{"pageNumber":"221","pageRowStart":"5500","pageSize":"25","recordCount":41062,"records":[{"id":70224964,"text":"70224964 - 2021 - Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt","interactions":[],"lastModifiedDate":"2021-10-11T15:41:58.169094","indexId":"70224964","displayToPublicDate":"2021-09-01T10:37:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt","docAbstract":"

Freshwater streams can exchange nutrients and carbon with the surrounding terrestrial environment through various mechanisms including physical erosion, flooding, leaf drop, and snowmelt. These aquatic-terrestrial interactions are crucial in carbon mobilization, transformation, ecosystem productivity, and have important implications for the role of freshwater ecosystems in the global carbon budget. We utilized high-frequency oxygen, temperature, and carbon dioxide (CO2) data to infer watershed connectivity in Boulder Creek, a mid-sized (1160 km2) watershed located in Colorado, USA. Daily modeled gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and reaeration coefficients (K600) were paired with high-frequency, in-situ dissolved CO2 data to characterize changes in metabolic regime and carbon flux on a stream influenced by seasonal snowmelt. GPP and ER were correlated (ρ = −0.72, p ≪ 0.001) during the non-snowmelt period and NEP was frequently negative. Mean FCO2 during the non-snowmelt period was approximately 302 (±171) mmol C m−2 d−1 and was primarily supported by watershed CO2 inputs. During snowmelt, GPP and ER were not significantly correlated (ρ = −0.22, p = 0.05), and mean NEP was significantly more negative than during non-snowmelt. Watershed connectivity was higher during snowmelt, as evidenced by significantly higher FCO2 (843 ± 338 mmol C m−2 d−1) and greater allochthonous CO2 inputs than during non-snowmelt periods, emphasizing the effects of seasonal differences in aquatic-terrestrial linkages in this stream. We suggest that our understanding of watershed carbon budgets is subject to temporal dynamics which control the degree of connectivity between terrestrial and aquatic ecosystems.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006296","usgsCitation":"Reed, A.P., Stets, E.G., Murphy, S.F., and Mullins, E., 2021, Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt: Journal of Geophysical Research Biogeosciences, v. 126, no. 9, e2021JG006296, 16 p., https://doi.org/10.1029/2021JG006296.","productDescription":"e2021JG006296, 16 p.","ipdsId":"IP-113327","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450975,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jg006296","text":"Publisher Index Page"},{"id":436214,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P991TMNQ","text":"USGS data release","linkHelpText":"Modeled Stream Metabolism in Boulder Creek near Boulder, CO (2016 - 2018)"},{"id":390389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Boulder","otherGeospatial":"Boulder Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.43922424316406,\n 39.95343802330847\n ],\n [\n -105.15975952148438,\n 39.95343802330847\n ],\n [\n -105.15975952148438,\n 40.054949943999496\n ],\n [\n -105.43922424316406,\n 40.054949943999496\n ],\n [\n -105.43922424316406,\n 39.95343802330847\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Ariel P. 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":219992,"corporation":false,"usgs":true,"family":"Reed","given":"Ariel","email":"","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. 0000-0001-5375-0196 estets@usgs.gov","orcid":"https://orcid.org/0000-0001-5375-0196","contributorId":194490,"corporation":false,"usgs":true,"family":"Stets","given":"Edward","email":"estets@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":824894,"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":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":824895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullins, Emily 0000-0002-6710-0327","orcid":"https://orcid.org/0000-0002-6710-0327","contributorId":219993,"corporation":false,"usgs":true,"family":"Mullins","given":"Emily","email":"","affiliations":[],"preferred":true,"id":824896,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228264,"text":"70228264 - 2021 - Developing bare-earth digital elevation models from structure-from-motion data on barrier islands","interactions":[],"lastModifiedDate":"2023-06-09T14:08:15.215958","indexId":"70228264","displayToPublicDate":"2021-09-01T08:49:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Developing bare-earth digital elevation models from structure-from-motion data on barrier islands","docAbstract":"

