The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111968","usgsCitation":"Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C., Storey, J.C., and Jenstrom, D., 2020, Landsat 9: Empowering open science and applications through continuity: Remote Sensing of Environment, v. 248, 111968, 13 p., https://doi.org/10.1016/j.rse.2020.111968.","productDescription":"111968, 13 p.","ipdsId":"IP-118603","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":378748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":799592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":799593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markham, Brian 0000-0002-9612-8169","orcid":"https://orcid.org/0000-0002-9612-8169","contributorId":139286,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":799594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCorkel, Joel","contributorId":192459,"corporation":false,"usgs":false,"family":"McCorkel","given":"Joel","email":"","affiliations":[],"preferred":false,"id":799595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":799596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":799597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jenstrom, Del","contributorId":241119,"corporation":false,"usgs":false,"family":"Jenstrom","given":"Del","email":"","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":799598,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212502,"text":"70212502 - 2020 - Reconstructing the velocity and deformation of a rapid landslide using multiview video","interactions":[],"lastModifiedDate":"2020-08-18T14:18:20.660046","indexId":"70212502","displayToPublicDate":"2020-07-23T09:13:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6454,"text":"Journal of Geophysical Research - Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing the velocity and deformation of a rapid landslide using multiview video","docAbstract":"Noncontact measurements of spatially varied ground surface deformation during landslide motion can provide important constraints on landslide mechanics. Here, we present and test a new method for extracting measurements of rapid landslide surface displacement and velocity (accelerations of approximately 1 m/s2) using sequences of stereo images obtained from a pair of inexpensive, stationary 4K video cameras with nominal frame rates of 29.97 Hz. The method combines elements of Structure from Motion with those of optical flow to extract data on 3‐D evolution of the ground surface during slope failure. We apply the method to an experiment at the U.S. Geological Survey debris‐flow flume in which a high‐speed, liquefying landslide was triggered by gradually adding water to a 6‐m3 prism of loosely packed sediment on a 31° slope. Strip‐scanning lidar measurements made during the experiment corroborate our video‐based measurements, but the latter encompassed the entire landslide surface and were much lower in cost. Our video‐based measurements enabled computation of depth‐integrated landslide dilation/contraction rates. The range of computed rates was within the ranges inferred from independent measurements of evolving pore water pressures and reasonable estimates of the hydraulic permeability of the sediment. Dilation and contraction rates play a crucial role in landslide mechanics. The dilation and contraction we observe contradict the incompressible flow assumption used in many studies that have employed noncontact methods to infer landslide properties.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JF005348","collaboration":"Colorado School of Mines; Oregon State University","usgsCitation":"Rapstine, T.D., Rengers, F.K., Allstadt, K.E., Iverson, R.M., Smith, J.B., Obryk, M., Logan, M., and Olsen, M.J., 2020, Reconstructing the velocity and deformation of a rapid landslide using multiview video: Journal of Geophysical Research - Earth Surface, v. 125, e2019JF005348, 18 p., https://doi.org/10.1029/2019JF005348.","productDescription":"e2019JF005348, 18 p.","ipdsId":"IP-116947","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Blue River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.42340087890624,\n 44.08758502824516\n ],\n [\n -122.20642089843749,\n 44.08758502824516\n ],\n [\n -122.20642089843749,\n 44.209772586984485\n ],\n [\n -122.42340087890624,\n 44.209772586984485\n ],\n [\n -122.42340087890624,\n 44.08758502824516\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationDate":"2020-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rapstine, Thomas D 0000-0001-5939-9587","orcid":"https://orcid.org/0000-0001-5939-9587","contributorId":224777,"corporation":false,"usgs":true,"family":"Rapstine","given":"Thomas","email":"","middleInitial":"D","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796609,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iverson, Richard M. 0000-0002-7369-3819 riverson@usgs.gov","orcid":"https://orcid.org/0000-0002-7369-3819","contributorId":536,"corporation":false,"usgs":true,"family":"Iverson","given":"Richard","email":"riverson@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":796611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Obryk, Maciej K. 0000-0002-8182-8656","orcid":"https://orcid.org/0000-0002-8182-8656","contributorId":203477,"corporation":false,"usgs":true,"family":"Obryk","given":"Maciej","middleInitial":"K.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":796613,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Logan, M. 0000-0002-3558-2405","orcid":"https://orcid.org/0000-0002-3558-2405","contributorId":238816,"corporation":false,"usgs":true,"family":"Logan","given":"M.","affiliations":[{"id":47793,"text":"USGS - Cascades Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":796614,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Olsen, M. J.","contributorId":238817,"corporation":false,"usgs":false,"family":"Olsen","given":"M.","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":796615,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211275,"text":"gip203 - 2020 - Hydrologic technician postcard","interactions":[],"lastModifiedDate":"2020-07-27T13:58:46.638491","indexId":"gip203","displayToPublicDate":"2020-07-23T07:06:16","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"203","displayTitle":"Hydrologic Technician Postcard","title":"Hydrologic technician postcard","docAbstract":"Hydrologic technicians collect water data related to water quantity, quality, availability, and movement in surface-water and groundwater environments.
For more information, visit https://www.usajobs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip203","usgsCitation":"U.S. Geological Survey, 2020, Hydrologic technican postcard: U.S. Geological Survey General Information Product 203, 2 p., https://doi.org/10.3133/gip203.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117273","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0203/gip203.pdf","text":"Postcard","size":"326 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 203"},{"id":376604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0203/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Chemists design analytical methods, analyze samples, and review instrument results to ensure high-quality, defensible data are provided to our Nation’s decision makers.
For more information, visit https://www.usajobs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip200","usgsCitation":"U.S. Geological Survey, 2020, Chemist postcard: U.S. Geological Survey General Information Product 200, 2 p., https://doi.org/10.3133/gip200.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117271","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376583,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0200/gip200.pdf","text":"Postcard","size":"284 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 200"},{"id":376603,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0200/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Life history theory in ornithology has been mostly based on temperate birds in part because a relative paucity of biological data has been described for tropical species. Expanding our knowledge about life histories of tropical birds can help us to better understand global trends in life history strategies. To aid in this endeavor, we studied Mountain Wren-Babblers (Gypsophila crassus) breeding in Malaysian Borneo from 2009 to 2017. Relatively small (mean = 28.8 g), dark brown birds, they were cooperative breeders and foraged and cared for the nest in groups of typically 4 or 5 birds. We located 145 nests, which were globular and partially domed (91.8 mm mean opening height accounted for half of 180.7 mm total mean nest height), constructed from fern fronds on the outside and dead leaves on the inside, and most often placed on banks. Brooding attentiveness decreased with nestling age and was rare after day 7 once they began growing their primary feathers. Provisioning rate slightly increased with nestling age. Nestling growth rate constants were typical of many tropical birds, asymptoting a few days prior to fledging. Predation accounted for nearly all nest failures (87 of 88), with a daily nest predation rate for the total nesting period of 0.056 and nest success decreasing with elevation. Daily predation rate was highest during lay (0.117) and lowest during incubation (0.046). We compared these results with related species to identify potential explanations for the trends we described. The most notable result from these comparisons was that Mountain Wren-Babblers have a long incubation period (23.5 d) and adults only incubate for a small part of the day. This anomalous behavior emphasizes the importance of understanding the great variation in tropical life history strategies to ultimately improve life history theory.
