The U.S. Geological Survey (USGS) developed and released a survey to assess the terrestrial analog needs of the planetary science community. The goal was to assess the current state of terrestrial analog studies and determine community needs related to the use of field sites for training and research, data dissemination and archiving, and sample collections.
The survey was designed to gather feedback from community members who have a self-described interest in the use of terrestrial analogs. The web-based questionnaire contained a total of 33 questions and was designed to take <10 minutes to complete. The questionnaire was divided into four sections: (1) “Respondent Details,” (2) “Field Analog Use,” (3) “Data Portal Use,” and (4) “Geologic Materials Collection Use.” Comment boxes were provided for 12 of the 33 questions, which allowed respondents to provide more detailed comments to individual questions. The questionnaire received a total of 248 responses. We identified 21 notable findings which are matched with one or more recommendations to be addressed by the planetary science community.
In general, the findings highlight the importance of terrestrial analog studies to the planetary science community. The findings address how and why the community uses terrestrial analogs, areas in which further support can lead to a greater return on investment, and how the community can better manage data and samples from these studies.
The results from this survey identify a need for additional training opportunities and analog-focused workshops. There is a gap in formal education related to field techniques for a significant part of researchers who conduct fieldwork. There is also a subset of the community who are interested in conducting field-based studies but are, however, unaware of relevant sites and methods. Workshops would provide an opportunity for scientists at all career stages to share their results and discuss common challenges such as logistics, field safety, funding, and data and sample archiving. Trainings, workshops, and better communication may also lead to increased field-analog work at locations in closer proximity to home institutions, reducing costs associated with large field expeditions and ultimately leading to more available funding for more localized field studies.
The survey also shows that the ability to archive a diverse array of field data is a major challenge for terrestrial studies and finds that existing practices are not compliant with National Aeronautics and Space Administration (NASA) data management policies. The survey points to a strong need for a central data repository, allowing for easier access to existing analog data and the archiving of new field data.
The community would benefit from additional physical sample archiving, consolidated into several key institutions to promote easier access, such as NASA and USGS centers. Though scientists would still need to acquire their own samples in the field for certain studies, many studies would benefit from an archive of existing samples and associated data for widely used analog sites, reducing redundant sampling practices.
This report finds that a coordinated effort to improve and standardize training, data archiving, sample curation, and communication regarding terrestrial analog studies will best serve the planetary science community in our exploration goals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241042","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"Edgar, L.A., Rumpf, M.E., Skinner, J.A., Jr., Gullikson, A.L., Keszthelyi, L., Hunter, M.A., Gaither, T., 2024, Assessing community needs for terrestrial analog studies: U.S. Geological Survey Open-File Report 2024–1042, 63 p., https://doi.org/10.3133/ofr20241042.","productDescription":"iii, 63 p.","onlineOnly":"Y","ipdsId":"IP-124254","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":464364,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1042/covrthb.jpg"},{"id":464365,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1042/ofr20241042.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Citizen science commonly uses social networking platforms because they provide the easiest way to contact people. Social networking platforms can also be especially effective in that they gather people by interest and region. By sharing questionnaires and collecting photographs in angling-themed Facebook groups, we assessed the applicability of social networking groups in citizen science surveys to evaluate the distribution patterns of invasive bigheaded carp in Hungary. Altogether, we received 1,234 responses from 29 Facebook groups to four survey solicitations, with responses coming from 417 locations from across Hungary. The majority of responses were received in the first (mean ± SD; 74.3% ± 5.3) and second days (12.5% ± 5.5) after a survey questionnaire was posted. Group size was positively correlated with reach but inversely with reach ratio (reached members divided by the total number of group members). We collected 311 photos of 622 bigheaded carp, of which 470 were H. molitrix and 67 were H. nobilis. Although spatial bias occurred both in responses to survey questionnaires and available photographs, presence of bigheaded carp was confirmed in all medium and large rivers and many lentic habitats in Hungary. We demonstrated that social media groups can be used to survey interested members of the public and facilitate rapid data collection, providing an effective platform for citizen science. Repeated, spaced sharing of survey solicitations in as many groups as possible (regardless of group size) is the cornerstone for effective data collection. Our survey provided more effective and inexpensively obtained information about the distribution of bigheaded carp than standard catch-based methods.
","language":"English","publisher":"Ubiquity Press","doi":"10.5334/cstp.780","usgsCitation":"Vitál, Z., Chapman, D., Halasi-Kovács, B., and Mozsár, A., 2024, Themed social networking groups as effective sources of data: A country-wide survey on invasive bigheaded carp (Hipophthalmichthys molitrix and H. nobilis) detection and distribution: Citizen Science: Theory and Practice, v. 9, no. 1, 29, 14 p., https://doi.org/10.5334/cstp.780.","productDescription":"29, 14 p.","ipdsId":"IP-158985","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":488378,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5334/cstp.780","text":"Publisher Index Page"},{"id":483722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Hungary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 19.905749040559556,\n 48.16445870730942\n ],\n [\n 19.646587056605625,\n 48.262319010335716\n ],\n [\n 19.47381240063592,\n 48.118342076328645\n ],\n [\n 18.825907440751536,\n 48.072184012061996\n ],\n [\n 18.774075043960778,\n 47.84077194152326\n ],\n [\n 18.653132784781832,\n 47.77114616748298\n ],\n [\n 17.651039780159408,\n 47.77114616748298\n ],\n [\n 17.314129201019796,\n 48.02598450726734\n ],\n [\n 17.11543834665531,\n 47.98552594077279\n ],\n [\n 17.003134820275022,\n 47.86395979415386\n ],\n [\n 17.029051018669804,\n 47.71886558279843\n ],\n [\n 16.5625594475527,\n 47.75953279753682\n ],\n [\n 16.407062257180257,\n 47.67234986658315\n ],\n [\n 16.71805663792503,\n 47.520887155888005\n ],\n [\n 16.44161718837492,\n 47.409925547425814\n ],\n [\n 16.44161718837492,\n 46.999097587853385\n ],\n [\n 16.268842532405188,\n 47.016769767732626\n ],\n [\n 16.1047066092342,\n 46.8752285993159\n ],\n [\n 16.28611999800259,\n 46.84569374552734\n ],\n [\n 16.57119818035133,\n 46.46621228452659\n ],\n [\n 17.460987658593552,\n 45.91002968951361\n ],\n [\n 18.065698954485782,\n 45.747503011802735\n ],\n [\n 18.4803581288121,\n 45.747503011802735\n ],\n [\n 18.713603914371276,\n 45.91604008269749\n ],\n [\n 18.869101104743663,\n 45.84988995631136\n ],\n [\n 19.68978072059778,\n 46.16788887622772\n ],\n [\n 20.510460336451843,\n 46.16788887622772\n ],\n [\n 20.648680061226855,\n 46.14993774774203\n ],\n [\n 20.795538518801777,\n 46.26352932418982\n ],\n [\n 21.10653289954655,\n 46.269501321699835\n ],\n [\n 22.24684562894288,\n 47.6897981229599\n ],\n [\n 22.480091414502084,\n 47.82337424756798\n ],\n [\n 22.69605973446386,\n 47.800168248983425\n ],\n [\n 22.89475058882843,\n 47.97396052110528\n ],\n [\n 22.81700199364164,\n 48.118342076328645\n ],\n [\n 22.626949872075784,\n 48.112574584327916\n ],\n [\n 22.46281394890471,\n 48.262319010335716\n ],\n [\n 22.229568163346727,\n 48.44028881239183\n ],\n [\n 21.754437859430936,\n 48.36573198861302\n ],\n [\n 21.41752728029016,\n 48.594788498150564\n ],\n [\n 21.10653289954655,\n 48.52618014157136\n ],\n [\n 20.78689978600312,\n 48.606214177684194\n ],\n [\n 20.510460336451843,\n 48.554778265864115\n ],\n [\n 20.329046947684617,\n 48.3312842835895\n ],\n [\n 19.905749040559556,\n 48.16445870730942\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vitál, Zoltán","contributorId":352562,"corporation":false,"usgs":false,"family":"Vitál","given":"Zoltán","affiliations":[{"id":84260,"text":"Hungarian University of Agriculture and Life Sciences","active":true,"usgs":false}],"preferred":false,"id":931686,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":931687,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Halasi-Kovács, Béla","contributorId":352563,"corporation":false,"usgs":false,"family":"Halasi-Kovács","given":"Béla","affiliations":[{"id":84260,"text":"Hungarian University of Agriculture and Life Sciences","active":true,"usgs":false}],"preferred":false,"id":931688,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mozsár, Attila","contributorId":352564,"corporation":false,"usgs":false,"family":"Mozsár","given":"Attila","affiliations":[{"id":84261,"text":"Balaton Limnological Research Institute","active":true,"usgs":false}],"preferred":false,"id":931689,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261033,"text":"70261033 - 2024 - Surveying waterfowl broods in wetlands using aerial drones","interactions":[],"lastModifiedDate":"2024-11-20T16:52:17.116684","indexId":"70261033","displayToPublicDate":"2024-11-20T09:46:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Surveying waterfowl broods in wetlands using aerial drones","docAbstract":"Effective waterfowl management relies on the collection of relevant demographic data to inform land management decisions; however, some types of data are difficult to obtain. For waterfowl, brood surveys are difficult to conduct because wetland habitats often obscure ducklings from being visually assessed. Here, we used Unoccupied Aerial Systems (UAS) to assess what wetland habitat characteristics influenced brood abundance in Suisun Marsh, California, USA. Using a thermal imaging camera, we surveyed 17 wetland units encompassing 332 ha of flooded area on seven waterfowl hunting clubs during the waterfowl breeding season. Additionally, using a combination of multispectral imagery collected from the UAS flights and LiDAR data from the previous year, we mapped habitat composition within each unit to relate to brood observation counts. From June 3-7, 2019, we identified 113 individual broods comprised of 827 ducklings. We found a positive relationship between the number of broods observed and the proportion of the unit that was flooded. We also found a positive relationship between the number of broods observed and the area of effective habitat, a metric of flooded habitat within a specific distance of flooded vegetation. Brood surveys using UAS could complement the traditional Breeding Population Survey and provide local managers with fine-scale and timely information regarding shifts in brood abundance in the region.","language":"English","publisher":"eScholarship","doi":"10.15447/sfews.2024v22iss3art2","usgsCitation":"Mackell, D.A., Casazza, M.L., Overton, C.T., Buffington, K., Freeman, C., Ackerman, J.T., and Thorne, K., 2024, Surveying waterfowl broods in wetlands using aerial drones: San Francisco Estuary and Watershed Science, v. 22, no. 3, 3, 16 p., https://doi.org/10.15447/sfews.2024v22iss3art2.","productDescription":"3, 16 p.","ipdsId":"IP-159150","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":466754,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2024v22iss3art2","text":"Publisher Index Page"},{"id":464360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Suisun Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.08563453767741,\n 38.23592936614091\n ],\n [\n -122.08563453767741,\n 38.115419770174015\n ],\n [\n -122.00107572096182,\n 38.115419770174015\n ],\n [\n -122.00107572096182,\n 38.23592936614091\n ],\n [\n -122.08563453767741,\n 38.23592936614091\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mackell, Desmond Alexander 0000-0002-1682-2581","orcid":"https://orcid.org/0000-0002-1682-2581","contributorId":266036,"corporation":false,"usgs":true,"family":"Mackell","given":"Desmond","email":"","middleInitial":"Alexander","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918976,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918977,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918978,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918979,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Chase M. 0000-0003-2284-1380","orcid":"https://orcid.org/0000-0003-2284-1380","contributorId":335090,"corporation":false,"usgs":false,"family":"Freeman","given":"Chase M.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":918980,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918981,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918982,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70261527,"text":"70261527 - 2024 - Model sensitivity analysis for coastal morphodynamics: Investigating sediment parameters and bed composition in Delft3D","interactions":[],"lastModifiedDate":"2025-05-13T15:59:07.445123","indexId":"70261527","displayToPublicDate":"2024-11-20T09:00:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Model sensitivity analysis for coastal morphodynamics: Investigating sediment parameters and bed composition in Delft3D","docAbstract":"Numerical simulation of sediment transport and subsequent morphological evolution rely on accurate parameterizations of sediment characteristics. However, these data are often not available or are spatially and/or temporally limited. This study approaches the problem of limited sediment grain-size data with a series of simulations assessing model sensitivity to sediment parameters and initial bed composition configurations in Delft3D, leading to improved modeling practices. A previously validated Delft3D sediment transport and morphology model for Dauphin Island, Alabama, USA, is used as the benchmark case. A method for the generation of representative sediment grain sizes and their spatially varying distributions is presented via end-member analysis of in situ surficial sediment samples. Derived sediment classes and their spatial distributions are applied to two sensitivity case simulations with increasing bed composition complexity. First, multiple sediment classes are applied in a single fully mixed layer, regardless of sediment type. Second, multiple sediment classes are applied in a thin, fully mixed transport layer with underlayers containing only the non-cohesive sediment classes below. Simulations were carried out in a probabilistic, Delft3D MorMerge configuration to capture long-term morphology change for 10 years. We found there is sensitivity to the inclusion of additional sediment classes and sediment distribution made evident in bed level and morphology change. Inclusion of highly mobile fine sediments altered model results in each sensitivity case. The model was also found to be sensitive to initial bed composition in terms of bed level and morphology change, with notable differences between sensitivity cases on decadal timescales, indicating an armoring effect in the second sensitivity case, which used the transport and underlayer bed configuration. The results of this study offer guidance for numerical modelers concerned with sediment behavior in coastal and estuarine environments.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse12112108","usgsCitation":"Jenkins, R., Smith, C., Passeri, D., and Ellis, A.M., 2024, Model sensitivity analysis for coastal morphodynamics: Investigating sediment parameters and bed composition in Delft3D: Journal of Marine Science and Engineering, v. 12, no. 11, 2108, 29 p., https://doi.org/10.3390/jmse12112108.","productDescription":"2108, 29 p.","ipdsId":"IP-170386","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466755,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse12112108","text":"Publisher Index Page"},{"id":465109,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.39043742176105,\n 30.854668400719234\n ],\n [\n -88.39043742176105,\n 30.17648303472457\n ],\n [\n -87.49364396321667,\n 30.17648303472457\n ],\n [\n -87.49364396321667,\n 30.854668400719234\n ],\n [\n -88.39043742176105,\n 30.854668400719234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":920895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":920896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":920897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellis, Alisha M. 0000-0002-1785-020X aellis@usgs.gov","orcid":"https://orcid.org/0000-0002-1785-020X","contributorId":192957,"corporation":false,"usgs":true,"family":"Ellis","given":"Alisha","email":"aellis@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":920898,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261053,"text":"70261053 - 2024 - Methodology for inclusion of produced and stored carbon dioxide in the U.S. Geological Survey Federal lands greenhouse gas inventory","interactions":[],"lastModifiedDate":"2024-11-21T15:07:34.730743","indexId":"70261053","displayToPublicDate":"2024-11-20T08:58:39","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Methodology for inclusion of produced and stored carbon dioxide in the U.S. Geological Survey Federal lands greenhouse gas inventory","docAbstract":"The U.S. Geological Survey (USGS) has developed two new carbon dioxide (CO2) emissions and sequestration accounting methods for use in future reports. The first method is a Federal lease-produced CO2 emissions calculation for an update of the report, “Federal Lands Greenhouse Gas Emissions and Sequestration in the United States.” The methodology to incorporate Federal lease CO2 production emissions into the updated report relies on CO2 sales royalty data from the Office of Natural Resources Revenue (ONRR). The end usage points for the gas include enhanced oil recovery with CO2 (CO2-EOR), food and beverage, and chemical production. CO2-EOR is the main end point for natural CO2 production in the United States; it accounted for 94% of usage in 2022 [1]. Federal lands emissions from this sector are estimated at 460 metric tons of CO2 in 2022, a very small amount relative to most other Federal lands emissions sector estimates.
The second new method, planned for a separate report, is a calculation of the geologic storage of CO2 on Federal lands. The second method estimates the CO2 stored under Federal surface lands and documents Federal climate change mitigation efforts. Currently, there is no storage of CO2 at an industrial level on Federal lands, however multiple proposals and projects are planned. This method was developed on non-Federal lands datasets in an effort to prepare for when these activities on Federal lands will require accounting. National estimates for CO2 geologic storage using this method, but without a Federal lands filtering step, totaled 8.0 million metric tons (Mt) in 2022.
The two methods described here are new benchmark methods in a collection of accounting procedures to document the current state of greenhouse gas emissions and their storage on Federal lands. These benchmarks can then be used to measure any subsequent changes in emissions from or carbon storage beneath Federal lands. While the magnitude of the values is currently non-existent to small, emissions mitigation goals established by decision makers indicate that these values will grow, and their documentation will take on greater value and use.
