Purpose
Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy.
Methods
The objective of this study was to quantify cash and cover crop residue nitrogen and carbon traits using partial least squares regression models and a combination of 1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected ASD spectra (n = 296) of 11 cover crop species and three cash crop species, and 2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches.
Results
Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 – 0.98; RMSE = 0.24 – 4.25%) and results suggest that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 – 0.75; RMSE = 2.71 – 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38 – 1.85%). PRISMA imagery models were also impacted by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation.
Conclusion
As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s11119-024-10159-4","usgsCitation":"Jennewein, J., Hively, W.D., Lamb, B.T., Daughtry, C.S., Thapa, R., Thieme, A., Reberg-Horton, C., and Mirsky, S., 2025, Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues: Precision Agriculture, v. 25, p. 2165-2197, https://doi.org/10.1007/s11119-024-10159-4.","productDescription":"33 p.","startPage":"2165","endPage":"2197","ipdsId":"IP-157137","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":492073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11119-024-10159-4","text":"Publisher Index Page"},{"id":491838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daughtry, Craig S.T.","contributorId":214079,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S.T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":941935,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thapa, Resham","contributorId":291894,"corporation":false,"usgs":false,"family":"Thapa","given":"Resham","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":941936,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":941937,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reberg-Horton, Chris","contributorId":357608,"corporation":false,"usgs":false,"family":"Reberg-Horton","given":"Chris","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":941938,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mirsky, Steven","contributorId":292000,"corporation":false,"usgs":false,"family":"Mirsky","given":"Steven","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":941939,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263241,"text":"70263241 - 2025 - Hydrodynamic and salinity tesponse to tidal restoration in the Herring River Estuary, MA, considering present and future sea levels","interactions":[],"lastModifiedDate":"2025-02-03T14:58:38.887466","indexId":"70263241","displayToPublicDate":"2024-05-16T08:53:04","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Hydrodynamic and salinity tesponse to tidal restoration in the Herring River Estuary, MA, considering present and future sea levels","docAbstract":"Coastal salt marshes are crucial ecosystems that provide habitat for a variety of species, improve water quality, and play a major role in the global carbon cycle. However, many salt marshes have been severely damaged by human activities such as diking and draining for urban development. Recently, there has been a noticeable shift toward the prioritization of coastal marsh restoration to re-establish their ecosystem services. The removal of anthropogenic barriers such as dikes, sluices, and culverts is a critical component of many projects because it allows for the restoration of tidal flow to support natural hydrologic regimes and salinity conditions, which play a dominant role in determining the ecological and biogeochemical functioning of marshes. This study examines how proposed removal of hydraulic structures will influence the hydrologic potential for marsh restoration in the Herring River Estuary in Cape Cod, Massachusetts, USA. Construction of dikes, roadways, and low-capacity culverts over the last century has substantially restricted tidal flow in the Herring River Estuary, leading to degradation of salt marsh habitat. The estuary is now undergoing the first phase of a restoration project to re-introduce natural hydrologic conditions, increase salinity, and restore salt marsh habitat. To assess how the Herring River Estuary will respond to human- and climate-driven modifications, we develop and apply a validated hydrodynamic model to simulate the complex tidal and salinity dynamics of the estuary under a range of restoration and sea level rise scenarios. We then quantify how salinity and critical hydrologic variables, including tidal range and depth of mean high water, will evolve for various restoration scenarios considering present and future sea levels. The results of this research can inform coastal management and restoration plans that re-create the natural functioning of the system while protecting critical infrastructure and reducing the risk of restoration failure.
","conferenceTitle":"World Environmental and Water Resources Congress 2024","conferenceDate":"May 19-22, 2024","conferenceLocation":"Milwaukee, WI","language":"English","publisher":"ASCE","doi":"10.1061/9780784485477.065","usgsCitation":"Naseri, K., Hummel, M.A., Befus, K.M., Smith, T.P., Eagle, M.J., and Kroeger, K.D., 2025, Hydrodynamic and salinity tesponse to tidal restoration in the Herring River Estuary, MA, considering present and future sea levels, World Environmental and Water Resources Congress 2024, Milwaukee, WI, May 19-22, 2024, p. 739-751, https://doi.org/10.1061/9780784485477.065.","productDescription":"15 p.","startPage":"739","endPage":"751","ipdsId":"IP-166785","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":481600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Herring River Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.03563796878046,\n 41.96028204250854\n ],\n [\n -70.057717500137,\n 41.95553185904734\n ],\n [\n -70.06507734392336,\n 41.929296137234985\n ],\n [\n -70.04758035680993,\n 41.930225912910544\n ],\n [\n -70.05167687363391,\n 41.950367075905746\n ],\n [\n -70.03601984746726,\n 41.95656377966937\n ],\n [\n -70.03563796878046,\n 41.96028204250854\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Naseri, Kasra","contributorId":350423,"corporation":false,"usgs":false,"family":"Naseri","given":"Kasra","affiliations":[{"id":12734,"text":"University of Texas at Arlington","active":true,"usgs":false}],"preferred":false,"id":925992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hummel, Michelle A. 0000-0002-5524-2547","orcid":"https://orcid.org/0000-0002-5524-2547","contributorId":330478,"corporation":false,"usgs":false,"family":"Hummel","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":78907,"text":"University of Texas at Arlington, Arlington, TX USA","active":true,"usgs":false}],"preferred":false,"id":925993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Befus, Kevin M.","contributorId":242636,"corporation":false,"usgs":false,"family":"Befus","given":"Kevin","email":"","middleInitial":"M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":925994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Timothy P.","contributorId":220144,"corporation":false,"usgs":false,"family":"Smith","given":"Timothy","email":"","middleInitial":"P.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":925995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":925996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":925997,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266503,"text":"70266503 - 2025 - Seasonal spatial ecology of Lake Trout in Lake Erie","interactions":[],"lastModifiedDate":"2025-05-09T15:25:35.326609","indexId":"70266503","displayToPublicDate":"2023-09-25T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal spatial ecology of Lake Trout in Lake Erie","docAbstract":"Objective
Lake Trout Salvelinus namaycush are native coldwater apex predators that play an important role in maintaining ecosystem functionality and diversity in the Laurentian Great Lakes. Following population collapses, rehabilitation efforts were widely initiated in the Great Lakes to reestablish self‐sustaining Lake Trout populations. Lake Erie may pose a challenge to these rehabilitation efforts due to limited availability of appropriate oxythermal habitat. Our goal was to investigate seasonal habitat use of adult Lake Trout in Lake Erie to inform management and rehabilitation efforts.
Methods
We used acoustic telemetry in Lake Erie, which was equipepd with a lake‐wide acoustic receiver grid, to quantify Lake Trout seasonal region occupancy, dispersal distances, bottom depth occupancy, space use extent, and space use overlap.
Result
We found that 32% of fish tagged in the eastern basin and all fish from the western basin dispersed more than 100 km from their tagging location, which represents a greater proportion of the population moving long distances than what has been previously documented in the Great Lakes. During stratification, Lake Trout were detected almost exclusively in the offshore eastern basin in areas where water depth exceeded 25 m. During nonstratified seasons, fish used other regions of the lake, occupying areas of highly variable depths. During fall, most fish tagged in the eastern basin occupied habitat along the southern shore of the eastern basin. Fish tagged in the western basin returned to this region in the fall of subsequent years despite occupying the offshore eastern basin during stratification and having depth occupancy, home range size, and overlap similar to that of eastern basin‐tagged fish. Fish size was positively correlated with receiver depth during winter and spring, and with home range overlap during spring and summer.