Control of the parr-smolt transformation (or smoltification) is crucial for the husbandry and successful seawater (SW) transfer of Atlantic salmon (Salmo salar) reared in freshwater (FW) hatcheries. Photoperiod is an important environmental signal that initiates the complex physiological, morphological and behavioural changes that coincide with marine migration. While the use of long-day photoperiods to initiate smoltification has been well studied, this study investigated how three preparatory photoperiods in FW (LD 08:16, LD 12:12, LD 16:08) preceding exposure to 24-h light (LD 24:0) may influence or enhance smolt performance and growth post-SW transfer. After the photoperiod treatment phase (8 weeks), all groups were exposed to LD 24:0 for 8 weeks (FW) and then transferred to SW for a further 8 weeks. Exposure to LD 16:08 induced rapid development of smolt-related characteristics such as increased gill NKA activity, gill NKAα1b mRNA, and plasma cortisol, and decreased gill NKAα1a mRNA levels and condition factor through the 8-week treatment phase. Subsequent exposure to a LD 24:0 photoperiod resulted in a partial reversal of several of these characteristics, suggesting these fish went through a partial desmoltification. Exposure to LD 12:12 for 8 weeks prior to LD 24:0 elicited an intermediary response in smoltification attributes compared to LD 16:08 and LD 08:16. The LD 12:12 group adapted to SW and showed no negative effects on growth or physiological responses after transfer to SW. Exposure to a shortened photoperiod (LD 08:16) did not elicit any smoltification-related changes prior to LD 24:0, however, exposure to LD 24:0 increased gill NKA activity, plasma cortisol, changes in NKAα1a and NKAα1b mRNA, and the ratio of NKAα1b: NKAα1a. These results were confirmed by the expected changes in NKAα1a and NKAα 1b-positive immuno-reactive gill ionocytes. In summary, after exposure to LD 24:0 fish in the LD 08:16 group showed similar levels of change to those of the LD 16:08 group during the initial FW phase (prior to exposure to LD 24:0). After SW transfer, all groups were able to upregulate SW-specific NKAα1b mRNA and acclimate to SW, even though no increase in cortisol was evident. By the end of the study, there was no difference in SW growth among the groups. Overall, our data indicate that LD 16:08 advanced hypoosmoregulatory characteristics prior to LD 24:0 exposure. In addition, the physiological and molecular indicators measured in this group suggest that fish could have been transferred to SW immediately after 8 weeks in LD 16:08, with no added benefit of successive exposure to LD 24:0, which is typically used by industry to induce smoltification.
Forster’s terns (Sterna forsteri), historically one of the most numerous colonial-breeding waterbirds in South San Francisco Bay, California, have experienced recent decreases in the number of nesting colonies and overall breeding population size. The South Bay Salt Pond Restoration Project aims to restore 50–90 percent of former salt evaporation ponds to tidal marsh habitat in South San Francisco Bay. During phase 1 of the South Bay Salt Pond Restoration Project, the breaching of several pond levees to begin the process of tidal marsh restoration inundated island nesting habitat that had been used by Forster’s terns, American avocets (Recurvirostra americana), and other waterbirds. Additional nesting habitat could be lost as more managed ponds are converted to tidal marsh in the future. To address this issue, the South Bay Salt Pond Restoration Project organized the construction of new nesting islands in managed ponds that will not be restored to tidal marsh, thereby providing enduring island nesting habitat for waterbirds. In 2012, 16 new islands were constructed in Pond A16 in the Alviso complex of the Don Edwards San Francisco Bay National Wildlife Refuge, which increased the number of islands in this pond from 4 to 20. However, despite a long history of nesting on the four islands in Pond A16 before the 2012 construction activities, no Forster’s terns have nested in Pond A16 during the 7-year period (2012–18) after island construction.
During the 2017 and 2019 breeding seasons, we used social attraction measures (decoys and colony call playback systems) to attract Forster’s terns to islands within Pond A16 to re-establish nesting colonies. We maintained these systems from March through August in each year. To evaluate the effect of these social attraction measures, we completed surveys (between April and August) where we recorded the number and location of all Forster’s terns and other waterbirds using Pond A16, and we monitored waterbird nests. We compared bird survey and nest monitoring data collected in 2017 and 2019 to data collected in 2015 and 2016, prior to the implementation of social attraction measures, allowing for direct evaluation of the effect of social attraction efforts on Forster’s terns.
To increase the visibility and stakeholder involvement of this project, we engaged in multiple outreach activities in 2017, 2019, and 2020, including the development of a project website and educational video; publication of popular articles in 2017 and 2020; the development of outreach materials describing the project to the general public; and public presentations to relay findings to managers, stakeholders, and the general public.
The relative abundance of Forster’s terns in Pond A16, after adjusting for the overall South San Francisco Bay breeding population each year, was higher during the nesting period in 2017 and 2019 (when social attraction was used) than in 2015 and 2016 (before social attraction was used). Furthermore, more Forster’s terns were observed during the pre-nesting and nesting periods in the areas of Pond A16 where decoys and call systems were deployed. Although no Forster’s tern nests were observed in Pond A16 before social attraction was implemented (2015, 2016), or during the first-year social attraction was implemented (2017), 35 Forster’s tern nests were recorded during the second year of social attraction implementation in 2019. These 35 nests represent a re-establishment of a Forster’s tern nesting colony to Pond A16 for the first time in 8 years. As social attraction efforts often benefit from multiple years of decoy and call system deployment, results from 2017 and 2019 suggest that continued implementation of social attraction measures could help to ensure Forster’s tern breeding colonies persist in Pond A16 and other areas of South San Francisco Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201081","collaboration":"Prepared in cooperation with the San Francisco Bay Bird Observatory","usgsCitation":"Hartman, C.A., Ackerman, J.T., Herzog, M.P., Wang, Y., and Strong, C., 2020, Establishing Forster’s Tern (Sterna forsteri) nesting sites at pond A16 using social attraction for the South Bay Salt Pond restoration project: U.S. Geological Survey Open-File Report 2020–1081, 28 p., https://doi.org/10.3133/ofr20201081.","productDescription":"vii, 28 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-118152","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":376595,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1081/covrthb.jpg"},{"id":376596,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1081/ofr20201081.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.28057861328124,\n 37.40452830389465\n ],\n [\n -121.90155029296875,\n 37.40452830389465\n ],\n [\n -121.90155029296875,\n 37.55709809310769\n ],\n [\n -122.28057861328124,\n 37.55709809310769\n ],\n [\n -122.28057861328124,\n 37.40452830389465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Although landscape-scale restoration efforts are gaining traction worldwide, their success is generally unknown. We review landscape-scale restorations to gain insight to whether focal ecological outcomes have been achieved, in the face of changing environmental conditions.
Only 9% of the 477 articles that resulted from our search were studies of landscape-scale restorations. The majority (73%) of the landscape restorations from our study have occurred since the 1990s, indicating that this type of restoration has gained in popularity in the last 30 years. Furthermore, 67% of these restoration studies occurred in a single country: China. Many scientific studies have addressed the ability of a species to shift ranges with climate change, yet few of the landscape-scale restoration studies used for our study addressed this question. Instead, 87% of the studies focused on ecosystem function, rather than community-level processes, as a result of restoration.
There is a clear need for more research to be undertaken on the ecological outcomes of landscape-scale restorations to understand whether they enable species and communities to shift their ranges or adapt to climate change. Conservation practitioners could utilize our decision matrix as a tool to guide restoration of individual sites within a landscape context, as well as current and future climatic conditions, to guide ecological outcomes of interest. Optimal biodiversity maintenance requires habitat conservation in concert with restoration activities at the landscape scale, and the latter, likely increasingly so in a world of changing climate.