Soil moisture maps provide quantitative information that, along with climate and energy balance, is critical to integrate with hydrologic processes for characterizing landscape conditions. However, soil moisture maps are difficult to produce for natural landscapes because of vegetation cover and complex topography. Satellite-based L-band microwave sensors are commonly used to develop spatial soil moisture data products, but most existing L-band satellites provide only coarse scale (one to tens of kilometers grid size), information that is unsuitable for measuring soil moisture variation at hillslope or watershed-scales. L-band sensors are typically deployed on satellite platforms and aircraft but have been too large to deploy on small uncrewed aircraft systems (UAS). There is a need for greater spatial resolution and development of effective measures of soil moisture across a variety of natural vegetation types. To address these challenges, a novel UAS-based L-band radiometer system was evaluated that has recently been tested in agricultural settings. In this study, L-band UAS was used to map soil moisture at 3–50-m (m) resolution in a 13 square kilometer (km2) mixed grassland-forested landscape in Sonoma County, California. The results represent the first application of this technology in a natural landscape with complex topography and vegetation. The L-band inversion of the radiative transfer model produced soil moisture maps with an average unbiased root mean squared error (ubRMSE) of 0.07 m3/m3 and a bias of 0.02 m3/m3. Improved fine-scale soil moisture maps developed using UAS-based systems may be used to help inform wildfire risk, improve hydrologic models, streamflow forecasting, and early detection of landslides.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2024.1337953","usgsCitation":"Stern, M.A., Ferrell, R., Flint, L.E., Kozanitas, M., Ackerly, D., Elston, J., Stachura, M., Dai, E., and Thorne, J.H., 2024, Fine-scale surficial soil moisture mapping using UAS-based L-band remote sensing in a mixed oak-grassland landscape: Frontiers in Remote Sensing, v. 5, 1337953, 12 p., https://doi.org/10.3389/frsen.2024.1337953.","productDescription":"1337953, 12 p.","ipdsId":"IP-159618","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499941,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2024.1337953","text":"Publisher Index Page"},{"id":499713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Sonoma County","city":"Santa Rosa","otherGeospatial":"Mayacamas Mountains, Pepperwood Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.71354880264356,\n 38.57616137564548\n ],\n [\n -122.71354880264356,\n 38.565372954642044\n ],\n [\n -122.68982536456959,\n 38.565372954642044\n ],\n [\n -122.68982536456959,\n 38.57616137564548\n ],\n [\n -122.71354880264356,\n 38.57616137564548\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ferrell, Ryan","contributorId":366124,"corporation":false,"usgs":false,"family":"Ferrell","given":"Ryan","affiliations":[{"id":37798,"text":"Pepperwood Preserve","active":true,"usgs":false}],"preferred":false,"id":955320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Lorraine E. 0000-0002-7868-441X","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":306090,"corporation":false,"usgs":false,"family":"Flint","given":"Lorraine","email":"","middleInitial":"E.","affiliations":[{"id":66369,"text":"Earth Knowledge, Inc.","active":true,"usgs":false}],"preferred":false,"id":955321,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kozanitas, Melina","contributorId":366125,"corporation":false,"usgs":false,"family":"Kozanitas","given":"Melina","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":955322,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ackerly, David","contributorId":139541,"corporation":false,"usgs":false,"family":"Ackerly","given":"David","affiliations":[{"id":7102,"text":"University of California, Berkeley, Dept. of Civil & Envir. Engineering","active":true,"usgs":false}],"preferred":false,"id":955323,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elston, Jack","contributorId":334719,"corporation":false,"usgs":false,"family":"Elston","given":"Jack","affiliations":[{"id":80215,"text":"Black Swift Technologies","active":true,"usgs":false}],"preferred":false,"id":955324,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stachura, Maciej","contributorId":334720,"corporation":false,"usgs":false,"family":"Stachura","given":"Maciej","affiliations":[{"id":80215,"text":"Black Swift Technologies","active":true,"usgs":false}],"preferred":false,"id":955325,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dai, Eryan","contributorId":366129,"corporation":false,"usgs":false,"family":"Dai","given":"Eryan","affiliations":[{"id":87362,"text":"Weather Stream Inc.","active":true,"usgs":false}],"preferred":false,"id":955326,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thorne, James H.","contributorId":173762,"corporation":false,"usgs":false,"family":"Thorne","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":955327,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260969,"text":"ofr20241063 - 2024 - High-Flow Experimental Outcomes to Inform Everglades Restoration, 2010–22","interactions":[],"lastModifiedDate":"2024-12-02T18:42:31.825148","indexId":"ofr20241063","displayToPublicDate":"2024-11-18T13:52:27","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-1063","displayTitle":"High-flow experimental outcomes to inform Everglades restoration, 2010–22","title":"High-Flow Experimental Outcomes to Inform Everglades Restoration, 2010–22","docAbstract":"The Decompartmentalization Physical Model (DPM) was an experimental facility in the central Everglades operated between 2010 and 2022 to release high flows through a levee-enclosed area of degraded ridge and slough wetland that had been isolated from flow for sixty years. The purpose of DPM experimental program was to make measurements before, during, and after seasonal high-flow releases that could help guide the Congressionally authorized Everglades restoration project known as the Decompartmentalization and Sheet Flow Enhancement Project.
The DPM facility was operated by the South Florida Water Management District, with the U.S. Geological Survey (USGS) and several universities participating in experimental design and leading aspects of the research. The USGS research at DPM focused on measuring high-flow hydraulics and its sedimentary and ecological responses in downstream wetlands. USGS investigated interactions between flow and vegetation and microtopography that influenced flow velocity and water depth, bed shear stress, sediment entrainment, and the resulting downstream transport of suspended sediment and fate of particle-associated phosphorus. USGS also investigated high-flow changes in water-column mixing and gas exchange and resulting effects on metabolism of the aquatic ecosystem (primary productivity and respiration). USGS also investigated effects of built structures such as levee gaps that were constructed to reconnect levee-enclosed basins. This report describes the methods and results of the USGS-led data collection at DPM.
The USGS studies at DPM have identified factors that influence effectiveness of restoration, specifically how high-flow releases maximize sheet flow and affect sediment and nutrient dynamics while minimizing undesirable outcomes caused by past management that bypassed wetlands by conveying polluted water through canals to ecologically sensitive downstream areas. The DPM high-flow experiments reconnected the Water Conservation Area 3A and Water Conservation Area 3B basins, and it therefore has become a central feature of the restoration’s Decompartmentalization and Sheet Flow Enhancement Project. DPM’s scientific findings have already influenced the adaptive management of Everglades restoration in guiding elements of the final design and implementation of the Central Everglades Planning Project-South. In addition to serving Everglades restoration, the DPM has the potential to influence similar adaptive management programs throughout the nation’s network of federal and state-managed river corridors, floodplains, and riparian ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20241063","usgsCitation":"Harvey, J., Choi, J., Larsen, L., Skalak, K., Maglio, M., Quion, K., Swartz, A., Lin, J.T.Y., Gomez-Velez, J., and Schmadel, N., 2024, High-flow experimental outcomes to inform Everglades restoration, 2010–22: U.S. Geological Survey Open-File Report 2024–1063, 72 p., https://doi.org/10.3133/ofr20241063.","productDescription":"Report: xi, 72 p.; 3 Data Releases","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-148372","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":464267,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1063/coverthb.jpg"},{"id":464268,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1063/ofr20241063.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":464271,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241063/full"},{"id":464270,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1063/images"},{"id":464269,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1063/ofr20241063.XML"},{"id":464274,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A9SQ85","text":"USGS Data Release","description":"Harvey, J.W., Choi, J., Quion, K., Lin, J.T., Swartz, A., Larsen, L.G., Haase, K., and Schmadel, N., 2024, High-flow Experimental Outcomes for Everglades Hydraulics and Aquatic Metabolism: U.S. Geological Survey, data release, https://doi.org/10.5066/P9A9SQ85.","linkHelpText":"- High-flow Experimental Outcomes for Everglades Hydraulics and Aquatic Metabolism"},{"id":464272,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DQYB1O","text":"USGS Data Release","description":"Harvey, J.W., and Choi, J., 2022, Biophysical Data for Simulating Overland Flow in the Everglades: U.S. Geological Survey data release, https://doi.org/10.5066/P9DQYB1O.","linkHelpText":"- Biophysical Data for Simulating Overland Flow in the Everglades"},{"id":464273,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SP0HM1","text":"USGS Data Release","description":"Harvey, J.W., Choi, J., Larsen, L., Skalak, K., Maglio, M.M., Quion, K.M., Lin, T., Psaltakis, J.W., Buskirk, B.A., Swartz, A.G., Lewis, J.M., Gomez-Velez, J.D., and Schmadel, N.M., 2022, High-Flow Field Experiments to Inform Everglades Restoration: Experimental Data 2010 to 2022 (ver. 2.0, October 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9SP0HM1.","linkHelpText":"- High-Flow Field Experiments to Inform Everglades Restoration: Experimental Data 2010 to 2022 (ver. 2.0, October 2023)"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.1076224101966,\n 26.691819233104567\n ],\n [\n -82.1076224101966,\n 24.751056659514802\n ],\n [\n -79.55347920896048,\n 24.751056659514802\n ],\n [\n -79.55347920896048,\n 26.691819233104567\n ],\n [\n -82.1076224101966,\n 26.691819233104567\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2402028121","usgsCitation":"Zhi, W., Baniecki, H., Liu, J., Boyer, E.W., Shen, C., Shenk, G.W., Liu, X., and Li, L., 2024, Increasing phosphorus loss despite widespread concentration decline in US rivers: PNAS, v. 121, no. 48, e2402028121, 9 p., https://doi.org/10.1073/pnas.2402028121.","productDescription":"e2402028121, 9 p.","ipdsId":"IP-167332","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":489078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2402028121","text":"Publisher Index Page"},{"id":464807,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"121","issue":"48","noUsgsAuthors":false,"publicationDate":"2024-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhi, Wei 0000-0001-5485-1095","orcid":"https://orcid.org/0000-0001-5485-1095","contributorId":336775,"corporation":false,"usgs":false,"family":"Zhi","given":"Wei","email":"","affiliations":[{"id":68932,"text":"Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA","active":true,"usgs":false}],"preferred":false,"id":920317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baniecki, Hubert 0000-0001-6661-5364","orcid":"https://orcid.org/0000-0001-6661-5364","contributorId":346942,"corporation":false,"usgs":false,"family":"Baniecki","given":"Hubert","email":"","affiliations":[{"id":83024,"text":"University of Warsaw, Warsaw, Poland","active":true,"usgs":false}],"preferred":false,"id":920318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liu, Jiangtao","contributorId":346943,"corporation":false,"usgs":false,"family":"Liu","given":"Jiangtao","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":920319,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":920320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shen, Chaopeng","contributorId":152465,"corporation":false,"usgs":false,"family":"Shen","given":"Chaopeng","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":920321,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":920322,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liu, Xiaofeng 0000-0002-8296-7076","orcid":"https://orcid.org/0000-0002-8296-7076","contributorId":317075,"corporation":false,"usgs":false,"family":"Liu","given":"Xiaofeng","email":"","affiliations":[{"id":68932,"text":"Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA","active":true,"usgs":false}],"preferred":false,"id":920323,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Li, Li","contributorId":223548,"corporation":false,"usgs":false,"family":"Li","given":"Li","affiliations":[],"preferred":false,"id":920324,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70260970,"text":"70260970 - 2024 - Three-dimensional temperature maps of the Williston Basin, USA: Implications for deep hot sedimentary and enhanced geothermal resources","interactions":[],"lastModifiedDate":"2024-11-27T16:09:53.482598","indexId":"70260970","displayToPublicDate":"2024-11-15T11:29:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"title":"Three-dimensional temperature maps of the Williston Basin, USA: Implications for deep hot sedimentary and enhanced geothermal resources","docAbstract":"As part of U.S. Geological Survey's (USGS) efforts to identify and assess geothermal energy resources of the US, a three-dimensional (3D) geologic and thermal model has been constructed for the Williston Basin, USA. The geologic model consists of all sedimentary units above the Proterozoic and Archean crystalline rock (called basement herein), with a total sedimentary thickness of up to 5 km near the basin center. Twenty-nine geologic units were mapped from interpreted formation tops from 16,465 wells. A 3D temperature model was constructed to a depth of 7 km by constructing a 3D heat flow model for the sedimentary units, followed by estimating underlying temperature using a one-dimensional (1D) analytic solution for heat flow within the underlying crystalline basement. Using the sedimentary basin model, heat flow was simulated in 3D and was calibrated using three temperature datasets: 1) 24 high-confidence static temperature logs (equilibrium thermal profiles), 2) more than15,000 drill stem test (DST) measurements from >7,000 wells, and 3) more than 45,000 bottomhole temperature (BHT) measurements from >14,000 wells. The DST and BHT datasets provide broad spatial coverage, but are lower confidence, primarily because measurements were made prior to attaining thermal equilibrium. DST and BHT measurements were binned regionally to develop representative thermal profiles that generally agree with these lower quality data (hereafter called pseudowell temperature profiles). Layer properties (primarily thermal conductivity and compaction curves) were set to best estimate values, then the heat flow model was calibrated to fit pseudowell and static temperature logs primarily by adjusting basal heat flow to approximate the overall temperature profile. Minor adjustments to thermal conductivity allowed adjusting changes in slope at lithologic contacts. Resulting maps include 3D temperature and basal (bottom of sedimentary units) heat flow estimates, which are used as input for the temperature model of the basement. The crystalline basement temperature model uses an analytic 1D solution to the heat flow equation that requires estimates of heat flow and temperature at the upper boundary (i.e., the sediment/basement contact), radiogenic heat production within the crystalline basement, and reference thermal conductivity (i.e., uncorrected for temperature). Two regions of high heat flow are identified: 1) in western North Dakota along the North American Central Plains Conductivity Anomaly and 2) in eastern Montana near the Poplar dome. Within the sedimentary column in the center of the basin of the basin, an area of approximately 100,000 km2 is predicted to have moderate- to high-temperature geothermal resources (>90 °C) under the thickest sequences of sediments. Where thick insulation and high heat flow coincide, electric-grade resources can be less than 4 km deep. Assuming a maximum feasible drilling depth of 7 km, temperatures are predicted to be as high as 175 °C. The geologic model may be used to identify strata at sufficient temperatures that may have natural permeability or that may have conditions that favor development of enhanced/engineered geothermal systems resources.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2024.103196","usgsCitation":"Gelman, S.E., and Burns, E.R., 2024, Three-dimensional temperature maps of the Williston Basin, USA: Implications for deep hot sedimentary and enhanced geothermal resources: Geothermics, v. 125, 103196, 9 p., https://doi.org/10.1016/j.geothermics.2024.103196.","productDescription":"103196, 9 p.","ipdsId":"IP-165645","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":466763,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geothermics.2024.103196","text":"Publisher Index Page"},{"id":464292,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.05487036081263,\n 49.09401622161886\n ],\n [\n -107.05487036081263,\n 45.9204646960259\n ],\n [\n -100.81731222757732,\n 45.9204646960259\n ],\n [\n -100.81731222757732,\n 49.09401622161886\n ],\n [\n -107.05487036081263,\n 49.09401622161886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gelman, Sarah E. 0000-0003-2549-9509","orcid":"https://orcid.org/0000-0003-2549-9509","contributorId":270004,"corporation":false,"usgs":true,"family":"Gelman","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":918758,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270062,"text":"70270062 - 2024 - Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands","interactions":[],"lastModifiedDate":"2025-08-08T15:24:31.376369","indexId":"70270062","displayToPublicDate":"2024-11-15T10:19:32","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands","docAbstract":"Expansions in the extent and infestation levels of exotic annual grass (EAG) within the rangelands of the western United States are well documented. Land managers are tasked with developing plans to limit EAG spread and prevent irreversible ecosystem deterioration. The most common EAG species and the subject of extensive study is Bromus tectorum (cheatgrass). Cheatgrass has spread rapidly in western rangelands since its initial invasion more than 100 years ago. Another concerning aggressive EAG, Taeniatherum caput-medusae (medusahead), is also commonly found in some of these areas. To control the spread of EAGs, researchers have investigated applying several control methods during different developmental stages of cheatgrass and medusahead. These control strategies require accurate maps of the timing and spatial patterns of the developmental stages to apply mitigation strategies in the correct areas at the right time. In this study, we developed annual phenological datasets for cheatgrass and medusahead with two objectives. The first objective was to determine if cheatgrass and medusahead can be differentiated at 30 m resolution using their phenological differences. The second objective was to establish an annual phenology metric regression tree model used to map the growing seasons of cheatgrass and medusahead. Harmonized Landsat and Sentinel-2 (HLS)-derived predicted weekly cloud-free 30 m normalized difference vegetation index (NDVI) images were used to develop these metric maps. The result of this effort was maps that identify the start and end of sustained growing season time for cheatgrass and medusahead at 30 m for the Snake River Plain and Northern Basin and Range ecoregions. These phenological datasets also identify the start and end-of-season NDVI values, along with maximum NDVI throughout the study period. These metrics may be utilized to characterize annual growth patterns for cheatgrass and medusahead. This approach can be utilized to plan time-sensitive control measures such as herbicide applications or cattle grazing.