with predators and resource availability. This successful adjustment of movement and behavior following extreme disturbance could help facilitate resilience at broader ecological scales.</p>","language":"English","publisher":"BMC","doi":"10.1186/s40462-024-00488-4","usgsCitation":"Calhoun, K., Connor, T., Gaynor, K., Van Scoyoc, A., Mcinturff, M.C., Kreling, S., and Brashares, J., 2024, Movement behavior in a dominant ungulate underlies successful adjustment to a rapidly changing landscape following megafire: Movement Ecology, v. 12, 53, 15 p., https://doi.org/10.1186/s40462-024-00488-4.","productDescription":"53, 15 p.","ipdsId":"IP-147496","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":492091,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-024-00488-4","text":"Publisher Index Page"},{"id":492008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Mendocino County","otherGeospatial":"Hopland Research and Extension Center","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -123.19199932451019,\n              39.34410383705571\n            ],\n            [\n              -123.19199932451019,\n              38.95674957822277\n            ],\n            [\n              -122.6413335916133,\n              38.95674957822277\n            ],\n            [\n              -122.6413335916133,\n              39.34410383705571\n            ],\n            [\n              -123.19199932451019,\n              39.34410383705571\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Calhoun, Kendall L.","contributorId":357766,"corporation":false,"usgs":false,"family":"Calhoun","given":"Kendall L.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":942541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connor, Thomas","contributorId":357767,"corporation":false,"usgs":false,"family":"Connor","given":"Thomas","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":942542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaynor, Kaitlyn M.","contributorId":357768,"corporation":false,"usgs":false,"family":"Gaynor","given":"Kaitlyn M.","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":942543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Scoyoc, Amy","contributorId":357769,"corporation":false,"usgs":false,"family":"Van Scoyoc","given":"Amy","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":942544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mcinturff, Michael C 0000-0002-4858-1292","orcid":"https://orcid.org/0000-0002-4858-1292","contributorId":337290,"corporation":false,"usgs":true,"family":"Mcinturff","given":"Michael","email":"","middleInitial":"C","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":942545,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kreling, Samantha E.S.","contributorId":357770,"corporation":false,"usgs":false,"family":"Kreling","given":"Samantha E.S.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":942546,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brashares, Justin S.","contributorId":357771,"corporation":false,"usgs":false,"family":"Brashares","given":"Justin S.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":942547,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70266736,"text":"70266736 - 2024 - Movement patterns of a small-bodied minnow suggest nomadism in a fragmented, desert river","interactions":[],"lastModifiedDate":"2025-05-12T15:12:59.392385","indexId":"70266736","displayToPublicDate":"2024-07-31T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Movement patterns of a small-bodied minnow suggest nomadism in a fragmented, desert river","docAbstract":"<p>Background</p><p>Unfettered movement among habitats is crucial for fish to access patchily distributed resources and complete their life cycle, but many riverscapes in the American Southwest are fragmented by dams. The federally endangered Rio Grande silvery minnow (<i>Hybognathus amarus</i>, RGSM) persists in a fragmented remnant of its former range (ca. 95% range reduction), and its movement ecology is understudied. </p><p>Methods</p><p>We tracked movements of hatchery-reared RGSM, tagged with passive integrated transponder tags, using stationary and mobile antennas from 2019–2022. We quantified probability of movement and total distance moved by RGSM released above and below a dam. We then assessed how well two prevailing riverine movement theories (i.e., restricted movement paradigm [RMP] and colonization cycle hypothesis [CCH]) explained RGSM movement patterns. </p><p>Results</p><p>We detected 36.8% of released RGSM (n = 37,215) making at least one movement. Movements were leptokurtic and substantially greater than expected based on the RMP for both stationary (1.7–5.9 m) and mobile (30.3–77.8 m) individuals. On average, RGSM were detected at large for 75 days and moved a total of 12.2 rkm within a year. The maximum total distance moved by RGSM was 103 rkm. Similarly, we observed a multimodal distribution of detected range sizes with a mean detected range of 2.4 rkm and a maximum detected range of 78.2 rkm. We found little support for an upstream movement bias, as expected under the CCH, and most movements (74%) were directed downstream. </p><p>Conclusions</p><p>Our data suggest RGSM are highly mobile, with the ability to make long-distance movements. Neither movement theory adequately described movement patterns of RGSM; instead, our findings support a nomadic movement pattern and an apparent drift paradox matching recent studies of other pelagic-broadcast spawning minnows who persist upstream despite experiencing downstream drift as larvae. Resolution of the drift paradox may be achieved through further, targeted studies into different aspects of the species’ life history. Quantification of RGSM movement provides crucial insights into the species’ movement ecology and may help define the appropriate scale of recovery efforts.</p>","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-024-00490-w","usgsCitation":"Chavez, M., Budy, P., Pennock, C., Archdeacon, T., and MacKinnon, P., 2024, Movement patterns of a small-bodied minnow suggest nomadism in a fragmented, desert river: Movement Ecology, v. 12, no. 1, 52, 16 p., https://doi.org/10.1186/s40462-024-00490-w.","productDescription":"52, 16 p.","ipdsId":"IP-160669","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488398,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-024-00490-w","text":"Publisher Index Page"},{"id":485717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio Grande Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -107.31501922274943,\n              35.106549536841754\n            ],\n            [\n              -107.31501922274943,\n              33.32320923069706\n            ],\n            [\n              -106.3385899856597,\n              33.32320923069706\n            ],\n            [\n              -106.3385899856597,\n              35.106549536841754\n            ],\n            [\n              -107.31501922274943,\n              35.106549536841754\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Chavez, Martinique J.","contributorId":354893,"corporation":false,"usgs":false,"family":"Chavez","given":"Martinique J.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":936630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pennock, Casey A.","contributorId":354894,"corporation":false,"usgs":false,"family":"Pennock","given":"Casey A.","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":936631,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Archdeacon, Thomas P.","contributorId":354895,"corporation":false,"usgs":false,"family":"Archdeacon","given":"Thomas P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":936633,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"MacKinnon, Peter D.","contributorId":354897,"corporation":false,"usgs":false,"family":"MacKinnon","given":"Peter D.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":936634,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70256400,"text":"sir20245033 - 2024 - Assessment of long-term changes in surface-water extent within Klamath Marsh, south-central Oregon, 1985–2021","interactions":[],"lastModifiedDate":"2026-02-03T18:28:42.919405","indexId":"sir20245033","displayToPublicDate":"2024-07-30T12:53:26","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5033","displayTitle":"Assessment of Long-Term Changes in Surface-Water Extent Within Klamath Marsh, South-Central Oregon, 1985–2021","title":"Assessment of long-term changes in surface-water extent within Klamath Marsh, south-central Oregon, 1985–2021","docAbstract":"<p>The annual maximum extent of surface water in Klamath Marsh has naturally fluctuated in response to periods of wet and dry conditions in the surrounding basin. Field observations during the 2010s indicate that the annual maximum extent of surface water has been declining and the marsh is not responding to hydrologic inputs as it had historically. This report describes the results of a hydrologic evaluation of Klamath Marsh to characterize and understand multi-year declines in the surface-water extent and increased intermittency of streamflow exiting the marsh.</p><p>Landsat imagery collected during 1985–2021 was processed to create a time series of annual maximum surface-water extent to assess changes in surface-water inundation within the marsh. A 50-percent decrease in the mean surface area of annual total open-water extent (TOWE) during the latter half of the study period (2003–21) compared to the first half (1985–2003) was observed in this 37-year time-series dataset. The change in open-water extent was offset by a corresponding increase in dry land in the marsh.</p><p>Time series of streamflow, groundwater level, total annual precipitation, annual mean temperature, and anthropogenic water use and water management were compiled and evaluated to improve understanding of the factors affecting TOWE. Statistically significant downward trends in the regional groundwater table and streamflow into and out of the marsh were identified as well as statistically significant upward trends in annual mean temperature. Statistically significant correlations among TOWE, streamflow, and groundwater level also were identified. The decreasing trends could not be attributed to changes in total annual precipitation or changing anthropogenic groundwater use within the study area.</p><p>Declines in the open-water extent of Klamath Marsh since 2000 principally are due to a decoupling of the groundwater and surface-water system beneath the marsh because of regional declines in groundwater level. Regional increases in air temperature and the reestablishment of more than 55,000 acres of forested land within the study area have likely contributed to increasing evapotranspiration, leaving less water available for groundwater recharge and stream base flow and resulting in basin-wide declines in streamflow and groundwater levels.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245033","collaboration":"Prepared in cooperation with the Klamath Tribes","usgsCitation":"Kennedy, J.J., Johnson, H.M., Gingerich, S.B., 2024, Assessment of long-term changes in surface-water extent within Klamath Marsh, south-central Oregon, 1985–2021: U.S. Geological Survey Scientific Investigations Report 2024–5033, 32 p., https://doi.org/10.3133/sir20245033.","productDescription":"Report: ix, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-153514","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":499460,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117151.htm","linkFileType":{"id":5,"text":"html"}},{"id":431674,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.XML"},{"id":431673,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5033/images"},{"id":431672,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RC7RJM","text":"USGS data release","description":"USGS data release","linkHelpText":"Klamath Marsh January through June maximum surface water extent, 1985–2021"},{"id":431671,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245033/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5033"},{"id":431670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.pdf","text":"Report","size":"6.