","language":"English","publisher":"Springer","doi":"10.1007/s40823-020-00056-7","usgsCitation":"von Holle, B., Yelenik, S.G., and Gornish, E.S., 2020, Restoration at the landscape scale as a means of mitigation and adaptation to climate change: Current Landscape Ecology Reports, v. 5, p. 85-97, https://doi.org/10.1007/s40823-020-00056-7.","productDescription":"13 p.","startPage":"85","endPage":"97","ipdsId":"IP-118681","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":455910,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s40823-020-00056-7","text":"Publisher Index Page"},{"id":378357,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2020-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"von Holle, Betsy 0000-0002-3116-3027","orcid":"https://orcid.org/0000-0002-3116-3027","contributorId":240595,"corporation":false,"usgs":false,"family":"von Holle","given":"Betsy","email":"","affiliations":[{"id":48109,"text":"National Science Foundation, Division of Environmental Biology","active":true,"usgs":false}],"preferred":false,"id":798523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769 syelenik@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":5251,"corporation":false,"usgs":true,"family":"Yelenik","given":"Stephanie","email":"syelenik@usgs.gov","middleInitial":"G.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":798524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gornish, Elise S 0000-0002-2055-4874","orcid":"https://orcid.org/0000-0002-2055-4874","contributorId":240596,"corporation":false,"usgs":false,"family":"Gornish","given":"Elise","email":"","middleInitial":"S","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":798525,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211309,"text":"70211309 - 2020 - The utility of zooarchaeological data to guide listing efforts for an imperiled mussel species (Bivalvia: Unionidae: Pleurobema riddellii)","interactions":[],"lastModifiedDate":"2023-03-27T17:18:32.279224","indexId":"70211309","displayToPublicDate":"2020-07-22T09:19:10","publicationYear":"2020","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}},"displayTitle":"The utility of zooarchaeological data to guide listing efforts for an imperiled mussel species (Bivalvia: Unionidae: Pleurobema riddellii)","title":"The utility of zooarchaeological data to guide listing efforts for an imperiled mussel species (Bivalvia: Unionidae: Pleurobema riddellii)","docAbstract":"The status of species in freshwater systems shift over time due to natural and anthropogenic causes. Determining the magnitude and cause of these shifts requires a long-term perspective. This process is complicated when there are also questions about the taxonomic validity of a species. Addressing these issues is important because both can undermine conservation and management efforts if incorrect. Pleurobema riddellii, Louisiana Pigtoe, is under review for protection under the U.S. Endangered Species Act, but its status in the Trinity River basin, where the taxon was described, remains in doubt due to questions about its taxonomy and occurrence within this basin. To address these questions, we compared shell morphometrics of P. riddellii dating to the late Holocene with modern P. riddellii, late Holocene Fusconaia sp., and modern Fusconaia sp. using multivariate analyses to test associations between the putative morphotypes. Based on these analyses, we demonstrate that P. riddellii was likely present in the Trinity during the late Holocene, which indicates questions about its taxonomic validity or presence in this basin are unfounded. Our study further highlights the role zooarchaeological studies can play in status assessments and their utility in better understanding biogeographic patterns for rare species.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.253","usgsCitation":"Randklev, C.R., Wolverton, S., Johnson, N., Smith, C.H., DuBose, T., Robertson, C., and Conley, J., 2020, The utility of zooarchaeological data to guide listing efforts for an imperiled mussel species (Bivalvia: Unionidae: Pleurobema riddellii): Conservation Science and Practice, v. 2, no. 9, e253, 12 p., https://doi.org/10.1111/csp2.253.","productDescription":"e253, 12 p.","ipdsId":"IP-113751","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455913,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.253","text":"Publisher Index Page"},{"id":376664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Trinity River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.119140625,\n 31.090574094954192\n ],\n [\n -94.39453125,\n 31.090574094954192\n ],\n [\n -94.39453125,\n 33.7243396617476\n ],\n [\n -97.119140625,\n 33.7243396617476\n ],\n [\n -97.119140625,\n 31.090574094954192\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Randklev, Charles R.","contributorId":202530,"corporation":false,"usgs":false,"family":"Randklev","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":793687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolverton, Steve","contributorId":229617,"corporation":false,"usgs":false,"family":"Wolverton","given":"Steve","email":"","affiliations":[{"id":34637,"text":"University of North Texas","active":true,"usgs":false}],"preferred":false,"id":793688,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":793689,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Chase H. 0000-0002-1499-0311","orcid":"https://orcid.org/0000-0002-1499-0311","contributorId":225140,"corporation":false,"usgs":false,"family":"Smith","given":"Chase","email":"","middleInitial":"H.","affiliations":[{"id":13716,"text":"Baylor University","active":true,"usgs":false}],"preferred":false,"id":793690,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DuBose, Traci","contributorId":229618,"corporation":false,"usgs":false,"family":"DuBose","given":"Traci","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":793691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Robertson, Clint","contributorId":206217,"corporation":false,"usgs":false,"family":"Robertson","given":"Clint","affiliations":[{"id":37288,"text":"Texas Parks and Wildife","active":true,"usgs":false}],"preferred":false,"id":793692,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Conley, Julian","contributorId":229619,"corporation":false,"usgs":false,"family":"Conley","given":"Julian","email":"","affiliations":[{"id":41695,"text":"Eastern Tennessee State University","active":true,"usgs":false}],"preferred":false,"id":793693,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250355,"text":"70250355 - 2020 - Mountain stoneflies may tolerate warming streams: Evidence from organismal physiology and gene expression","interactions":[],"lastModifiedDate":"2023-12-05T13:04:38.611795","indexId":"70250355","displayToPublicDate":"2020-07-22T07:00:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Mountain stoneflies may tolerate warming streams: Evidence from organismal physiology and gene expression","docAbstract":"Rapid glacier recession is altering the physical conditions of headwater streams. Stream temperatures are predicted to rise and become increasingly variable, putting entire meltwater-associated biological communities at risk of extinction. Thus, there is a pressing need to understand how thermal stress affects mountain stream insects, particularly where glaciers are likely to vanish on contemporary timescales. In this study, we measured the critical thermal maximum (CTMAX) of stonefly nymphs representing multiple species and a range of thermal regimes in the high Rocky Mountains, USA. We then collected RNA-sequencing data to assess how organismal thermal stress translated to the cellular level. Our focal species included the meltwater stonefly, Lednia tumana, which was recently listed under the U.S. Endangered Species Act due to climate-induced habitat loss. For all study species, critical thermal maxima (CTMAX > 20°C) far exceeded the stream temperatures mountain stoneflies experience (<10°C). Moreover, while evidence for a cellular stress response was present, we also observed constitutive expression of genes encoding proteins known to underlie thermal stress (i.e., heat shock proteins) even at low temperatures that reflected natural conditions. We show that high-elevation aquatic insects may not be physiologically threatened by short-term exposure to warm temperatures and that longer-term physiological responses or biotic factors (e.g., competition) may better explain their extreme distributions.
The headwaters of the Snake River are in the mountains of northwestern Wyoming. Maintaining the recognized high quality of water in Grand Teton National Park is a National Park Service (NPS) priority. To characterize and understand the water resources of Grand Teton National Park, the NPS established a monitoring program to monitor the quality of area surface waters. Beginning in 2006, water was sampled by the NPS and analyzed for a range of chemical species at the Snake River above Jackson Lake at Flagg Ranch streamgage 13010065 (hereafter referred to as “Snake River at Flagg Ranch”), a site where the U.S. Geological Survey (USGS) previously sampled and analyzed water from 1986 through 2004. The USGS, in cooperation with the NPS, evaluated water-quality data collected by both entities to determine if discharge and total dissolved solids (referred to as dissolved solids) have changed in the Snake River at the Flagg Ranch.
To understand potential changes with time in dissolved solids, discharge was analyzed between January 1986 and December 2018, which corresponds with the time period when water-quality data were collected. Mean annual discharge varied during this time, with high, low, mean, and median flows generally increasing from 1986 through 1998, decreasing through 2005, and then generally increasing through 2018.
Combining water-quality data collected by the USGS and NPS provides a longer, more complete dataset for analyses. During the period of time when NPS was the sampling agency, specific conductance data were collected, but dissolved-solids data were not. The specific conductance data from both agencies were evaluated to determine if combining the data was justified. The interquartile ranges of data collected by both agencies are similar, and rapid, large changes in values during the period of transition between USGS and NPS sampling do not occur. The USGS and NPS datasets are not statistically different in the spring, summer, or fall, but are statistically different in the winter. The winter differences could be a function of the lack of wintertime NPS sampling, which excludes higher-concentration, lower-discharge data or a function of changes in the actual concentration in the stream. Although there is some difference in the winter datasets, the similarity in sampling methods and general overall data characteristics justifies combining the data for trend analyses.