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16224258","usgsCitation":"Benedict, T.D., Boyte, S., and Dahal, D., 2024, Differentiating cheatgrass and medusahead phenological characteristics in western United States rangelands: Remote Sensing, v. 16, no. 22, 4258, 21 p., https://doi.org/10.3390/rs16224258.","productDescription":"4258, 21 p.","ipdsId":"IP-171996","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494184,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16224258","text":"Publisher Index Page"},{"id":493848,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100.28187929406397,\n 48.82060636906533\n ],\n [\n -124.99361768794509,\n 48.88697493321598\n ],\n [\n -124.99361768794509,\n 30.85327470627726\n ],\n [\n -99.69664006714606,\n 30.849197853937767\n ],\n [\n -100.28187929406397,\n 48.82060636906533\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"22","noUsgsAuthors":false,"publicationDate":"2024-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Benedict, Trenton David 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":346111,"corporation":false,"usgs":true,"family":"Benedict","given":"Trenton","middleInitial":"David","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":945268,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":945269,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":192023,"corporation":false,"usgs":false,"family":"Dahal","given":"Devendra","affiliations":[],"preferred":false,"id":945270,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260924,"text":"gip245 - 2024 - The U.S. Geological Survey National Water Quality Network—Surface Water—2023","interactions":[],"lastModifiedDate":"2025-12-22T21:22:29.719591","indexId":"gip245","displayToPublicDate":"2024-11-15T07:39:14","publicationYear":"2024","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":"245","displayTitle":"The U.S. Geological Survey National Water Quality Network—Surface Water—2023","title":"The U.S. Geological Survey National Water Quality Network—Surface Water—2023","docAbstract":"The U.S. Geological Survey (USGS) National Water Quality Network for surface water (NWQN-SW) was established in 2013 to develop long-term, comparable assessments of surface-water quality in support of national, regional, state, and local needs related to water-quality management and policy. Waterquality samples are collected at each site and measured for a variety of parameters. In 2023, the NWQN-SW consisted of 109 sites, each of them paired with a streamgage, operated by the USGS or other agencies that provide continuous information on streamflow conditions. The waterquality data and the streamflow information from the NWQN-SW is then used to assess the status and trends of water-quality conditions and potential impacts on human and aquatic health.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip245","usgsCitation":"Riskin, Melissa, 2024, The U.S. Geological Survey National Water Quality Network—Surface Water—2023: U.S. Geological Survey General Information Product 245, https://doi.org/10.3133/gip245.","productDescription":"1 p.","ipdsId":"IP-167530","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":481905,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip247","text":"GIP 247","description":"GIP 247","linkHelpText":"— The U.S. Geological Survey National Water Quality Network—Groundwater—2023"},{"id":481904,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip244","text":"GIP 244","description":"GIP 244","linkHelpText":"— The U.S. Geological Survey National Atmospheric Deposition Program, National Trends Network—2022 (ver. 1.1)"},{"id":481903,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip243","text":"GIP 243","description":"GIP 243","linkHelpText":"— U.S. Geological Survey Groundwater Climate Response Network—2023"},{"id":481902,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip242","text":"GIP 242","description":"GIP 242","linkHelpText":"— The U.S. Geological Survey National Streamgage Network—2023"},{"id":464107,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/245/gip245.jpg"},{"id":464108,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/245/gip245.pdf","text":"Report","size":"864 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 245"},{"id":497911,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117795.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.67138671875,\n 54.686534234529695\n ],\n [\n -129.9462890625,\n 55.36662484928637\n ],\n [\n -130.1220703125,\n 56.145549500679074\n ],\n [\n -131.9677734375,\n 56.9449741808516\n ],\n [\n -135.3076171875,\n 59.833775202184206\n ],\n [\n -136.38427734375,\n 59.65664225341022\n ],\n [\n -136.6259765625,\n 59.23217626921806\n ],\n [\n -137.52685546875,\n 58.938673187948304\n ],\n [\n -137.65869140625,\n 59.33318942659219\n ],\n [\n -138.8232421875,\n 60.009970961180386\n ],\n [\n -139.21874999999997,\n 60.108670463036\n ],\n [\n -139.04296875,\n 60.403001945865476\n ],\n [\n -139.85595703125,\n 60.337823495982015\n ],\n [\n -140.99853515625,\n 60.337823495982015\n ],\n [\n -141.15234374999997,\n 69.71810669906763\n ],\n [\n -143.4375,\n 70.17020068549206\n ],\n [\n -145.1953125,\n 70.08056215839737\n ],\n [\n -149.765625,\n 70.58341752317065\n ],\n [\n -152.40234375,\n 70.61261423801925\n ],\n [\n -152.314453125,\n 70.95969716686398\n ],\n [\n -157.1484375,\n 71.35706654962706\n ],\n [\n -159.9609375,\n 70.8734913192635\n ],\n [\n -162.0703125,\n 70.31873847853124\n ],\n [\n -163.916015625,\n 69.06856318696033\n ],\n [\n -166.376953125,\n 68.942606818121\n ],\n [\n -166.376953125,\n 68.26938680456564\n ],\n [\n -163.30078125,\n 66.86108230224609\n ],\n [\n -161.982421875,\n 66.47820814385636\n ],\n [\n -163.564453125,\n 66.08936427047088\n ],\n [\n -163.564453125,\n 66.6181218846659\n ],\n [\n -165.76171875,\n 66.40795547978848\n ],\n [\n -168.0908203125,\n 65.69447579373418\n ],\n [\n -166.55273437499997,\n 65.14611484756372\n ],\n [\n -166.904296875,\n 65.05360170595502\n ],\n [\n -166.3330078125,\n 64.41592147626879\n ],\n [\n -162.861328125,\n 64.39693778132846\n ],\n [\n -160.927734375,\n 64.90491004905083\n ],\n [\n -161.0595703125,\n 64.47279382008166\n ],\n [\n -161.4990234375,\n 64.49172504435471\n ],\n [\n -160.8837890625,\n 63.87939001720202\n ],\n [\n -161.1474609375,\n 63.470144746565424\n ],\n [\n -162.6416015625,\n 63.64625919492172\n ],\n [\n -163.212890625,\n 63.05495931065107\n ],\n [\n -164.2236328125,\n 63.37183226679281\n ],\n [\n -166.1572265625,\n 61.75233128411639\n ],\n [\n -165.3662109375,\n 60.54377524118842\n ],\n [\n -167.431640625,\n 60.326947742998414\n ],\n [\n -167.255859375,\n 59.866883195210214\n ],\n [\n -165.8935546875,\n 59.7563950493563\n ],\n [\n -162.68554687499997,\n 59.734253447591364\n ],\n [\n -162.3779296875,\n 60.174306261926034\n ],\n [\n -161.806640625,\n 59.46740794183739\n ],\n [\n -162.0263671875,\n 59.108308258604964\n ],\n [\n -161.806640625,\n 58.768200159239576\n ],\n [\n -162.20214843749997,\n 58.65408464530598\n ],\n [\n -160.83984375,\n 58.44773280389084\n ],\n [\n -159.9609375,\n 58.6769376725869\n ],\n [\n -159.08203125,\n 58.309488840677645\n ],\n [\n -156.88476562499997,\n 58.92733441827545\n ],\n [\n -157.5,\n 58.516651799363785\n ],\n [\n -157.8076171875,\n 57.61010702068388\n ],\n [\n -161.54296875,\n 56.022948079627454\n ],\n [\n -168.6181640625,\n 53.4357192066942\n ],\n [\n -174.9462890625,\n 52.26815737376817\n ],\n [\n -178.2421875,\n 51.83577752045248\n ],\n [\n -173.1884765625,\n 51.590722643120145\n ],\n [\n -162.5537109375,\n 54.23955053156177\n ],\n [\n -155.302734375,\n 55.52863052257191\n ],\n [\n -151.4794921875,\n 57.51582286553883\n ],\n [\n -146.9970703125,\n 60.08676274626006\n ],\n [\n -145.546875,\n 60.21799073323445\n ],\n [\n -144.228515625,\n 59.689926220143356\n ],\n [\n -142.3828125,\n 59.93300042374631\n ],\n [\n -138.3837890625,\n 58.83649009392136\n ],\n [\n -135.6591796875,\n 56.31653672211301\n ],\n [\n -133.2421875,\n 54.521081495443596\n ],\n [\n -130.67138671875,\n 54.686534234529695\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.796875,\n 44.902577996288876\n ],\n [\n -67.67578124999999,\n 45.583289756006316\n ],\n [\n -67.939453125,\n 47.57652571374621\n ],\n [\n -69.2578125,\n 47.338822694822\n ],\n [\n -71.19140625,\n 45.27488643704891\n ],\n [\n -75.146484375,\n 44.96479793033101\n ],\n [\n -78.046875,\n 43.644025847699496\n ],\n [\n -79.1015625,\n 43.51668853502906\n ],\n [\n -79.1015625,\n 42.87596410238256\n ],\n [\n -82.68310546875,\n 41.65649719441145\n ],\n [\n -83.14453125,\n 42.049292638686836\n ],\n [\n -83.07861328125,\n 42.374778361114195\n ],\n [\n -82.529296875,\n 42.601619944327965\n ],\n [\n -82.24365234375,\n 43.6599240747891\n ],\n [\n -82.41943359375,\n 45.058001435398275\n ],\n [\n -83.60595703125,\n 45.85941212790755\n ],\n [\n -83.49609375,\n 46.027481852486645\n ],\n [\n -83.7158203125,\n 46.164614496897094\n ],\n [\n -83.95751953125,\n 46.07323062540835\n ],\n [\n -84.24316406249999,\n 46.558860303117164\n ],\n [\n -84.72656249999999,\n 46.558860303117164\n ],\n [\n -84.90234375,\n 46.92025531537451\n ],\n [\n -88.41796875,\n 48.3416461723746\n ],\n [\n -89.3408203125,\n 47.96050238891509\n ],\n [\n -90.76904296874999,\n 48.122101028190805\n ],\n [\n -90.87890625,\n 48.22467264956519\n ],\n [\n -91.51611328125,\n 48.10743118848039\n ],\n [\n -92.2412109375,\n 48.37084770238366\n ],\n [\n -92.39501953125,\n 48.23930899024907\n ],\n [\n -92.94433593749999,\n 48.61838518688487\n ],\n [\n -93.44970703125,\n 48.63290858589535\n ],\n [\n -94.7021484375,\n 48.748945343432936\n ],\n [\n -94.833984375,\n 49.23912083246698\n ],\n [\n -95.1416015625,\n 49.396675075193976\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -123.22265625000001,\n 48.99463598353405\n ],\n [\n -123.0908203125,\n 48.80686346108517\n ],\n [\n -123.24462890625,\n 48.66194284607006\n ],\n [\n -123.1787109375,\n 48.32703913063476\n ],\n [\n -124.78271484375,\n 48.472921272487824\n ],\n [\n -124.93652343749999,\n 48.16608541901253\n ],\n [\n -124.365234375,\n 46.58906908309182\n ],\n [\n -124.541015625,\n 44.15068115978094\n ],\n [\n -124.93652343749999,\n 42.69858589169842\n ],\n [\n -124.541015625,\n 41.22824901518529\n ],\n [\n -124.73876953125,\n 40.43022363450862\n ],\n [\n -124.03564453125,\n 39.35129035526705\n ],\n [\n -124.01367187499999,\n 38.8225909761771\n ],\n [\n -122.05810546875,\n 36.12012758978146\n ],\n [\n -120.95947265624999,\n 34.88593094075317\n ],\n [\n -120.80566406250001,\n 34.08906131584994\n ],\n [\n -118.21289062499999,\n 32.2313896627376\n ],\n [\n -117.22412109375,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.713355353177555\n ],\n [\n -114.78515624999999,\n 32.491230287947594\n ],\n [\n -110.98388671874999,\n 31.3348710339506\n ],\n [\n -108.21533203125,\n 31.297327991404266\n ],\n [\n -108.2373046875,\n 31.765537409484374\n ],\n [\n -106.435546875,\n 31.765537409484374\n ],\n [\n -104.9853515625,\n 30.600093873550072\n ],\n [\n -104.47998046875,\n 29.592565403314087\n ],\n [\n -103.20556640625,\n 28.94086176940557\n ],\n [\n -102.65625,\n 29.76437737516313\n ],\n [\n -102.3486328125,\n 29.84064389983441\n ],\n [\n -101.49169921875,\n 29.7453016622136\n ],\n [\n -100.83251953125,\n 29.267232865200878\n ],\n [\n -100.30517578125,\n 28.246327971048842\n ],\n [\n -99.60205078124999,\n 27.586197857692664\n ],\n [\n -99.47021484375,\n 27.31321389856826\n ],\n [\n -99.228515625,\n 26.52956523826758\n ],\n [\n -98.2177734375,\n 26.05678288577881\n ],\n [\n -97.75634765625,\n 26.03704188651584\n ],\n [\n -97.44873046875,\n 25.839449402063185\n ],\n [\n -97.20703125,\n 25.93828707492375\n ],\n [\n -96.8994140625,\n 26.194876675795218\n ],\n [\n -96.78955078125,\n 27.858503954841247\n ],\n [\n -93.75732421875,\n 29.420460341013133\n ],\n [\n -90.2197265625,\n 28.998531814051795\n ],\n [\n -88.22021484375,\n 29.05616970274342\n ],\n [\n -87.91259765625,\n 30.14512718337613\n ],\n [\n -86.5283203125,\n 30.183121842195515\n ],\n [\n -85.2978515625,\n 29.49698759653577\n ],\n [\n -84.13330078125,\n 29.80251790576445\n ],\n [\n -82.81494140625,\n 28.555576049185973\n ],\n [\n -83.21044921875,\n 27.800209937418252\n ],\n [\n -82.77099609375,\n 26.941659545381516\n ],\n [\n -82.08984375,\n 25.878994400196202\n ],\n [\n -81.5625,\n 25.264568475331583\n ],\n [\n -82.28759765625,\n 24.467150664739002\n ],\n [\n -82.0458984375,\n 24.046463999666567\n ],\n [\n -80.6396484375,\n 24.56710835257599\n ],\n [\n -79.78271484375,\n 25.34402602913433\n ],\n [\n -79.60693359375,\n 27.27416111737468\n ],\n [\n -80.68359375,\n 30.713503990354965\n ],\n [\n -80.66162109375,\n 31.50362930577303\n ],\n [\n -76.81640625,\n 34.07086232376631\n ],\n [\n -75.16845703124999,\n 35.263561862152095\n ],\n [\n -75.498046875,\n 37.055177106660814\n ],\n [\n -73.58642578125,\n 39.90973623453719\n ],\n [\n -71.3671875,\n 40.84706035607122\n ],\n [\n -69.63134765625,\n 40.9964840143779\n ],\n [\n -70.0048828125,\n 42.342305278572816\n ],\n [\n -70.3564453125,\n 42.89206418807337\n ],\n [\n -67.2802734375,\n 44.37098696297173\n ],\n [\n -67.0166015625,\n 44.69989765840318\n ],\n [\n -66.796875,\n 44.902577996288876\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.56640625,\n 18.771115062337024\n ],\n [\n -154.68749999999997,\n 19.642587534013032\n ],\n [\n -156.9287109375,\n 21.453068633086783\n ],\n [\n -159.521484375,\n 22.43134015636061\n ],\n [\n -160.5322265625,\n 21.983801417384697\n ],\n [\n -159.9609375,\n 21.207458730482642\n ],\n [\n -158.291015625,\n 20.92039691397189\n ],\n [\n -156.97265625,\n 19.932041306115536\n ],\n [\n -155.9619140625,\n 18.8543103618898\n ],\n [\n -155.56640625,\n 18.771115062337024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Water Quality Network Surface Water Coordinator
Observing Systems Division
Water Mission Area
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Contact Publications Warehouse
As of October 2023, the U.S. Geological Survey (USGS) operated more than 660 sites across the United States and its territories as part of the Groundwater Climate Response Network (CRN). The CRN is comprised of wells and springs selected to monitor the effects of climate variability, such as droughts, on groundwater levels and spring discharge nationwide. The CRN includes more than 550 locations with realtime data and more than 100 sites with non-real-time data available to the public on the CRN web mapper and the USGS National Water Dashboard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip243","usgsCitation":"Fine, J., 2024, U.S. Geological Survey Groundwater Climate Response Network—2023: U.S. Geological Survey General Information Product 243, https://doi.org/10.3133/gip243.","productDescription":"1 p.","ipdsId":"IP-167529","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":481896,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip245","text":"GIP 245","description":"GIP 245","linkHelpText":"— The U.S. Geological Survey National Water Quality Network—Surface Water—2023"},{"id":481895,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip244","text":"GIP 244","description":"GIP 244","linkHelpText":"— The U.S. Geological Survey National Atmospheric Deposition Program, National Trends Network—2022 (ver. 1.1)"},{"id":481897,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip247","text":"GIP 247","description":"GIP 247","linkHelpText":"— The U.S. Geological Survey National Water Quality Network—Groundwater—2023"},{"id":481894,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/gip242","text":"GIP 242","description":"GIP 242","linkHelpText":"— The U.S. Geological Survey National Streamgage Network—2023"},{"id":464075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/243/gip243.pdf","text":"Report","size":"297 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 2043"},{"id":464074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/243/gip243.jpg"}],"contact":"National Groundwater Networks Coordinator
Observing Systems Division
Water Mission Area
U.S. Geological Survey
2730 Deer Run Rd.
Carson City, NV 89701
Contact Publications Warehouse
In 2016, the Secretary of the U.S. Department of the Interior requested that the U.S. Geological Survey (USGS) produce a publicly available and annually updated database of estimated greenhouse gas emissions associated with the extraction and use of fossil fuels from Federal lands. The first report in this series included emissions estimates from 2005 to 2014 and were reported for 29 States and two offshore areas. Native American and Tribal lands were not included in that analysis. This report recalculates those previous years (2005–14) with updated data and methods and extends the estimates to 2022. Nationwide emissions from fossil fuels produced on Federal lands in 2022 were 1,081.2 million metric tons of carbon dioxide equivalent (MMT CO2 Eq.) for CO2, 33.4 MMT CO2 Eq. for methane (CH4), and 4.3 MMT CO2 Eq. for nitrous oxide (N2O). Compared to 2005, the 2022 totals represent decreases in emissions for all three greenhouse gases (by 17 percent for CO2, 37 percent for CH4, and 30 percent for N2O). Emissions from fossil fuels produced on Federal lands represent, on average, 21.8 percent of U.S. emissions for CO2, 6.1 percent for CH4, and 1.3 percent for N2O over the 18 years included in this estimate. The trends and relative magnitudes of the greenhouse gas emissions estimated are roughly parallel to the Federal lands fossil fuel production volumes.