Conclusion
The results of this study can begin to inform management decisions regarding stocking locations, harvest regulations, and habitat restoration to facilitate the continued rehabilitation of this important native species.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/tafs.10430","usgsCitation":"Funnell, T., Brenden, T., Kraus, R., MacDougall, T., Markham, J., Murray, C.R., Robinson, J., and Vandergoot, C., 2025, Seasonal spatial ecology of Lake Trout in Lake Erie: Transactions of the American Fisheries Society, v. 152, no. 5, p. 672-693, https://doi.org/10.1002/tafs.10430.","productDescription":"22 p.","startPage":"672","endPage":"693","ipdsId":"IP-147724","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":489775,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10430","text":"Publisher Index Page"},{"id":485651,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.64944329899429,\n 41.51580044260001\n ],\n [\n -82.51869251123252,\n 41.29110603376694\n ],\n [\n -81.28782100546219,\n 41.660571299524435\n ],\n [\n -78.97904490351347,\n 42.371465449623486\n ],\n [\n -78.53770879510445,\n 42.8933192827144\n ],\n [\n -79.71226204625849,\n 43.01731540218874\n ],\n [\n -80.99357134777507,\n 42.83042392061225\n ],\n [\n -83.40636998730963,\n 42.19691200067402\n ],\n [\n -83.64944329899429,\n 41.51580044260001\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"152","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Funnell, Tyler","contributorId":354777,"corporation":false,"usgs":false,"family":"Funnell","given":"Tyler","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brenden, Travis","contributorId":354778,"corporation":false,"usgs":false,"family":"Brenden","given":"Travis","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacDougall, Tom","contributorId":354780,"corporation":false,"usgs":false,"family":"MacDougall","given":"Tom","affiliations":[{"id":12864,"text":"OMNRF","active":true,"usgs":false}],"preferred":false,"id":936383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Markham, James","contributorId":354781,"corporation":false,"usgs":false,"family":"Markham","given":"James","affiliations":[{"id":39079,"text":"NYSDEC","active":true,"usgs":false}],"preferred":false,"id":936384,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murray, Charles Richard","contributorId":34115,"corporation":false,"usgs":true,"family":"Murray","given":"Charles","email":"","middleInitial":"Richard","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":936608,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Robinson, Jason","contributorId":354782,"corporation":false,"usgs":false,"family":"Robinson","given":"Jason","affiliations":[{"id":39079,"text":"NYSDEC","active":true,"usgs":false}],"preferred":false,"id":936385,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vandergoot, Christopher S.","contributorId":354783,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936386,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70240684,"text":"70240684 - 2025 - Can big data inform invasive dreissenid mussel risk assessments of habitat suitability?","interactions":[],"lastModifiedDate":"2025-02-11T15:38:29.891089","indexId":"70240684","displayToPublicDate":"2023-02-14T07:00:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Can big data inform invasive dreissenid mussel risk assessments of habitat suitability?","docAbstract":"Invasion risk assessments of habitat suitability provide insight on early detection effort allocation; however, sufficient data are rarely available to inform assessments. We explored tradeoffs of leveraging big data from the National Water Quality Portal (WQP), a standardized water quality database in the United States, to inform calcium- and pH-based risk assessments of invasive mussel (Dreissena polymorpha and Dreissena rostriformis bugensis) habitat suitability in the Pacific Northwest’s Columbia River Basin. We evaluated risk assessment sensitivity to alternative metrics of summarizing WQP data, tested if the large number of WQP observations resulted in accurate risk prediction of sites lacking WQP data, and characterized the spatial distribution of suitable habitat. Risk assessments were insensitive to how data were summarized at a site. Predictive accuracy was low when interpolating risk to sites lacking data. High-risk sites based on calcium clustered in two water basins, whereas high-risk sites based on pH were at similarly high frequencies. Finally, we found that data gaps still exist in the Columbia River Basin despite the large volumes of WQP data. We conclude that WQP-based risk assessments of habitat suitability could be considered as a starting place for estimating dreissenid invasion risk within an adaptive framework, rather than as a final solution.
Introduction: Throughout their range, bighorn sheep (Ovis canadensis) populations have seen significant disease-associated declines. Unfortunately, understanding of the underlying epidemiological processes driving the disease dynamics in this species has hindered conservation efforts aimed at improving the health and long-term viability of these populations. Individual response to pathogen exposure emerges from dynamic interactions between competing evolutionary processes within the host and pathogen. The host’s adaptive immune system recognizes pathogens and mounts a defensive response. Pathogens have evolved strategies to overcome adaptive immune defenses including maintaining high genetic diversity through rapid evolution. The outcomes of this evolutionary warfare determine the success of pathogen invasion of the host and ultimately the success of conservation efforts.
Methods: During an epizootic dominated by a single strain, we explore these host-pathogen dynamics by examining the variation in effects of pathogen invasion on captive bighorn sheep with differing histories of exposure to genetically diverse strains of Mycoplasma ovipneumoniae (Movi). We monitored clinical signs of disease and sampled animals and their environment to detect spread of Movi among 37 bighorn sheep separated into nine pens based on known exposure
Results: We documented Movi transmission within and across pens and we detected Movi DNA in air, water, and invertebrate samples. Higher levels of antibody to Movi prior to the epizootic were associated with a lower likelihood of presenting clinical signs of pneumonia. Nonetheless, higher antibody levels in symptomatic individuals were associated with more severe progressive disease, increased probability and speed of pneumonia-induced mortality, and reduced likelihood of returning to a healthy state. Bighorn sheep with previous exposure to a strain other than the predominant epizootic strain were more likely to recover.
Discussion: Our results indicate that Movi-strain variability was sufficient to overwhelm the adaptive host immunological defenses. This outcome indicates, in free-ranging herds, past exposure is likely insufficient to protect bighorn sheep from infection by new Movi strains, although it influences the progression of disease and recovery within the herd. Therefore, given Movi-strain variability and the lack of immunological protection from past exposure, focusing management efforts on minimizing the introduction of Movi into bighorn herds, through separation of domestic and bighorn sheep and avoidance of management activities that create commingling of bighorn sheep carrying differing Movi strains, will likely be the most effective approach for reducing the effects of disease and achieving bighorn sheep conservation goals.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.1039234","usgsCitation":"Walsh, D.P., Felts, B., Cassirer, E.F., Besser, T., and Jenks, J., 2025, Host vs. pathogen evolutionary arms race: Effects of exposure history on individual response to a genetically diverse pathogen: Frontiers in Ecology and Evolution, v. 10, 1039234, 15 p., https://doi.org/10.3389/fevo.2022.1039234.","productDescription":"1039234, 15 p.","ipdsId":"IP-147044","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490647,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.1039234","text":"Publisher Index Page"},{"id":489256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, South Dakota, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.12550692958686,\n 48.622740734449025\n ],\n [\n -125.12550692958686,\n 42.255746531119684\n ],\n [\n -104.19379023399964,\n 43.101197064942625\n ],\n [\n -97.25752073214774,\n 42.79596056344036\n ],\n [\n -96.55090638798809,\n 45.96616875671981\n ],\n [\n -104.16278350002719,\n 45.839641419987615\n ],\n [\n -104.21271815015305,\n 44.95911011336172\n ],\n [\n -112.65464676168163,\n 44.983358203048724\n ],\n [\n -116.30103568420452,\n 49.06546837440046\n ],\n [\n -125.12550692958686,\n 48.622740734449025\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2023-01-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felts, Brandi L.","contributorId":301873,"corporation":false,"usgs":false,"family":"Felts","given":"Brandi L.","affiliations":[],"preferred":false,"id":938832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cassirer, E. Frances","contributorId":23404,"corporation":false,"usgs":true,"family":"Cassirer","given":"E.","email":"","middleInitial":"Frances","affiliations":[],"preferred":false,"id":938833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Besser, Thomas E.","contributorId":187454,"corporation":false,"usgs":false,"family":"Besser","given":"Thomas E.","affiliations":[],"preferred":false,"id":938834,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenks, Jonathan A.","contributorId":264322,"corporation":false,"usgs":false,"family":"Jenks","given":"Jonathan A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":938835,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70270789,"text":"70270789 - 2025 - Restoring aquatic habitats through dam removal","interactions":[],"lastModifiedDate":"2025-08-26T16:01:39.307249","indexId":"70270789","displayToPublicDate":"2022-11-10T10:54:38","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-148-2022","title":"Restoring aquatic habitats through dam removal","docAbstract":"This report presents results from a four-year project (2018–2022) to document the effects of small, run-of-river dams and dam removal on water quality (stream temperature and dissolved oxygen (DO)), aquatic macroinvertebrates, and fishes. Temperature and DO are critical water quality parameters that shape biogeochemical processes and biotic assemblages in streams. Macroinvertebrate and fish assemblages can be reflective of habitat and water quality due to their diversity and sensitivity to high temperatures and low DO and are often used as indicators of ecosystem health (e.g., Clean Water Act Section 401). This study aimed to better explain the responses of these important ecological parameters to small dam removals, which may support a more comprehensive understanding of the benefits of restoration to aquatic ecosystems.
We collected pre- and post-restoration water quality data and macroinvertebrate samples at 16 small dams in Massachusetts that have been removed (10 sites) or are currently being considered for removal (6 sites). General results from these monitoring efforts indicate that:
● 15 of 16 small dams increased impoundment water temperatures and warming persisted downstream at 11 of those sites, relative to upstream. Dam removal reduced summer impoundment warming at 7 of 10 removal sites and reduced downstream warming at 5 of 10 sites. These in-stream temperature improvements occurred within 5 years after dam removal.
● 13 of 16 small dams negatively impacted dissolved oxygen (DO) concentrations within the impoundments, but the magnitude of impact varied across sites. Negative impoundment DO impacts did not consistently translate downstream, and downstream responses to dam removal were generally minimal and variable across sites. Dam removal significantly reduced negative impoundment DO impacts within 1 year after removal at 7 of 10 sites, and sites with greater pre-removal impacts experienced the greatest magnitude of DO recovery after dam removal.
● Interannual variability in dam impacts on water quality across sites suggests periods of extreme weather (i.e., droughts or high precipitation) due to climate change may exacerbate adverse impacts from run-of-river dams.
● Macroinvertebrate assemblages within dam impoundments differed from assemblages in adjacent un-impounded stream sections and exhibited a loss of sensitive organisms (an average of 17% fewer). Dam removal led to more similar macroinvertebrate assemblages throughout most stream sections, and recovery of sensitive taxa occurred relatively quickly (1-3 years).