Because the dissolved-solids parameter is useful for managers, it is often calculated from specific conductance using a linear regression model when dissolved-solids data are absent. For this study, creating a modeled dataset of dissolved solids for the NPS data collection period of time provided a longer, more complete dataset of dissolved-solids concentrations.
The concentrations of dissolved solids over time are identified by season and indicate that samples collected in the fall and winter have higher concentrations than samples collected in spring and summer. Specifically, the mean dissolved-solids concentrations in fall and winter are around 188 milligrams per liter (mg/L), whereas the mean concentrations are around 130 mg/L in spring and summer. This difference is generally attributed to the dilution of spring and summer samples by snowmelt generated runoff during the high-flow period of the year.
Trend analyses of dissolved-solids concentrations and loads indicate that an upward trend in concentration from 1986 to 2018 is likely, and a downward trend in load is highly likely. Comparing 1986 to 2018, dissolved-solids concentration is estimated to have increased by 2.25 mg/L (1.4 percent). During that same period, the dissolved-solids load is estimated to have decreased 11.8 million kilograms per year (12-percent decrease). This decrease is consistent with the estimated decrease in annual mean of daily mean discharge. Because 10 percent of the total change in dissolved-solids load is related to a change in the concentration-discharge relationship and 2 percent is related to changes in discharge, the decreased load is related less to changes in discharge and more to landscape scale processes that are affecting the concentration-discharge relationship.
As noted above, the data collected by the USGS and NPS are generally comparable with regards to sampling and analytical methods, and data collected by both agencies were used as one dataset for trend analyses. The current NPS sampling schedule, however, is creating a dataset biased towards lower concentration dissolved-solids data, which occurs during higher summer flows, by only sampling during April through November. From 1986 to 2018, the percentage of NPS samples is small enough that the effect on trends is expected to be minimal. Because of the importance of low flow (winter season) data, it is likely that an April through November sampling regime may affect the ability to detect trends or determine seasonality in the future. Collection of winter data in particular is important based on the findings that the changes in the modeled concentration-discharge relationship over time have been most pronounced during the winter (represented by February) months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205062","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Miller, O.L., and Eddy-Miller, C.A., 2020, Discharge and dissolved-solids characteristics and trends of Snake River above Jackson Lake at Flagg Ranch, Wyoming, 1986–2018: U.S. Geological Survey Scientific Investigations Report 2020–5062, 19 p., https://doi.org/10.3133/sir20205062.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-116863","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":376357,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5062/coverthb.jpg"},{"id":376358,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5062/sir20205062.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5062"}],"country":"United States","state":"Wyoming","otherGeospatial":"Flagg Ranch watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.05804443359375,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.081666311450526\n ],\n [\n -110.23681640625,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.457309801319305\n ],\n [\n -111.05804443359375,\n 44.081666311450526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Boseman Avenue
Helena, MT 59601
Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
West Valley City, UT 84119–2047
The July 2019 Ridgecrest earthquake sequence in southeastern California was characterized as surprising because only ~35% of the rupture occurred on previously mapped faults. Employing more detailed inspection of pre-event high-resolution topography and imagery in combination with field observations, we document evidence of active faulting in the landscape along the entire fault system. Scarps, deflected drainages, and lineaments and contrasts in topography, vegetation, and ground color demonstrate previous slip on a dense network of orthogonal faults, consistent with patterns of surface rupture observed in 2019. Not all of these newly mapped fault strands ruptured in 2019. Outcrop-scale field observations additionally reveal tufa lineaments and sheared Quaternary deposits. Neotectonic features are commonly short (<2 km), discontinuous, and display en echelon patterns along both the M 6.4 and M 7.1 ruptures. These features are generally more prominent and better preserved outside the late Pleistocene lake basins. Fault expression may also be related to deformation style: scarps and topographic lineaments are more prevalent in areas where substantial vertical motion occurred in 2019. Where strike-slip displacement dominated in 2019, the faults are mainly expressed by less prominent tonal and vegetation features. Both the NE- and NW-trending active fault systems are subparallel to regional bedrock fabrics that were established as early as ~150 Ma, and may be reactivating these older structures. Overall, we estimate that 50-70% (i.e., an additional 15-35%) of the 2019 surface ruptures could have been recognized as active faults with detailed inspection of pre-event data. Similar detailed mapping of potential neotectonic features could help improve seismic hazard analyses in other regions of eastern California and elsewhere that have distributed faulting or incompletely mapped faults. In areas where faults cannot be resolved as single thoroughgoing structures, a zone of potential faulting should be used as a hazard model input.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200041","usgsCitation":"Thompson Jobe, J., Philibosian, B.E., Chupik, C., Dawson, T.E., Bennett, S.E., Gold, R.D., DuRoss, C., Ladinsky, T.C., Kendrick, K.J., Haddon, E., Pierce, I., Swanson, B.J., and Seitz, G., 2020, Evidence of previous faulting along the 2019 Ridgecrest, California earthquake ruptures: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1427-1456, https://doi.org/10.1785/0120200041.","productDescription":"30 p.","startPage":"1427","endPage":"1456","ipdsId":"IP-115636","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":436866,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENA24Y","text":"USGS data release","linkHelpText":"Pre-existing features associated with active faulting in the vicinity of the 2019 Ridgecrest, California earthquake sequence"},{"id":376748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.30603027343749,\n 34.46127728843705\n ],\n [\n -116.49902343749999,\n 34.46127728843705\n ],\n [\n -116.49902343749999,\n 36.59788913307022\n ],\n [\n -119.30603027343749,\n 36.59788913307022\n ],\n [\n -119.30603027343749,\n 34.46127728843705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson Jobe, Jessica 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":225113,"corporation":false,"usgs":false,"family":"Thompson Jobe","given":"Jessica","email":"","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":793963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chupik, Colin","contributorId":217357,"corporation":false,"usgs":false,"family":"Chupik","given":"Colin","email":"","affiliations":[{"id":39606,"text":"Univ. of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":793965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dawson, Timothy E.","contributorId":24429,"corporation":false,"usgs":false,"family":"Dawson","given":"Timothy","email":"","middleInitial":"E.","affiliations":[{"id":7099,"text":"Calif. 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Survey","active":true,"usgs":false}],"preferred":false,"id":793966,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennett, Scott E.K. 0000-0002-9772-4122 sekbennett@usgs.gov","orcid":"https://orcid.org/0000-0002-9772-4122","contributorId":5340,"corporation":false,"usgs":true,"family":"Bennett","given":"Scott","email":"sekbennett@usgs.gov","middleInitial":"E.K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":793967,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":793968,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":793969,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ladinsky, Tyler C.","contributorId":201083,"corporation":false,"usgs":false,"family":"Ladinsky","given":"Tyler","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":793970,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kendrick, Katherine J. 0000-0002-9839-6861","orcid":"https://orcid.org/0000-0002-9839-6861","contributorId":207907,"corporation":false,"usgs":true,"family":"Kendrick","given":"Katherine","email":"","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793971,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Haddon, Elizabeth 0000-0001-7601-7755 ehaddon@usgs.gov","orcid":"https://orcid.org/0000-0001-7601-7755","contributorId":196407,"corporation":false,"usgs":true,"family":"Haddon","given":"Elizabeth","email":"ehaddon@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":793972,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pierce, Ian","contributorId":217358,"corporation":false,"usgs":false,"family":"Pierce","given":"Ian","email":"","affiliations":[{"id":39606,"text":"Univ. of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":793973,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Swanson, Brian J.","contributorId":216334,"corporation":false,"usgs":false,"family":"Swanson","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":793974,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Seitz, Gordon G.","contributorId":17303,"corporation":false,"usgs":false,"family":"Seitz","given":"Gordon