In 2021, Federal lands of the conterminous United States stored 70,424 MMT CO2 Eq. in terrestrial ecosystems. Soils stored most of the terrestrial ecosystem carbon (66 percent), followed by live vegetation (25 percent), deadwood (5 percent), and litter (4 percent). Carbon sequestration on Federal lands was highly variable over time, owing primarily to interannual variability in climate and weather, and variability in land use and land cover (LULC) change and disturbances, among these are wildfires and logging. Between 2005 and 2021, Federal lands sequestered an average of 83 MMT CO2 Eq./yr. By subtracting the cumulative effects of LULC and disturbance-related CO2 losses to the atmosphere from the total, we estimate that ecosystems at the national level sequestered CO2 at an annual mean rate of 17 MMT CO2 Eq./yr in a term called the net ecosystem exchange (NEE). This annual NEE sequestration value represents about 1.4 percent of average fossil fuel emissions over the same period.
The USGS estimates presented in this report represent an accounting for the emissions resulting from fossil fuel extraction on Federal lands and the end-use combustion of those fuels, as well as for the sequestration of carbon in terrestrial ecosystems on Federal lands. A combined net CO2 emissions estimate, which is the difference between the emitted and sequestered CO2 from both the fossil fuel and ecosystems estimates, provides context for evaluating the greenhouse gas contributions of activities on these lands. The estimates included in this report can provide context for future energy decisions, as well as a basis to track change in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245103","usgsCitation":"Merrill, M.D., Sleeter, B.M., and Freeman, P.A., 2024, Federal lands greenhouse gas emissions and sequestration in the United States—Estimates for 2005–22: U.S. Geological Survey Scientific Investigations Report 2024–5103, 39 p., https://doi.org/10.3133/sir20245103. [Supersedes USGS Scientific Investigations Report 2018–5131.]","productDescription":"Report: viii, 39 p.; Data Release","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167699","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":463585,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245103/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5103 HTML"},{"id":463583,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5103/coverthb.jpg"},{"id":463587,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5103/images"},{"id":463586,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5103/sir20245103.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5103 XML"},{"id":497912,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117792.htm","linkFileType":{"id":5,"text":"html"}},{"id":465340,"rank":7,"type":{"id":18,"text":"Project Site"},"url":"https://energy.usgs.gov/fedghg/","text":"Interactive map","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- Federal Lands Emissions and Sequestration in the United States: Estimates 2005-22"},{"id":463588,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14CVD9R","text":"USGS data release","linkHelpText":"Federal lands greenhouse gas emissions and sequestration in the United States—Estimates for 2005–22 - data"},{"id":463584,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5103/sir20245103.pdf","text":"Report","size":"2.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5103 PDF"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.67138671875,\n 54.686534234529695\n ],\n [\n -129.9462890625,\n 55.36662484928637\n ],\n [\n -130.1220703125,\n 56.145549500679074\n ],\n [\n -131.9677734375,\n 56.9449741808516\n ],\n [\n -135.3076171875,\n 59.833775202184206\n ],\n [\n -136.38427734375,\n 59.65664225341022\n ],\n [\n -136.6259765625,\n 59.23217626921806\n ],\n [\n -137.52685546875,\n 58.938673187948304\n ],\n [\n -137.65869140625,\n 59.33318942659219\n ],\n [\n -138.8232421875,\n 60.009970961180386\n ],\n [\n -139.21874999999997,\n 60.108670463036\n ],\n [\n -139.04296875,\n 60.403001945865476\n ],\n [\n -139.85595703125,\n 60.337823495982015\n ],\n [\n -140.99853515625,\n 60.337823495982015\n ],\n [\n -141.15234374999997,\n 69.71810669906763\n ],\n [\n -143.4375,\n 70.17020068549206\n ],\n [\n -145.1953125,\n 70.08056215839737\n ],\n [\n -149.765625,\n 70.58341752317065\n ],\n [\n -152.40234375,\n 70.61261423801925\n ],\n [\n -152.314453125,\n 70.95969716686398\n ],\n [\n -157.1484375,\n 71.35706654962706\n ],\n [\n -159.9609375,\n 70.8734913192635\n ],\n [\n -162.0703125,\n 70.31873847853124\n ],\n [\n -163.916015625,\n 69.06856318696033\n ],\n [\n -166.376953125,\n 68.942606818121\n ],\n [\n -166.376953125,\n 68.26938680456564\n ],\n [\n -163.30078125,\n 66.86108230224609\n ],\n [\n -161.982421875,\n 66.47820814385636\n ],\n [\n -163.564453125,\n 66.08936427047088\n ],\n [\n -163.564453125,\n 66.6181218846659\n ],\n [\n -165.76171875,\n 66.40795547978848\n ],\n [\n -168.0908203125,\n 65.69447579373418\n ],\n [\n -166.55273437499997,\n 65.14611484756372\n ],\n [\n -166.904296875,\n 65.05360170595502\n ],\n [\n -166.3330078125,\n 64.41592147626879\n ],\n [\n -162.861328125,\n 64.39693778132846\n ],\n [\n -160.927734375,\n 64.90491004905083\n ],\n [\n -161.0595703125,\n 64.47279382008166\n ],\n [\n -161.4990234375,\n 64.49172504435471\n ],\n [\n -160.8837890625,\n 63.87939001720202\n ],\n [\n -161.1474609375,\n 63.470144746565424\n ],\n [\n -162.6416015625,\n 63.64625919492172\n ],\n [\n -163.212890625,\n 63.05495931065107\n ],\n [\n -164.2236328125,\n 63.37183226679281\n ],\n [\n -166.1572265625,\n 61.75233128411639\n ],\n [\n -165.3662109375,\n 60.54377524118842\n ],\n [\n -167.431640625,\n 60.326947742998414\n ],\n [\n -167.255859375,\n 59.866883195210214\n ],\n [\n -165.8935546875,\n 59.7563950493563\n ],\n [\n -162.68554687499997,\n 59.734253447591364\n ],\n [\n -162.3779296875,\n 60.174306261926034\n ],\n [\n -161.806640625,\n 59.46740794183739\n ],\n [\n -162.0263671875,\n 59.108308258604964\n ],\n [\n -161.806640625,\n 58.768200159239576\n ],\n [\n -162.20214843749997,\n 58.65408464530598\n ],\n [\n -160.83984375,\n 58.44773280389084\n ],\n [\n -159.9609375,\n 58.6769376725869\n ],\n [\n -159.08203125,\n 58.309488840677645\n ],\n [\n -156.88476562499997,\n 58.92733441827545\n ],\n [\n -157.5,\n 58.516651799363785\n ],\n [\n -157.8076171875,\n 57.61010702068388\n ],\n [\n -161.54296875,\n 56.022948079627454\n ],\n [\n -168.6181640625,\n 53.4357192066942\n ],\n [\n -174.9462890625,\n 52.26815737376817\n ],\n [\n -178.2421875,\n 51.83577752045248\n ],\n [\n -173.1884765625,\n 51.590722643120145\n ],\n [\n -162.5537109375,\n 54.23955053156177\n ],\n [\n -155.302734375,\n 55.52863052257191\n ],\n [\n -151.4794921875,\n 57.51582286553883\n ],\n [\n -146.9970703125,\n 60.08676274626006\n ],\n [\n -145.546875,\n 60.21799073323445\n ],\n [\n -144.228515625,\n 59.689926220143356\n ],\n [\n -142.3828125,\n 59.93300042374631\n ],\n [\n -138.3837890625,\n 58.83649009392136\n ],\n [\n -135.6591796875,\n 56.31653672211301\n ],\n [\n -133.2421875,\n 54.521081495443596\n ],\n [\n -130.67138671875,\n 54.686534234529695\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.796875,\n 44.902577996288876\n ],\n [\n -67.67578124999999,\n 45.583289756006316\n ],\n [\n -67.939453125,\n 47.57652571374621\n ],\n [\n -69.2578125,\n 47.338822694822\n ],\n [\n -71.19140625,\n 45.27488643704891\n ],\n [\n -75.146484375,\n 44.96479793033101\n ],\n [\n -78.046875,\n 43.644025847699496\n ],\n [\n -79.1015625,\n 43.51668853502906\n ],\n [\n -79.1015625,\n 42.87596410238256\n ],\n [\n -82.68310546875,\n 41.65649719441145\n ],\n [\n -83.14453125,\n 42.049292638686836\n ],\n [\n -83.07861328125,\n 42.374778361114195\n ],\n [\n -82.529296875,\n 42.601619944327965\n ],\n [\n -82.24365234375,\n 43.6599240747891\n ],\n [\n -82.41943359375,\n 45.058001435398275\n ],\n [\n -83.60595703125,\n 45.85941212790755\n ],\n [\n -83.49609375,\n 46.027481852486645\n ],\n [\n -83.7158203125,\n 46.164614496897094\n ],\n [\n -83.95751953125,\n 46.07323062540835\n ],\n [\n -84.24316406249999,\n 46.558860303117164\n ],\n [\n -84.72656249999999,\n 46.558860303117164\n ],\n [\n -84.90234375,\n 46.92025531537451\n ],\n [\n -88.41796875,\n 48.3416461723746\n ],\n [\n -89.3408203125,\n 47.96050238891509\n ],\n [\n -90.76904296874999,\n 48.122101028190805\n ],\n [\n -90.87890625,\n 48.22467264956519\n ],\n [\n -91.51611328125,\n 48.10743118848039\n ],\n [\n -92.2412109375,\n 48.37084770238366\n ],\n [\n -92.39501953125,\n 48.23930899024907\n ],\n [\n -92.94433593749999,\n 48.61838518688487\n ],\n [\n -93.44970703125,\n 48.63290858589535\n ],\n [\n -94.7021484375,\n 48.748945343432936\n ],\n [\n -94.833984375,\n 49.23912083246698\n ],\n [\n -95.1416015625,\n 49.396675075193976\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -123.22265625000001,\n 48.99463598353405\n ],\n [\n -123.0908203125,\n 48.80686346108517\n ],\n [\n -123.24462890625,\n 48.66194284607006\n ],\n [\n -123.1787109375,\n 48.32703913063476\n ],\n [\n -124.78271484375,\n 48.472921272487824\n ],\n [\n -124.93652343749999,\n 48.16608541901253\n ],\n [\n -124.365234375,\n 46.58906908309182\n ],\n [\n -124.541015625,\n 44.15068115978094\n ],\n [\n -124.93652343749999,\n 42.69858589169842\n ],\n [\n -124.541015625,\n 41.22824901518529\n ],\n [\n -124.73876953125,\n 40.43022363450862\n ],\n [\n -124.03564453125,\n 39.35129035526705\n ],\n [\n -124.01367187499999,\n 38.8225909761771\n ],\n [\n -122.05810546875,\n 36.12012758978146\n ],\n [\n -120.95947265624999,\n 34.88593094075317\n ],\n [\n -120.80566406250001,\n 34.08906131584994\n ],\n [\n -118.21289062499999,\n 32.2313896627376\n ],\n [\n -117.22412109375,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.713355353177555\n ],\n [\n -114.78515624999999,\n 32.491230287947594\n ],\n [\n -110.98388671874999,\n 31.3348710339506\n ],\n [\n -108.21533203125,\n 31.297327991404266\n ],\n [\n -108.2373046875,\n 31.765537409484374\n ],\n [\n -106.435546875,\n 31.765537409484374\n ],\n [\n -104.9853515625,\n 30.600093873550072\n ],\n [\n -104.47998046875,\n 29.592565403314087\n ],\n [\n -103.20556640625,\n 28.94086176940557\n ],\n [\n -102.65625,\n 29.76437737516313\n ],\n [\n -102.3486328125,\n 29.84064389983441\n ],\n [\n -101.49169921875,\n 29.7453016622136\n ],\n [\n -100.83251953125,\n 29.267232865200878\n ],\n [\n -100.30517578125,\n 28.246327971048842\n ],\n [\n -99.60205078124999,\n 27.586197857692664\n ],\n [\n -99.47021484375,\n 27.31321389856826\n ],\n [\n -99.228515625,\n 26.52956523826758\n ],\n [\n -98.2177734375,\n 26.05678288577881\n ],\n [\n -97.75634765625,\n 26.03704188651584\n ],\n [\n -97.44873046875,\n 25.839449402063185\n ],\n [\n -97.20703125,\n 25.93828707492375\n ],\n [\n -96.8994140625,\n 26.194876675795218\n ],\n [\n -96.78955078125,\n 27.858503954841247\n ],\n [\n -93.75732421875,\n 29.420460341013133\n ],\n [\n -90.2197265625,\n 28.998531814051795\n ],\n [\n -88.22021484375,\n 29.05616970274342\n ],\n [\n -87.91259765625,\n 30.14512718337613\n ],\n [\n -86.5283203125,\n 30.183121842195515\n ],\n [\n -85.2978515625,\n 29.49698759653577\n ],\n [\n -84.13330078125,\n 29.80251790576445\n ],\n [\n -82.81494140625,\n 28.555576049185973\n ],\n [\n -83.21044921875,\n 27.800209937418252\n ],\n [\n -82.77099609375,\n 26.941659545381516\n ],\n [\n -82.08984375,\n 25.878994400196202\n ],\n [\n -81.5625,\n 25.264568475331583\n ],\n [\n -82.28759765625,\n 24.467150664739002\n ],\n [\n -82.0458984375,\n 24.046463999666567\n ],\n [\n -80.6396484375,\n 24.56710835257599\n ],\n [\n -79.78271484375,\n 25.34402602913433\n ],\n [\n -79.60693359375,\n 27.27416111737468\n ],\n [\n -80.68359375,\n 30.713503990354965\n ],\n [\n -80.66162109375,\n 31.50362930577303\n ],\n [\n -76.81640625,\n 34.07086232376631\n ],\n [\n -75.16845703124999,\n 35.263561862152095\n ],\n [\n -75.498046875,\n 37.055177106660814\n ],\n [\n -73.58642578125,\n 39.90973623453719\n ],\n [\n -71.3671875,\n 40.84706035607122\n ],\n [\n -69.63134765625,\n 40.9964840143779\n ],\n [\n -70.0048828125,\n 42.342305278572816\n ],\n [\n -70.3564453125,\n 42.89206418807337\n ],\n [\n -67.2802734375,\n 44.37098696297173\n ],\n [\n -67.0166015625,\n 44.69989765840318\n ],\n [\n -66.796875,\n 44.902577996288876\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.56640625,\n 18.771115062337024\n ],\n [\n -154.68749999999997,\n 19.642587534013032\n ],\n [\n -156.9287109375,\n 21.453068633086783\n ],\n [\n -159.521484375,\n 22.43134015636061\n ],\n [\n -160.5322265625,\n 21.983801417384697\n ],\n [\n -159.9609375,\n 21.207458730482642\n ],\n [\n -158.291015625,\n 20.92039691397189\n ],\n [\n -156.97265625,\n 19.932041306115536\n ],\n [\n -155.9619140625,\n 18.8543103618898\n ],\n [\n -155.56640625,\n 18.771115062337024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geology, Energy & Minerals Science Center
U.S. Geological Survey
954 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Imaging spectroscopy has been a recognized and established remote sensing technology since the 1980s, mainly using airborne and field-based platforms to identify and quantify key bio- and geo-chemical surface and atmospheric compounds, based on characteristic spectral reflectance features in the visible-near infrared (VNIR) and short-wave infrared (SWIR). Spaceborne missions, a leap in technology, were sparse, starting with the CHRIS/PROBA and EO1/Hyperion missions in the early 2000s, and providing spectroscopy data with limited spectral coverage and/or low data quality in the SWIR. Since 2019, several countries and agencies have successfully launched a number of spaceborne imaging spectroscopy systems into orbit or deployed them on the International Space Station (ISS) such as DESIS, PRISMA, HISUI, GF-5, EnMAP and EMIT. Among these recent missions, the German Environmental Mapping and Analysis Program (EnMAP) stands for its long-term development, sophisticated design with on-board calibration, high data quality requirements, and extensive accompanying science program. EnMAP was launched in April 2022 and, following a successful commissioning phase, started its operational activities in November 2022. The EnMAP mission encompasses global coverage from 80° N to 80° S through on-demand data acquisitions. Data are free and open access with 30 m spatial resolution, a high spectral resolution with a spectral sampling distance of 6.5 nm and 10 nm in the VNIR and SWIR regions respectively, and a high signal-to-noise ratio. In this paper, we aim to present the mission's current status, coverage, science capabilities and performance two years after launch. We show the potential of EnMAP for space-based imaging spectroscopy to operate in various environments, including high and low light levels, dense forests, Antarctic glaciers, and arid agricultural areas. EnMAP enables various applications in fields such as agriculture and forestry, soil compositional, raw materials, and methane mapping, as well as water quality assessment, and snow and ice properties. The results show that EnMAP's performance exceeds the mission requirements, and highlights the significant potential for contribution to scientific exploitation in various geo- and biochemical sciences. EnMAP is also expected to serve as a key tool for the development and testing of data processing algorithms for upcoming global operational missions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2024.114379","usgsCitation":"Chabrillat, S., Foerster, S., Segl, K., Beamish, A., Brell, M., Asadzadeh, S., Milewski, R., Ward, K., Brosinsky, A., Koch, K., Scheffler, D., Guillaso, S., Kokhanovsky, A., Roessner, S., Guanter, L., Kauffman, H., Pinnel, N., Carmona, E., Storch, T., Hank, T., Berger, K., Wocher, M., Hostert, P., van der Linden, S., Okujeni, A., Janz, A., Jakimow, B., Bracher, A., Soppa, M., Alvarado, L., Buddenbaum, H., Heim, B., Heiden, U., Moreno, J.M., Ong, C., Bohn, N., Green, R., Bachmann, M., Kokaly, R.F., Schodlok, M., Painter, T., Gascon, F., Buongiorno, F., Mottus, M., Brando, V., Feilhauer, H., Betz, M., Baur, S., Feckl, R., Schickling, A., Krieger, V., Bock, M., La Porta, L., and Fischer, S., 2024, The EnMAP spaceborne imaging spectroscopy mission: Initial scientific results two years after launch: Remote Sensing of Environment, v. 315, 114379, 21 p., https://doi.org/10.1016/j.rse.2024.114379.","productDescription":"114379, 21 p.","ipdsId":"IP-163728","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":487026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2024.114379","text":"Publisher Index Page"},{"id":481742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"315","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chabrillat, Sabine 0000-0001-8600-5168","orcid":"https://orcid.org/0000-0001-8600-5168","contributorId":243560,"corporation":false,"usgs":false,"family":"Chabrillat","given":"Sabine","email":"","affiliations":[{"id":48729,"text":"Helmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZ","active":true,"usgs":false}],"preferred":false,"id":926522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foerster, Saskia","contributorId":305903,"corporation":false,"usgs":false,"family":"Foerster","given":"Saskia","email":"","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Segl, Karl","contributorId":305901,"corporation":false,"usgs":false,"family":"Segl","given":"Karl","email":"","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beamish, Alison","contributorId":350638,"corporation":false,"usgs":false,"family":"Beamish","given":"Alison","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brell, Maximilian","contributorId":305868,"corporation":false,"usgs":false,"family":"Brell","given":"Maximilian","email":"","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926526,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Asadzadeh, Saeid","contributorId":350639,"corporation":false,"usgs":false,"family":"Asadzadeh","given":"Saeid","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Milewski, Robert","contributorId":350640,"corporation":false,"usgs":false,"family":"Milewski","given":"Robert","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926528,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ward, Kathrin J.","contributorId":350641,"corporation":false,"usgs":false,"family":"Ward","given":"Kathrin J.","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926529,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brosinsky, Arlena","contributorId":350642,"corporation":false,"usgs":false,"family":"Brosinsky","given":"Arlena","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926530,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koch, Katrin","contributorId":350643,"corporation":false,"usgs":false,"family":"Koch","given":"Katrin","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926531,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Scheffler, Daniel","contributorId":305902,"corporation":false,"usgs":false,"family":"Scheffler","given":"Daniel","email":"","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926532,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Guillaso, Stephane","contributorId":350644,"corporation":false,"usgs":false,"family":"Guillaso","given":"Stephane","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926533,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kokhanovsky, Alexander","contributorId":350645,"corporation":false,"usgs":false,"family":"Kokhanovsky","given":"Alexander","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926534,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Roessner, Sigrid","contributorId":350646,"corporation":false,"usgs":false,"family":"Roessner","given":"Sigrid","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926535,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Guanter, Luis","contributorId":305900,"corporation":false,"usgs":false,"family":"Guanter","given":"Luis","email":"","affiliations":[{"id":66323,"text":"Universitat