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5033"},{"id":431669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5033/sir20245033.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.27115315992094,\n              43.15707158138778\n            ],\n            [\n              -122.27115315992094,\n              42.30\n            ],\n            [\n              -121.15,\n              42.30\n            ],\n            [\n              -121.15,\n              43.15707158138778\n            ],\n            [\n              -122.27115315992094,\n              43.15707158138778\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_or@usgs.gov\" data-mce-href=\"mailto:dc_or@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/oregon-water-science-center\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/oregon-water-science-center\">Oregon Water Science Center</a><br>U.S. Geological Survey<br>601 SW 2nd Avenue, Suite 1950<br>Portland, OR 97204</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Approach</li><li>Results</li><li>Discussion</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2024-07-30","noUsgsAuthors":false,"publicationDate":"2024-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Kennedy, Joseph J. 0000-0002-6608-2366","orcid":"https://orcid.org/0000-0002-6608-2366","contributorId":333051,"corporation":false,"usgs":false,"family":"Kennedy","given":"Joseph J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":907252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Henry M. 0000-0002-7571-4994 hjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7571-4994","contributorId":869,"corporation":false,"usgs":true,"family":"Johnson","given":"Henry","email":"hjohnson@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":907253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gingerich, Stephen B. 0000-0002-4381-0746 sbginger@usgs.gov","orcid":"https://orcid.org/0000-0002-4381-0746","contributorId":294524,"corporation":false,"usgs":false,"family":"Gingerich","given":"Stephen B.","email":"sbginger@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":907254,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70259319,"text":"70259319 - 2024 - Re-evaluating Marine Isotope Stage 5a paleo-sea-level trends from across the Florida Keys reef tract","interactions":[],"lastModifiedDate":"2024-10-04T14:29:30.770237","indexId":"70259319","displayToPublicDate":"2024-07-30T09:23:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7169,"text":"Quaternary Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Re-evaluating Marine Isotope Stage 5a paleo-sea-level trends from across the Florida Keys reef tract","docAbstract":"<p><span>Unraveling how Global Mean Sea Level (GMSL) fluctuated during past warm periods can improve our understanding of linkages between sea-level fluctuations, orbital forcing, and ice-sheet dynamics. Current estimates of GMSL for Marine Isotope Stages (MIS) 5a and 5c — two warm intervals following the relatively well-documented MIS 5e — contain meters of uncertainty and fewer data due to several challenges. These challenges include concealment of datable in-situ coral facies by MIS 1 deposits and inaccessibility due to submergence by modern sea level. We present a comprehensive dataset based on U–Th dating and stratigraphic correlation of 23 cores totaling over 170&nbsp;m of recovered coral-reef deposits across the tectonically stable Florida Keys Reef Tract (FKRT). Following detailed facies descriptions, 34 in-situ, minimally altered aragonitic coral samples (≤2.7% calcite) below the Holocene-Pleistocene boundary were targeted for U–Th geochronology. Fourteen closed-system coral U–Th ages from MIS 5a include the commonly used sea-level indicator&nbsp;</span><i>Acropora palmata</i><span>, but also the massive coral taxa&nbsp;</span><i>Pseudodiploria strigosa, Siderastrea siderea, Orbicella</i><span>&nbsp;spp., and&nbsp;</span><i>Porites astreoides</i><span>. Dating yielded ages in the range of 88–81 ka (average 2σ uncertainty of less than 200 years). These ages suggest MIS 5a reef initiation at ∼88 ka BP, a peak near 83 ka with minimum elevations between −6.0&nbsp;±&nbsp;0.5 and −5.6&nbsp;±&nbsp;0.5&nbsp;m MSL (2σ uncertainty and subsidence-corrected), and reef termination and sea-level fall by ∼81 ka BP. Notably, the range of peak MIS 5a relative sea-level estimates of −6.5 to −5.1 m MSL are more than 2&nbsp;m shallower (higher) than previous estimates of −11 to −9&nbsp;m. Our higher resolution regional sea-level reconstruction across four subregions of the Florida Keys reef tract aligns with changes in July insolation at 65° N: a trend that most other records, such as deep-sea sediments, do not have the accuracy and precision to resolve. Three massive coral samples from MIS 5c, consisting of&nbsp;</span><i>Pseudodiploria clivosa</i><span>, and&nbsp;</span><i>Orbicella</i><span>&nbsp;spp., yielded ages in the range of 104 to 99 ka (average 2σ uncertainty less than 200 years); however, because only one sample met the closed-system criteria, our ability to estimate MIS 5c sea level is relatively limited. More empirical estimates of sea-level from the MIS 5a and MIS 5c intervals based on numerical dating of reliable local sea-level constraints are critical for GMSL calculations and relating changes in sea-level amplitude and timing to global ice volume modeling and glacio-isostatic effects, all of which can improve predictions of future sea-level changes in coastal regions.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.qsa.2024.100222","usgsCitation":"Hsia, S., Toth, L., Mortlock, R.A., and Kerans, C., 2024, Re-evaluating Marine Isotope Stage 5a paleo-sea-level trends from across the Florida Keys reef tract: Quaternary Science Advances, v. 15, 100222, 17 p., https://doi.org/10.1016/j.qsa.2024.100222.","productDescription":"100222, 17 p.","ipdsId":"IP-166304","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466974,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.qsa.2024.100222","text":"Publisher Index Page"},{"id":462598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -79.23876328908999,\n              26.097759348420453\n            ],\n            [\n              -83.46117316122947,\n              26.097759348420453\n            ],\n            [\n              -83.46117316122947,\n              24.102407845404713\n            ],\n            [\n              -79.23876328908999,\n              24.102407845404713\n            ],\n            [\n              -79.23876328908999,\n              26.097759348420453\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hsia, Scarlette 0000-0002-2230-9004","orcid":"https://orcid.org/0000-0002-2230-9004","contributorId":339740,"corporation":false,"usgs":false,"family":"Hsia","given":"Scarlette","email":"","affiliations":[{"id":39890,"text":"University of Texas at Austin, Jackson School of Geosciences","active":true,"usgs":false}],"preferred":false,"id":914916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":914917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mortlock, Richard A.","contributorId":299718,"corporation":false,"usgs":false,"family":"Mortlock","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":914918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kerans, Charles","contributorId":75838,"corporation":false,"usgs":false,"family":"Kerans","given":"Charles","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":914919,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70257097,"text":"70257097 - 2024 - Modeling rare plant habitat together with public land managers using an iterative, coproduced process to inform decision-making on multiple-use public lands","interactions":[],"lastModifiedDate":"2024-08-13T14:43:37.410358","indexId":"70257097","displayToPublicDate":"2024-07-30T08:21:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Modeling rare plant habitat together with public land managers using an iterative, coproduced process to inform decision-making on multiple-use public lands","docAbstract":"<p><span>Public lands across the United States are managed for multiple uses, resources, and values ranging from energy development to rare plant conservation. Intensified energy development and other land use changes across the Southwestern United States have increased the need for proactive management to mitigate impacts to rare plants. Habitat suitability models can inform decision-making and lead to more effective conservation of rare plants and their habitats, but high-quality models that are suited for use at local scales are lacking for many species. Our team of scientists and managers developed ensembles of habitat suitability models for five rare plant species in New Mexico using a coproduced, iterative framework complemented by comprehensive ground truthing and tailoring of products for use in public land decisions. Our process resulted in substantial differences from initial models through changes to environmental predictors, species occurrence and background data, and development of new species-specific predictors. Involving species experts and end users in model development can strengthen the process and resulting model and build understanding and trust in final products. Both factors can promote use of models to inform public land permitting and planning decisions that may affect rare plants, including by guiding development away from highly suitable habitats.</span></p>","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.13179","usgsCitation":"Jarnevich, C.S., Carter, S.K., Davidson, Z.M., MacPhee, N.D., Alexander, P.J., Hayes, B., Belmaric, P.N., and Harms, B., 2024, Modeling rare plant habitat together with public land managers using an iterative, coproduced process to inform decision-making on multiple-use public lands: Conservation Science and Practice, v. 6, no. 8, e13179, 15 p., https://doi.org/10.1111/csp2.13179.","productDescription":"e13179, 15 p.","ipdsId":"IP-158708","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439242,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.13179","text":"Publisher Index Page"},{"id":432438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -108.69579033294093,\n              38.37268084260387\n            ],\n            [\n              -108.69579033294093,\n              35.269919346315746\n            ],\n            [\n              -103.45845941486846,\n              35.269919346315746\n            ],\n            [\n              -103.45845941486846,\n              38.37268084260387\n            ],\n            [\n              -108.69579033294093,\n              38.37268084260387\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"6","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":909380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":909381,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davidson, Zoe M. 0000-0003-2043-8598","orcid":"https://orcid.org/0000-0003-2043-8598","contributorId":336894,"corporation":false,"usgs":false,"family":"Davidson","given":"Zoe","email":"","middleInitial":"M.","affiliations":[{"id":80903,"text":"Bureau of Land Management Headquarters","active":true,"usgs":false}],"preferred":false,"id":909382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacPhee, Nicole D.","contributorId":337152,"corporation":false,"usgs":false,"family":"MacPhee","given":"Nicole","email":"","middleInitial":"D.