G.","affiliations":[{"id":7099,"text":"Calif. Geol. Survey","active":true,"usgs":false}],"preferred":false,"id":793975,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70263610,"text":"70263610 - 2020 - San Andreas fault exploration using refraction tomography and S-wave-type and Fϕ-mode guided waves","interactions":[],"lastModifiedDate":"2025-02-19T16:36:26.556871","indexId":"70263610","displayToPublicDate":"2020-07-21T10:28:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"San Andreas fault exploration using refraction tomography and S-wave-type and Fϕ-mode guided waves","docAbstract":"Surface ruptures from the 18 April 1906 M∼7.9 San Francisco earthquake were distributed over an ∼35‐meter‐wide zone at San Andreas Lake on the San Francisco Peninsula in California (Schussler, 1906). Since ∼1906, the surface ruptures have been largely covered by water, but with water levels at near‐historic low levels in 2008–2011, we observed that the 1906 surface ruptures were no longer visible. As a fault imaging test, we acquired refraction tomography and guided‐wave data across the 1906 surface ruptures in 2011. We found that individual fault traces, as mapped by Schussler (1906), can be identified on the basis of discrete low‐velocity zones (VS and VP, reduced ∼40% and ∼34%, respectively) and high‐amplitude guided waves. Guided waves have traditionally been observed as large‐amplitude waveforms over wide (hundreds of meters to kilometers) zones of faulting, but we demonstrate that by evaluating guided waves (including Rayleigh/Love‐ and P/SV‐types) in terms of peak ground velocity (PGV), individual near‐surface fault traces within a fault zone can be precisely located, even more than 100 yr after the surface ruptures. Such precise exploration can be used to focus paleoseismic trenching efforts and to identify or exclude faulting at specific sites. We evaluated PGV of both S‐wave‐type and Fϕ‐mode‐type guided waves and found that both wave types can be used to identify subsurface fault traces. At San Andreas Lake (main fault), S‐wave‐type guided waves travel up to 18% slower than S body waves, and Fϕ‐mode guided waves travel ∼60% slower than P body waves but ∼15% faster than S body waves. We found that guided‐wave amplitudes vary with frequency but are up to five times higher than those of body waves, including the S wave. Our data are consistent with the concept that guided waves can be a strong‐shaking hazard during large‐magnitude earthquakes.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200136","usgsCitation":"Catchings, R.D., Rymer, M., and Goldman, M., 2020, San Andreas fault exploration using refraction tomography and S-wave-type and Fϕ-mode guided waves: Bulletin of the Seismological Society of America, v. 110, no. 6, p. 3088-3102, https://doi.org/10.1785/0120200136.","productDescription":"15 p.","startPage":"3088","endPage":"3102","ipdsId":"IP-102153","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Calfornia","otherGeospatial":"San Andreas fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.44607249804032,\n 37.61113945668713\n ],\n [\n -122.44607249804032,\n 37.57485979697452\n ],\n [\n -122.39941099606784,\n 37.57485979697452\n ],\n [\n -122.39941099606784,\n 37.61113945668713\n ],\n [\n -122.44607249804032,\n 37.61113945668713\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rymer, Michael 0000-0002-5429-5073 mrymer@usgs.gov","orcid":"https://orcid.org/0000-0002-5429-5073","contributorId":220757,"corporation":false,"usgs":true,"family":"Rymer","given":"Michael","email":"mrymer@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927566,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263930,"text":"70263930 - 2020 - Liquefaction and related ground failure from July 2019 Ridgecrest earthquake sequence","interactions":[],"lastModifiedDate":"2025-02-28T16:19:07.872364","indexId":"70263930","displayToPublicDate":"2020-07-21T10:14:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Liquefaction and related ground failure from July 2019 Ridgecrest earthquake sequence","docAbstract":"The 2019 Ridgecrest earthquake sequence produced a 4 July M 6.5 foreshock and a 5 July M 7.1 mainshock, along with 23 events with magnitudes greater than 4.5 in the 24 hr period following the mainshock. The epicenters of the two principal events were located in the Indian Wells Valley, northwest of Searles Valley near the towns of Ridgecrest, Trona, and Argus. We describe observed liquefaction manifestations including sand boils, fissures, and lateral spreading features, as well as proximate non‐ground failure zones that resulted from the sequence. Expanding upon results initially presented in a report of the Geotechnical Extreme Events Reconnaissance Association, we synthesize results of field mapping, aerial imagery, and inferences of ground deformations from Synthetic Aperture Radar‐based damage proxy maps (DPMs). We document incidents of liquefaction, settlement, and lateral spreading in the Naval Air Weapons Station China Lake US military base and compare locations of these observations to pre‐ and postevent mapping of liquefaction hazards. We describe liquefaction and ground‐failure features in Trona and Argus, which produced lateral deformations and impacts on several single‐story masonry and wood frame buildings. Detailed maps showing zones with and without ground failure are provided for these towns, along with mapped ground deformations along transects. Finally, we describe incidents of massive liquefaction with related ground failures and proximate areas of similar geologic origin without ground failure in the Searles Lakebed. Observations in this region are consistent with surface change predicted by the DPM. In the same region, geospatial liquefaction hazard maps are effective at identifying broad percentages of land with liquefaction‐related damage. We anticipate that data presented in this article will be useful for future liquefaction susceptibility, triggering, and consequence studies being undertaken as part of the Next Generation Liquefaction project.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200025","usgsCitation":"Zimmaro, P., Nweke, C.C., Hernandez, J., Hudson, K., Hudson, M.B., Ahdi, S.K., Boggs, M., Davis, C.A., Goulet, C.A., Brandenberg, S.J., Hudnut, K.W., and Stewart, J., 2020, Liquefaction and related ground failure from July 2019 Ridgecrest earthquake sequence: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1549-1566, https://doi.org/10.1785/0120200025.","productDescription":"18 p.","startPage":"1549","endPage":"1566","ipdsId":"IP-119620","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487714,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/99z116kn","text":"External Repository"},{"id":482647,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.3,\n 36\n ],\n [\n -117.8,\n 36\n ],\n [\n -117.8,\n 35.55\n ],\n [\n -117.3,\n 35.55\n ],\n [\n -117.3,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmaro, Paolo","contributorId":219068,"corporation":false,"usgs":false,"family":"Zimmaro","given":"Paolo","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":929153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nweke, Chukwuebuka C","contributorId":217352,"corporation":false,"usgs":false,"family":"Nweke","given":"Chukwuebuka","email":"","middleInitial":"C","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":929154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hernandez, Janis","contributorId":216335,"corporation":false,"usgs":false,"family":"Hernandez","given":"Janis","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":929155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hudson, Kenneth S","contributorId":351629,"corporation":false,"usgs":false,"family":"Hudson","given":"Kenneth S","affiliations":[{"id":84020,"text":"University of Calif., Los Angeles","active":true,"usgs":false}],"preferred":false,"id":929156,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hudson, Martin B","contributorId":217360,"corporation":false,"usgs":false,"family":"Hudson","given":"Martin","email":"","middleInitial":"B","affiliations":[{"id":39607,"text":"Wood","active":true,"usgs":false}],"preferred":false,"id":929157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ahdi, Sean K","contributorId":217355,"corporation":false,"usgs":false,"family":"Ahdi","given":"Sean","email":"","middleInitial":"K","affiliations":[{"id":39605,"text":"Exponent, Inc. and UCLA","active":true,"usgs":false}],"preferred":false,"id":929158,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boggs, Matt","contributorId":224310,"corporation":false,"usgs":false,"family":"Boggs","given":"Matt","email":"","affiliations":[{"id":13444,"text":"US Navy","active":true,"usgs":false}],"preferred":false,"id":929159,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davis, Craig A.","contributorId":171490,"corporation":false,"usgs":false,"family":"Davis","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":929160,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Goulet, Christine A. 0000-0002-7643-357X","orcid":"https://orcid.org/0000-0002-7643-357X","contributorId":194805,"corporation":false,"usgs":false,"family":"Goulet","given":"Christine","email":"","middleInitial":"A.","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":929161,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Brandenberg, Scott J","contributorId":217350,"corporation":false,"usgs":false,"family":"Brandenberg","given":"Scott","email":"","middleInitial":"J","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":929162,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hudnut, Kenneth W. 0000-0002-3168-4797 hudnut@usgs.gov","orcid":"https://orcid.org/0000-0002-3168-4797","contributorId":2550,"corporation":false,"usgs":true,"family":"Hudnut","given":"Kenneth","email":"hudnut@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929163,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Stewart, Jonathan P.","contributorId":350854,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan P.","affiliations":[{"id":83855,"text":"University of California, Los