Politecnica de Valencia, 46022 Valencia, Spain","active":true,"usgs":false}],"preferred":false,"id":926536,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kauffman, Hermann","contributorId":350647,"corporation":false,"usgs":false,"family":"Kauffman","given":"Hermann","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926537,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Pinnel, Nicole","contributorId":305899,"corporation":false,"usgs":false,"family":"Pinnel","given":"Nicole","email":"","affiliations":[{"id":66316,"text":"German Aerospace Center (DLR), Earth Observation Center (EOC), 82234 Weßling, Germany","active":true,"usgs":false}],"preferred":false,"id":926538,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Carmona, Emiliano","contributorId":305874,"corporation":false,"usgs":false,"family":"Carmona","given":"Emiliano","email":"","affiliations":[{"id":66316,"text":"German Aerospace Center (DLR), Earth Observation Center (EOC), 82234 Weßling, Germany","active":true,"usgs":false}],"preferred":false,"id":926539,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Storch, Tobias","contributorId":305864,"corporation":false,"usgs":false,"family":"Storch","given":"Tobias","email":"","affiliations":[{"id":66316,"text":"German Aerospace Center (DLR), Earth Observation Center (EOC), 82234 Weßling, Germany","active":true,"usgs":false}],"preferred":false,"id":926540,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Hank, Tobias","contributorId":350648,"corporation":false,"usgs":false,"family":"Hank","given":"Tobias","affiliations":[{"id":83802,"text":"Ludwig-Maximilians-University München (LMU), Dept. of Geography, 80333 Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":926541,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Berger, Katja","contributorId":350649,"corporation":false,"usgs":false,"family":"Berger","given":"Katja","affiliations":[{"id":83803,"text":"Laboratory for Earth Observation (LEO), Image Processing Laboratory (IPL), University of Valencia, 46980 Paterna, València, Spain","active":true,"usgs":false}],"preferred":false,"id":926542,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Wocher, Mathias","contributorId":350650,"corporation":false,"usgs":false,"family":"Wocher","given":"Mathias","affiliations":[{"id":83802,"text":"Ludwig-Maximilians-University München (LMU), Dept. of Geography, 80333 Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":926543,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Hostert, Patrick","contributorId":294426,"corporation":false,"usgs":false,"family":"Hostert","given":"Patrick","affiliations":[{"id":63572,"text":"Humboldt University of Berlin","active":true,"usgs":false}],"preferred":false,"id":926544,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"van der Linden, Sebastian","contributorId":350651,"corporation":false,"usgs":false,"family":"van der Linden","given":"Sebastian","affiliations":[{"id":83804,"text":"University of Greifswald, Institute for Geography and Geology, 17489 Greifswald, Germany","active":true,"usgs":false}],"preferred":false,"id":926545,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Okujeni, Akpona","contributorId":350652,"corporation":false,"usgs":false,"family":"Okujeni","given":"Akpona","affiliations":[{"id":83805,"text":"Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":926546,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Janz, Andreas","contributorId":350653,"corporation":false,"usgs":false,"family":"Janz","given":"Andreas","affiliations":[{"id":83805,"text":"Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":926547,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Jakimow, Benjamin","contributorId":350654,"corporation":false,"usgs":false,"family":"Jakimow","given":"Benjamin","affiliations":[{"id":83805,"text":"Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":926548,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Bracher, Astrid","contributorId":305905,"corporation":false,"usgs":false,"family":"Bracher","given":"Astrid","email":"","affiliations":[{"id":66325,"text":"Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research, 27570 Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":926549,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Soppa, Mariana","contributorId":305906,"corporation":false,"usgs":false,"family":"Soppa","given":"Mariana","email":"","affiliations":[{"id":66325,"text":"Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research, 27570 Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":926550,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Alvarado, L.M.A.","contributorId":350655,"corporation":false,"usgs":false,"family":"Alvarado","given":"L.M.A.","affiliations":[{"id":66325,"text":"Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research, 27570 Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":926551,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Buddenbaum, H.","contributorId":350656,"corporation":false,"usgs":false,"family":"Buddenbaum","given":"H.","affiliations":[{"id":83806,"text":"Environmental Remote Sensing and Geoinformatics, Trier University, 54286 Trier, Germany","active":true,"usgs":false}],"preferred":false,"id":926552,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Heim, Birgit","contributorId":350657,"corporation":false,"usgs":false,"family":"Heim","given":"Birgit","affiliations":[{"id":66325,"text":"Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research, 27570 Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":926553,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Heiden, Uta","contributorId":350658,"corporation":false,"usgs":false,"family":"Heiden","given":"Uta","affiliations":[{"id":66316,"text":"German Aerospace Center (DLR), Earth Observation Center (EOC), 82234 Weßling, Germany","active":true,"usgs":false}],"preferred":false,"id":926554,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Moreno, Jose M.","contributorId":150464,"corporation":false,"usgs":false,"family":"Moreno","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":18029,"text":"D Ciencias Ambientales, U Castilla La Mancha, Toledo, Spain","active":true,"usgs":false}],"preferred":false,"id":926555,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Ong, Cindy 0000-0002-9168-2865","orcid":"https://orcid.org/0000-0002-9168-2865","contributorId":243558,"corporation":false,"usgs":false,"family":"Ong","given":"Cindy","email":"","affiliations":[{"id":36909,"text":"CSIRO","active":true,"usgs":false}],"preferred":false,"id":926556,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Bohn, Niklas","contributorId":305904,"corporation":false,"usgs":false,"family":"Bohn","given":"Niklas","email":"","affiliations":[{"id":66318,"text":"GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":926557,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Green, Robert O.","contributorId":56271,"corporation":false,"usgs":true,"family":"Green","given":"Robert O.","affiliations":[],"preferred":false,"id":926558,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Bachmann, Martin","contributorId":305897,"corporation":false,"usgs":false,"family":"Bachmann","given":"Martin","email":"","affiliations":[{"id":66316,"text":"German Aerospace Center (DLR), Earth Observation Center (EOC), 82234 Weßling, Germany","active":true,"usgs":false}],"preferred":false,"id":926559,"contributorType":{"id":1,"text":"Authors"},"rank":38},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":926560,"contributorType":{"id":1,"text":"Authors"},"rank":39},{"text":"Schodlok, Martin","contributorId":350659,"corporation":false,"usgs":false,"family":"Schodlok","given":"Martin","affiliations":[{"id":83807,"text":"Bundesanstalt für Geowissenschaften und Rohstoffe (BGR), Geozentrum Hannover, Germany","active":true,"usgs":false}],"preferred":false,"id":926561,"contributorType":{"id":1,"text":"Authors"},"rank":40},{"text":"Painter, Thomas H.","contributorId":225054,"corporation":false,"usgs":false,"family":"Painter","given":"Thomas H.","affiliations":[{"id":33607,"text":"University of California Los Angeles","active":true,"usgs":false}],"preferred":false,"id":926562,"contributorType":{"id":1,"text":"Authors"},"rank":41},{"text":"Gascon, Ferran","contributorId":350381,"corporation":false,"usgs":false,"family":"Gascon","given":"Ferran","affiliations":[{"id":38836,"text":"European Space Agency","active":true,"usgs":false}],"preferred":false,"id":926563,"contributorType":{"id":1,"text":"Authors"},"rank":42},{"text":"Buongiorno, Fabrizia","contributorId":350660,"corporation":false,"usgs":false,"family":"Buongiorno","given":"Fabrizia","affiliations":[{"id":83808,"text":"National Institute of Geophysics and Volcanology (INGV), Rome, Lazio, Italy","active":true,"usgs":false}],"preferred":false,"id":926564,"contributorType":{"id":1,"text":"Authors"},"rank":43},{"text":"Mottus, Matti","contributorId":350661,"corporation":false,"usgs":false,"family":"Mottus","given":"Matti","affiliations":[{"id":83809,"text":"Technical Research Centre of Finland (VTT), Espoo, Finland","active":true,"usgs":false}],"preferred":false,"id":926565,"contributorType":{"id":1,"text":"Authors"},"rank":44},{"text":"Brando, Vittorio Ernesto","contributorId":350662,"corporation":false,"usgs":false,"family":"Brando","given":"Vittorio Ernesto","affiliations":[{"id":83810,"text":"Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), Rome, Italy","active":true,"usgs":false}],"preferred":false,"id":926566,"contributorType":{"id":1,"text":"Authors"},"rank":45},{"text":"Feilhauer, Hannes","contributorId":334012,"corporation":false,"usgs":false,"family":"Feilhauer","given":"Hannes","email":"","affiliations":[],"preferred":false,"id":926567,"contributorType":{"id":1,"text":"Authors"},"rank":46},{"text":"Betz, Matthias","contributorId":305871,"corporation":false,"usgs":false,"family":"Betz","given":"Matthias","email":"","affiliations":[{"id":66317,"text":"OHB System AG, 82234 Weßling, Germany","active":true,"usgs":false}],"preferred":false,"id":926579,"contributorType":{"id":1,"text":"Authors"},"rank":47},{"text":"Baur, Simon","contributorId":305875,"corporation":false,"usgs":false,"family":"Baur","given":"Simon","email":"","affiliations":[{"id":66317,"text":"OHB System AG, 82234 Weßling, Germany","active":true,"usgs":false}],"preferred":false,"id":926580,"contributorType":{"id":1,"text":"Authors"},"rank":48},{"text":"Feckl, Rupert","contributorId":350667,"corporation":false,"usgs":false,"family":"Feckl","given":"Rupert","affiliations":[],"preferred":false,"id":926581,"contributorType":{"id":1,"text":"Authors"},"rank":49},{"text":"Schickling, Anke","contributorId":305915,"corporation":false,"usgs":false,"family":"Schickling","given":"Anke","email":"","affiliations":[],"preferred":false,"id":926568,"contributorType":{"id":1,"text":"Authors"},"rank":50},{"text":"Krieger, Vera","contributorId":350668,"corporation":false,"usgs":false,"family":"Krieger","given":"Vera","affiliations":[],"preferred":false,"id":926582,"contributorType":{"id":1,"text":"Authors"},"rank":51},{"text":"Bock, Michael","contributorId":350663,"corporation":false,"usgs":false,"family":"Bock","given":"Michael","affiliations":[{"id":83811,"text":"German Aerospace Center (DLR), Space Agency, Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":926569,"contributorType":{"id":1,"text":"Authors"},"rank":52},{"text":"La Porta, Laura","contributorId":305911,"corporation":false,"usgs":false,"family":"La Porta","given":"Laura","email":"","affiliations":[{"id":66327,"text":"German Aerospace Center (DLR), Space Agency, 53227 Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":926570,"contributorType":{"id":1,"text":"Authors"},"rank":53},{"text":"Fischer, Sebastian","contributorId":305912,"corporation":false,"usgs":false,"family":"Fischer","given":"Sebastian","email":"","affiliations":[{"id":66327,"text":"German Aerospace Center (DLR), Space Agency, 53227 Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":926571,"contributorType":{"id":1,"text":"Authors"},"rank":54}]}} ,{"id":70260455,"text":"dr1201 - 2024 - Post Carr Fire bioassessment data report, Whiskeytown National Recreation Area, Shasta County, California","interactions":[],"lastModifiedDate":"2024-11-13T15:27:55.660405","indexId":"dr1201","displayToPublicDate":"2024-11-12T08:22:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1201","displayTitle":"Post Carr Fire Bioassessment Data Report, Whiskeytown National Recreation Area, Shasta County, California","title":"Post Carr Fire bioassessment data report, Whiskeytown National Recreation Area, Shasta County, California","docAbstract":"The U.S. Geological Survey, in cooperation with the National Park Service, analyzed water and sediment chemistry, benthic macroinvertebrate assemblages, fish and amphibian assemblages, fish and invertebrate tissues, instream habitat characteristics, and sediment heterogeneity at 10 stream sites within Whiskeytown National Recreation Area, Shasta County, California, during August 2020, 2 years after the Carr Fire. The post-Carr Fire data were compared to available pre-Carr Fire data to help determine if there have been wildfire-induced changes in the aquatic communities within the Whiskeytown National Recreational Area. Benthic sediment results for metals of biological concern were compared with consensus-based probable effect concentrations from previously published sediment-quality guidelines. Results from 2020 sampling indicated exceedances of these guidelines at one site for cadmium, chromium, copper, and zinc; there were exceedances of the guidelines at three sites for nickel. Concentrations of metals of biological concern in fish and invertebrate tissue samples generally varied among sites and years, with no pattern with specific reference to the Carr Fire. Average zinc and lead concentrations in composite invertebrate samples were slightly higher in 2020 than in previous years, and arsenic levels were lower in 2020 for invertebrate and fish tissues. Post-Carr Fire stream-habitat and sediment-size characterization values did not change substantially when compared to pre-Carr Fire values, or had high variation among all sites and years. Fish and amphibian inventories demonstrated that fewer total fish and amphibians were collected post-Carr Fire, but higher numbers of native Sacramento Sucker (Catostomus occidentalis) and Sacramento Pikeminnow (Ptychocheilus grandis) were collected than in previous years. The combined histories of mining and frequent wildfires in the area pose an increased risk for metal contamination throughout the aquatic system. Continued monitoring for multilevel trophic effects of contaminants can provide information about the overall health of the Whiskeytown National Recreation Area and surrounding region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1201","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Wulff, M.L., Brown, L.R., and Violette, V.L., 2024, Post Carr Fire bioassessment data report, Whiskeytown National Recreation Area, Shasta County, California: U.S. Geological Survey Data Report 1201, 26 p., https://doi.org/10.3133/dr1201.","productDescription":"Report: viii, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-125210","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":463558,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90WGKWI","text":"USGS Data Release","description":"Wulff, M.L., Brown, L.R., and Violette, V.L., 2023, Post Carr Fire Bioassessment Data, Whiskeytown National Recreation Area, Shasta County, California, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P90WGKWI.","linkHelpText":"Post Carr Fire Bioassessment Data, Whiskeytown National Recreation Area, Shasta County, California, 2020"},{"id":463557,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1201/full"},{"id":463556,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1201/images"},{"id":463555,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1201/dr1201.XML"},{"id":463554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1201/dr1201.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":463553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1201/coverthb.jpg"}],"country":"United States","state":"California","county":"Shasta County","otherGeospatial":"Whiskeytown National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.7971714207388,\n 41.04817475780641\n ],\n [\n -122.7971714207388,\n 40.53716575964799\n ],\n [\n -122.2801689286946,\n 40.53716575964799\n ],\n [\n -122.2801689286946,\n 41.04817475780641\n ],\n [\n -122.7971714207388,\n 41.04817475780641\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Depleted hydrocarbon reservoirs are subsurface geological structures capable of sequestering vast quantities of carbon dioxide (CO2) as well as storing other energy gases for later usage, such as natural gas, and potentially hydrogen (H2). Here we outline a methodology to quantify multi-gas storage resources in depleted conventional gas reservoirs for usage in assessments by the United States Geological Survey (USGS) at the scale of sedimentary basins. The methodology consists first of quantifying accessible pore volume in a depleted reservoir for natural gas storage using up to three equations. Input data are derived from commonly reported or estimated reservoir parameters and natural gas production volumes, and equations may be combined in linear models to improve pore volume estimates. Storage estimates from these equations are tested and validated for 31 reservoirs in the Michigan Basin Province, USA that were previously converted to underground gas storage facilities and have known (federally reported) natural gas storage capacities. Secondly, natural gas storage capacities can be transformed via fluid substitution calculations to estimate the storage resources for non-native fluids, applied here for, CO2, H2, and methane-H2 blends, accounting for molecule-specific deviations from ideal gas behavior at reservoir pressures and temperatures as well as differing storage efficiencies. Importantly, the storage of non-native fluids may not be appropriate in all depleted gas reservoir settings due to potential risks like leakage, in particular in the case of H2 storage, requiring additional knowledge of caprock sealing capacity. Given this caveat, we demonstrate the fluid substitution method for natural gas reservoirs of the Northern Niagaran Reef and Southern Niagaran Reef USGS plays in the Michigan Basin Province, as these trends of Silurian pinnacle reefs are capped with tight-sealing evaporite facies. The deterministic equations outlined from this methodology can be incorporated into future probabilistic USGS gas storage assessments for CO2, H2, and natural gas resources in the United States.