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":909383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alexander, Patrick J.","contributorId":337153,"corporation":false,"usgs":false,"family":"Alexander","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":909384,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayes, Brandon","contributorId":337154,"corporation":false,"usgs":false,"family":"Hayes","given":"Brandon","email":"","affiliations":[{"id":80983,"text":"Student Services Contractor to USGS FORT","active":true,"usgs":false}],"preferred":false,"id":909385,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Belmaric, Pairsa N.","contributorId":337156,"corporation":false,"usgs":false,"family":"Belmaric","given":"Pairsa","email":"","middleInitial":"N.","affiliations":[{"id":80983,"text":"Student Services Contractor to USGS FORT","active":true,"usgs":false}],"preferred":false,"id":909386,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harms, Benjamin R","contributorId":267283,"corporation":false,"usgs":false,"family":"Harms","given":"Benjamin R","affiliations":[],"preferred":false,"id":909387,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70258229,"text":"70258229 - 2024 - 3-D geological modeling for numerical flow simulation studies of gas hydrate reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope","interactions":[],"lastModifiedDate":"2024-09-09T14:11:34.989533","indexId":"70258229","displayToPublicDate":"2024-07-29T07:09:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1513,"text":"Energy and Fuels","active":true,"publicationSubtype":{"id":10}},"title":"3-D geological modeling for numerical flow simulation studies of gas hydrate reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope","docAbstract":"<div id=\"abstractBox\" class=\"article_abstract-content hlFld-Abstract\"><p class=\"articleBody_abstractText\">Accurate reservoir evaluation requires reliable three-dimensional (3-D) geological models. This study conducted 3-D geological modeling for numerical flow simulation of the B1 sand gas hydrate reservoir at the Kuparuk State 7-11-12 pad, Prudhoe Bay Unit, Alaska North Slope. The model integrates well logs, core, and seismic data to address spatial heterogeneity in geological structures and reservoir properties. Two modeling types were performed: structural framework modeling and petrophysical property modeling. For structural framework modeling, seismic data and well log markers were used to reproduce subsurface structures characterized by a normal fault system. A volume-based modeling algorithm and stair-step gridding were applied. The resulting 3-D model comprised 2,640,000 grid cells across 264 layers, including seven fault grids. For petrophysical property modeling, total porosity was initially modeled using sequential Gaussian simulation with collocated cokriging. To reproduce the upward coarsening of the B1 sand, upscaled log-derived total porosity and a 3-D trend depicting total porosity variation were used as primary and secondary data, respectively. Gas hydrate saturation distribution was modeled similarly, with secondary data from estimated porosity distribution and seismic-derived acoustic impedance map enhancing accuracy. Results indicate higher gas hydrate saturation in the upper part of the B1 sand and areas with higher acoustic impedance. Intrinsic permeability was modeled from the total porosity and clay-bound water volume, and effective permeability was derived from the gas hydrate saturation and intrinsic permeability distributions based on the “Tokyo model”. Effective permeability distributions were influenced by the total porosity, gas hydrate saturation, and intrinsic permeability. Within the same layer, higher gas hydrate saturation leads to decreased effective permeability. In total, 100 sets of multiple scenarios were prepared, providing input data for dynamic flow simulations to evaluate the effects of lateral heterogeneity in reservoir properties and the hydraulic characteristics of faults on production behavior for preassessment before the long-term production test.</p></div>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.energyfuels.4c01665","usgsCitation":"Tamaki, M., Taninaka, M., Ohtsuki, S., Than Tin, A., Shimoda, N., Collett, T., and Boswell, R., 2024, 3-D geological modeling for numerical flow simulation studies of gas hydrate reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope: Energy and Fuels, v. 28, no. 16, p. 15248-15269, https://doi.org/10.1021/acs.energyfuels.4c01665.","productDescription":"22 p.","startPage":"15248","endPage":"15269","ipdsId":"IP-165534","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":489143,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2447061","text":"External Repository"},{"id":433603,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -149.5,\n              70.5\n            ],\n            [\n              -149.5,\n              70.2\n            ],\n            [\n              -149,\n              70.2\n            ],\n            [\n              -149,\n              70.5\n            ],\n            [\n              -149.5,\n              70.5\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"28","issue":"16","noUsgsAuthors":false,"publicationDate":"2024-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Tamaki, Machiko","contributorId":344039,"corporation":false,"usgs":false,"family":"Tamaki","given":"Machiko","affiliations":[{"id":82276,"text":"Japan Oil Engineering Co.","active":true,"usgs":false}],"preferred":false,"id":912666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taninaka, Misuzu","contributorId":344040,"corporation":false,"usgs":false,"family":"Taninaka","given":"Misuzu","email":"","affiliations":[{"id":82276,"text":"Japan Oil Engineering Co.","active":true,"usgs":false}],"preferred":false,"id":912667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ohtsuki, Satoshi","contributorId":344041,"corporation":false,"usgs":false,"family":"Ohtsuki","given":"Satoshi","affiliations":[{"id":82277,"text":"Japan Organization for Metals and Energy Security","active":true,"usgs":false}],"preferred":false,"id":912668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Than Tin, Aung","contributorId":344042,"corporation":false,"usgs":false,"family":"Than Tin","given":"Aung","email":"","affiliations":[{"id":82277,"text":"Japan Organization for Metals and Energy Security","active":true,"usgs":false}],"preferred":false,"id":912669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shimoda, Naoyuki","contributorId":344043,"corporation":false,"usgs":false,"family":"Shimoda","given":"Naoyuki","email":"","affiliations":[{"id":82277,"text":"Japan Organization for Metals and Energy Security","active":true,"usgs":false}],"preferred":false,"id":912670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220806,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":912671,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boswell, Ray","contributorId":344044,"corporation":false,"usgs":false,"family":"Boswell","given":"Ray","affiliations":[{"id":64933,"text":"National Energy Technology Laboratory","active":true,"usgs":false}],"preferred":false,"id":912672,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70259164,"text":"70259164 - 2024 - A Bayesian age from dispersed plagioclase and zircon dates in the Los Chocoyos ash, Central America","interactions":[],"lastModifiedDate":"2024-10-03T16:01:15.602206","indexId":"70259164","displayToPublicDate":"2024-07-29T06:41:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"A Bayesian age from dispersed plagioclase and zircon dates in the Los Chocoyos ash, Central America","docAbstract":"<div id=\"preview-section-abstract\"><div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0002\" class=\"abstract author\"><div id=\"abss0002\"><div id=\"spara012\" class=\"u-margin-s-bottom\">The eruption that produced the modern Atitlán caldera in Guatemala has a Volcanic Explosivity Index (VEI) greater than 8, making it the largest of the Quaternary in the Central American Volcanic Arc (CAVA). It generated ∼1220 km<sup>3</sup><span>&nbsp;</span>(730 km<sup>3</sup><span>&nbsp;</span>dense rock equivalent) of dispersed rhyolitic ash and unwelded pyroclastic flow deposits known as the Los Chocoyos ash. Despite its scale, the age of the Los Chocoyos ash has been difficult to constrain. Its inferred position in marine sediment off the coast of Ecuador suggests an oxygen isotope-based age of 84 ± 5 thousand years (ka) that has been used for decades. A recent effort to determine the eruption age of the Los Chocoyos ash used U-Th/He and U/Th disequilibrium methods that both yield overdispersed sets of dates from zircon. Using U/Th disequilibrium corrected U-Th/He dates, which include some that are up to 19 ± 8 kyr younger than the youngest U/Th disequilibrium dates, an eruption age of 75 ± 4 ka has been proposed, which overlaps the eruption age of the Youngest Toba Tuff,<span>&nbsp;</span><sup>40</sup>Ar/<sup>39</sup>Ar dated at 73.9 ± 0.6 ka. Critically, the above findings have led to the hypothesis that this pair of sulfate-rich supereruptions may be responsible for a global cooling event ∼74 ka. To further test this supereruption doublet hypothesis we present new<span>&nbsp;</span><sup>40</sup>Ar/<sup>39</sup>Ar and U-Th disequilibrium data from plagioclase and zircon rim surfaces, respectively, to quantify the timing of the Los Chocoyos eruption. We apply Bayesian estimation to the overdispersed sets of<span>&nbsp;</span><sup>40</sup>Ar/<sup>39</sup>Ar plagioclase and U-Th disequilibrium zircon dates. This approach accounts for retention of radiogenic<span>&nbsp;</span><sup>40</sup>Ar in plagioclase in a subset of crystals and for crystallization of zircon that may precede the time of eruption and yields remarkably consistent ages from two independent chronometers. The Bayesian estimate of 98 ± 6 ka calculated from 308 individual dates identifies the population nearest to the time of the Los Chocoyos eruption. Interpreted as an eruption age, this finding precludes a link between the Los Chocoyos eruption and global cooling ∼74 ka and rules out the supereruption doublet trigger.</div></div></div></div></div><div id=\"preview-section-introduction\"><br></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2024.118826","usgsCitation":"Baudry, A., Singer, B.S., Jicha, B., Jilly-Rehak, C.E., Vazquez, J.A., and Keller, C.B., 2024, A Bayesian age from dispersed plagioclase and zircon dates in the Los Chocoyos ash, Central America: Earth and Planetary Science Letters, v. 643, 118826, https://doi.org/10.1016/j.epsl.2024.118826.","productDescription":"118826","ipdsId":"IP-165532","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":462406,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"643","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baudry, Alec","contributorId":344647,"corporation":false,"usgs":false,"family":"Baudry","given":"Alec","email":"","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":914385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Singer, Bradley S.","contributorId":344649,"corporation":false,"usgs":false,"family":"Singer","given":"Bradley","email":"","middleInitial":"S.","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":914386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jicha, Brian","contributorId":213920,"corporation":false,"usgs":false,"family":"Jicha","given":"Brian","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":914387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jilly-Rehak, Christine E.","contributorId":344651,"corporation":false,"usgs":false,"family":"Jilly-Rehak","given":"Christine","email":"","middleInitial":"E.