Angeles, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":929164,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70214546,"text":"70214546 - 2020 - Factors influencing the probability of hydraulic fracturing induced seismicity in Oklahoma","interactions":[],"lastModifiedDate":"2020-10-01T14:41:33.329979","indexId":"70214546","displayToPublicDate":"2020-07-21T09:42:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Factors influencing the probability of hydraulic fracturing induced seismicity in Oklahoma","docAbstract":"Injection‐induced seismicity became an important issue over the past decade, and although much of the rise in seismicity is attributed to wastewater disposal, a growing number of cases have identified hydraulic fracturing (HF) as the cause. A recent study identified regions in Oklahoma where ≥75% of seismicity from 2010 to 2016 correlated with nearly 300 HF wells. To identify factors associated with increased probability of induced seismicity, we gathered publicly available information about the HF operations in these regions including: injected volume, number of wells on a pad, injected fluid (gel vs. slickwater), vertical depth of the well, proximity of the well to basement rock, and the formation into which the injection occurred. To determine the statistical strength of the trends, we applied logistic regression, bootstrapping, and odds ratios. We see no trend with total injected volume in our Oklahoma dataset, in contrast to strong trends observed in Alberta and Texas, but we note those regions have many more multiwell pads leading to larger cumulative volumes within a localized area. We found a >∼50% lower probability of seismicity with the use of gel compared to slickwater. We found that HF wells targeting older formations had a higher probability of seismicity; however, these wells also tend to be deeper, and we found the trend with well depth to be stronger than the trend with age of formation. When isolated to the Woodford formation, well depth produced the strongest relationship, increasing from ∼5%> to ∼50% probability from 1.5 to 5.5 km. However, no trend was seen in the proximity to basement parameter. Based on previously measured pore pressure gradients, we interpret the strong absolute depth relationship to be a result of the increasing formation overpressure measured in deeper portions of the basin that lower the stress change needed to induce seismicity.
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Robert","contributorId":217693,"corporation":false,"usgs":true,"family":"Skoumal","given":"Robert","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":799901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Currie, Brian S.","contributorId":207881,"corporation":false,"usgs":false,"family":"Currie","given":"Brian","email":"","middleInitial":"S.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":799902,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211261,"text":"ofr20201079 - 2020 - Evaluation of the Washington State Department of Transportation stormwater monitoring and effectiveness program for 2014–19","interactions":[],"lastModifiedDate":"2020-07-22T13:27:59.672556","indexId":"ofr20201079","displayToPublicDate":"2020-07-21T08:23:58","publicationYear":"2020","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":"2020-1079","displayTitle":"Evaluation of the Washington State Department of Transportation Stormwater Monitoring and Effectiveness Program for 2014–19","title":"Evaluation of the Washington State Department of Transportation stormwater monitoring and effectiveness program for 2014–19","docAbstract":"The U.S. Geological Survey was asked by the Washington State Department of Transportation to provide technical assistance as a third-party reviewer of their stormwater effectiveness monitoring program during the transition between the completion of the 2014 Washington State Department of Ecology permit requirements and start of the new 2019 Washington State Department of Ecology permit requirements. For the purposes of this evaluation, the U.S. Geological Survey reviewed Washington State Department of Transportation’s 2014 National Pollution Discharge Elimination System permit. This review focuses on sections S7, S8, G9, and appendix 4 of the permit that are specific to monitoring. These sections cover the methods of monitoring, the constituents that were monitored, laboratory requirements, reporting requirements, and data archival. Next, all quality-assurance project plans for the 2014 general permit and annual reports required for the permit were reviewed. The quality-assurance project plans and annual reports were reviewed to ensure that monitoring was executed and reported as required by the 2014 general permit. The monitoring requirements put forth from the permits were fully addressed in quality-assurance project plans and were completed and presented in the annual monitoring reports. Overall, the Washington State Department of Transportation monitoring program does not change much under its new 2019 permit. The Washington State Department of Transportation has followed through with the plan set out in each of its approved quality-assurance project plans and therefore, is in a good position to meet or exceed the new permit requirements in the upcoming 5-year permit cycle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201079","collaboration":"Prepared in cooperation with the Washington State Department of Transportation","usgsCitation":"Senter, C.A., and Sheibley, R.W., 2020, Evaluation of the Washington State Department of Transportation stormwater monitoring and effectiveness program for 2014–19: U.S. Geological Survey Open-File Report 2020–1079, 11 p., https://doi.org/10.3133/ofr20201079.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-117873","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":376565,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1079/ofr20201079.pdf","text":"Report","size":"394 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1079"},{"id":376564,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1079/coverthb.jpg"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Groundwater susceptibility to contamination was investigated by using environmental tracer-based groundwater age metrics in the south Atlantic and Gulf Coast principal aquifer systems of the Southeastern Coastal Plain, Mississippi embayment–Texas coastal uplands, and the Coastal Lowlands. Samples of dissolved gas, tritium, sulfur hexafluoride, tritiogenic helium, and carbon-14 were collected from 231 public supply wells in the 3 principal aquifer systems. Dissolved gas models were used to characterize recharge conditions and they identified recharge mechanisms that ranged from rapid, but short-lived, water table rises (possibly associated with large scale flooding), to slower diffuse recharge not associated with large water table fluctuations. Dissolved gas and geochemical correction models were used to calculate and (or) correct tracer concentrations before input to lumped parameter models of groundwater age. Lumped parameter models that were fit to tracer concentrations indicated groundwater was relatively old across the aquifer systems, with an estimated mean age of about 30,000 years. Estimates of groundwater age were related to hydrogeology, with increasing groundwater ages associated with greater depth, confinement, and distance from the recharge zone. Young groundwater with mean ages less than 2,000 years generally was in unconfined parts of the aquifer system, except for local areas of heavy groundwater extraction from unconfined aquifer units where estimated mean ages were up to 15,000 years. Lumped parameter model optimized age distributions describe the relative contribution of differing flow paths to the mean age, and a composite distribution of all samples from the three aquifer systems indicated that about 15 percent of the total sampled water had an age of less than 100 years. Various metrics of susceptibility, to land surface and geogenic contamination sources, derived from the age distributions, indicated geogenic sources as the primary threat to groundwater quality in the aquifer systems. Values of the susceptibility index (unitless) and fraction of recharge since 2,000 and 15,000 years before present are provided for assessment of individual well susceptibility. The data and interpretation methods presented here provide an additional means of investigating the susceptibility and sustainability of groundwater resources of the Southeastern Coastal Plain, Mississippi embayment–Texas coastal uplands, and the Coastal Lowlands aquifer systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205050","collaboration":"National Water-Quality ProgramNAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Inland fisheries, defined as finfish caught in lakes, rivers, and other water bodies, provide economic value and a source of protein at local and international levels. However, no comprehensive compilation of U.S. inland commercial fisheries exists. We sought to obtain data across all 50 states during 1990–2015 and noted a small, but significant, decline in harvest. The minimum harvest averaged 41,427 tonnes during 2009–2015 and peaked in 1995 with a minimum harvest of 49,951 tonnes. During 2009–2015, harvest and taxonomic composition varied regionally: eastern interior (the highest regional harvest, dominated by coregonines and carp), western interior (carp and Clupeidae), Gulf (catfish and Clupeidae), Pacific (salmonines), and Atlantic (the lowest regional harvest, dominated by catfish and Clupeidae). Our data compilation of commercial landings was more than double the current limited national inland harvest statistics, which might be indicative of an under appreciation for the value of inland fisheries that can have consequences when policy decisions are made regarding competing sectors for water usage.