","conferenceTitle":"17th International Conference on Greenhouse Gas Control Technologies, GHGT-17","conferenceDate":"October 20-24, 2024","conferenceLocation":"Calgary, Alberta, Canada","language":"English","publisher":"SSRN","doi":"10.2139/ssrn.5014690","usgsCitation":"Jones, M.M., Wiens, A.M., Buursink, M., Brennan, S., Freeman, P., Varela, B.A., Gallotti, J.S., and Warwick, P., 2024, A methodology to estimate CO2 and energy gas storage resources in depleted conventional gas reservoirs, 17th International Conference on Greenhouse Gas Control Technologies, GHGT-17, Calgary, Alberta, Canada, October 20-24, 2024, 11 p., https://doi.org/10.2139/ssrn.5014690.","productDescription":"11 p.","ipdsId":"IP-170771","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":494404,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2139/ssrn.5014690","text":"Publisher Index Page"},{"id":465302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.75849594232385,\n 41.756477118127776\n ],\n [\n -84.8005552678581,\n 41.76806804494862\n ],\n [\n -84.8005552678581,\n 41.69849108036351\n ],\n [\n -83.47195266732744,\n 41.7216917758135\n ],\n [\n -83.14562922158265,\n 42.045621932091876\n ],\n [\n -83.10678119232736,\n 42.33345675600043\n ],\n [\n -82.87369301679546,\n 42.37938867220606\n ],\n [\n -82.5939872061575,\n 42.56278054661732\n ],\n [\n -82.48521272424307,\n 42.66570229298421\n ],\n [\n -82.39974705988166,\n 43.02458842322994\n ],\n [\n -82.64060484126432,\n 44.03289284325294\n ],\n [\n -83.06793316307204,\n 44.09988558213914\n ],\n [\n -83.65065360190172,\n 43.668698111571615\n ],\n [\n -83.86820256573132,\n 43.730487390238466\n ],\n [\n -83.79050650722071,\n 43.949045304295396\n ],\n [\n -83.51857315074679,\n 44.05523352848033\n ],\n [\n -83.25440655181109,\n 44.43371319023686\n ],\n [\n -83.23886734010883,\n 45.079220498819666\n ],\n [\n -83.4719555156407,\n 45.3856295079126\n ],\n [\n -84.40431135343007,\n 45.690386669618704\n ],\n [\n -84.76171322257865,\n 45.820491266851775\n ],\n [\n -85.11134548587647,\n 45.79882823167293\n ],\n [\n -85.86499725342895,\n 45.079220529744305\n ],\n [\n -86.30786478693892,\n 44.7159747605769\n ],\n [\n -86.54095290279666,\n 44.016134626186215\n ],\n [\n -86.579800932052,\n 43.64621532110968\n ],\n [\n -86.25347748630794,\n 42.7342241373056\n ],\n [\n -86.75849594232385,\n 41.756477118127776\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Matthew M. 0000-0001-5996-1728","orcid":"https://orcid.org/0000-0001-5996-1728","contributorId":344228,"corporation":false,"usgs":true,"family":"Jones","given":"Matthew","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":918316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiens, Ashton M. 0000-0002-7030-0602","orcid":"https://orcid.org/0000-0002-7030-0602","contributorId":271176,"corporation":false,"usgs":true,"family":"Wiens","given":"Ashton","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buursink, Marc L. 0000-0001-6491-386X","orcid":"https://orcid.org/0000-0001-6491-386X","contributorId":203357,"corporation":false,"usgs":true,"family":"Buursink","given":"Marc L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918318,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brennan, Sean T. 0000-0002-7102-9359","orcid":"https://orcid.org/0000-0002-7102-9359","contributorId":204982,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":224150,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918320,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Varela, Brian A. 0000-0001-9849-6742 bvarela@usgs.gov","orcid":"https://orcid.org/0000-0001-9849-6742","contributorId":178091,"corporation":false,"usgs":true,"family":"Varela","given":"Brian","email":"bvarela@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918321,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gallotti, Joao S. 0000-0002-7901-029X","orcid":"https://orcid.org/0000-0002-7901-029X","contributorId":302892,"corporation":false,"usgs":true,"family":"Gallotti","given":"Joao","email":"","middleInitial":"S.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":918322,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":207248,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":918323,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263970,"text":"70263970 - 2024 - Length in assessing status of freshwater fish populations: A review","interactions":[],"lastModifiedDate":"2025-03-04T15:50:34.260958","indexId":"70263970","displayToPublicDate":"2024-11-10T09:39:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Length in assessing status of freshwater fish populations: A review","docAbstract":"Effective policy formulation regarding the conservation of freshwater fish necessitates an understanding of water‐specific prevailing conditions and trends. Assessing fish populations in inland waters is difficult and expensive because there are many independent systems that need to be evaluated. Therefore, numerous freshwater systems are beset by insufficient data and the lack of systematic assessments of their status. To alleviate this deficiency, the objective of this study was to review length‐based metrics that may have utility in evaluating the well‐being of freshwater fish populations.
Length measurements can serve as proxies for a range of ecological and population dynamics attributes that are essential for the effective management of fish and associated fisheries. A review of the historical development of length measurements in fish conservation is provided, along with an examination of the potential biases that may arise from the use of lengths in practical contexts. In addition, we examine techniques that enable the spatial and temporal visualization of length data sets, as well as a range of indices and metrics that can be computed using length measurements.
Building populations assessments around length may be a cost‐effective strategy that allows a first cut at managing a large number of waters. Length‐based assessments can signal if management intervention is necessary, if management policies are yielding the intended outcome, or if surveys beyond mere length are necessary.
Our review indicates that length offers a straightforward and efficient approach to evaluate the status of fish populations in inland systems. We encourage pursuing additional study and to this end propose specific areas for investigation.
Hydrologic connectivity, the network of water pathways linking aquatic habitats, is vital for the exchange of organisms and abiotic materials between rivers and adjacent waterbodies. This study quantified hydrologic connectivity for 1,283 lakes in the Lower Mississippi River floodplain using satellite imagery, streamgauge data, and geospatial information. We aimed to assess connection frequency patterns between lakes and streams. Eight metrics describing temporal aspects of hydrologic connectivity were estimated, identifying trends by lake features and by stream size. Each lake exhibited a distinct pattern of connection, with specific months of connectivity followed by disconnection, likely influenced by lake characteristics and seasonal precipitation. Larger lakes showed increased connectivity, likely due to their surface area and volume, while smaller lakes were more prone to isolation, especially during dry periods. Lakes connected to large streams exhibited more prolonged and recurring connections, with less seasonal variation. In contrast, lakes near agricultural areas experienced reduced connectivity. However, local factors such as levees and artificial channels often disrupted these general trends. This hydrologic connectivity analysis can provide insight to support floodplain management, facilitate development of frameworks that restore connectivity, promote preservation of ecological integrity, and support management of invasive species spread in agricultural floodplains.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2024.112808","usgsCitation":"Ahmad, H., Miranda, L.E., Dunn, C.G., Boudreau, M., and Colvin, M.E., 2024, Connectivity patterns between floodplain lakes and neighboring streams in the historical floodplain of the Lower Mississippi River: Ecological Indicators, v. 169, 112808, 12 p., https://doi.org/10.1016/j.ecolind.2024.112808.","productDescription":"112808, 12 p.","ipdsId":"IP-168197","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490657,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2024.112808","text":"Publisher Index Page"},{"id":490406,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1QIH9NJ","text":"USGS data release","linkHelpText":"Code for Connectivity patterns between floodplain lakes and neighboring streams in the historical floodplain of the Lower Mississippi River"},{"id":489294,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Mississippi River floodplain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.30878420635052,\n 38.86158462464857\n ],\n [\n -92.32018351048195,\n 38.86158462464857\n ],\n [\n -92.32018351048195,\n 28.20085112656909\n ],\n [\n -88.30878420635052,\n 28.20085112656909\n ],\n [\n -88.30878420635052,\n 38.86158462464857\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"169","noUsgsAuthors":false,"publicationDate":"2024-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahmad, Hafez","contributorId":353774,"corporation":false,"usgs":false,"family":"Ahmad","given":"Hafez","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":938775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":938776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":938777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boudreau, Melanie R.","contributorId":353778,"corporation":false,"usgs":false,"family":"Boudreau","given":"Melanie R.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":938778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Colvin, Michael E. 0000-0002-6581-4764","orcid":"https://orcid.org/0000-0002-6581-4764","contributorId":331490,"corporation":false,"usgs":true,"family":"Colvin","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":938779,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70260707,"text":"sir20235064I - 2024 - Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70260707,"text":"sir20235064I - 2024 - Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064I","publicationYear":"2024","noYear":false,"chapter":"I","displayTitle":"Peak Streamflow Trends in South Dakota and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2025-12-22T21:31:08.991933","indexId":"sir20235064I","displayToPublicDate":"2024-11-08T10:53:13","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5064","chapter":"I","displayTitle":"Peak Streamflow Trends in South Dakota and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020","docAbstract":"Peak-flow (flood) frequency analysis is essential to water-resources management applications, including the design of critical infrastructure such as bridges and culverts, and floodplain mapping. Federal guidelines for performing peak-flow flood frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C, which documents the guidelines for determining annual peak streamflow frequency, is that, for basins without major hydrologic alterations (for example, regulation, diversion, and urbanization), statistical properties of the distribution of annual peak streamflows are stationary; that is, the mean, variance, and skew are constant through time. Nonstationarity is a statistical property of a peak-flow series such that the long-term (on the order of decades) distributional properties change one or more times either gradually or abruptly through time. Individual nonstationarities may be attributed to one source such as flow regulation, land-use change, or climate but are often the result of a combination of sources, making detection and attribution of nonstationarities challenging.
In response to a growing concern regarding nonstationarity in peak streamflows in the region, the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, assessed the potential nonstationarity in peak streamflows in the north-central United States. This chapter characterizes the effects of natural hydroclimatic shifts and potential climate change on annual peak streamflows in the State of South Dakota. Annual peak and daily streamflow as well as model-simulated gridded climatic data were examined for temporal monotonic trends, change points, and other statistical properties indicative of changing climatic and environmental conditions.
Changes in annual peak and daily flows were evaluated among 13, 35, and 81 qualifying U.S. Geological Survey streamgages for the 75-, 50-, and 30-year trend periods through water year 2020 (the period from October 1, 2019, to September 30, 2020) in South Dakota, respectively. No qualifying streamgages were in the 100-year trend period in the State. Statistical tests for autocorrelation (independent and identically distributed assumption), monotonic trends, and change points in the median and scale are analyzed to evaluate potential stationarity violations (nonstationarity) for performing at-site peak-flow flood-frequency analysis. The trends are reported using a likelihood approach as an alternative to simply reporting significant trends with an arbitrary p-value cutoff point.
A distinct east-west spatial pattern of likely upward and downward monotonic trends and change points, respectively, was detected in 75- and 50-year trend periods, but an inconsistent spatial pattern was detected in the 30-year trend period. Additionally, change points in the median annual peak streamflows were detected in the late 1970s and early 1980s in the western part of the State, but in the east, the change point was more commonly detected in 1992–93. A similar east-west spatial pattern of likely upward and downward trends was detected in the annual peak-flow timing, the day of the year of the annal peak streamflow. In the western part of the State, the annual peak streamflows are arriving earlier, but in the east, the annual peak streamflows are arriving later. A peaks-over-threshold (POT) analysis where, on average, there are two events per year (POT2) and four events per year (POT4) was also used to evaluate changes in the frequency (count) of daily streamflows exceeding the threshold. Similar to detected changes in the annual peak streamflow, an east-west likely upward or downward change corresponding to an increase or decrease, respectively, in the frequency of daily streamflow greater than a POT2 and POT4 threshold was detected.
A monthly water-balance model was used to evaluate hydroclimatic variation in annual and seasonal precipitation, snowfall, potential evapotranspiration, and soil moisture storage for all qualifying streamgages in the 75-, 50-, and 30-year trend periods. Detected trends in the annual hydroclimatic metrics for the 75- and 50-year trend periods indicate a spatially consistent statewide increase in precipitation, decrease in snowfall, increase in potential evapotranspiration, and increase in soil moisture storage. Furthermore, detected trends in seasonal precipitation in the 75- and 50-year trend periods highlight a pronounced change in precipitation in winter and later into the summer season, especially in the 50-year trend period in the eastern part of the State. Statewide increases in seasonal soil moisture storage were also detected, highlighting year-round increasing flood magnitudes, particularly in the eastern part of the State.