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":914388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":914389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keller, C. Brenhin 0000-0001-7400-9428","orcid":"https://orcid.org/0000-0001-7400-9428","contributorId":330229,"corporation":false,"usgs":false,"family":"Keller","given":"C.","email":"","middleInitial":"Brenhin","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":914390,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70264868,"text":"70264868 - 2024 - Near-real-time earthquake-induced fatality estimation using crowdsourced data and few-shot large-language models","interactions":[],"lastModifiedDate":"2025-03-27T13:23:09.681442","indexId":"70264868","displayToPublicDate":"2024-07-27T10:51:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"Near-real-time earthquake-induced fatality estimation using crowdsourced data and few-shot large-language models","docAbstract":"<div id=\"d1e819\" class=\"u-margin-s-bottom\">When a damaging earthquake occurs, immediate information about casualties (e.g., fatalities and injuries) is critical for time-sensitive decision-making by emergency response and aid agencies in the first hours and days. Systems such as the Prompt Assessment of Global Earthquakes for Response (PAGER) by the U.S. Geological Survey (USGS) were developed to provide a forecast of such impacts within about 30 min of any significant earthquake globally. However, existing disaster-induced human loss estimation systems often rely on early casualty reports manually retrieved from global traditional media, which are labor-intensive, time-consuming, and have significant time latencies. Recent approaches use keyword matching and topic modeling to identify human casualty-relevant information from social media but tend to be error-prone when dealing with complex semantics in multi-lingual text data and parsing dynamically changing and conflicting human death and injury numbers shared by various unvetted sources in social media platforms.</div><div id=\"d1e821\" class=\"u-margin-s-bottom\">In this work, we introduce an end-to-end framework to significantly improve the timeliness and accuracy of global earthquake-induced human loss forecasting using multi-lingual, crowdsourced social media. Our framework integrates (i) a hierarchical casualty extraction model built upon large language models, prompt design, and few-shot learning to retrieve quantitative human loss claims from social media, (ii) a physical constraint-aware, dynamic-truth discovery model that discovers the truthful human loss from massive noisy and potentially conflicting human loss claims, and (iii) a Bayesian updating loss projection model that dynamically updates the final loss estimation using discovered truths. We test the framework in real-time on a series of global earthquake events in 2021 and 2022 and show that our framework effectively automates the retrieval of casualty information faster but with comparable accuracy to those now retrieved manually by the USGS. The code associated with this work is made available at:<span>&nbsp;</span><a class=\"anchor anchor-primary\" rel=\"noopener\" href=\"https://github.com/SusuXu-s-Lab/Hierarchical-Earthquake-Casualty-Information-Retrieval\" target=\"_blank\" data-mce-href=\"https://github.com/SusuXu-s-Lab/Hierarchical-Earthquake-Casualty-Information-Retrieval\"><span class=\"anchor-text-container\"><span class=\"anchor-text\">https://github.com/SusuXu-s-Lab/Hierarchical-Earthquake-Casualty-Information-Retrieval</span></span></a></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2024.104680","usgsCitation":"Wang, C., Engler, D.T., Li, X., Hou, J., Wald, D., Jaiswal, K.S., and Xu, S., 2024, Near-real-time earthquake-induced fatality estimation using crowdsourced data and few-shot large-language models: International Journal of Disaster Risk Reduction, v. 111, 104680, 18 p., https://doi.org/10.1016/j.ijdrr.2024.104680.","productDescription":"104680, 18 p.","ipdsId":"IP-153312","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":488692,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2024.104680","text":"Publisher Index Page"},{"id":483885,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","noUsgsAuthors":false,"publicationDate":"2024-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Chenguang","contributorId":352749,"corporation":false,"usgs":false,"family":"Wang","given":"Chenguang","affiliations":[],"preferred":false,"id":932109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engler, Davis T. 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265962,"corporation":false,"usgs":true,"family":"Engler","given":"Davis","email":"","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932105,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Xuechun","contributorId":317874,"corporation":false,"usgs":false,"family":"Li","given":"Xuechun","email":"","affiliations":[{"id":69176,"text":"Stonybrook University","active":true,"usgs":false}],"preferred":false,"id":932103,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hou, James","contributorId":221043,"corporation":false,"usgs":false,"family":"Hou","given":"James","email":"","affiliations":[],"preferred":false,"id":932104,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wald, David J. 0000-0002-8673-1706","orcid":"https://orcid.org/0000-0002-8673-1706","contributorId":352720,"corporation":false,"usgs":true,"family":"Wald","given":"David J.","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":932107,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932106,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xu, Susu","contributorId":300127,"corporation":false,"usgs":false,"family":"Xu","given":"Susu","email":"","affiliations":[{"id":65025,"text":"Stony Brook University, NY, USA","active":true,"usgs":false}],"preferred":false,"id":932108,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70257856,"text":"70257856 - 2024 - Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds","interactions":[],"lastModifiedDate":"2024-08-29T12:11:12.859224","indexId":"70257856","displayToPublicDate":"2024-07-27T07:05:55","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3251,"text":"Remote Sensing Letters","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds","docAbstract":"<div class=\"hlFld-Abstract\"><p class=\"last\">Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain models (DTMs). A survey of NAIP point cloud classification and related ground point-generated DTMs has not yet been undertaken. This study applies a Support Vector Machine (SVM) to classifying ground and nonground points from NAIP point clouds for test sites in Wyoming and Arizona, USA. Light detection and ranging (lidar) data from the U.S. Geological Survey 3D Elevation Program (3DEP) are used to validate the classified NAIP ground points and their corresponding DTMs. Comparing height differences between filtered NAIP ground points and 3DEP ground points, the SVM classifier’s results show that the vertical root mean square error value is 1.87 m and 1.69 m for the Wyoming and Arizona sites, respectively. If NAIP point clouds were continuously measured, the resulting availability of medium-resolution DTMs would benefit the application of multitemporal forest health monitoring and DTM generation.</p></div>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/2150704X.2024.2384095","usgsCitation":"Liu, J., Arundel, S., and Shavers, E.J., 2024, Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds: Remote Sensing Letters, v. 15, no. 8, https://doi.org/10.1080/2150704X.2024.2384095.","ipdsId":"IP-157783","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":433295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jung-Kuan 0000-0001-8461-8200","orcid":"https://orcid.org/0000-0001-8461-8200","contributorId":333940,"corporation":false,"usgs":true,"family":"Liu","given":"Jung-Kuan","email":"","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":911842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":911843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":911844,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70257805,"text":"70257805 - 2024 - High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements","interactions":[],"lastModifiedDate":"2024-08-28T11:55:23.849904","indexId":"70257805","displayToPublicDate":"2024-07-27T06:52:48","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements","docAbstract":"<div id=\"ab015\" class=\"abstract author\"><div id=\"as015\"><p id=\"sp0015\">Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18&nbsp;km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.</p></div></div><div id=\"ab005\" class=\"abstract graphical\" lang=\"en\"><br></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131717","usgsCitation":"Newman, C.P., Humphrey, E., Brennwald, M., Gardner, W.P., Palko, K.M., Gooseff, M., and Solomon, K., 2024, High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements: Journal of Hydrology, v. 640, 131717, 12 p., https://doi.org/10.1016/j.jhydrol.2024.131717.","productDescription":"131717, 12 p.","ipdsId":"IP-160271","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":439247,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.131717","text":"Publisher Index Page"},{"id":433241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Colorado River, Virgin River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -113.96736437517845,\n              38.013605174379165\n            ],\n            [\n              -113.96736437517845,\n              37.099133479396826\n            ],\n            [\n              -111.92390734392869,\n              37.099133479396826\n            ],\n            [\n              -111.92390734392869,\n              38.013605174379165\n            ],\n            [\n              -113.96736437517845,\n              38.013605174379165\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -108.67195421892819,\n              40.639091534844084\n            ],\n            [\n              -108.67195421892819,\n              39.01931084468799\n            ],\n            [\n              -105.11238390642816,\n              39.01931084468799\n            ],\n            [\n              -105.11238390642816,\n              40.639091534844084\n            ],\n            [\n              -108.67195421892819,\n              40.639091534844084\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"640","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":911726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Humphrey, Eric 0000-0002-1174-8458","orcid":"https://orcid.org/0000-0002-1174-8458","contributorId":303273,"corporation":false,"usgs":true,"family":"Humphrey","given":"Eric","email":"","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":911727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brennwald, Matthias","contributorId":343690,"corporation":false,"usgs":false,"family":"Brennwald","given":"Matthias","email":"","affiliations":[{"id":82160,"text":"Eawag","active":true,"usgs":false}],"preferred":false,"id":911728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gardner, W. 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,{"id":70259229,"text":"70259229 - 2024 - Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020","interactions":[],"lastModifiedDate":"2024-10-03T16:04:03.176966","indexId":"70259229","displayToPublicDate":"2024-07-25T09:16:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18722,"text":"Agrosystems, Geosciences & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020","docAbstract":"<p><span>California produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high-resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National Agriculture Imagery Program and Landsat imagery were used to classify nine crop types that are common in the study region: grain crops, field crops, rice, citrus and subtropical, deciduous fruit and nut, vineyard, berry and vegetable, pasture, and fallow/young perennial crop types. To create labeled data, we sampled 1253 fields and manually identified crop types for each examined year using high-resolution imagery and Landsat normalized difference vegetation index time series. We applied a random forest machine learning algorithm in Google Earth Engine. Results show that the mean overall classification accuracy of the nine-class map was 93.1%, with individual accuracies ranging from 99.3% (rice) to 89.5% (fallow/young perennial). Mann–Kendall trend tests showed significant (</span><i>p</i><span> less than 0.05) declines in field crop and pasture area during the study period, while deciduous fruit and nut, citrus and subtropical, and fallow/young perennial crop types experienced significant increases. At an aggregate level, there was a general shift from annual crop types to perennial crop types. These data provide a 16-year time span of spatially explicit crop type classifications, trends, and patterns in central California that can be used to aid managers and decision makers for resource planning or hazard mitigation.