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10483","usgsCitation":"Murray, D.N., Bunnell, D.B., Rogers, M.W., Lynch, A., Beard, and Funge-Smith, S., 2020, Trends in inland commercial fisheries in the United States: Fisheries Magazine, v. 45, no. 11, p. 585-596, https://doi.org/10.1002/fsh.10483.","productDescription":"12 p.","startPage":"585","endPage":"596","ipdsId":"IP-107636","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"links":[{"id":465881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":922644,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Funge-Smith, Simon 0000-0001-9974-5333","orcid":"https://orcid.org/0000-0001-9974-5333","contributorId":245642,"corporation":false,"usgs":false,"family":"Funge-Smith","given":"Simon","email":"","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":922645,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211307,"text":"70211307 - 2020 - On the use of receiver operating character tests for evaluating spatial earthquake forecasts","interactions":[],"lastModifiedDate":"2020-09-10T20:11:59.974668","indexId":"70211307","displayToPublicDate":"2020-07-20T09:00:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"On the use of receiver operating character tests for evaluating spatial earthquake forecasts","docAbstract":"Spatial forecasts of triggered earthquake distributions have been ranked using receiver operating characteristic (ROC) tests. The test is a binary comparison between regions of positive and negative forecast against positive and negative presence of earthquakes. Forecasts predicting only positive changes score higher than Coulomb methods, which predict positive and negative changes. I hypothesize that removing the possibility of failures in negative forecast realms yields better ROC scores. I create a ‘perfect’ Coulomb forecast where all earthquakes only fall into positive stress change areas and compare with an informationless all-positive forecast. The ‘perfect’ Coulomb forecast barely beats the informationless forecast, and adding as few as 4 earthquakes occurring in the negative stress regions causes the Coulomb forecast to be no better than an informationless forecast under a ROC test. ROC tests also suffer from data imbalance when applied to earthquake forecasts because there are many more negative cases than positive.","language":"English","publisher":"Wiley","doi":"10.1029/2020GL088570","usgsCitation":"Parsons, T.E., 2020, On the use of receiver operating character tests for evaluating spatial earthquake forecasts: Geophysical Research Letters, v. 47, no. 17, e2020GL088570, 7 p., https://doi.org/10.1029/2020GL088570.","productDescription":"e2020GL088570, 7 p.","ipdsId":"IP-118312","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":376662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"17","noUsgsAuthors":false,"publicationDate":"2020-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":793682,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70213093,"text":"70213093 - 2020 - ShakeAlert Earthquake Early Warning System Performance During the 2019 Ridgecrest Earthquake Sequence","interactions":[],"lastModifiedDate":"2020-09-09T15:43:45.358709","indexId":"70213093","displayToPublicDate":"2020-07-20T08:47:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"ShakeAlert Earthquake Early Warning System Performance During the 2019 Ridgecrest Earthquake Sequence","docAbstract":"During July 2019, a sequence of earthquakes including a Mw6.4 foreshock and a Mw7.1 mainshock occurred near Ridgecrest, California. ShakeAlert, the U.S. Geological Survey (USGS) ShakeAlert public Earthquake Early Warning (EEW) system being developed for the U.S. West Coast, was operational during this time, though public alerting was only available within LA County. ShakeAlert created alert messages for many of the earthquakes, including the two largest events, and for many of the larger aftershocks. In this study, we dissect log files and replay data through the system to reconstruct the sequence of events and analyze the performance of the system during that time period. While the system performed reasonably well overall, the sequence also revealed various issues and short comings that will be addressed in impending and future system upgrades, with most parts of the system working as they should. ShakeAlert correctly detected and rapidly characterized both the Mw6.4 and Mw7.1 earthquakes within 6.9 s of their origin times and created alert messages that were available to ShakeAlert’s pilot users. No public alerts were sent out by the ShakeAlertLA cellphone app (the only publicly available alerting method at the time) because the predicted shaking for LA County was below the app’s alerting threshold of MMI 4.0. For the Mw6.4 event this was accurate. For the Mw7.1 event, public alerts for LA County were warranted, but ShakeAlert underpredicted the shaking levels because both the point-source and the finite-fault algorithms underestimated the magnitude of the earthquake by 0.8 units. A number of software and hardware issues that were responsible for the magnitude underestimation of the mainshock have been identified and will be addressed in future ShakeAlert releases. We also analyze the hypothetical alerting performance of ShakeAlert had public alerting been available throughout southern California with a lower alerting threshold of 〖MMI〗_alert=2.5MMI 2.5. We find that, despite the magnitude underestimation, ShakeAlert could have provided timely warnings to a large fraction of affected sites, including some of the near-epicentral sites with high ground motion intensities.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200032","usgsCitation":"Chung, A., Meier, M., Andrews, J., Bose, M., Crowell, B., McGuire, J., and Smith, D., 2020, ShakeAlert Earthquake Early Warning System Performance During the 2019 Ridgecrest Earthquake Sequence: Bulletin of the Seismological Society of America, v. 110, p. 1904-1923, https://doi.org/10.1785/0120200032.","productDescription":"20 p.","startPage":"1904","endPage":"1923","ipdsId":"IP-115548","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":455944,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20200727-125421047","text":"External Repository"},{"id":378269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.77069091796875,\n 35.552339944156195\n ],\n [\n -117.57843017578126,\n 35.552339944156195\n ],\n [\n -117.57843017578126,\n 35.67068501330236\n ],\n [\n -117.77069091796875,\n 35.67068501330236\n ],\n [\n -117.77069091796875,\n 35.552339944156195\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Chung, Angela","contributorId":141196,"corporation":false,"usgs":false,"family":"Chung","given":"Angela","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":798234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meier, Men-Andrin","contributorId":201882,"corporation":false,"usgs":false,"family":"Meier","given":"Men-Andrin","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":798235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andrews, Jennifer","contributorId":187764,"corporation":false,"usgs":false,"family":"Andrews","given":"Jennifer","affiliations":[],"preferred":false,"id":798236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bose, Maren","contributorId":222639,"corporation":false,"usgs":false,"family":"Bose","given":"Maren","email":"","affiliations":[{"id":40575,"text":"Swiss Seismological Service, Swiss Federal Institute of Technology Zürich (ETH Zürich), Zürich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":798237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crowell, Brendan","contributorId":171723,"corporation":false,"usgs":false,"family":"Crowell","given":"Brendan","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":798238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":798239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Deborah 0000-0002-8317-7762","orcid":"https://orcid.org/0000-0002-8317-7762","contributorId":201885,"corporation":false,"usgs":true,"family":"Smith","given":"Deborah","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":798240,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217309,"text":"70217309 - 2020 - Avian eggshell thickness in relation to egg morphometrics, embryonic development, and mercury contamination","interactions":[],"lastModifiedDate":"2021-01-18T13:36:47.518525","indexId":"70217309","displayToPublicDate":"2020-07-20T07:35:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Avian eggshell thickness in relation to egg morphometrics, embryonic development, and mercury contamination","docAbstract":"Eggshell thickness is important for physiological, ecological, and ecotoxicological studies on birds; however, empirical eggshell thickness measurements for many species and regions are limited. We measured eggshell thickness at the equator and the egg poles for 12 avian species and related eggshell thickness to egg morphometrics, embryonic development, egg status, and mercury contamination. Within an egg, eggshells were approximately 5.1% thicker at the equator than the sharp pole of the egg, although this difference varied among species (0.6%–9.8%). Within Forster's tern (Sterna forsteri), where eggshell thickness was measured at 5 equally spaced positions along the longitude of the egg, eggshell thickness changed more rapidly near the sharp pole of the egg compared to near the blunt pole of the egg. Within species, eggshell thickness was related to egg width and egg volume for six of the 12 species but was not related to egg length for any species. Among species, mean eggshell thickness was strongly related to species mean egg width, egg length, egg volume, and bird body mass, although species mean body mass was the strongest predictor of species mean eggshell thickness. Using three species (American avocet [Recurvirostra americana], black‐necked stilt [Himantopus mexicanus], and Forster's tern), whose nests were carefully monitored, eggshell thickness (including the eggshell membrane) did not differ among viable, naturally abandoned, dead, or failed‐to‐hatch eggs; was not related to total mercury concentrations of the egg content; and did not decrease with embryonic age. Our study also provides a review of all existing eggshell thickness data for these 12 species.