● Fish species richness increased upstream at 2 of 10 removal sites, suggesting potential increases in fish passage from downstream reaches. However, particular species, such as American Eel (Anguilla rostrata), exhibited both positive and negative responses to dam removal across study sites. Incorporating more sites with pre-and post-dam removal fish data could allow for better understanding factors explaining site-specific differences.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css92498424","usgsCitation":"Abbott, K., Roy, A.H., and Nislow, K., 2025, Restoring aquatic habitats through dam removal: Cooperator Science Series CSS-148-2022, ii, 161 p., https://doi.org/10.3996/css92498424.","productDescription":"ii, 161 p.","ipdsId":"IP-142787","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","noUsgsAuthors":false,"publicationDate":"2022-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Abbott, Katherine M.","contributorId":347949,"corporation":false,"usgs":false,"family":"Abbott","given":"Katherine M.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":947078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":947079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nislow, Keith","contributorId":201434,"corporation":false,"usgs":false,"family":"Nislow","given":"Keith","affiliations":[{"id":27110,"text":"U.S. Dept of Agriculture, Forest Service","active":true,"usgs":false}],"preferred":false,"id":947080,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269697,"text":"70269697 - 2025 - Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System","interactions":[],"lastModifiedDate":"2025-07-30T14:37:59.419554","indexId":"70269697","displayToPublicDate":"2021-02-01T09:32:01","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8912,"text":"International Journal of Applied Earth Observations and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System","docAbstract":"The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ETa), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ETa, and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ETa yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ETa estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (rah) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2021.102298","usgsCitation":"Comini de Andrade, B., de Andrade Pinto, E., Ruhoff, A., and Senay, G.B., 2025, Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System: International Journal of Applied Earth Observations and Geoinformation, v. 98, 102298, 16 p., https://doi.org/10.1016/j.jag.2021.102298.","productDescription":"102298, 16 p.","ipdsId":"IP-125399","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2021.102298","text":"Publisher Index Page"},{"id":493182,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Urucuia Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -43,\n -9.75\n ],\n [\n -47,\n -9.75\n ],\n [\n -47,\n -16\n ],\n [\n -43,\n -16\n ],\n [\n -43,\n -9.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"98","noUsgsAuthors":false,"publicationDate":"2021-02-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Comini de Andrade, Bruno César","contributorId":358913,"corporation":false,"usgs":false,"family":"Comini de Andrade","given":"Bruno César","affiliations":[{"id":85711,"text":"Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.","active":true,"usgs":false}],"preferred":false,"id":944465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Andrade Pinto, Eber José","contributorId":358914,"corporation":false,"usgs":false,"family":"de Andrade Pinto","given":"Eber José","affiliations":[{"id":85714,"text":"Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, and Serviço Geológico do Brasil-CPRM, Belo Horizonte, Brazil","active":true,"usgs":false}],"preferred":false,"id":944466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruhoff, Anderson","contributorId":269919,"corporation":false,"usgs":false,"family":"Ruhoff","given":"Anderson","email":"","affiliations":[{"id":56044,"text":"Universidade Federal do Rio Grande do Sul","active":true,"usgs":false}],"preferred":false,"id":944467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944468,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266753,"text":"70266753 - 2025 - Reevaluation of thermal maturity and stages of petroleum formation of the Mississippian Barnett Shale, Fort Worth Basin, Texas","interactions":[],"lastModifiedDate":"2025-05-12T14:03:38.358281","indexId":"70266753","displayToPublicDate":"2017-12-01T08:59:16","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Reevaluation of thermal maturity and stages of petroleum formation of the Mississippian Barnett Shale, Fort Worth Basin, Texas","docAbstract":"New data including measured reflectance (%Ro), programmed open-system pyrolysis data, and kerogen elemental analyses obtained on the Mississippian Barnett Shale in the Fort Worth Basin, Texas, indicate that secondary-gas generation starts at 1.5% Ro and not at the previously prescribed 1.1% Ro. Oil-cracking kinetic parameters derived from pyrolysis experiments in the presence and absence of water indicate that secondary-gas generation will not occur at a thermal maturity as low as 1.1% Ro and requires a minimum thermal maturity of 1.5% Ro. This difference is especially important in using the Barnett Shale as an analog for evaluating other possible shale-gas plays. The new reflectance measurements have a good relationship with hydrogen indices (HIs) and compare well with other published data sets. However, the relationship does not compare well with the previously published data used to prescribe 1.1% Ro as the start of secondary-gas generation in the Barnett Shale. This discrepancy is attributed to differences in measured %Ro values and not attributed to differences in the HI values. Lack of publicly available information on the previously reported %Ro values makes it difficult to ascertain the reason for their lower values. These lower %Ro values also have impact on the previously prescribed relationship for estimating %Ro from the temperature at maximum yield by programmed open-system pyrolysis (Tmax). As a result, the new data do not agree with a previously described relationship, and the considerable scatter makes the new relationship unreliable. However, the relationship between the HI and %Ro has less scatter, which indicates that HI offers a better proxy in calculating %Ro than Tmax for the Barnett Shale. Comparison of various programmed open-system pyrolysis methods (i.e., Rock-Eval II, Rock-Eval 6, Source Rock Analyzer, and Hawk) indicates that variations in HI are within ±10% of one another. An HI of at least 44 mg/g total organic carbon is prescribed as a more certain limit for the start of secondary-gas generation and prospective in situ gas-shale accumulations.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/01251716053","usgsCitation":"Lewan, M., and Pawlewicz, M., 2025, Reevaluation of thermal maturity and stages of petroleum formation of the Mississippian Barnett Shale, Fort Worth Basin, Texas: AAPG Bulletin, v. 101, no. 12, p. 1945-1970, https://doi.org/10.1306/01251716053.","productDescription":"26 p.","startPage":"1945","endPage":"1970","ipdsId":"IP-074437","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":485708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Fort Worth basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -100,\n 34\n ],\n [\n -100,\n 30.5\n ],\n [\n -96.9,\n 30.5\n ],\n [\n -96.9,\n 34\n ],\n [\n -100,\n 34\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"101","issue":"12","noUsgsAuthors":false,"publicationDate":"2017-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Lewan, Michael 0000-0001-6347-1553 mlewan@usgs.gov","orcid":"https://orcid.org/0000-0001-6347-1553","contributorId":173938,"corporation":false,"usgs":true,"family":"Lewan","given":"Michael","email":"mlewan@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":936687,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pawlewicz, M.J.","contributorId":354949,"corporation":false,"usgs":false,"family":"Pawlewicz","given":"M.J.","affiliations":[{"id":6605,"text":"USGS","active":true,"usgs":false}],"preferred":false,"id":936688,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70261981,"text":"70261981 - 2024 - Autonomous samplers and environmental DNA metabarcoding: Sampling day and primer choice have greatest impact on fish detection probabilities","interactions":[],"lastModifiedDate":"2025-01-07T14:54:29.601342","indexId":"70261981","displayToPublicDate":"2025-09-13T08:43:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19881,"text":"Metabarcoding & Metagenomics","active":true,"publicationSubtype":{"id":10}},"title":"Autonomous samplers and environmental DNA metabarcoding: Sampling day and primer choice have greatest impact on fish detection probabilities","docAbstract":"Unprecedented rates of biodiversity loss and ecosystem function necessitate the use of rapid, efficacious, and cost-effective biomonitoring tools. The combination of autonomous samplers and high throughput sequencing (i.e., “metabarcoding”) of environmental DNA (eDNA) samples enables characterization of entire communities at high frequency and can be an important tool for conservation and management, allowing researchers to track fluctuations in biodiversity. We deployed two autonomous samplers at two U.S. Geological Survey streamgage sites in the upper Snake River (Wyoming and Idaho, USA) to collect eDNA samples from July-September 2021 and 2022 to characterize fish diversity. We used a probabilistic approach to evaluate the effects of water temperature, water discharge, filter pore size, water volume filtered, number of samples collected, timing, and primers on the probability of detecting eDNA from fish species known to be present. We detected eDNA from 13/15 species present in these areas of the Snake River. Overall, we did not find evidence that filter pore size, water volume filtered, water discharge, and water temperature affected the probability of detecting fish species’ eDNA. By contrast, primers and sampling day affected fish detection probabilities, indicating that primer choice and sampling day can either over- or under- estimate species diversity. These results indicate that users would ideally consider sampling on non-consecutive days and which primer set will maximize species detections.