</span></p>","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/agg2.20553","usgsCitation":"Smith, B.W., Soulard, C.E., and Walker, J., 2024, Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020: Agrosystems, Geosciences & Environment, v. 7, no. 3, e20553, 16 p., https://doi.org/10.1002/agg2.20553.","productDescription":"e20553, 16 p.","ipdsId":"IP-157770","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":466976,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/agg2.20553","text":"Publisher Index Page"},{"id":462483,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -120.39755424898114,\n              34.420043865180986\n            ],\n            [\n              -118.3779343983518,\n              35.235828590126644\n            ],\n            [\n              -118.14248794924036,\n              35.84498396138433\n            ],\n            [\n              -121.41402389397426,\n              40.45021878284146\n            ],\n            [\n              -124.11418123355082,\n              39.15848742125334\n            ],\n            [\n              -120.39755424898114,\n              34.420043865180986\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"7","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Britt Windsor 0000-0003-1556-2383","orcid":"https://orcid.org/0000-0003-1556-2383","contributorId":287481,"corporation":false,"usgs":true,"family":"Smith","given":"Britt","email":"","middleInitial":"Windsor","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":914531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":914532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":914533,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256998,"text":"70256998 - 2024 - Collision structures of the Prince William terrane and Chugach terrane docking along the Shumagin and Unimak convergent margins, Alaska, USA","interactions":[],"lastModifiedDate":"2024-10-07T16:12:59.020563","indexId":"70256998","displayToPublicDate":"2024-07-25T08:41:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Collision structures of the Prince William terrane and Chugach terrane docking along the Shumagin and Unimak convergent margins, Alaska, USA","docAbstract":"<p><span>Western Alaska’s convergent margins are composed of tectonostratigraphic terranes. On land, terrane assembly is recognized along boundaries or sutures between neighboring geologic elements with distinctly different origins. In marine areas where rock outcrops are covered by sediment, recognizing terrane sutures is problematic. A fault in seismic dip line 5 of the ALEUT project has been interpreted as a terrane suture. It is imaged intermittently down to the 30+-km-deep plate interface. Processing of ALEUT strike line 7 revealed the suture at ~18 km depths extending 300 km along the margin. Upper structures in line 5 are like the structures of adjacent seismic transects where imaging is only 8−10 km deep. They were previously not recognized as the upper reaches of terrane sutures and show structural details obscured at greater depths. The composite data are the basis for a simple tectonic model of terrane docking.</span></p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02757.1","usgsCitation":"von Huene, R.E., and Miller, J.J., 2024, Collision structures of the Prince William terrane and Chugach terrane docking along the Shumagin and Unimak convergent margins, Alaska, USA: Geosphere, v. 20, no. 5, p. 1276-1285, https://doi.org/10.1130/GES02757.1.","productDescription":"10 p.","startPage":"1276","endPage":"1285","ipdsId":"IP-153757","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":488998,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02757.1","text":"Publisher Index Page"},{"id":432273,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Southwest Alaska Trench","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -164,\n              56\n            ],\n            [\n              -164,\n              52.75\n            ],\n            [\n              -156,\n              52.75\n            ],\n            [\n              -156,\n              56\n            ],\n            [\n              -164,\n              56\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"von Huene, Roland E. 0000-0003-1301-3866 rvonhuene@usgs.gov","orcid":"https://orcid.org/0000-0003-1301-3866","contributorId":191070,"corporation":false,"usgs":true,"family":"von Huene","given":"Roland","email":"rvonhuene@usgs.gov","middleInitial":"E.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":909108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, John J. 0000-0002-9098-0967 jmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-0967","contributorId":3785,"corporation":false,"usgs":true,"family":"Miller","given":"John","email":"jmiller@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":909109,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70256096,"text":"fs20243031 - 2024 - The 3D Elevation Program—Supporting Michigan’s economy","interactions":[],"lastModifiedDate":"2024-07-25T14:14:30.587319","indexId":"fs20243031","displayToPublicDate":"2024-07-24T19:50:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3031","displayTitle":"The 3D Elevation Program—Supporting Michigan’s Economy","title":"The 3D Elevation Program—Supporting Michigan’s economy","docAbstract":"High-quality elevation data are proving to be a resource of value in addressing many important economic issues in Michigan. The expanding statewide availability of current and accurate high-resolution elevation data can help support agriculture and precision farming, natural resource conservation, flood risk management, and geologic resource assessment and hazard mitigation. Water supply and quality assessment, coastal zone management, infrastructure and construction management, solar potential and other renewable energy programs, and identification of features of interest or concern such as archaeological and historical sites are other notable areas that are supported with accurate, high-resolution elevation data. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243031","usgsCitation":"Rachol, C.M., 2024, The 3D Elevation Program—Supporting Michigan’s economy: U.S. Geological Survey Fact Sheet 2024-3031, 2 p., https://doi.org/10.3133/fs20243031.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-125866","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":431287,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3031/fs20243031.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3031 XML"},{"id":431286,"rank":3,"type":{"id":39,"text":"HTML 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 \"}}]}","contact":"<p>Director, <a href=\"https://www.usgs.gov/programs/national-geospatial-program\" data-mce-href=\"https://www.usgs.gov/programs/national-geospatial-program\">National Geospatial Program</a><br>U.S. Geological Survey<br>MS 511<br>12201 Sunrise Valley Drive<br>Reston, VA 20192</p><p>Email: <a href=\"mailto:3DEP@usgs.gov\" data-mce-href=\"mailto:3DEP@usgs.gov\">3DEP@usgs.gov</a></p>","tableOfContents":"<ul><li>Introduction</li><li>Status of 3DEP in Michigan</li><li>Agriculture and Precision Farming</li><li>Natural Resources Conservation</li><li>Flood Risk Management</li><li>Geologic Resource Assessment and Hazard Mitigation</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2024-07-24","noUsgsAuthors":false,"publicationDate":"2024-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Rachol, Cynthia M. 0000-0001-9984-3435 crachol@usgs.gov","orcid":"https://orcid.org/0000-0001-9984-3435","contributorId":3488,"corporation":false,"usgs":true,"family":"Rachol","given":"Cynthia","email":"crachol@usgs.gov","middleInitial":"M.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":906680,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70256144,"text":"ofr20241044 - 2024 - Restoration of common loon (Gavia immer) in Minnesota—2023 annual report","interactions":[],"lastModifiedDate":"2024-07-25T00:14:00.597697","indexId":"ofr20241044","displayToPublicDate":"2024-07-24T13:37:34","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1044","displayTitle":"Restoration of Common Loon (<em>Gavia immer</em>) in Minnesota—2023 Annual Report","title":"Restoration of common loon (Gavia immer) in Minnesota—2023 annual report","docAbstract":"<p>The <i>Deepwater Horizon</i> mobile drilling platform exploded on April 20, 2010. The resulting massive oil spill injured natural resources in the Gulf of Mexico, including wintering common loons (<i>Gavia immer</i>). We report on activities completed under the “Restoration of Common Loons in Minnesota” project in calendar year 2023, which was funded by the Open Ocean Trustee Implementation Group. In 2022, a subset of monitored breeding territories was identified as focal territories, which are sampling units for the study. The U.S. Geological Survey, in cooperation with the Minnesota Department of Natural Resources, monitored 98 common loon focal territories and an additional 43 nonfocal territories in 2023 across 56 study lakes in Minnesota. We collaborated with lake associations and private citizens to deploy 42 artificial nesting platforms within 44 focal treatment territories. The remaining 54 focal territories were controls. Territorial surveys were completed from May 8 to August 11, 2023, to evaluate occupancy, nest success, and chick survival. At least one nest attempt was observed in 31 of 44 treatment territories and a second nest attempt was observed after a failed initial attempt in 6 treatment territories. However, only one nest was on an artificial nesting platform in a treatment territory; the remaining nest locations were natural. At least one nest attempt was observed in 37 of 54 control territories, and a second nest attempt was observed after a failed initial attempt in 5 control territories. Chicks or other evidence of hatching were observed in 17 of 54 control territories and 17 of 44 treatment territories, with 1 of those successful treatment nests occurring on an artificial nesting platform. This report includes no formal analysis, but we plan to analyze data after collection of all field data in subsequent years.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241044","collaboration":"Prepared in cooperation with the Minnesota Department of Natural Resources and Minnesota Pollution Control Agency","usgsCitation":"Beatty, W.S., Amoth, K., Bergstrom, K., Fara, L.J., Gray, B.R., Houdek, S.C., Jech, J., Kenow, K.P., Rabasco, R., Rettler, S., Wellik, M., and Yang, S., 2024, Restoration of common loon (<em>Gavia immer</em>) in Minnesota—2023 annual report: U.S. Geological Survey Open-File Report 2024–1044, 6 p., https://doi.org/10.3133/ofr20241044.","productDescription":"Report: vi, 6 p.; 2 Data Releases","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-162805","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences 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Document"},"url":"https://pubs.usgs.gov/publication/ofr20241044/full"},{"id":431371,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LA536E","text":"USGS data release","linkHelpText":"Summary of detection data for breeding common loons in north-central Minnesota (2021–2022)"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -96.57221262524712,\n              48.473840582024934\n            ],\n            [\n              -96.57221262524712,\n              46.234012124497895\n            ],\n            [\n              -93.01264231274689,\n              46.234012124497895\n            ],\n            [\n              -93.01264231274689,\n              48.473840582024934\n            ],\n            [\n              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0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":224795,"corporation":false,"usgs":true,"family":"Beatty","given":"William S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906891,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amoth, Kelly","contributorId":328850,"corporation":false,"usgs":false,"family":"Amoth","given":"Kelly","email":"","affiliations":[{"id":78507,"text":"Minnesota