Based on the results of these stationarity tests for the qualifying streamgages in South Dakota among the 75-, 50-, and 30-year trend periods, consistent temporal and spatial patterns of nonstationarity were detected among the 75- and 50-year trend periods. Furthermore, when nonstationarity is detected in daily streamflow, increased streamflow and volume (increasing frequency in POT), as well as potentially bridge scour, may have implications on culvert and highway design in the eastern part of South Dakota. Thus, when performing at-site peak-flow flood-frequency analyses in South Dakota, potential nonstationarities and alternative approaches are important considerations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064I","collaboration":"Prepared in cooperation with the South Dakota Department of Transportation","usgsCitation":"Barth, N.A., and Sando, S.K., 2024, Peak streamflow trends in South Dakota and their relation to changes in climate, water years 1921–2020, chap. I of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 70 p., https://doi.org/10.3133/sir20235064I.","productDescription":"Report: x, 70 p.; Data Release; Dataset","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-146340","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":497916,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117775.htm","linkFileType":{"id":5,"text":"html"}},{"id":463794,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235064I/full"},{"id":463793,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R71WWZ","text":"USGS data release","linkHelpText":"Peak streamflow data, climate data, and results from investigating hydroclimatic trends and climate change effects on peak streamflow in the Central United States, 1920–2020"},{"id":463788,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5064/i/coverthb.jpg"},{"id":463792,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":463791,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5064/i/images/"},{"id":463790,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5064/i/sir20235064i.XML"},{"id":463789,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5064/i/sir20235064i.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5064–I"}],"country":"United States","state":"South Dakota","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.054487,44.180381],[-104.055914,44.874986],[-104.057698,44.997431],[-104.039681,44.998041],[-104.040114,45.374214],[-104.045443,45.94531],[-100.430597,45.943638],[-99.005754,45.939944],[-98.414518,45.936504],[-96.56328,45.935238],[-96.564002,45.91956],[-96.56703,45.915682],[-96.56442,45.909415],[-96.568315,45.902902],[-96.568772,45.888072],[-96.571354,45.886673],[-96.571871,45.871846],[-96.574667,45.866816],[-96.572984,45.861602],[-96.574517,45.843098],[-96.583085,45.820024],[-96.596704,45.811801],[-96.612512,45.794442],[-96.627778,45.786239],[-96.638726,45.770171],[-96.641941,45.759871],[-96.652226,45.746809],[-96.662595,45.738682],[-96.672665,45.732336],[-96.711157,45.717561],[-96.745086,45.701576],[-96.75035,45.698782],[-96.760866,45.687518],[-96.835769,45.649648],[-96.844211,45.639583],[-96.852392,45.61484],[-96.857751,45.605962],[-96.801987,45.555414],[-96.79384,45.550724],[-96.76528,45.521414],[-96.745487,45.488712],[-96.743486,45.480649],[-96.738446,45.473499],[-96.732739,45.458737],[-96.692541,45.417338],[-96.680454,45.410499],[-96.617726,45.408092],[-96.60118,45.403181],[-96.562142,45.38609],[-96.521787,45.375645],[-96.489065,45.357071],[-96.469246,45.324941],[-96.468027,45.318619],[-96.46191,45.313884],[-96.453067,45.298115],[-96.451232,44.718375],[-96.453049,43.500415],[-96.598928,43.500457],[-96.599182,43.496011],[-96.586274,43.491099],[-96.580997,43.481384],[-96.586364,43.478251],[-96.584603,43.46961],[-96.587929,43.464878],[-96.600039,43.45708],[-96.60286,43.450907],[-96.594254,43.434153],[-96.587884,43.431685],[-96.575181,43.431756],[-96.570224,43.428601],[-96.573579,43.419228],[-96.562728,43.412782],[-96.557586,43.406792],[-96.537116,43.395063],[-96.531159,43.39561],[-96.529152,43.397735],[-96.525453,43.396317],[-96.521572,43.38564],[-96.521323,43.374607],[-96.526467,43.368314],[-96.527223,43.362257],[-96.526635,43.351833],[-96.524289,43.347214],[-96.534913,43.336473],[-96.528817,43.316561],[-96.525564,43.312467],[-96.530392,43.300034],[-96.553087,43.29286],[-96.555246,43.294803],[-96.56911,43.295535],[-96.573556,43.29917],[-96.581052,43.297118],[-96.579094,43.293797],[-96.577588,43.2788],[-96.580904,43.2748],[-96.582876,43.274594],[-96.582939,43.276536],[-96.586317,43.274319],[-96.58522,43.268878],[-96.576804,43.268308],[-96.564165,43.260239],[-96.554968,43.259998],[-96.552591,43.257769],[-96.552963,43.247281],[-96.565253,43.244241],[-96.571194,43.238961],[-96.568505,43.231554],[-96.56044,43.224219],[-96.554937,43.226775],[-96.540088,43.225698],[-96.535741,43.22764],[-96.526865,43.224071],[-96.519273,43.21769],[-96.500759,43.220767],[-96.496454,43.223652],[-96.485264,43.224183],[-96.476697,43.222014],[-96.470626,43.207225],[-96.473777,43.198766],[-96.473834,43.189804],[-96.472395,43.185644],[-96.465146,43.182971],[-96.467292,43.164066],[-96.466537,43.150281],[-96.459978,43.143516],[-96.450361,43.142237],[-96.443431,43.133825],[-96.440801,43.123129],[-96.436589,43.120842],[-96.439335,43.113916],[-96.462855,43.091419],[-96.462636,43.089614],[-96.455337,43.088129],[-96.454088,43.084197],[-96.455209,43.075053],[-96.46085,43.064033],[-96.468207,43.06186],[-96.473165,43.06355],[-96.476905,43.062383],[-96.490365,43.050789],[-96.501748,43.048632],[-96.510256,43.049917],[-96.518431,43.042068],[-96.509145,43.037297],[-96.512916,43.029962],[-96.510995,43.024701],[-96.499187,43.019213],[-96.49167,43.009707],[-96.496699,42.998807],[-96.509986,42.995126],[-96.512886,42.991424],[-96.512237,42.985937],[-96.516724,42.981458],[-96.520773,42.980385],[-96.515922,42.972886],[-96.506148,42.971348],[-96.503132,42.968192],[-96.500308,42.959391],[-96.504857,42.954659],[-96.509472,42.945151],[-96.519994,42.93976],[-96.516419,42.935438],[-96.516888,42.932512],[-96.525536,42.935511],[-96.541689,42.922576],[-96.536564,42.905656],[-96.542847,42.903737],[-96.539397,42.899964],[-96.536007,42.900901],[-96.528886,42.89795],[-96.526357,42.891852],[-96.540116,42.889678],[-96.537851,42.878475],[-96.546394,42.874464],[-96.549659,42.870281],[-96.550469,42.863742],[-96.546556,42.857273],[-96.541708,42.858871],[-96.545502,42.849956],[-96.554709,42.846142],[-96.554203,42.843648],[-96.549976,42.840705],[-96.551285,42.836606],[-96.556162,42.836675],[-96.560572,42.839373],[-96.56284,42.836309],[-96.563058,42.831051],[-96.565605,42.830434],[-96.571353,42.837155],[-96.581604,42.837521],[-96.58238,42.833657],[-96.577813,42.828719],[-96.585699,42.818041],[-96.596008,42.815044],[-96.595664,42.810426],[-96.590913,42.808987],[-96.595283,42.792982],[-96.602575,42.787767],[-96.603784,42.78372],[-96.61949,42.784034],[-96.626406,42.773518],[-96.632142,42.770863],[-96.632212,42.761512],[-96.628741,42.757532],[-96.621235,42.758084],[-96.619494,42.754792],[-96.630485,42.750378],[-96.639704,42.737071],[-96.631931,42.725086],[-96.624704,42.725497],[-96.624446,42.714294],[-96.630617,42.70588],[-96.612555,42.698402],[-96.61017,42.694568],[-96.59908,42.697296],[-96.596625,42.695122],[-96.596405,42.688514],[-96.58562,42.687076],[-96.575299,42.682665],[-96.574064,42.67801],[-96.578148,42.672765],[-96.572261,42.670776],[-96.569194,42.675509],[-96.566684,42.675942],[-96.556244,42.664396],[-96.5599,42.662819],[-96.559962,42.658543],[-96.556214,42.657949],[-96.546827,42.661491],[-96.542366,42.660736],[-96.537877,42.655431],[-96.537881,42.646446],[-96.526766,42.641184],[-96.516338,42.630435],[-96.515918,42.624994],[-96.518542,42.62035],[-96.530896,42.617129],[-96.529894,42.610432],[-96.525671,42.609312],[-96.517048,42.615343],[-96.509468,42.61273],[-96.500183,42.594106],[-96.501037,42.589247],[-96.494777,42.585741],[-96.49545,42.579474],[-96.485796,42.575001],[-96.489328,42.5708],[-96.498709,42.57087],[-96.498041,42.558153],[-96.476952,42.556079],[-96.479909,42.524195],[-96.490802,42.520331],[-96.49297,42.517282],[-96.490089,42.512441],[-96.477454,42.509589],[-96.473339,42.503537],[-96.476909,42.497795],[-96.476509,42.493595],[-96.474409,42.491895],[-96.46255,42.490788],[-96.456348,42.492478],[-96.443408,42.489495],[-96.478792,42.479635],[-96.501321,42.482749],[-96.508587,42.486691],[-96.515891,42.49427],[-96.520683,42.504761],[-96.528753,42.513273],[-96.538036,42.518131],[-96.548791,42.520547],[-96.567896,42.517877],[-96.591121,42.50541],[-96.603468,42.50446],[-96.611489,42.506088],[-96.625958,42.513576],[-96.628179,42.516963],[-96.632882,42.528987],[-96.63533,42.54764],[-96.643589,42.557604],[-96.658754,42.566426],[-96.681369,42.574486],[-96.7093,42.603753],[-96.711546,42.614758],[-96.709485,42.621932],[-96.687788,42.645992],[-96.687082,42.652093],[-96.691269,42.6562],[-96.728024,42.666882],[-96.746949,42.666223],[-96.76406,42.661985],[-96.793238,42.666024],[-96.800986,42.669758],[-96.802178,42.672237],[-96.800485,42.692466],[-96.801652,42.698774],[-96.806219,42.704149],[-96.843419,42.712024],[-96.860436,42.720797],[-96.886845,42.725222],[-96.906797,42.7338],[-96.924156,42.730327],[-96.948902,42.719465],[-96.961576,42.719841],[-96.964776,42.722455],[-96.965833,42.727096],[-96.96123,42.740623],[-96.96888,42.754278],[-96.97912,42.76009],[-96.99282,42.759481],[-97.02485,42.76243],[-97.033229,42.765904],[-97.065592,42.772189],[-97.096128,42.76934],[-97.131331,42.771929],[-97.134461,42.774494],[-97.138216,42.783428],[-97.150763,42.795566],[-97.166978,42.802087],[-97.200431,42.805485],[-97.210126,42.809296],[-97.213084,42.813007],[-97.213957,42.820143],[-97.218269,42.829561],[-97.217411,42.843519],[-97.218825,42.845848],[-97.237868,42.853139],[-97.251764,42.855432],[-97.267946,42.852583],[-97.289859,42.855499],[-97.306677,42.867604],[-97.336156,42.856802],[-97.359569,42.854816],[-97.368643,42.858419],[-97.376695,42.865195],[-97.393966,42.86425],[-97.408315,42.868334],[-97.417066,42.865918],[-97.431951,42.851542],[-97.442279,42.846224],[-97.452177,42.846048],[-97.470529,42.850455],[-97.49149,42.851625],[-97.504847,42.858477],[-97.531867,42.850105],[-97.561928,42.847552],[-97.591916,42.853837],[-97.603762,42.858329],[-97.611811,42.858367],[-97.657846,42.844626],[-97.686506,42.842435],[-97.72045,42.847439],[-97.774456,42.849774],[-97.817075,42.861781],[-97.828496,42.868797],[-97.84527,42.867734],[-97.875345,42.858724],[-97.877003,42.854394],[-97.875849,42.847725],[-97.878976,42.843673],[-97.879878,42.835395],[-97.888562,42.817251],[-97.908983,42.794909],[-97.921434,42.788352],[-97.936716,42.775754],[-97.950147,42.769619],[-97.977588,42.769923],[-98.000348,42.763256],[-98.017228,42.762411],[-98.035034,42.764205],[-98.059838,42.772772],[-98.062913,42.781119],[-98.067388,42.784759],[-98.094574,42.799309],[-98.107688,42.810633],[-98.127489,42.820127],[-98.137912,42.832728],[-98.146933,42.839823],[-98.167523,42.836925],[-98.189765,42.841628],[-98.219826,42.853157],[-98.25181,42.872824],[-98.280007,42.874996],[-98.325864,42.8865],[-98.34623,42.902747],[-98.42074,42.931924],[-98.430934,42.931504],[-98.437285,42.928393],[-98.444145,42.929242],[-98.448309,42.936428],[-98.467356,42.947556],[-98.490483,42.977948],[-98.49855,42.99856],[-100.472742,42.999288],[-101.625424,42.996238],[-101.849982,42.999329],[-104.053127,43.000585],[-104.055488,43.853476],[-104.054487,44.180381]]]},\"properties\":{\"name\":\"South Dakota\",\"nation\":\"USA \"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Understanding the geomorphic processes and causes for long-term hydrogeomorphic changes along the Upper Mississippi River System (UMRS) is necessary for scientific studies ranging from habitat needs assessments, sediment transport, and nutrient processing, and making sound management decisions and prioritizing ecological restoration activities. From 2018 through 2020 the U.S. Geological Survey and U.S. Army Corps of Engineers led a series of calls and meetings, and a workshop to develop a draft UMRS hydrogeomorphic change conceptual model and hierarchical classification scheme. This project was funded through an Upper Mississippi River Restoration 2018 science in support of restoration proposal entitled, “Conceptual Model and Hierarchical Classification of Hydrogeomorphic Settings in the Upper Mississippi River System.” This report documents the background leading up to and the major findings from the workshop. The resulting conceptual model focuses on the drivers and boundary conditions that affect the major hydrogeomorphic processes along the valley corridor using a continuum of spatial and temporal scales and resolutions. The draft hierarchical classification was based on three existing and three new nested geospatial datasets that ultimately can be used to characterize hydrogeomorphic settings that span the UMRS valley corridor. The conceptual model and hierarchical classification will help characterize recent (mid-1990s through mid-2010s) decadal-scale processes and sources for potential hydrogeomorphic change that span a range of spatial scales from watershed hydrology and sediment sources to channel hydraulics and sediment transport.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241051","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Fitzpatrick, F.A., Rogala, J.T., Hendrickson, J.S., Sawyer, L., Stone, J., Erwin, S., Brauer, E.J., and Vaughan, A.A., 2024, Upper Mississippi River System hydrogeomorphic change conceptual model and hierarchical classification: U.S. Geological Survey Open-File Report 2024–1051, 24 p., https://doi.org/10.3133/ofr20241051.","productDescription":"vi, 24 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154820","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":463608,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1051/ofr20241051.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1051"},{"id":463607,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1051/coverthb.jpg"},{"id":463611,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241051/full"},{"id":463609,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1051/ofr20241051.XML"},{"id":463610,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1051/images/"},{"id":497918,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117772.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, South Dakota, Wisconsin","otherGeospatial":"Upper Mississippi River System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.50342683246095,\n 40.74721296729754\n ],\n [\n -86.22485822523954,\n 41.12212957640983\n ],\n [\n -86.47633619015835,\n 41.49085772032035\n ],\n [\n -87.70997806528162,\n 41.54161184649007\n ],\n [\n -87.96780499446993,\n 42.13710516972478\n ],\n [\n -88.45256695171824,\n 43.2601947483673\n ],\n [\n -88.90796053029793,\n 43.953704040844826\n ],\n [\n -89.31966888436784,\n 43.88619768580165\n ],\n [\n -88.82098034404291,\n 45.84620970391856\n ],\n [\n -89.77751644898966,\n 46.14284357021026\n ],\n [\n -92.3836871160787,\n 46.36717699384113\n ],\n [\n -93.57654031001726,\n 47.5843117955113\n ],\n [\n -96.69363739805445,\n 47.520997934475815\n ],\n [\n -96.5298693029927,\n 46.26717241479707\n ],\n [\n -97.88294379082475,\n 46.288663271101996\n ],\n [\n -96.96809715353103,\n 45.32804185048016\n ],\n [\n -95.79932895452089,\n 43.51835880034011\n ],\n [\n -93.73471399415848,\n 39.80187490699902\n ],\n [\n -90.99752932951242,\n 36.93622008574626\n ],\n [\n -89.6631770431625,\n 36.64968232220457\n ],\n [\n -89.28540446277691,\n 37.166100746132656\n ],\n [\n -87.50342683246095,\n 40.74721296729754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Rapid-response petrological monitoring is a major advance for volcano observatories, allowing them to build and validate models of plumbing systems that supply eruptions in near-real-time. The depth of magma storage has recently been identified as high-priority information for volcanic observatories, yet this information is not currently obtainable via petrological monitoring methods on timescales relevant to eruption response. Fluid inclusion barometry (using micro-thermometry or Raman spectroscopy) is a well-established petrological method to estimate magma storage depths and has been proposed to have potential as a rapid-response monitoring tool, although this has not been formally demonstrated. To address this deficiency, we performed a near-real-time rapid-response simulation for the September 2023 eruption of Kīlauea, Hawaiʻi. We show that Raman-based fluid inclusion barometry can robustly determine reservoir depths within a day of receiving samples — a transformative timescale that has not previously been achieved by petrological methods. Fluid inclusion barometry using micro-thermometric techniques has typically been limited to systems with relatively deep magma storage (>0.4 g/cm3 or >7 km) where measurements of CO2 density are easy and accurate because the CO2 fluid homogenizes into the liquid phase. Improvements of the accuracy of Raman spectroscopy measurements of fluids with low CO2 density over the past couple of decades has enabled measurements of fluid inclusions from shallower magmatic systems. However, one caveat of examining shallower systems is that the fraction of H2O in the fluid may be too high to reliably convert CO2 density to pressure. To test the global applicability of rapid response fluid inclusion barometry, we compiled a global melt inclusion dataset (>4000 samples) and calculate the fluid composition at the point of vapor saturation (XH2O). We show that fluid inclusions in crystal-hosts from mafic compositions (<57 wt. % SiO2) — likely representative of magmas recharging many volcanic systems worldwide — trap fluids with XH2O low enough to make fluid inclusion barometry useful at many of the world’s most active and hazardous mafic volcanic systems (e.g., Iceland, Hawaiʻi, Galápagos Islands, East African Rift, Réunion, Canary Islands, Azores, Cabo Verde).