Infectious disease epidemics present a difficult task for policymakers, requiring the implementation of control strategies under significant time constraints and uncertainty. Mathematical models can be used to predict the outcome of control interventions, providing useful information to policymakers in the event of such an epidemic. However, these models suffer in the early stages of an outbreak from a lack of accurate, relevant information regarding the dynamics and spread of the disease and the efficacy of control. As such, recommendations provided by these models are often incorporated in an ad hoc fashion, as and when more reliable information becomes available. In this work, we show that such trial-and-error-type approaches to management, which do not formally take into account the resolution of uncertainty and how control actions affect this, can lead to sub-optimal management outcomes. We compare three approaches to managing a theoretical epidemic: a non-adaptive management (AM) approach that does not use real-time outbreak information to adapt control, a passive AM approach that incorporates real-time information if and when it becomes available, and an active AM approach that explicitly incorporates the future resolution of uncertainty through gathering real-time information into its initial recommendations. The structured framework of active AM encourages the specification of quantifiable objectives, models of system behaviour and possible control and monitoring actions, followed by an iterative learning and control phase that is able to employ complex control optimisations and resolve system uncertainty. The result is a management framework that is able to provide dynamic, long-term projections to help policymakers meet the objectives of management. We investigate in detail the effect of different methods of incorporating up-to-date outbreak information. We find that, even in a highly simplified system, the method of incorporating new data can lead to different results that may influence initial policy decisions, with an active AM approach to management providing better information that can lead to more desirable outcomes from an epidemic.
Biological nitrogen fixation (BNF) supports terrestrial primary productivity and plays key roles in mediating human-induced changes in global nitrogen (N) and carbon cycling. However, there are still critical uncertainties in our understanding of the amount of BNF occurring across terrestrial ecosystems, and of how terrestrial BNF will respond to global change. We synthesized BNF data from Latin America, a region reported to sustain some of the highest BNF rates on Earth, but that is underrepresented in previous data syntheses. We used meta-analysis and modeling approaches to estimate BNF rates across Latin America's major biomes and to evaluate the potential effects of increased N deposition and land-use change on these rates. Unmanaged tropical and subtropical moist forests sustained observed and predicted total BNF rates of 10 ± 1 and 14 ± 1 kg N ha−1 y−1, respectively, supporting the hypothesis that these forests sustain lower BNF rates than previously thought. Free-living BNF accounted for two-thirds of the total BNF in these forests. Despite an average 30% reduction of free-living BNF in response to experimental N-addition, our results suggest free-living BNF rate responses to current and projected N deposition across tropical and subtropical moist forests are small. In contrast, the conversion of unmanaged ecosystems to crop and pasture lands increased BNF rates across all terrestrial biomes, mostly in savannas, grasslands, and dry forests, increasing BNF rates 2-fold. The information obtained here provides a more comprehensive understanding of BNF patterns for Latin America.
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The basal high resistivity zone has a higher proportion of Type II sedimentary organic matter than the overlying TMS, indicating it is more prone to oil generation. Optical petrography and electron microscopy reveal a heterogeneous clay matrix with ubiquitous pyrite grains, quartz, feldspar, glaucony, foraminifera, shell fragments, and rarer occurrences of apatite and crinoid fragments as well as liptinite, alginite, inertinite, and vitrinite. Our petrographic observations suggest that higher abundances of detrital quartz grains coupled with minimal authigenic cements result in higher porosity and permeability. However, the TMS is also more clay-rich than other unconventional shale oil and gas plays, which can impair the effectiveness of hydraulic fracture stimulation. Thin section observations reveal alternating clay and calcium carbonate laminae that are interpreted to reflect changes in sediment flux. Planktonic foraminifera indicate an overlying oxygenated water column while benthic inoceramid fragments and pervasive authigenic pyrite suggest anoxic or dysoxic bottom water conditions. Apatite fragments in thin section suggest mixing events and an influx of nutrient-rich sediments. Overall, these observations suggest that a variety of paleodepositional environments occurred in the TMS and the lithofacies diversity resulting from these small-scale depositional cycles makes it difficult to determinatively identify areas conducive to enhanced economic hydrocarbon recovery.","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2020.104580","collaboration":"None","usgsCitation":"Lohr, C., Valentine, B.J., Hackley, P.C., and Dulong, F.T., 2020, Characterization of the unconventional Tuscaloosa marine shale reservoir in southwestern Mississippi, USA: Insights from optical and SEM petrography: Marine and Petroleum Geology, v. 121, 104580, 24 p., https://doi.org/10.1016/j.marpetgeo.2020.104580.","productDescription":"104580, 24 p.","ipdsId":"IP-112257","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":455967,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpetgeo.2020.104580","text":"Publisher Index Page"},{"id":376788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi, Lousianna","otherGeospatial":"Southwestern Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.021484375,\n 30.012030680358613\n ],\n [\n -88.41796875,\n 30.012030680358613\n ],\n [\n -88.41796875,\n 32.02670629333614\n ],\n [\n -92.021484375,\n 32.02670629333614\n ],\n [\n -92.021484375,\n 30.012030680358613\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lohr, Celeste D. 0000-0001-6287-9047 clohr@usgs.gov","orcid":"https://orcid.org/0000-0001-6287-9047","contributorId":3866,"corporation":false,"usgs":true,"family":"Lohr","given":"Celeste D.","email":"clohr@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":794040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valentine, Brett J. 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":3846,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","middleInitial":"J.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":794041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":794042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dulong, Frank T. 0000-0001-7388-647X fdulong@usgs.gov","orcid":"https://orcid.org/0000-0001-7388-647X","contributorId":650,"corporation":false,"usgs":true,"family":"Dulong","given":"Frank","email":"fdulong@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":794043,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211221,"text":"70211221 - 2020 - Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine","interactions":[],"lastModifiedDate":"2020-07-20T13:33:45.743932","indexId":"70211221","displayToPublicDate":"2020-07-18T07:32:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine","docAbstract":"Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 Countriescovering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3,351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) cloud-platform. We used the pixel-based supervised Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 94 percent with roughly 14 percent omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of croplands occupying 18 percent of the land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. For the entire Landsat-derived 30-m product the overall accuracy was 93.8% with cropland class providing producers accuracy of 86.5% (errors of omissions = 13.5%) and users accuracy of 85.7% (errors of commissions = 14.3%). This Landsat-derived 30-m cropland product (GFSAD30) provided 10-30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30m global land cover (GLC30) product providing an overall accuracy of 88.8 percent (Kappa 0.7) with producers cropland similarity of 89.2 percent (errors of omissions = 10.8%) and users cropland similarity of 81.8 percent (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strength of GFSAD30 product, compared to other products, were in: 1. Identifying precise location of croplands, and 2. Capturing fragmented croplands. The cropland extent map dataset is available through NASAs Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global Croplands