Pollution Control Agency, St. Paul, Minnesota","active":true,"usgs":false}],"preferred":false,"id":906892,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergstrom, Katelyn","contributorId":334553,"corporation":false,"usgs":false,"family":"Bergstrom","given":"Katelyn","email":"","affiliations":[{"id":80175,"text":"Minnesota Department of Natural Resources (MN DNR)","active":true,"usgs":false}],"preferred":false,"id":906893,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fara, Luke J. 0000-0002-1143-4395","orcid":"https://orcid.org/0000-0002-1143-4395","contributorId":202973,"corporation":false,"usgs":true,"family":"Fara","given":"Luke J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906894,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gray, Brian R. 0000-0001-7682-9550 brgray@usgs.gov","orcid":"https://orcid.org/0000-0001-7682-9550","contributorId":2615,"corporation":false,"usgs":true,"family":"Gray","given":"Brian","email":"brgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906895,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Houdek, Steven C. 0000-0001-9452-6596 shoudek@usgs.gov","orcid":"https://orcid.org/0000-0001-9452-6596","contributorId":4423,"corporation":false,"usgs":true,"family":"Houdek","given":"Steven","email":"shoudek@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906896,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jech, Jayden","contributorId":334552,"corporation":false,"usgs":false,"family":"Jech","given":"Jayden","email":"","affiliations":[{"id":80175,"text":"Minnesota Department of Natural Resources (MN DNR)","active":true,"usgs":false}],"preferred":false,"id":906897,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kenow, Kevin P. 0000-0002-3062-5197 kkenow@usgs.gov","orcid":"https://orcid.org/0000-0002-3062-5197","contributorId":3339,"corporation":false,"usgs":true,"family":"Kenow","given":"Kevin","email":"kkenow@usgs.gov","middleInitial":"P.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906898,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rabasco, Robert","contributorId":328846,"corporation":false,"usgs":false,"family":"Rabasco","given":"Robert","email":"","affiliations":[{"id":78505,"text":"Minnesota Department of Natural Resources, Brainerd, Minnesota","active":true,"usgs":false}],"preferred":false,"id":906899,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rettler, Spencer","contributorId":328847,"corporation":false,"usgs":false,"family":"Rettler","given":"Spencer","email":"","affiliations":[{"id":78506,"text":"Minnesota Department of Natural Resources, Grand Rapids, Minnesota","active":true,"usgs":false}],"preferred":false,"id":906900,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wellik, Michael 0000-0002-3123-3988 mwellik@usgs.gov","orcid":"https://orcid.org/0000-0002-3123-3988","contributorId":340322,"corporation":false,"usgs":false,"family":"Wellik","given":"Michael","email":"mwellik@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":906901,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Yang, Steven","contributorId":328849,"corporation":false,"usgs":false,"family":"Yang","given":"Steven","email":"","affiliations":[{"id":78507,"text":"Minnesota Pollution Control Agency, St. Paul, Minnesota","active":true,"usgs":false}],"preferred":false,"id":906902,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}}
,{"id":70261500,"text":"70261500 - 2024 - Potential for spatial coexistence of a transboundary migratory species and wind energy development","interactions":[],"lastModifiedDate":"2024-12-12T16:17:50.855245","indexId":"70261500","displayToPublicDate":"2024-07-24T10:08:50","publicationYear":"2024","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":"Potential for spatial coexistence of a transboundary migratory species and wind energy development","docAbstract":"<p><span>Global expansion in wind energy development is a notable achievement of the international community’s effort to reduce carbon emissions during energy production. However, the increasing number of wind turbines have unintended consequences for migratory birds and bats. Wind turbine curtailment and other mitigation strategies can reduce fatalities, but improved spatial and temporal data are needed to identify the most effective way for wind energy development and volant migratory species to coexist. Mexican free-tailed bats (</span><i>Tadarida brasiliensis mexicana</i><span>) account for a large proportion of known bat fatalities at wind facilities in the southwestern US. We examined the geographic concordance between existing wind energy generation facilities, areas of high wind potential amenable for future deployment of wind facilities, and seasonally suitable habitat for these bats. We used ecological niche modeling to determine species distribution during each of 4 seasons. We used a multi-criteria GIS-based approach to produce a wind turbine siting suitability map. We identified seasonal locations with highest and lowest potential for the species’ probability of occurrence, providing a potential explanation for the higher observed fatalities during fall migration. Thirty percent of 33,606 wind turbines within the southwestern US occurred in highly suitable areas for Mexican free-tailed bats, primarily in west Texas. There is also broad spatial overlap between areas of high wind potential and areas of suitable habitat for Mexican free-tailed bats. Because of this high degree of overlap, our results indicate that post-construction strategies, such as curtailing the timing of operations and deterrents, would be more effective for bat conservation than strategic siting of new wind energy installations.</span></p>","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-66490-3","usgsCitation":"Huang, T., Feng, X., Derbridge, J.J., Libby, K., Diffendorfer, J., Thogmartin, W.E., McCracken, G., Medellin, R., and Lopez-Hoffman, L., 2024, Potential for spatial coexistence of a transboundary migratory species and wind energy development: Scientific Reports, v. 14, 17050, 11 p., https://doi.org/10.1038/s41598-024-66490-3.","productDescription":"17050, 11 p.","ipdsId":"IP-117864","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":466978,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-66490-3","text":"Publisher Index Page"},{"id":465068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -92.47553206933844,\n              15.195823510229175\n            ],\n            [\n              -87.8489299849073,\n              17.515394892042707\n            ],\n            [\n              -86.69187668195985,\n              21.411133478434067\n            ],\n            [\n              -89.22876810857116,\n              21.631901891107603\n            ],\n            [\n              -90.7271444797507,\n              20.398715741133728\n            ],\n            [\n              -93.64195681314828,\n             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,{"id":70256174,"text":"70256174 - 2024 - NASA Champions 2024: Data strategies for when to use cloud, coding strategies for parallelization, & first examples of big science in the Cloud","interactions":[],"lastModifiedDate":"2024-07-26T11:15:44.469332","indexId":"70256174","displayToPublicDate":"2024-07-24T06:12:10","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"NASA Champions 2024: Data strategies for when to use cloud, coding strategies for parallelization, & first examples of big science in the Cloud","docAbstract":"<p>From April-May 2024, the NASA Mentors who span eleven Distributed Active Archive Centers (DAACs) co-led the third Champions Cohort with the NASA Openscapes project team, this year focusing on, teaching lessons they adapted for geospatial and cloud analysis. The Cohort included nine international research teams from academia and government that were curious about working with NASA Earthdata in the Cloud. Many teams were interested in using data from multiple DAACs. User cloud adaption takes time, given the new conceptual mindsets and technical skillsets it requires. During the ten weeks we worked together, NASA Mentors refined and extended previous lessons to focus on thinking through and planning the transition to using the Cloud for science research and applications, and initial experiments using the Cloud through our 2i2c JupyterHub. Below are these updates and YouTube clips!</p><p>There were also recurring themes/questions that we have heard before, some of which remain as open questions and continue to remain a challenge. Importantly, Amazon Web Services (AWS) Cloud onboarding, when to use what resources, how to set them up, and how to discuss needs with organizational leadership and IT staff, which often falls outside the scope of NASA DAACs, yet it’s a key element of helping users adopt the Cloud and use NASA data in the Cloud. It is encouraging to hear some of the champions starting to have conversations with their institutions, IT departments, and making their needs known, which is likely a big part of the solution, too. We are thankful to NASA Openscapes Champions for informing and nudging these conversations! All of this work is underpinned by Openscapes and NASA’s commitment to open science practices and a kinder collaborative culture. This cohort is funded by NASA and is part of our NASA Openscapes Framework project.</p>","language":"English","publisher":"NASA","usgsCitation":"Thornton, M., Taglialatela, C., Lopez, L., Fisher, M., Hunzinger, A., Jami, M., Lind, B.M., Nickles, C., Teucher, A., Merrelli, A., Robinson, E., and Lowndes, J., 2024, NASA Champions 2024: Data strategies for when to use cloud, coding strategies for parallelization, & first examples of big science in the Cloud, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-167616","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":431422,"rank":1,"type":{"id":15,"text":"Index 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,{"id":70256095,"text":"fs20243026 - 2024 - The Native American Research Assistantship Program—Building capacity for Indigenous water-resources monitoring","interactions":[],"lastModifiedDate":"2024-07-23T20:16:33.355013","indexId":"fs20243026","displayToPublicDate":"2024-07-23T13:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3026","displayTitle":"The Native American Research Assistantship Program: Building Capacity for Indigenous Water-Resources Monitoring","title":"The Native American Research Assistantship Program—Building capacity for Indigenous water-resources monitoring","docAbstract":"<p>Intertribal networks for collecting and analyzing hydrologic and environmental data are growing. The U.S. Geological Survey can be a key partner with Tribal Nations in the further development of network capacity. A first step is the internship opportunity available through the partnership between the USGS and The Wildlife Society: The Native American Research Assistantship Program.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243026","usgsCitation":"Hare-Red Corn, E., Breault, R.F., and Sorenson, J.R., 2024, The Native American Research Assistantship Program—Building capacity for Indigenous water-resources monitoring: U.S. Geological Survey Fact Sheet 2024–3026, 2 p., https://doi.org/10.3133/fs20243026.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158895","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":431260,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3026/images/"},{"id":431259,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3026/fs20243026.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3026 XML"},{"id":431258,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243026/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3026 HTML"},{"id":431257,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3026/fs20243026.pdf","text":"Report","size":"5.