","language":"English","publisher":"Oxford Academic","doi":"10.1093/petrology/egae119","usgsCitation":"DeVitre, C., Wieser, P.E., Bearden, A.T., Richie, A., Rangel, B., Gleeson, M., Grimsich, J., Lynn, K.J., Downs, D.T., Deligne, N.I., and Mulliken, K.M., 2024, Depths in a day - A new era of rapid-response Raman-based barometry using fluid inclusions: Journal of Petrology, v. 65, no. 11, egae119, 15 p., https://doi.org/10.1093/petrology/egae119.","productDescription":"egae119, 15 p.","ipdsId":"IP-158109","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466776,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egae119","text":"Publisher Index Page"},{"id":464235,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.2575579892722,\n 19.41802727097236\n ],\n [\n -155.2575579892722,\n 19.40813592330987\n ],\n [\n -155.2414055520364,\n 19.40813592330987\n ],\n [\n -155.2414055520364,\n 19.41802727097236\n ],\n [\n -155.2575579892722,\n 19.41802727097236\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"65","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-11-07","publicationStatus":"PW","contributors":{"authors":[{"text":"DeVitre, Charlotte","contributorId":346229,"corporation":false,"usgs":false,"family":"DeVitre","given":"Charlotte","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":918713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wieser, Penny E. 0000-0002-1070-8323","orcid":"https://orcid.org/0000-0002-1070-8323","contributorId":272601,"corporation":false,"usgs":false,"family":"Wieser","given":"Penny","email":"","middleInitial":"E.","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":918714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bearden, Alexander T.","contributorId":346329,"corporation":false,"usgs":false,"family":"Bearden","given":"Alexander","email":"","middleInitial":"T.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":918715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richie, Araela","contributorId":346330,"corporation":false,"usgs":false,"family":"Richie","given":"Araela","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":918716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rangel, Berenise","contributorId":346222,"corporation":false,"usgs":false,"family":"Rangel","given":"Berenise","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":918717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gleeson, Matthew","contributorId":346331,"corporation":false,"usgs":false,"family":"Gleeson","given":"Matthew","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":918718,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grimsich, John","contributorId":346332,"corporation":false,"usgs":false,"family":"Grimsich","given":"John","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":918719,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lynn, Kendra J. 0000-0001-7886-4376","orcid":"https://orcid.org/0000-0001-7886-4376","contributorId":290327,"corporation":false,"usgs":true,"family":"Lynn","given":"Kendra","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918720,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Downs, Drew T. 0000-0002-9056-1404 ddowns@usgs.gov","orcid":"https://orcid.org/0000-0002-9056-1404","contributorId":173516,"corporation":false,"usgs":true,"family":"Downs","given":"Drew","email":"ddowns@usgs.gov","middleInitial":"T.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918721,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Deligne, Natalia I. 0000-0001-9221-8581","orcid":"https://orcid.org/0000-0001-9221-8581","contributorId":257389,"corporation":false,"usgs":true,"family":"Deligne","given":"Natalia","email":"","middleInitial":"I.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918722,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mulliken, Katherine M. 0000-0003-4190-5060","orcid":"https://orcid.org/0000-0003-4190-5060","contributorId":217810,"corporation":false,"usgs":false,"family":"Mulliken","given":"Katherine","email":"","middleInitial":"M.","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":918723,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70260482,"text":"sir20245095 - 2024 - Real-time pier scour monitoring and observations at three scour-critical sites in Idaho, water years 2020–22","interactions":[],"lastModifiedDate":"2025-12-22T21:36:02.002108","indexId":"sir20245095","displayToPublicDate":"2024-11-06T13:33:30","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5095","displayTitle":"Real-Time Pier Scour Monitoring and Observations at Three Scour-Critical Sites in Idaho, Water Years 2020–22","title":"Real-time pier scour monitoring and observations at three scour-critical sites in Idaho, water years 2020–22","docAbstract":"To observe real-time pier scour at three scour-critical sites in Idaho, the U.S. Geological Survey, in cooperation with Idaho Transportation Department, installed and operated fixed real-time (15-minute interval) bed elevation scour sonar sensors at three bridge locations associated with U.S. Geological Survey streamflow gaging stations for water years 2020 through 2022. Daily mean and peak streamflow conditions during the 3-year study were at or below average except for the peak flow in 2022. Each of the three sites included in the study had a coarse bed with an armored channel. Observed pier scour at each of the three sites was less than 20 percent than the stated minimum depth to the pier pile tip. The below average daily mean and peak streamflow during the study period may have resulted in below average scour.
Observed pier scour data during spring runoff (water years 2020–22) were compared to both Coarse Bed and Hydraulic Engineering Circular 18 (HEC-18) general pier scour design equation estimates to better understand how the observed pier scour data compared to design pier scour equation estimates during the same observational periods. For the 3-year study period, the Coarse Bed design equation generally overpredicted scour by about 2.5 times less than the HEC-18 general pier scour equation. The risk associated with each design equation was summarized using a reliability index to describe how each prediction might be expected to reliably overestimate scour depth. Overall, the Coarse Bed design scour equation provided more reasonable scour depth estimates than the HEC-18 general pier scour equation but with more risk to underestimating scour depth. Because these data are limited (3 sites, 3 years, and during average streamflow conditions), further research is needed to compare observed scour data to estimates predicted by the Coarse Bed design equation and other design equations.
This study demonstrated that real-time pier scour monitoring is a useful method and countermeasure at critical bridge sites. A recently developed rapid deployment real-time pier scour monitoring method may be a useful method to consider for future studies. Real-time monitoring at scour critical sites may be a useful tool to confirm previous scour evaluation estimates where site inspection scour observations conflict with the scour evaluation estimates. Considering alternative scour monitoring and evaluation methods, including the rapid estimation method, and updating pier scour calculations using the most recent coarse-bed pier scour equation may offer a more cost-effective solution to identifying and updating scour critical coding for bridges in Idaho. For scour critical bridge sites, the real-time pier scour monitoring methods used for this study provided an effective real-time local pier scour monitoring countermeasure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245095","collaboration":"Prepared in cooperation with the Idaho Transportation Department","usgsCitation":"Fosness, R.L., and Schauer, P.V., 2024, Real-time pier scour monitoring and observations at three scour-critical sites in Idaho, water years 2020–22: U.S. Geological Survey Scientific Investigations Report 2024–5095, 23 p., https://doi.org/10.3133/sir20245095.","productDescription":"Report; vii, 23 p.p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128131","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":497921,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117770.htm","linkFileType":{"id":5,"text":"html"}},{"id":463605,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5095/images"},{"id":463604,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90332LD","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydraulic assessment summary at selected real-time pier scour monitoring sites in Idaho, 2020–2022"},{"id":463603,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245095/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5095"},{"id":463602,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5095/sir20245095.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5095"},{"id":463601,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5095/sir20245095.jpg"},{"id":463606,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5095/sir20245095.XML"}],"country":"United States","state":"Idaho","otherGeospatial":"Boise River, Payette River, St. Joe River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.19181903119929,\n 47.27634704869766\n ],\n [\n -116.19181903119929,\n 47.2713170479783\n ],\n [\n -116.18399082696462,\n 47.2713170479783\n ],\n [\n -116.18399082696462,\n 47.27634704869766\n ],\n [\n -116.19181903119929,\n 47.27634704869766\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.98113730832642,\n 43.79475425455084\n ],\n [\n -116.98113730832642,\n 43.738517550064955\n ],\n [\n -116.90322501094207,\n 43.738517550064955\n ],\n [\n -116.90322501094207,\n 43.79475425455084\n ],\n [\n -116.98113730832642,\n 43.79475425455084\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.64141007600492,\n 43.905644822917225\n ],\n [\n -116.64141007600492,\n 43.88897692930604\n ],\n [\n -116.61377308255302,\n 43.88897692930604\n ],\n [\n -116.61377308255302,\n 43.905644822917225\n ],\n [\n -116.64141007600492,\n 43.905644822917225\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4250
Recent advances in remote sensing have produced global glacier surface elevation change data. Parsing these elevation change signals into contributions from the climate (i.e. climatic mass balance) and glacier dynamics (i.e. flux divergence) is critical to enhance our process-based understanding of glacier change. In this study, we evaluate three approaches for direct, continuous measurements of the climatic mass balance, flux divergence, and elevation change at a site on Gulkana Glacier in Alaska using low-cost GNSS sensors, GNSS interferometric reflectometry (GNSS-IR), banded ablation stakes with time-lapse cameras, and combinations thereof. Cumulative climatic mass balance over the season was 4.85 m and the three approaches were within 0.08 m through early July before the snowpack melted, and within 0.28 m through mid-August. The flux divergence increased from 0.52 ± 0.03 cm d-1 before June 3 to roughly 0.73 cm d-1 after June 27. We demonstrate a single GNSS system fixed atop an ablation stake can measure contemporaneous climatic mass balance, flux divergence, and elevation change based on the antenna’s position and GNSS-IR techniques. The ability of these systems to measure glacier mass balance and flux divergence offers unique opportunities for year-round observations on mountain glaciers in the future.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/jog.2024.54","usgsCitation":"Wells, A., Rounce, D.R., Sass, L., Florentine, C., Garbo, A., Baker, E., and McNeil, C., 2024, GNSS reflectometry from low-cost sensors for continuous in situ contemporaneous glacier mass balance and flux divergence: Journal of Glaciology, v. 70, e5, 12 p., https://doi.org/10.1017/jog.2024.54.","productDescription":"e5, 12 p.","ipdsId":"IP-164695","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":466780,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/jog.2024.54","text":"Publisher Index Page"},{"id":464746,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulkana Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -145.68362267602475,\n 63.35411541798791\n ],\n [\n -145.68362267602475,\n 63.196253345505795\n ],\n [\n -145.0204214654105,\n 63.196253345505795\n ],\n [\n -145.0204214654105,\n 63.35411541798791\n ],\n [\n -145.68362267602475,\n 63.35411541798791\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"70","noUsgsAuthors":false,"publicationDate":"2024-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Albin","contributorId":346929,"corporation":false,"usgs":false,"family":"Wells","given":"Albin","email":"","affiliations":[{"id":12943,"text":"Carnegie Mellon University","active":true,"usgs":false}],"preferred":false,"id":920224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rounce, David R.","contributorId":290361,"corporation":false,"usgs":false,"family":"Rounce","given":"David","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":920225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":920226,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":920227,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garbo, Adam","contributorId":346930,"corporation":false,"usgs":false,"family":"Garbo","given":"Adam","email":"","affiliations":[{"id":39169,"text":"University of Ottawa","active":true,"usgs":false}],"preferred":false,"id":920228,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, Emily 0000-0002-0938-3496 ehbaker@usgs.gov","orcid":"https://orcid.org/0000-0002-0938-3496","contributorId":200570,"corporation":false,"usgs":true,"family":"Baker","given":"Emily","email":"ehbaker@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":920229,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McNeil, Christopher J. 0000-0003-4170-0428 cmcneil@usgs.gov","orcid":"https://orcid.org/0000-0003-4170-0428","contributorId":5803,"corporation":false,"usgs":true,"family":"McNeil","given":"Christopher J.","email":"cmcneil@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":920230,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268340,"text":"70268340 - 2024 - River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements","interactions":[],"lastModifiedDate":"2025-06-23T14:40:02.847232","indexId":"70268340","displayToPublicDate":"2024-11-05T09:37:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7942,"text":"Earth Surface Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements","docAbstract":"Measuring suspended-sand fluxes in rivers remains a scientific challenge due to their high spatial and temporal variability. To capture the vertical and lateral gradients of concentration in the cross-section, measurements with point samples are performed. However, the uncertainty related to these measurements is rarely evaluated, as few studies of the major sources of error exist. Therefore, the aim of this study is to develop a method to determine the cross-sectional sand flux and estimate its uncertainty. This SDC (for sand discharge computing) method combines suspended-sand concentrations from point samples with ADCP (acoustic Doppler current profiler) high-resolution depth and velocity measurements. The MAP (for multitransect averaged profile) method allows obtaining an average of several ADCP transects on a regular grid, including the unmeasured areas. The suspended-sand concentrations are integrated vertically by fitting a theoretical exponential suspended-sand profile to the data using Bayesian modeling. The lateral integration is based on the water depth as a proxy for the local bed shear stress to evaluate the bed concentration and sediment diffusion along the river cross-section. The estimation of uncertainty combines ISO standards and semi-empirical methods with a Bayesian approach to estimate the uncertainty due to the vertical integration. The new method is applied to data collected in four rivers under various hydro-sedimentary conditions: the Colorado, Rhône, Isère, and Amazon rivers, with computed flux uncertainties ranging between 18 % and 32 %. The relative difference between the suspended-sand flux in 21 cases calculated with the proposed SDC method compared to the International Organization for Standardization (ISO) 4363 standard method ranges between −40 % and +23 %. This method that comes with a flexible, open-source code is the first to propose an applicable uncertainty estimation that could be adapted to other flux computation methods.
","language":"English","publisher":"European Geophysical Union","doi":"10.5194/esurf-12-1243-2024","usgsCitation":"Marggraf, J., Dramais, G., Le Coz, J., Calmel, B., Camenen, B., Topping, D.J., Santini, W., Pierrefeu, G., and Lauters, F., 2024, River suspended-sand flux computation with uncertainty estimation using water samples and high-resolution ADCP measurements: Earth Surface Dynamics, v. 12, no. 6, p. 1243-1266, https://doi.org/10.5194/esurf-12-1243-2024.","productDescription":"24 p.","startPage":"1243","endPage":"1266","ipdsId":"IP-123898","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":491495,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/esurf-12-1243-2024","text":"Publisher Index Page"},{"id":491101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Marggraf, Jessica","contributorId":350702,"corporation":false,"usgs":false,"family":"Marggraf","given":"Jessica","affiliations":[{"id":83813,"text":"RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":940855,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dramais, Guillaume","contributorId":357236,"corporation":false,"usgs":false,"family":"Dramais","given":"Guillaume","affiliations":[{"id":85354,"text":"1RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":940856,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Le Coz, Jerome","contributorId":350703,"corporation":false,"usgs":false,"family":"Le Coz","given":"Jerome","affiliations":[{"id":83813,"text":"RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":940857,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calmel, Blaise","contributorId":357237,"corporation":false,"usgs":false,"family":"Calmel","given":"Blaise","affiliations":[{"id":85354,"text":"1RiverLy, INRAE, 5 Rue de la Doua, Villeurbanne, 69100, France","active":true,"usgs":false}],"preferred":false,"id":940858,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Camenen, Benoit","contributorId":238956,"corporation":false,"usgs":false,"family":"Camenen","given":"Benoit","email":"","affiliations":[{"id":47840,"text":"Scientist, IRSTEA, Lyon, France","active":true,"usgs":false}],"preferred":false,"id":940859,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":940860,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Santini, William","contributorId":357238,"corporation":false,"usgs":false,"family":"Santini","given":"William","affiliations":[{"id":85355,"text":"IRD-GET, Institut de Recherche pour le Développement, Laboratoire GET (IRD, CNRS, UPS, CNES), Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":940861,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pierrefeu, Gilles","contributorId":238958,"corporation":false,"usgs":false,"family":"Pierrefeu","given":"Gilles","email":"","affiliations":[{"id":47841,"text":"Senior Engineer, CNR, Lyon, France","active":true,"usgs":false}],"preferred":false,"id":940862,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lauters, François","contributorId":357239,"corporation":false,"usgs":false,"family":"Lauters","given":"François","affiliations":[{"id":85356,"text":"Service Etudes Eau Environnement, EDF, 134 Chemin de l'étang, Saint Martin Le Vinoux, 38950, France","active":true,"usgs":false}],"preferred":false,"id":940863,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260957,"text":"70260957 - 2024 - Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy","interactions":[],"lastModifiedDate":"2024-12-10T15:37:31.896607","indexId":"70260957","displayToPublicDate":"2024-11-05T09:14:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy","docAbstract":"Making timely management decisions is often hindered by uncertainty. Monitoring reduces two key types of uncertainty. First, it serves to reduce structural uncertainty of how the system works and provides support for expectations of how a system works. Second, it serves to reduce parametric uncertainty of the drivers of system dynamics. By combining monitoring data and quantitative models, we can reduce structural and parametric uncertainty. To demonstrate this, we focus on the Shenandoah salamander (Plethodon shenandoah), a United States Federally Endangered Species. Early work suggested that P. shenandoah extinction risk results from competition with a conspecific (Plethodon cinereus). However, more recent work has found equivocal support for this claim, instead suggesting that abiotic factors, such as moisture and temperature, drive P. shenandoah persistence. Using long-term monitoring data, we find that while competition may play a part in P. shenandoah extinction risk, measures of surface moisture are better predictors of occupancy dynamics. Further, we find decreased detection rates of P. shenandoah when P. cinereus is present, suggesting a conflation of detection probability with actual competition, which cautions against making inference from unadjusted observations of occurrence. Using multiple lines of inquiry allows for more robust understanding of system drivers in the face of high uncertainty, increasing opportunities to manage extinction risk.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s00442-024-05631-x","usgsCitation":"Werba, J.A., DiRenzo, G.V., Brand, A., and Campbell Grant, E.H., 2024, Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy: Oecologia, v. 206, p. 305-316, https://doi.org/10.1007/s00442-024-05631-x.","productDescription":"12 p.","startPage":"305","endPage":"316","ipdsId":"IP-148195","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":464231,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Blue Ridge Mountains, Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.60935855780521,\n 38.68748637317097\n ],\n [\n -78.60935855780521,\n 38.31093059502825\n ],\n [\n -78.13191468279777,\n 38.31093059502825\n ],\n [\n -78.13191468279777,\n 38.68748637317097\n ],\n [\n -78.60935855780521,\n 38.68748637317097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"206","noUsgsAuthors":false,"publicationDate":"2024-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Werba, Jo Avital 0000-0002-5295-7790","orcid":"https://orcid.org/0000-0002-5295-7790","contributorId":338728,"corporation":false,"usgs":true,"family":"Werba","given":"Jo","email":"","middleInitial":"Avital","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":918691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":918692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brand, Adrianne 0000-0003-2664-0041","orcid":"https://orcid.org/0000-0003-2664-0041","contributorId":304281,"corporation":false,"usgs":true,"family":"Brand","given":"Adrianne","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":918693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":918694,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}