","language":"English","publisher":"Pensoft","doi":"10.3897/mbmg.8.122375","usgsCitation":"Jones-Slobodian, D.N., Augustine, B., Hutchins, P., Birch, J.M., Yamahara, K., Jensen, S.L., Richardson, R.T., Trott, R., Campbell, J., Barnhart, E.P., and Sepulveda, A., 2024, Autonomous samplers and environmental DNA metabarcoding: Sampling day and primer choice have greatest impact on fish detection probabilities: Metabarcoding & Metagenomics, v. 8, e122375, 23 p., https://doi.org/10.3897/mbmg.8.122375.","productDescription":"e122375, 23 p.","ipdsId":"IP-161729","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":490040,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/mbmg.8.122375","text":"Publisher Index Page"},{"id":465748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2024-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones-Slobodian, Devin Nicole 0000-0001-9215-2930","orcid":"https://orcid.org/0000-0001-9215-2930","contributorId":305357,"corporation":false,"usgs":true,"family":"Jones-Slobodian","given":"Devin","middleInitial":"Nicole","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":922531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, Ben 0000-0001-6935-6361","orcid":"https://orcid.org/0000-0001-6935-6361","contributorId":245736,"corporation":false,"usgs":true,"family":"Augustine","given":"Ben","email":"","affiliations":[{"id":49304,"text":"Department of Natural Resources, Cornell University","active":true,"usgs":false}],"preferred":false,"id":922532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hutchins, Patrick Ross 0000-0001-5232-0821","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":256658,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick Ross","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":922533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birch, James M.","contributorId":255728,"corporation":false,"usgs":false,"family":"Birch","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":922534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yamahara, Kevan","contributorId":239853,"corporation":false,"usgs":false,"family":"Yamahara","given":"Kevan","email":"","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":922535,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jensen, Scott L.","contributorId":207327,"corporation":false,"usgs":false,"family":"Jensen","given":"Scott","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":922536,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Richardson, Rodney T.","contributorId":332908,"corporation":false,"usgs":false,"family":"Richardson","given":"Rodney","middleInitial":"T.","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":922537,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Trott, Regina","contributorId":332903,"corporation":false,"usgs":false,"family":"Trott","given":"Regina","email":"","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":922538,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Campbell, James 0000-0002-2760-3149","orcid":"https://orcid.org/0000-0002-2760-3149","contributorId":218045,"corporation":false,"usgs":true,"family":"Campbell","given":"James","email":"","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922539,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922540,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":922541,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70262524,"text":"70262524 - 2024 - Groundwater/surface-water interaction","interactions":[],"lastModifiedDate":"2025-01-22T16:50:42.818223","indexId":"70262524","displayToPublicDate":"2025-01-10T10:44:12","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"chapter":"2","title":"Groundwater/surface-water interaction","docAbstract":"No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Groundwater science relevant to the Great Lakes Water Quality Agreement: An updated status report","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"U.S. Environmental Protection Agency and Environment and Climate Change Canada","usgsCitation":"Reeves, H.W., Danielescu, S., Priebe, E., and Zhang, H., 2024, Groundwater/surface-water interaction, 15 p.","productDescription":"15 p.","startPage":"9","endPage":"23","ipdsId":"IP-167804","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":480779,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://binational.net/2025/01/10/groundwater-science-updated-status-report/"},{"id":480933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reeves, Howard W. 0000-0001-8057-2081 hwreeves@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-2081","contributorId":2307,"corporation":false,"usgs":true,"family":"Reeves","given":"Howard","email":"hwreeves@usgs.gov","middleInitial":"W.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Danielescu, Serban","contributorId":349778,"corporation":false,"usgs":false,"family":"Danielescu","given":"Serban","affiliations":[],"preferred":false,"id":924784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Priebe, Elizabeth","contributorId":349779,"corporation":false,"usgs":false,"family":"Priebe","given":"Elizabeth","affiliations":[],"preferred":false,"id":924785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Helen","contributorId":295491,"corporation":false,"usgs":false,"family":"Zhang","given":"Helen","email":"","affiliations":[{"id":63895,"text":"Ontario Ministry of the Environment","active":true,"usgs":false}],"preferred":false,"id":924786,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70262206,"text":"70262206 - 2024 - Immediate effect of floating solar energy deployment on greenhouse gas dynamics in ponds","interactions":[],"lastModifiedDate":"2025-01-15T15:04:12.077019","indexId":"70262206","displayToPublicDate":"2025-01-06T09:01:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Immediate effect of floating solar energy deployment on greenhouse gas dynamics in ponds","docAbstract":"Floating photovoltaic (FPV) solar energy offers promise for renewable electricity production that spares land for other societal benefits. FPV deployment may alter greenhouse gas (GHG) production and emissions from waterbodies by changing physical, chemical, and biological processes, which can have implications for the carbon cost of energy production with FPV. Here, we use an ecosystem-scale experiment to assess how GHG dynamics in ponds respond to installation of operationally representative FPV. Following FPV deployments of 70% array coverage, daily whole-pond GHG emissions increased by 26.8% on a carbon dioxide-equivalent (CO2-eq) basis, and dissolved oxygen availability rapidly decreased. Despite increased emissions following FPV deployment, FPV-derived GHG emissions from waterbodies are likely lower than landscape GHG emissions associated with terrestrial solar and hydropower production on a CO2-eq kWh–1 basis. Adaptive management strategies like bubbler installation may reduce the magnitude of FPV impacts on GHG and dissolved oxygen dynamics.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.4c06363","usgsCitation":"Ray, N.E., Holgerson, M., and Grodsky, S.M., 2024, Immediate effect of floating solar energy deployment on greenhouse gas dynamics in ponds: Environmental Science and Technology, v. 58, no. 50, p. 22104-22113, https://doi.org/10.1021/acs.est.4c06363.","productDescription":"10 p.","startPage":"22104","endPage":"22113","ipdsId":"IP-165832","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466694,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1021/acs.est.4c06363","text":"External Repository"},{"id":466411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"50","noUsgsAuthors":false,"publicationDate":"2024-12-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Ray, Nicholas E.","contributorId":333311,"corporation":false,"usgs":false,"family":"Ray","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":923502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holgerson, Meredith A.","contributorId":348513,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923503,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grodsky, Steven Mark 0000-0003-0846-7230","orcid":"https://orcid.org/0000-0003-0846-7230","contributorId":328517,"corporation":false,"usgs":true,"family":"Grodsky","given":"Steven","email":"","middleInitial":"Mark","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923504,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261548,"text":"70261548 - 2024 - Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales","interactions":[],"lastModifiedDate":"2024-12-13T15:13:03.033614","indexId":"70261548","displayToPublicDate":"2025-01-01T07:58:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales","docAbstract":"Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g., at the catchment and stream reach level). We applied the SPAtially Referenced Regression On Watershed attributes (SPARROW) model in three regions: the Upper Midwest part of the United States, New Zealand, and the Grande River Basin in southeastern Brazil. For each region, we compared predictions of nitrogen delivery between models developed using novel large-scale datasets and those developed using local-scale datasets. Large-scale models tended to underperform the local-scale models in poorly monitored areas. Despite this, large-scale models are well suited to generate hypotheses about relative effects of different nutrient source categories (point and urban, agricultural, native vegetation) and to identify knowledge gaps across spatial scales when data are scarce. Regardless of the spatial resolution of the predictors used in the models, a representative network of water quality monitoring stations is key to improve the performance of large-scale models used to estimate loads and yields. We discuss avenues of research to understand how this large-scale modelling approach can improve decision making for managing catchments at local scales, particularly in data poor regions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2024.122520","usgsCitation":"Suarez-Castro, A.F., Robertson, D., Lehner, B., de Souza, M.L., Kittridge, M., Saad, D., Linke, S., McDowell, R.W., Ranjbar, M.H., Ausseil, O., and Hamilton, D.P., 2024, Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales: Water Research, v. 268, no. Part A, 122520, 11 p., https://doi.org/10.1016/j.watres.2024.122520.","productDescription":"122520, 11 p.","ipdsId":"IP-164752","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":466696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2024.122520","text":"Publisher Index Page"},{"id":465110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil, New Zealand, United 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NZ","active":true,"usgs":false}],"preferred":false,"id":920991,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ranjbar, Mohammad H.","contributorId":347161,"corporation":false,"usgs":false,"family":"Ranjbar","given":"Mohammad","email":"","middleInitial":"H.","affiliations":[{"id":80337,"text":"Griffith University, Australia","active":true,"usgs":false}],"preferred":false,"id":920992,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ausseil, Olivier","contributorId":347162,"corporation":false,"usgs":false,"family":"Ausseil","given":"Olivier","email":"","affiliations":[{"id":83091,"text":"Girffith University, Australia","active":true,"usgs":false}],"preferred":false,"id":920993,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hamilton, David P. 0000-0002-9341-8777","orcid":"https://orcid.org/0000-0002-9341-8777","contributorId":347163,"corporation":false,"usgs":false,"family":"Hamilton","given":"David","email":"","middleInitial":"P.","affiliations":[{"id":83086,"text":"Griffith University, Brisbane, Australia","active":true,"usgs":false}],"preferred":false,"id":920994,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70261890,"text":"sir20245126 - 2024 - Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska","interactions":[],"lastModifiedDate":"2025-07-10T15:28:29.176134","indexId":"sir20245126","displayToPublicDate":"2024-12-31T15:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5126","displayTitle":"Simulating Present and Future Groundwater/Surface-Water Interactions and Stream Temperatures in Beaver Creek, Kenai Peninsula, Alaska","title":"Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska","docAbstract":"In many places, coldwater ecosystems are facing increasing pressure from anthropogenic warming. This study examined stream temperatures and the water balance in the Beaver Creek watershed on the Kenai Peninsula in south-central Alaska—an area that is experiencing rapid warming. Low-gradient streams near the Kenai coast provide important spawning and rearing habitat for salmon but may be especially vulnerable to rising temperatures, because of long residence times, inflows from abundant riparian wetlands, and reliance on groundwater discharge that may also warm, or decrease in volume with rising evapotranspiration. In recent decades, observed maximum 7-day temperatures have consistently exceeded statistical (regression-based) projections. Here we simulate total streamflows and temperatures with a physics-based model that links the Soil Water Balance, MODFLOW 6 and SNTEMP simulation codes on a 7-day timestep. The model is based on existing data and groundwater levels, instream flows, and stream temperatures collected during 2019–23. Future climate scenarios were developed for 2023–50 from downscaled climate projections.