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3026 PDF"},{"id":431256,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3026/coverthb.jpg"}],"contact":"<p><a href=\"mailto:dc_ny@usgs.gov\" data-mce-href=\"mailto:dc_ny@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-york-water-science-center\" data-mce-href=\"https://www.usgs.gov/centers/new-york-water-science-center\">New York Water Science Center</a><br>U.S. Geological Survey<br>425 Jordan Road<br>Troy, NY 12180–8349</p>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-07-23","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hare-Red Corn, Electa","contributorId":340245,"corporation":false,"usgs":false,"family":"Hare-Red Corn","given":"Electa","email":"","affiliations":[{"id":81515,"text":"Pawnee Nation","active":true,"usgs":false}],"preferred":false,"id":906676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breault, Robert F. 0000-0002-2517-407X rbreault@usgs.gov","orcid":"https://orcid.org/0000-0002-2517-407X","contributorId":218990,"corporation":false,"usgs":true,"family":"Breault","given":"Robert","email":"rbreault@usgs.gov","middleInitial":"F.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sorenson, Jason R. 0000-0001-5553-8594","orcid":"https://orcid.org/0000-0001-5553-8594","contributorId":203249,"corporation":false,"usgs":true,"family":"Sorenson","given":"Jason R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906678,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70259218,"text":"70259218 - 2024 - Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better","interactions":[],"lastModifiedDate":"2024-10-02T13:35:21.858349","indexId":"70259218","displayToPublicDate":"2024-07-23T08:15:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better","docAbstract":"<p><span>Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500&nbsp;m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000&nbsp;m and over two meteorological data sets, coarser (≈12&nbsp;km) and finer (4&nbsp;km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR035982","usgsCitation":"Barnhart, T.B., Putman, A.L., Heldmyer, A.J., Rey, D., Hammond, J., Driscoll, J.M., and Sexstone, G., 2024, Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better: Water Resources Research, v. 60, no. 7, e2023WR035982, 21 p., https://doi.org/10.1029/2023WR035982.","productDescription":"e2023WR035982, 21 p.","ipdsId":"IP-154162","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - 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Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":914515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":914517,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914518,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70266476,"text":"70266476 - 2024 - Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease","interactions":[],"lastModifiedDate":"2025-05-08T15:08:37.666349","indexId":"70266476","displayToPublicDate":"2024-07-22T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5548,"text":"Spatial Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease","docAbstract":"To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.","language":"English","publisher":"Elsevier","doi":"10.1016/j.spasta.2024.100850","usgsCitation":"Reyes, J., Ma, T., McGahan, I., Storm, D., Walsh, D.P., and Zhu, J., 2024, Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease: Spatial Statistics, v. 62, 100850, 15 p., https://doi.org/10.1016/j.spasta.2024.100850.","productDescription":"100850, 15 p.","ipdsId":"IP-155343","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1016/j.spasta.2024.100850","text":"Publisher Index Page"},{"id":485559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":354688,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":354689,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":936164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian P.","contributorId":354690,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":354692,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936167,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Jun","contributorId":354695,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936168,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70256125,"text":"70256125 - 2024 - Multi-decadal vegetation transformations of a New Mexico ponderosa pine landscape after severe fires and aerial seeding","interactions":[],"lastModifiedDate":"2024-09-11T16:20:26.613489","indexId":"70256125","displayToPublicDate":"2024-07-21T06:37:46","publicationYear":"2024","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":"Multi-decadal vegetation transformations of a New Mexico ponderosa pine landscape after severe fires and aerial seeding","docAbstract":"<p>Wildfires and climate change are having transformative effects on vegetation composition and structure, and post-fire management may have long-lasting impacts on ecosystem reorganization. Post-fire aerial seeding treatments are commonly used to reduce runoff and soil erosion, but little is known about how seeding treatments affect native vegetation recovery over long periods of time, particularly in type-converted forests which have been dramatically transformed by the effects of repeated, high-severity fire. In this study, we analyze and report on a rare long-term (23-year) dataset that documents vegetation dynamics following a 1996 post-fire aerial seed treatment and subsequent 2011 high-severity reburn in a dry conifer forest of northern New Mexico in the southwestern United States. Repeated surveys between 1997 – 2019 of 49 permanent transects were used to test for differences in vegetation cover, richness, and diversity between seeded and unseeded areas, and to characterize the development of seeded and unseeded vegetation communities through time and across gradients of burn severity, elevation, and soil-available water capacity. Post-fire seeding led to a clear and sustained divergence in herbaceous community composition. Seeded plots had much higher cover of non-native graminoids, primarily <i>Bromus inermis</i>, a likely contaminant in the seed mix. High-severity reburning in all plots in 2011 reduced native graminoid cover by half at seeded plots compared to both pre-fire levels and to plots that were unseeded following the initial 1996 fire. In addition, increased fire severity was associated with increased non-native graminoid cover and reduced native graminoid cover, native species richness, and species diversity. This study documents a fire-driven ecosystem transformation from a former conifer forest into a shrub-grass system, reinforced by aerial seeding of grasses and high-severity reburning. This unique long-term dataset illustrates that post-fire seeding carries significant risk of unwanted non-native species invasions that persist through subsequent fires – indicating that alternative post-fire management actions merit consideration to better support native ecosystem resilience in the face of emergent climate change and increasing disturbance. Lastly, this study highlights the importance of long-term monitoring of post-fire vegetation dynamics, as short-term assessments will miss key elements of the full complexity of ecosystem responses to fire and post-fire management actions.</p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.3008","usgsCitation":"Wion, A.P., Stevens, J., Beeley, K., Oertel, R., Margolis, E.Q., and Allen, C., 2024, Multi-decadal vegetation transformations of a New Mexico ponderosa pine landscape after severe fires and aerial seeding: Ecological Applications, v. 34, no. 6, e3008, 21 p., https://doi.org/10.1002/eap.3008.","productDescription":"e3008, 21 p.","ipdsId":"IP-158911","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":498298,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.3008","text":"Publisher Index Page"},{"id":431346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Mountains, San Miguel Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -107.61072300933778,\n              36.66992633929999\n            ],\n            [\n              -107.61072300933778,\n              35.363708672581055\n            ],\n            [\n              -105.69910191558768,\n              35.363708672581055\n            ],\n            [\n              -105.69910191558768,\n              36.66992633929999\n            ],\n            [\n              -107.61072300933778,\n              36.66992633929999\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"34","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Wion, Andreas Paul 0000-0002-0701-2843","orcid":"https://orcid.org/0000-0002-0701-2843","contributorId":335166,"corporation":false,"usgs":true,"family":"Wion","given":"Andreas","email":"","middleInitial":"Paul","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":906778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Jens T. 0000-0002-2234-1960","orcid":"https://orcid.org/0000-0002-2234-1960","contributorId":289230,"corporation":false,"usgs":false,"family":"Stevens","given":"Jens T.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":906779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beeley, Kay","contributorId":340264,"corporation":false,"usgs":false,"family":"Beeley","given":"Kay","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":906780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oertel, Rebecca","contributorId":340265,"corporation":false,"usgs":false,"family":"Oertel","given":"Rebecca","email":"","affiliations":[{"id":81531,"text":"Fort Collins Science Center *retired","active":true,"usgs":false}],"preferred":false,"id":906781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":906782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allen, Craig D.","contributorId":289211,"corporation":false,"usgs":false,"family":"Allen","given":"Craig D.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":906783,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70256106,"text":"70256106 - 2024 - The influence of vesicularity on grain morphology in basaltic pyroclasts from Mauna Loa and Kīlauea volcanoes","interactions":[],"lastModifiedDate":"2024-07-22T11:42:53.520956","indexId":"70256106","displayToPublicDate":"2024-07-20T06:41:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3841,"text":"Journal of Applied Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"The influence of vesicularity on grain morphology in basaltic pyroclasts from Mauna Loa and Kīlauea volcanoes","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Vesicularity of individual pyroclasts from airfall tephra deposits is an important parameter that is commonly measured at basaltic volcanoes. Conventional methods used to determine pyroclast vesicularity on a large number of clasts has the potential to be time consuming, particularly when rapid analysis is required. Here we propose dynamic image analysis on two-dimensional (2D) projection shapes of crushed pyroclasts from tephra deposits as a new method to estimate vesicularity. This method relies on the influence of vesicles and uses grain morphology as a proxy for vesicle size and abundance. Pyroclasts from a variety of basaltic tephra deposits from the volcanoes of Mauna Loa and Kīlauea were analyzed. Vesicularities between 52–98% were measured via nitrogen-gas pycnometry. The same pyroclasts were then crushed and sieved, and their grain shapes measured using dynamic image analysis on a CAMSIZER®. This yields values for the mean sphericity, elongation, compactness, and Krumbein roundness of the grains. Our data show that grains become increasingly irregular with increasing vesicularity, with the degree of correlation between shape parameters and vesicularity depending on the size of measured grains. Shape irregularities in small grains (60–250 µm) are mostly area-based, with elongation being the best vesicularity indicator, whereas shape irregularities in large grains (250–700 µm) are mostly perimeter-based, with Krumbein roundness as the best vesicularity indicator. Using mean shape parameter values with all grain sizes included, grain elongation is the most well-correlated shape parameter with vesicularity, with the best fitted model explaining 76% of variation in the observations. Microscope images of thin sections of intact pyroclasts, as well as from crushed pyroclasts, were analyzed using CSDCorrections 1.6 software in ImageJ to find local vesicularity, vesicle size, grain size, grain elongation, and vesicle spatial distribution by stereological conversion. Observed correlation between grain shape and vesicularity can be explained by the local effect of vesicles on the shape of the solid structure in between those vesicles. Grain shape depends not only on vesicularity, but also on vesicle to grain size ratio and the spatial distribution of vesicles. The influence of vesicles on grain shape is best captured by grains with the size of the solid structure in between vesicles, which generally increases with decreasing vesicularity. Dynamic image analysis is a useful tool to quickly gauge vesicularity, which could be used in near-real-time during an eruption response. However, this method is best suited for highly vesicular (&gt; 80%) basaltic pyroclasts from tephra deposits with few microlites and phenocrysts. Further research on crushing techniques, optimum grain size for shape measurements, and Krumbein roundness measurements for the grain size range of 250–700 µm might enable application of this method to lower vesicularity pyroclasts.</p></div></div><div id=\"Sec1-section\" class=\"c-article-section\"><br></div>","language":"English","publisher":"Springer Nature","doi":"10.1186/s13617-024-00145-w","usgsCitation":"van Helden, K., Schmith, J., and Downs, D.T., 2024, The influence of vesicularity on grain morphology in basaltic pyroclasts from Mauna Loa and Kīlauea volcanoes: Journal of Applied Volcanology, v. 13, 6, 22 p., https://doi.org/10.1186/s13617-024-00145-w.","productDescription":"6, 22 p.","ipdsId":"IP-158926","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":439263,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1186/s13617-024-00145-w","text":"Publisher Index Page"},{"id":431298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Mauna Loa Volcano, Kīlauea volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -155.7324967305703,\n              19.58657920442002\n            ],\n            [\n              -155.7324967305703,\n              19.35855058744275\n            ],\n            [\n              -155.46501586329234,\n              19.35855058744275\n            ],\n            [\n              -155.46501586329234,\n              19.58657920442002\n            ],\n            [\n              -155.7324967305703,\n              19.58657920442002\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -155.390947864021,\n              19.500061663805354\n            ],\n            [\n              -155.390947864021,\n              19.314095563291104\n            ],\n            [\n              -155.17296628967014,\n              19.314095563291104\n            ],\n            [\n              -155.17296628967014,\n              19.500061663805354\n            ],\n            [\n              -155.390947864021,\n              19.500061663805354\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2024-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"van Helden, Kira","contributorId":334955,"corporation":false,"usgs":false,"family":"van Helden","given":"Kira","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":906711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmith, Johanne 0000-0002-0912-7441","orcid":"https://orcid.org/0000-0002-0912-7441","contributorId":334956,"corporation":false,"usgs":true,"family":"Schmith","given":"Johanne","affiliations":[{"id":80292,"text":"Hawaiian Volcano Observatory","active":true,"usgs":false}],"preferred":true,"id":906712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Downs, Drew T. 0000-0002-9056-1404 ddowns@usgs.gov","orcid":"https://orcid.org/0000-0002-9056-1404","contributorId":173516,"corporation":false,"usgs":true,"family":"Downs","given":"Drew","email":"ddowns@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":906713,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256103,"text":"70256103 - 2024 - Integrating depth measurements from gaging stations with image archives for spectrally based remote sensing of river bathymetry","interactions":[],"lastModifiedDate":"2024-07-22T11:40:29.026382","indexId":"70256103","displayToPublicDate":"2024-07-20T06:36:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Integrating depth measurements from gaging stations with image archives for spectrally based remote sensing of river bathymetry","docAbstract":"<div class=\"article-section__content en main\"><p>Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA<span>&nbsp;</span><i>R</i><sup>2</sup><span>&nbsp;</span>(<i>p</i>&nbsp;&lt;&nbsp;0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR037295","usgsCitation":"Legleiter, C.J., Overstreet, B., and Kinzel, P.J., 2024, Integrating depth measurements from gaging stations with image archives for spectrally based remote sensing of river bathymetry: Water Resources Research, v. 60, no. 7, e2024WR037295, 33 p., https://doi.org/10.1029/2024WR037295.","productDescription":"e2024WR037295, 33 p.","ipdsId":"IP-160793","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439264,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr037295","text":"Publisher Index Page"},{"id":431297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Willamette River basin, Delaware River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -75.99460284334587,\n              38.837173118399136\n            ],\n            [\n              -74.06100909334606,\n              38.837173118399136\n            ],\n            [\n              -74.06100909334606,\n              42.12840813758996\n            ],\n            [\n              -75.99460284334587,\n              42.12840813758996\n            ],\n            [\n              -75.99460284334587,\n              38.837173118399136\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -120.6052321751901,\n              42.976477236568854\n            ],\n            [\n              -120.6052321751901,\n              45.954927496432504\n            ],\n            [\n              -123.59351342519011,\n              45.954927496432504\n            ],\n            [\n              -123.59351342519011,\n              42.976477236568854\n            ],\n            [\n              -120.6052321751901,\n              42.976477236568854\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"60","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":906708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Overstreet, Brandon 0000-0001-7845-6671 boverstreet@usgs.gov","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":169201,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"boverstreet@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":906710,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256023,"text":"sir20245052 - 2024 - A predictive analysis of water use for Providence, Rhode Island","interactions":[],"lastModifiedDate":"2026-02-03T19:35:50.433499","indexId":"sir20245052","displayToPublicDate":"2024-07-19T14:18:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5052","displayTitle":"A Predictive Analysis of Water Use for Providence, Rhode Island","title":"A predictive analysis of water use for Providence, Rhode Island","docAbstract":"<p>To explain the drivers of historical water use in the public water systems (PWSs) that serve populations in Providence, Rhode Island, and surrounding areas, and to forecast future water use, a machine-learning model (cubist regression) was developed by the U.S. Geological Survey in cooperation with Providence Water to model daily per capita rates of domestic, commercial, and industrial water use. The PWSs in this area form a connected network that sources water from the Scituate Reservoir in Rhode Island. The cubist regression model was trained and tested on daily per capita rates for three categories of water use (domestic, commercial, and industrial) that were developed from quarterly water sales data and U.S. Census Bureau population estimates within each PWS service area from January 2005 through December 2021. The model was then used to make forecasts of future water use under varying scenarios of climate change, population growth, and economic growth for the years 2030 and 2040.</p><p>The resulting daily per capita rates, which were modeled from the historical data, had an r<sup>2</sup> value of 0.94 and root mean square error of 6.7 gallons per capita daily. Results of the model were used to estimate total water use (the product of daily per capita rates and population) for all public water systems over the historical study period. Daily per capita rates in the study area decreased from 2005 to 2021, while population increased during that same period. “Category of water use” was the variable with the greatest explanatory power for modeling daily per capita rates. Overall, both daily per capita rates and total water use were projected to decrease in 2030 and 2040, in comparison to historical values from 2005 to 2021. Daily per capita rates and total water use were forecasted to decrease as economic growth rates increase. Daily per capita rates were expected to decrease as population growth rates increase; however, total water use was less sensitive to population growth rates than daily per capita rates. Effects of climate change were minimal over the 2030 and 2040 forecasting horizon for the scenarios tested.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245052","collaboration":"Prepared in cooperation with Providence Water","usgsCitation":"Chamberlin, C.A., 2024, A predictive analysis of water use for Providence, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2024–5052, 36 p., https://doi.org/10.3133/sir20245052.","productDescription":"Report: viii, 36 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152679","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499474,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117188.htm","linkFileType":{"id":5,"text":"html"}},{"id":431062,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94XIQ7W","text":"USGS data release","linkHelpText":"Model archive, input data, modeled estimates of water use 2005-2021, and forecasts of water use in 2030 and 2040 in Providence, Rhode Island"},{"id":431061,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5052/sir20245052.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5052 XML"},{"id":431060,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5052/images/"},{"id":431059,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245052/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5052 HTML"},{"id":431058,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5052/sir20245052.pdf","text":"Report","size":"4.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5052 PDF"},{"id":431057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5052/coverthb.jpg"}],"country":"United States","state":"Rhode Island","city":"Providence","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -71.6320030327788,\n              41.56235835697041\n            ],\n            [\n              -71.17676197086665,\n              41.56235835697041\n            ],\n            [\n              -71.17676197086665,\n              42.025783641742635\n            ],\n            [\n              -71.6320030327788,\n              42.025783641742635\n            ],\n            [\n              -71.6320030327788,\n              41.56235835697041\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_nweng@usgs.gov\" data-mce-href=\"mailto:dc_nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Acknowledgements</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-07-19","noUsgsAuthors":false,"publicationDate":"2024-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Chamberlin, Catherine A. 0000-0002-1307-4784","orcid":"https://orcid.org/0000-0002-1307-4784","contributorId":331334,"corporation":false,"usgs":true,"family":"Chamberlin","given":"Catherine","email":"","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906428,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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