Results indicate that groundwater discharge is about 64 percent of the total streamflow during the months of May through September. Total streamflow and groundwater discharge are expected to remain similar to current conditions through 2050. Stream temperatures are expected to rise; by midcentury, near the Beaver Creek mouth the model predicts 34 to 63 additional days per year with average weekly temperatures above 13 degrees Celsius, 14 to 81 additional days with average weekly temperatures above 15 degrees Celsius, and routine exceedances of 20 degrees Celsius during the warmest periods. Projected stream temperatures vary spatially. Areas of high groundwater inflows in the lower main stem and some tributaries may be most resilient to warming air temperatures during dry conditions. During storm events, groundwater-dominated tributaries may have the coolest stream temperatures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245126","usgsCitation":"Leaf, A.T., Haserodt, M.J., Meyer, B.E., Westenbroek, S.M., and Koch, J.C., 2024, Simulating present and future groundwater/surface-water interactions and stream temperatures in Beaver Creek, Kenai Peninsula, Alaska: U.S. Geological Survey Scientific Investigations Report 2024–5126, 111 p., https://doi.org/10.3133/sir20245126.","productDescription":"Report: ix, 111 p.; 2 Data Releases; Dataset","numberOfPages":"126","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-167012","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":465606,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5126/sir20245126.XML"},{"id":465583,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14UAWGB","text":"USGS data release","linkHelpText":"Surface water and groundwater hydrology and temperature, Beaver Creek, Kenai Peninsula, Alaska, 2022–2023"},{"id":465585,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/national-hydrography/access-national-hydrography-products","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":465584,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"http://doi.org/10.5066/P9K30VAP","text":"USGS data release","linkHelpText":"Soil water balance, groundwater flow, and stream temperature models for Beaver Creek, Alaska, 2019 to 2050"},{"id":492014,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118271.htm","linkFileType":{"id":5,"text":"html"}},{"id":465607,"rank":8,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5126/images/"},{"id":465605,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245126/full"},{"id":465582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5126/sir20245126.pdf","text":"Report","size":"34.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5126"},{"id":465581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5126/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaver Creek, Kenai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -152.3330540056542,\n 60.957367731319806\n ],\n [\n -152.38912986860709,\n 59.25155522317334\n ],\n [\n -148.50867874659667,\n 59.25563148250791\n ],\n [\n -148.50867874659667,\n 60.95766646209441\n ],\n [\n -152.3330540056542,\n 60.957367731319806\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The Sharptooth walking catfish (Clarias gariepinus) is native to parts of the Middle East, Asia, and Africa but is one of the world's most invasive freshwater fish species. The species’ ability to invade is partly due to its ability to breathe atmospheric oxygen, thereby avoiding hypoxia or poor water quality in receiving waterbodies; further, it can crawl over moist land to disperse to new systems. In 2018, two individuals purported to be Sharptooth walking catfish were collected and identified within the Canal de Patillas, a canal in the Guayama municipality in southeastern Puerto Rico. Several other specimens were also collected the following year. Given that this invasion was in its infancy, environmental DNA (eDNA) surveillance was employed as a rapid and efficient surveillance tool to help delineate the extent of this invasion. Water samples were collected and analyzed for Clarias eDNA from 66 sites encompassing a wide range of habitats in canals, lakes, and a river surrounding the area where they were first reported. Using eDNA-metabarcoding, we detected Clarias eDNA at 40 of the 63 sites throughout the sampled waterbodies. Furthermore, our molecular analysis indicated that the Whitespotted Clarias (C. fuscus) is the likely invader, and the original specimens, classified as C. gariepinus, were misidentified based on the use of morphological rather than genetic features for species classification. Environmental DNA surveillance indicated that the distribution of Clarias catfish in this system is substantially more extensive than previously documented. These findings suggest that additional survey efforts could help determine the invasion's full spatial extent and leading edge to inform the scope and scale of any potential management responses.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css20629765","usgsCitation":"Paine, R.T., Rogers, M.W., and Rosenberger, A.E., 2024, Environmental DNA reveals invasion of Puerto Rican waterways by non-native Clarias catfish: Cooperator Science Series CSS-164-2024, ii, 27 p., https://doi.org/10.3996/css20629765.","productDescription":"ii, 27 p.","ipdsId":"IP-173700","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494512,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto 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synchrony in seed production is associated with trait similarity and climate across North America","docAbstract":"Mast seeding, the synchronous and highly variable production of seed crops in perennial plants, is a population level phenomenon and has cascading effects in ecosystems. Mast seeding studies are typically conducted at the population/species level. Much less is known about synchrony in mast seeding between species because the necessary long-term data are rarely available. To investigate synchrony between species within communities, we used long-term data from seven forest communities in the US Long-Term Ecological Research (LTER) network, ranging from tropical rainforest to taiga. We focus on cross-species synchrony and i) quantify synchrony in reproduction overall and within LTER sites, ii) test for relationships between synchrony with trait and phylogenetic similarity, and iii) investigate how climate conditions are related to levels of synchrony. Overall, synchrony in reproduction between woody plant species was greater than expected by chance, but also spanned a wide range of values between species. Based on 11 traits for 103 species (plus phylogenetic relatedness), cross-species synchrony in reproduction was driven primarily by trait similarity with phylogeny being largely unimportant, and synchrony was higher in sites with greater climatic water deficit. Synchronous masting within a community has consequences for understanding forest regeneration dynamics and consumer-resource interactions.","language":"English","publisher":"Wiley","doi":"10.1111/ele.14498","usgsCitation":"LaMontagne, J.M., Greene, D.F., Holland, E., Johnstone, J.F., Schulze, M., Zimmerman, J.K., Lyon, N.J., Chen, A., Miller, T.E., Nigro, K.M., Snell Rebecca S., Barton, J.H., Chaudhary, V., Cleavitt, N.L., Crone, E., Koenig, W.D., Macias, D., Pearse, I.S., and Redmond, M.D., 2024, Community synchrony in seed production is associated with trait similarity and climate across North America: Ecology Letters, v. 27, no. 12, e14498, 15 p., https://doi.org/10.1111/ele.14498.","productDescription":"e14498, 15 p.","ipdsId":"IP-162505","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":501361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.14498","text":"Publisher Index Page"},{"id":501146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -174.57469846241375,\n 59.07732223048754\n ],\n [\n -132.94118629888828,\n 46.634323740727496\n ],\n [\n -102.87615155066331,\n 14.159655778644463\n ],\n [\n -86.60507271467972,\n 18.528872570028568\n ],\n [\n -76.70651169792454,\n 26.155075649047504\n ],\n [\n -55.08810680677202,\n 45.22442345962252\n ],\n [\n -93.39098226670879,\n 53.00878959626715\n ],\n [\n -134.4869880449841,\n 65.05021822139254\n ],\n [\n -154.62493341933438,\n 66.11219190375385\n ],\n [\n -174.57469846241375,\n 59.07732223048754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"LaMontagne, Jalene M.","contributorId":223096,"corporation":false,"usgs":false,"family":"LaMontagne","given":"Jalene","middleInitial":"M.","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":957043,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greene, David F.","contributorId":302454,"corporation":false,"usgs":false,"family":"Greene","given":"David","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":957044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holland, E. 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It is known only from Mud Cave, a small linear cave on the 3,200 ha Caney Mountain Conservation Area in Ozark County, Missouri. The species is listed as “Endangered” by the state of Missouri, and “Threatened” by the American Fisheries Society. Previous studies documented its population range and limited life history notes; but little is known about Mud Cave’s habitat. We conducted a one-year study to document the cave’s water chemistry, quality, and interior wetted pool volume, and how this habitat varies monthly, to establish a baseline for future study.
","language":"English","publisher":"International Association of Astacology","doi":"10.5869/fc.2024.v29-1.121","collaboration":"Missouri Department of Conservation","usgsCitation":"Ellingsworth, E.A., DiStefano, R., Westhoff, J.T., and O'Brian, B., 2024, The endangered Caney Mountain cave crayfish: A preliminary study of its habitat with brief life history notes: Freshwater Crayfish, v. 29, no. 1, p. 121-132, https://doi.org/10.5869/fc.2024.v29-1.121.","productDescription":"12 p.","startPage":"121","endPage":"132","ipdsId":"IP-166086","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","county":"Ozark County","otherGeospatial":"Caney Mountain Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.45151688083625,\n 36.66449702263134\n ],\n [\n -92.45151688083625,\n 36.625266091515115\n ],\n [\n -92.40079722946713,\n 36.625266091515115\n ],\n [\n -92.40079722946713,\n 36.66449702263134\n ],\n [\n -92.45151688083625,\n 36.66449702263134\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Ellingsworth, Emila A.","contributorId":360156,"corporation":false,"usgs":false,"family":"Ellingsworth","given":"Emila","middleInitial":"A.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":946864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DiStefano, R.J.","contributorId":360159,"corporation":false,"usgs":false,"family":"DiStefano","given":"R.J.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":946865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Westhoff, Jacob Thomas 0000-0002-2347-5098","orcid":"https://orcid.org/0000-0002-2347-5098","contributorId":288958,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"Thomas","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":946866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O'Brian, B.M.","contributorId":360164,"corporation":false,"usgs":false,"family":"O'Brian","given":"B.M.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":946867,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271332,"text":"70271332 - 2024 - Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin","interactions":[],"lastModifiedDate":"2025-09-05T15:31:48.53114","indexId":"70271332","displayToPublicDate":"2024-12-31T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin","docAbstract":"The spread of non-native freshwater mussels in North America is a growing threat that has already resulted in substantial ecological and economic damage to infested areas. A primary vector by which invasive mussels spread is watercraft that are transported over land from an infested waterbody to an uninfested waterbody. Management efforts such as watercraft inspection and detection programs that attempt to intercept infected watercraft can help limit this type of spread, but optimizing the effectiveness of these programs under limited resources is complicated. Studies have looked at developing watercraft inspection and decontamination program strategies that focus on policy-specific objectives such as maximizing the number of infested watercraft interceptions. However, there is limited work that has considered the heterogeneous impact of protection efforts across different regions and waterbodies. Knowledge about this heterogeneity can highlight regions that would benefit the most from protection as well as regions that would require less effort to protect, e.g., areas with naturally unsuitable water quality for dreissenids. To this end, we construct a composite relative risk index (CRR) for watersheds within the Missouri River Basin, a region in the United States on the front line of dreissenid spread. The CRR uses a model that mirrors an expected value model but uses relative indexing as a proxy for the model components. The CRR incorporates a wide array of data sets to account for the direct and indirect damages from a potential infestation along with the risk of an infestation occurring. Our results suggest that the relative priority of a specific watershed—measured through CRR—can depend on whether we consider the entire Missouri River Basin or just the watersheds in the same state. This also indicates substantial state-level heterogeneity in the CRR. Another contribution is that the CRR index includes user-specified weights for certain parameters so that a user can adjust the relative importance of various factors to match their specific context. An accompanying web tool allows users to view the CRR results and adjust multiple parameters to see the resulting impacts on the CCR for watersheds in the Missouri River Basin.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2024.112526","usgsCitation":"Raymond, J., Bair, L., Counihan, T., Daniel, W., Duntugan, S., Neilson, M., and Springborn, M.R., 2024, Don’t move a mussel: The role of key environmental drivers and management scale in assessing spatial variation in dreissenid spread risk in the Missouri River Basin: Ecological Indicators, v. 170, 112526, 14 p., https://doi.org/10.1016/j.ecolind.2024.112526.","productDescription":"112526, 14 p.","ipdsId":"IP-160154","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":495378,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2024.112526","text":"Publisher Index 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Field work and monitoring took place during 2012–18. The area is underlain by sedimentary formations that form a fractured-rock aquifer used for drinking water and industrial supply. The EPA placed the Site on the National Priorities List in 1989, identifying tetrachloroethylene (PCE) and trichloroethylene (TCE) as contaminants of concern.
During 2012–18, the USGS conducted field activities that included drilling an 82-foot (ft)-deep monitoring well (MG 2220) in 2016, reconstructing a 208-ft-deep former industrial production well (MG 668 [Granite Knitting Mill]), and collecting borehole geophysical and video logs and water levels from those and five additional wells, which ranged in depth from about 50 to 200 ft below land surface. Continuous water levels were collected during 2014–17, and a synoptic set of water levels were measured in April 2018 in the seven wells.
The borehole geophysical logs (caliper, acoustic televiewer, natural gamma, single-point resistance, vertical flow, and fluid temperature and resistivity) and borehole video logs in the seven wells were evaluated to assess potential for lithologic correlation and to identify and describe water-bearing features, which included both low- and high-angle fractures and other openings oriented along dipping bedding planes, joints, or possible faults. Borehole geophysical logs collected by USGS in 1992 in a 300-ft-deep former production well near the Site were also evaluated. Few to no distinctive features were identified on geophysical logs (natural gamma and single-point resistance) that could be used for correlation, thus limiting this approach to determining local geologic structure. Extensive fracturing in the upper 62 ft of monitoring well MG 2220 indicates that the well was likely drilled through a zone of faulting, and other evidence of faulting is present in the area near the Site. Assessment of continuous water levels showed hydraulic connections among some wells as indicated by rising or falling water levels in response to changes in pumping rates at nearby wells. A map of water levels measured in April 2018 indicates potential for groundwater flow generally toward the stream to the south and southwest of the Site, but the limited water-level data are insufficient to describe vertical groundwater gradients or lateral gradients in any detail.
Review of 1999–2022 volatile organic compound (VOC) monitoring data collected by the Pennsylvania Department of Environmental Protection for five monitoring wells indicates that the highest groundwater concentrations of PCE and TCE were found in samples from extraction well MG 2201 (S-1) downgradient from, and nearest to, the previously identified Site contaminant source area, and these concentrations fluctuated through time. PCE concentrations were higher than TCE concentrations in samples from all five monitoring wells and were much higher than TCE concentrations in samples from extraction well MG 2201 (S-1). Temporally variable recharge is a possible factor affecting observed fluctuations in PCE concentrations in groundwater samples from well extraction MG 2201 (S-1), as indicated by a general inverse relation between PCE concentrations and water levels in a nearby long-term observation well. The PCE concentration of 1,830 micrograms per liter (μg/L) in a May 2018 water sample from monitoring well MG 2220 was more than four times the PCE concentration of 444 μg/L in a December 2017 sample from the nearby extraction well MG 2201 (S-1), which is open to fewer fractures. Low concentrations of VOCs were measured in surface water at two stream sites downgradient from wells with the highest groundwater VOC concentrations at the Site, indicating that discharge of contaminated groundwater to the stream is likely.
Development of a conceptual model of the groundwater system was constrained by limited data. In areas with no pumping, groundwater-flow directions generally are thought to be controlled by topography and geologic structure (bedding orientation) and likely to the south and southwest of the Site, with local flow directions affected by orientations of fractures, joints, and local faults. Additional investigations that could help improve the conceptual model of the groundwater system and help delineate the extent of groundwater contamination and its transport are discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241080","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., Risser, D.W., Goode, D.J., and Bird, P.H., 2024, Hydrologic investigations and a preliminary conceptual model of the groundwater system at North Penn Area 1 Superfund Site, Souderton, Montgomery County, Pennsylvania: U.S. Geological Survey Open-File Report 2024–1080, 78 p., https://doi.org/10.3133/ofr20241080.","productDescription":"xi, 78 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151018","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":494216,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118273.htm","linkFileType":{"id":5,"text":"html"}},{"id":465486,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1080/ofr20241080.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1080 XML"},{"id":465485,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241080/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1080 HTML"},{"id":465479,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1080/images/"},{"id":465476,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1080/ofr20241080.pdf","text":"Report","size":"18.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1080 PDF"},{"id":465475,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1080/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Montgomery County","city":"Souderton","otherGeospatial":"North Penn Area 1 Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.33380565402877,\n 40.30337215850042\n ],\n [\n -75.33067431094733,\n 40.30297414782885\n ],\n [\n -75.32310689850118,\n 40.30864557850933\n ],\n [\n -75.32121504538941,\n 40.31133187946756\n ],\n [\n -75.32415067952832,\n 40.31496319053656\n ],\n [\n -75.33002194780529,\n 40.3133714069823\n ],\n [\n -75.33432754454195,\n 40.307053646040714\n ],\n [\n -75.33380565402877,\n 40.30337215850042\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
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Climate-driven changes in high-elevation forest distribution and reductions in snow and ice cover have major implications for ecosystems and global water security. In the Greater Yellowstone Ecosystem of the Rocky Mountains (United States), recent melting of a high-elevation (3,091 m asl) ice patch exposed a mature stand of whitebark pine (Pinus albicaulis) trees, located ~180 m in elevation above modern treeline, that date to the mid-Holocene (c. 5,950 to 5,440 cal y BP). Here, we used this subfossil wood record to develop tree-ring-based temperature estimates for the upper-elevation climate conditions that resulted in ancient forest establishment and growth and the subsequent regional ice-patch growth and downslope shift of treeline. Results suggest that mid-Holocene forest establishment and growth occurred under warm-season (May-Oct) mean temperatures of 6.2 °C (±0.2 °C), until a multicentury cooling anomaly suppressed temperatures below 5.8 °C, resulting in stand mortality by c. 5,440 y BP. Transient climate model simulations indicate that regional cooling was driven by changes in summer insolation and Northern Hemisphere volcanism. The initial cooling event was followed centuries later (c. 5,100 y BP) by sustained Icelandic volcanic eruptions that forced a centennial-scale 1.0 °C summer cooling anomaly and led to rapid ice-patch growth and preservation of the trees. With recent warming (c. 2000–2020 CE), warm-season temperatures now equal and will soon exceed those of the mid-Holocene period of high treeline. It is likely that perennial ice cover will again disappear from the region, and treeline may expand upslope so long as plant-available moisture and disturbance are not limiting.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2412162121","usgsCitation":"Pederson, G.T., Stahle, D.K., McWethy, D.B., Toohey, M., Jungclaus, J., Lee, C., Martin, J.T., Alt, M., Kichas, N.E., Chellman, N.J., McConnell, J.R., and Whitlock, C., 2024, Dynamic treeline and cryosphere response to pronounced mid-Holocene climatic variability in the US Rocky Mountains: Proceedings of the National Academy of Sciences, v. 122, e2412162121, 11 p., https://doi.org/10.1073/pnas.2412162121.","productDescription":"e2412162121, 11 p.","ipdsId":"IP-163460","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":466697,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2412162121","text":"Publisher Index Page"},{"id":465611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Oregon","active":true,"usgs":false}],"preferred":false,"id":922193,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70261819,"text":"ofr20241029 - 2024 - Investigation of land cover within wetland complexes at Dixie Meadows, Churchill County, Nevada, from October 2015 to January 2022","interactions":[],"lastModifiedDate":"2025-08-15T16:09:44.08473","indexId":"ofr20241029","displayToPublicDate":"2024-12-27T14:30:00","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":"2024-1029","displayTitle":"Investigation of Land Cover Within Wetland Complexes at Dixie Meadows, Churchill County, Nevada, from October 2015 to January 2022","title":"Investigation of land cover within wetland complexes at Dixie Meadows, Churchill County, Nevada, from October 2015 to January 2022","docAbstract":"The U.S. Geological Survey investigated land cover at subannual time steps within six wetland areas in Dixie Valley, Churchill County, Nevada, from October 2015 to January 2022. As requested by the U.S. Fish and Wildlife Service, we used aerial photography and satellite remote sensing data to map surface water and other land cover types within the wetland complexes. We identified five land cover classes using the green normalized difference vegetation index (gNDVI) and its inverse relationship to the normalized difference water index (NDWI) within three U.S. Department of Agriculture National Agriculture Imagery Program aerial images (acquired in 2015, 2017, and 2019) and 110 European Space Agency Sentinel-2 satellite images (acquired 2015–2022). The relative wetness of soil conditions within each land cover class is estimated by comparison to previously published observations of relative conductivity measured by 79 field-based sensors within the wetlands from 2019 to 2021. We mapped the areal coverage of the five land cover classes for approximately 385 acres (1,559,000 square meters [m²]) comprising six individual wetland complexes as well as a larger 1,298- acre (5,254,000-m2) area of interest inclusive of the wetland complexes and adjacent landscape. Land cover of open water (Class 5) primarily within ponds at one of the wetland complexes comprised 8,333 m2, on average, of the wetland complexes. Land cover of mixed shallow surface water, saturated soil, and vegetation (Class 4) comprised 111,723 m2 on average of the wetland complexes. Land cover of dense green vegetation canopy cover (Class 3) that often (46 percent of observations) had underlying surface water or saturated soil conditions comprised 592,522 m2 on average of the wetland complexes. The remaining areas of the wetland complexes not mapped as these three land cover types (Classes 2 and 1) had sparse vegetation or bare soil cover and commonly (greater than or equal to 67 percent of observations) had dry soil conditions. The investigation of land cover detailed in this report could inform future efforts to map land cover more precisely via higher resolution remote sensing or ground-based surveying or could be incorporated with other environmental monitoring data to characterize habitat and hydrology of the wetland complexes at Dixie Meadows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241029","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Sankey, J.B., Bransky, N.D., and Caster, J.J., 2024, Investigation of land cover within wetland complexes at Dixie Meadows, Churchill County, Nevada, from October 2015 to January 2022: U.S. Geological Survey Open-File Report 2024–1029, 10 p., https://doi.org/10.3133/ofr20241029.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150955","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":494217,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118272.htm","linkFileType":{"id":5,"text":"html"}},{"id":465474,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1029/images/"},{"id":465473,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1029/ofr20241029.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1029 XML"},{"id":465466,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90U1VAM","text":"USGS data release","linkHelpText":"Land cover classification data for wetland complexes at Dixie Meadows, Nevada from October 2015 to January 2022"},{"id":465472,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241029/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1029 HTML"},{"id":465465,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1029/ofr20241029.pdf","text":"Report","size":"5.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1029 PDF"},{"id":465464,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1029/coverthb.jpg"}],"country":"United States","state":"Nevada","county":"Churchill County","otherGeospatial":"Dixie Meadows","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.0333,\n 39.808333\n ],\n [\n -118.091667,\n 39.808333\n ],\n [\n -118.091667,\n 39.75\n ],\n [\n -118.0333,\n 39.75\n ],\n [\n -118.0333,\n 39.808333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Wildfires in the western United States (US) have been demonstrated to affect water quality, including dissolved organic carbon (DOC), in streams. Elevated post-wildfire DOC concentration poses a potential risk to drinking water treatment systems. In-stream measurements of fluorescent dissolved organic matter (fDOM), a proxy for DOC, have shown potential to detect dynamic changes in DOC. High frequency monitoring of water temperature, turbidity, and fDOM was used in conjunction with discrete sampling during targeted storm events and at fixed intervals to estimate DOC in five western US streams following wildfires in 2020 and 2021 with the objective to characterise and compare responses to wildfire among sites. The elevated turbidity conditions typical after wildfire presented a challenge to fDOM measurements and there was a need to identify appropriate turbidity corrections at burned sites. A combination of established and novel methods corrected fDOM concentrations for turbidity effects up to 800 Formazin nephelometric units (FNU). Pre-wildfire high frequency water quality data in adjacent burned and unburned watersheds allowed for separation of climate effects on DOC at one of the sites. Hydrology, climate and landcover were more important drivers of post-wildfire DOC yield than wildfire characteristics. Seasonal patterns of DOC were unchanged by wildfire in snowmelt-driven watersheds. Large, transient spikes in DOC concentration following frontal and convective storms were observed post-wildfire at all burned sites, but not at the unburned site. These spikes often exceeded operational thresholds for drinking water treatment. This study highlights the ability to develop high frequency DOC estimates in surface waters up to 800 FNU using fDOM sensors and targeted storm sampling and emphasises the value of high frequency pre-wildfire data in adjacent burned and unburned watersheds for separating climate and wildfire effects.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70023","usgsCitation":"Akie, G.A., Clow, D.W., Murphy, S.F., Clark, G.D., Meador, M.R., and Ebel, B., 2024, An intercomparison of DOC estimated from fDOM sensors in wildfire affected streams of the western United States: Hydrological Processes, v. 38, no. 12, e70023, 20 p., https://doi.org/10.1002/hyp.70023.","productDescription":"e70023, 20 p.","ipdsId":"IP-164617","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":489141,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70023","text":"Publisher Index Page"},{"id":481139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Colorado, Montana, 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\"}}]}","volume":"38","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Akie, Garrett Alexander 0000-0002-6356-7106","orcid":"https://orcid.org/0000-0002-6356-7106","contributorId":290236,"corporation":false,"usgs":true,"family":"Akie","given":"Garrett","email":"","middleInitial":"Alexander","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":924966,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Gregory D. 0000-0003-0066-8193 gmclark@usgs.gov","orcid":"https://orcid.org/0000-0003-0066-8193","contributorId":224364,"corporation":false,"usgs":true,"family":"Clark","given":"Gregory","email":"gmclark@usgs.gov","middleInitial":"D.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meador, Michael R. 0000-0001-5956-3340 mrmeador@usgs.gov","orcid":"https://orcid.org/0000-0001-5956-3340","contributorId":219878,"corporation":false,"usgs":true,"family":"Meador","given":"Michael","email":"mrmeador@usgs.gov","middleInitial":"R.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":924968,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":924969,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261835,"text":"70261835 - 2024 - Self-guided decision support groundwater modelling with Python","interactions":[],"lastModifiedDate":"2024-12-30T15:49:23.123549","indexId":"70261835","displayToPublicDate":"2024-12-24T09:16:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19861,"text":"Journal of Open Source Education","active":true,"publicationSubtype":{"id":10}},"title":"Self-guided decision support groundwater modelling with Python","docAbstract":"The GMDSI tutorial notebooks repository provides learners with a comprehensive set of tutorials for self-guided training on decision-support groundwater modelling using Python-based tools. Although targeted at groundwater modelling, they are based around model-agnostic tools and readily transferable to other environmental modelling workflows. The tutorials are divided into three parts. The first covers fundamental theoretical concepts. These are intended as background reading for reference on an as-needed basis. Tutorials in the second part introduce learners to some of the core concepts parameter estimation in a groundwater modelling context, as well as providing a gentle introduction to the PEST, PEST++ and pyEMU software. Lastly, the third part demonstrates how to implement highly-parameterized applied decision-support modelling workflows. The tutorials aim to provide examples of both “how to use” the software as well as “how to think” about using the software. A key advantage to using notebooks in this context is that the workflows described run the same code as practitioners would run on a large-scale real- world application. Using a small synthetic model facilitates rapid progression through the workflow.","language":"English","publisher":"Open Journals","doi":"10.21105/jose.00240","usgsCitation":"Hugman, R., White, J., Fienen, M., Hemmings, B., and Markovich, K., 2024, Self-guided decision support groundwater modelling with Python: Journal of Open Source Education, v. 7, no. 82, 240, 6 p., https://doi.org/10.21105/jose.00240.","productDescription":"240, 6 p.","ipdsId":"IP-166010","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":466700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/jose.00240","text":"Publisher Index Page"},{"id":465530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hugman, Rui 0000-0003-0891-3886","orcid":"https://orcid.org/0000-0003-0891-3886","contributorId":299138,"corporation":false,"usgs":false,"family":"Hugman","given":"Rui","affiliations":[{"id":64778,"text":"Univeristy of Flinders","active":true,"usgs":false}],"preferred":false,"id":921991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":214251,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":921992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hemmings, Brioch","contributorId":260167,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":921994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Markovich, Katie","contributorId":347560,"corporation":false,"usgs":false,"family":"Markovich","given":"Katie","affiliations":[{"id":83190,"text":"INTERA Geosciences","active":true,"usgs":false}],"preferred":false,"id":921995,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261520,"text":"sim3514 - 2024 - Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California","interactions":[],"lastModifiedDate":"2025-08-15T16:11:32.54982","indexId":"sim3514","displayToPublicDate":"2024-12-23T10:32:03","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3514","displayTitle":"Geologic Map and Structure Sections Along the Southern Part of the Bartlett Springs Fault Zone and Adjacent Areas from Cache Creek to Lake Berryessa, Northern Coast Ranges, California","title":"Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California","docAbstract":"Located in the Coast Ranges of northern California, the Bartlett Springs Fault Zone is the easternmost fault in the San Andreas Fault system in northern California. The fault is a right-lateral, strike-slip structure considered capable of producing an earthquake of moment magnitude 7. The purpose of this mapping is to better characterize the geology and earthquake hazards associated with the southern part of the Bartlett Springs Fault Zone and to help identify any evidence of active uplift on the faults bounding the Coast Ranges. Although the area immediately surrounding the Bartlett Springs Fault Zone is sparsely populated, its southern segment presents a potential seismic hazard to northern California communities as far away as the San Francisco Bay region and Sacramento. There are also nearby water resources, mineral resources, and public lands used for public recreation.
The Coast Ranges of northern California are a series of northwest-southeast-oriented mountain ranges and valleys located north of the San Francisco Bay region, between the Pacific Ocean to the west and the Sacramento Valley to the east. The region has rugged terrain, high mountain peaks that reach more than 2,400 meters above sea level, isolated and narrow valley bottoms on which most human settlements are located, and large drainage systems that tend to follow the northwest-southeast-oriented topographic grain. The physiographic character of the region is shaped by its bedrock geology, deformational history, and active faulting.
The basement rocks of the northern Coast Ranges consist of the Franciscan Complex and the Great Valley complex, the latter of which consists of two informal units, the Coast Range ophiolite and the Great Valley sequence. The Franciscan Complex and the Great Valley complex are in structural contact along the Coast Range Fault, a regional-scale structure and fundamental crustal boundary.
The Franciscan Complex and the Great Valley complex are superposed by active, northwest-southeast-striking strike-slip faults that are associated with seismicity swarms. These active strike-slip faults can produce moderate to large earthquakes that have moment magnitudes of 7–8. In places, these active structures bound large ranges and valleys, suggesting that much of the modern topographic expression is the result of active deformation processes.
This report contains new 1:24,000-scale geologic mapping along the southern part of the Bartlett Springs Fault Zone between Clear Lake and Lake Berryessa. The map area spans 738 square kilometers in northern Napa County, southern Lake County, and parts of Yolo and Colusa Counties. The south and east borders of the map are 90 kilometers north of San Francisco and 70 kilometers west of Sacramento, respectively. The map area is within the Knoxville mining district, which has a history of mercury and gold mining dating back to the mid-19th century. The two main towns in the region, Lower Lake and Clearlake, California, are west-northwest of the map area. Approximately 71,000 people live in the cities and rural communities located within a 40-kilometer radius of the center of the map area.
The bedrock geology, cross sections, and structural data presented herein are critical for evaluating the long-term evolution of the Bartlett Springs Fault Zone. This work will supplement studies on local seismic hazards, liquefaction potential, landslide hazards, earthquake geology, natural resources, groundwater resources, engineering geology, and tectonic history by providing the background information for site-specific investigations on these subjects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3514","usgsCitation":"Melosh, B.L., Bodtker, J.W., and Valin, Z.C., 2024, Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California: U.S. Geological Survey Scientific Investigations Map 3514, 2 sheets, scale 1:24,000, 20 p. pamphlet, https://doi.org/10.3133/sim3514.","productDescription":"Pamphlet: vi, 20 p.; 2 Sheets: 46.15 x 78.86 inches and 58.26 x 41.78 inches; Data Release","numberOfPages":"20","additionalOnlineFiles":"Y","ipdsId":"IP-128914","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":494218,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118060.htm","linkFileType":{"id":5,"text":"html"}},{"id":465095,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1YJRCZD","text":"USGS Data Release","description":"Melosh, B.L., Bodtker, J.W., Valin, Z.C., and Sullivan, K., 2024, Geospatial database of the geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California: U.S. Geological Survey data release, https://doi.org/10.5066/P1YJRCZD.","linkHelpText":"Geospatial database of the geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California"},{"id":465094,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3514/covrthb.jpg"},{"id":465093,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_sheet2.pdf","text":"Sheet 2","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465092,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_sheet1.pdf","text":"Sheet 1","size":"30 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465091,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_pamphlet.pdf","text":"Pamphlet","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Northern Coast Ranges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5457,\n 39.0012\n ],\n [\n -122.5457,\n 38.6099\n ],\n [\n -122.2368,\n 38.6099\n ],\n [\n -122.2368,\n 39.0012\n ],\n [\n -122.5457,\n 39.0012\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
Numerous mercury (Hg) sources can contribute to biological burdens within the Great Lakes, including atmospheric deposition (e.g., precipitation), non-point source land runoff (e.g., watershed), and legacy contamination. Due to these different environmental entry points, it is often difficult to ascertain if legacy Hg contamination contributes to contemporary fish consumption advisories within Areas of Concern (AOCs), as designated by the United States-Canada Great Lakes Water Quality Agreement. In this study, we aimed to assess the contributions of legacy Hg to sediments in nearshore wetland habitats and co-located prey items (dragonfly larvae and yellow perch) within the St. Louis River AOC using Hg stable isotopes. We observed that nearshore sediments had the same Hg source portfolio as previously examined main channel sites. Furthermore, this data confirmed that two major Hg sources were contributing to sediments within nearshore regions of the St. Louis River AOC: legacy and watershed Hg. The contribution of legacy Hg was estimated in biota and demonstrated that up to 64% of the Hg in fish tissue in the lower estuary (St. Louis Bay) was from legacy sources, but that this percentage declined substantially when examining upstream regions of the AOC. These data indicate the influence of legacy Hg to the food web varies spatially within the St. Louis River. We further found that watershed Hg sources are an important Hg contributor to the St. Louis River, which likely applies to other impacted and unimpacted tributaries across the Great Lakes region.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102494","usgsCitation":"Janssen, S., Hoffman, J.C., and Krabbenhoft, D.P., 2024, New tools for a legacy problem: How isotope tracers inform area of concern actions in the St. Louis River in Lake Superior: Journal of Great Lakes Research (JGLR), v. 51, no. 1, 102494, 9 p., https://doi.org/10.1016/j.jglr.2024.102494.","productDescription":"102494, 9 p.","ipdsId":"IP-170550","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102494","text":"Publisher Index Page"},{"id":490198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Louis River in Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.25467395030219,\n 47.005307600372475\n ],\n [\n -92.25467395030219,\n 46.60173825387926\n ],\n [\n -91.04335848560507,\n 46.60173825387926\n ],\n [\n -91.04335848560507,\n 47.005307600372475\n ],\n [\n -92.25467395030219,\n 47.005307600372475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoffman, Joel C.","contributorId":84244,"corporation":false,"usgs":false,"family":"Hoffman","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":939290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":939291,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}