Unoccupied aerial systems can collect aerial imagery that can be used to develop structure-from-motion products with a temporal resolution well-suited to monitoring dynamic barrier island environments. However, topographic data created using photogrammetric techniques such as structure-from-motion represent the surface elevation including the vegetation canopy. Additional processing is required for estimating bare-earth elevation, which is critical for understanding the underlying geomorphology of these islands. In this study, we used a vegetation and elevation survey to produce bare-earth digital elevation models from structure-from-motion-derived elevation products for two sites on Dauphin Island, Alabama (USA). One site was exposed to high wave energy and included a mix of beach, dune, and barrier flat habitats that were dominated by supratidal/upland herbaceous vegetation. The second site was exposed to low wave energy and was dominated by intertidal marsh. Aerial imagery was collected in late fall of 2018 and spring of 2019. We tested several machine learning algorithms for predicting and removing elevation bias for vegetated areas using predictors that included spectral indices from unoccupied aerial systems-based multispectral imagery and landscape position information (e.g., relative topography and distance from shore). Models were developed for each site and season. We also explored how well the model from one season generalized to data from a different season for the same site. For developing initial digital surface models, we found that utilizing a minimum bin algorithm, as opposed to interpolation, led to lower elevation bias. For bias removal, Gaussian process regression performed the best and led to a root mean square error for the bare-earth digital elevation models of around 0.10 m for the high energy site and 0.15 m for the low energy site. Compared to the digital surface models, the root mean square error for the bare-earth digital elevation models was reduced by at least 29 percent for the high energy site and 69 percent for the low energy site. For all models, common predictors included surface elevation, vegetation greenness, and distance from the shoreline. The models produced comparable results when trained using data from a different season. The error estimates for all analyses were within published elevation standards for lidar data for vegetated areas. With calibration, this approach could be portable to other areas or data, such as aerial lidar (conventional or unoccupied), to provide an efficient and repeatable framework for monitoring geomorphology or provide baseline elevations for predicting changes to these environments under future conditions.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2021.08.014","usgsCitation":"Enwright, N., Kranenburg, C.J., Patton, B., Dalyander, P., Brown, J., Piazza, S., and Cheney, W.C., 2021, Developing bare-earth digital elevation models from structure-from-motion data on barrier islands: ISPRS Journal of Photogrammetry and Remote Sensing, v. 180, p. 269-282, https://doi.org/10.1016/j.isprsjprs.2021.08.014.","productDescription":"14 p.; Data Release","startPage":"269","endPage":"282","ipdsId":"IP-127598","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450987,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2021.08.014","text":"Publisher Index Page"},{"id":436215,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RA15I0","text":"USGS data release","linkHelpText":"Barrier island vegetation and elevation survey, Dauphin Island, AL, 2018-19"},{"id":395611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417857,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PX0O3"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.33969116210938,\n 30.211608223816906\n ],\n [\n -88.06159973144531,\n 30.211608223816906\n ],\n [\n -88.06159973144531,\n 30.286938665455985\n ],\n [\n -88.33969116210938,\n 30.286938665455985\n ],\n [\n -88.33969116210938,\n 30.211608223816906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"180","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":217794,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. 0000-0002-2955-0167 ckranenburg@usgs.gov","orcid":"https://orcid.org/0000-0002-2955-0167","contributorId":169234,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine","email":"ckranenburg@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patton, Brett 0000-0002-7396-3452 pattonb@usgs.gov","orcid":"https://orcid.org/0000-0002-7396-3452","contributorId":5458,"corporation":false,"usgs":true,"family":"Patton","given":"Brett","email":"pattonb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":833555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":833556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Jenna A. 0000-0003-3137-7073","orcid":"https://orcid.org/0000-0003-3137-7073","contributorId":208564,"corporation":false,"usgs":true,"family":"Brown","given":"Jenna A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Piazza, Sarai 0000-0001-6962-9008","orcid":"https://orcid.org/0000-0001-6962-9008","contributorId":220329,"corporation":false,"usgs":true,"family":"Piazza","given":"Sarai","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cheney, Wyatt C 0000-0003-1009-8411","orcid":"https://orcid.org/0000-0003-1009-8411","contributorId":274998,"corporation":false,"usgs":false,"family":"Cheney","given":"Wyatt","email":"","middleInitial":"C","affiliations":[{"id":56693,"text":"Cheney Consulting at the U.S. Geological Survey Wetland and Aquatic Research Center","active":true,"usgs":false}],"preferred":false,"id":833559,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224258,"text":"70224258 - 2021 - Hydrate formation on marine seep bubbles and the implications for water column methane dissolution","interactions":[],"lastModifiedDate":"2021-09-16T12:27:12.757011","indexId":"70224258","displayToPublicDate":"2021-09-01T07:25:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9107,"text":"Journal of Geophysical Research - Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Hydrate formation on marine seep bubbles and the implications for water column methane dissolution","docAbstract":"

Methane released from seafloor seeps contributes to a number of benthic, water column, and atmospheric processes. At seafloor seeps within the methane hydrate stability zone, crystalline gas hydrate shells can form on methane bubbles while the bubbles are still in contact with the seafloor or as the bubbles begin ascending through the water column. These shells reduce methane dissolution rates, allowing hydrate-coated bubbles to deliver methane to shallower depths in the water column than hydrate-free bubbles. Here, we analyze seafloor videos from six deepwater seep sites associated with a diverse range of bubble-release processes involving hydrate formation. Bubbles that grow rapidly are often hydrate-free when released from the seafloor. As bubble growth slows and seafloor residence time increases, a hydrate coating can form on the bubble's gas-water interface, fully coating most bubbles within ∼10 s of the onset of hydrate formation at the seafloor. This finding agrees with water-column observations that most bubbles become hydrate-coated after their initial ∼150 cm of rise, which takes about 10 s. Whether a bubble is coated or not at the seafloor affects how much methane a bubble contains and how quickly that methane dissolves during the bubble's rise through the water column. A simplified model shows that, after rising 150 cm above the seafloor, a bubble that grew a hydrate shell before releasing from the seafloor will have ∼5% more methane than a bubble of initial equal volume that did not grow a hydrate shell after it traveled to the same height.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JC017363","usgsCitation":"Fu, X., Waite, W., and Ruppel, C.D., 2021, Hydrate formation on marine seep bubbles and the implications for water column methane dissolution: Journal of Geophysical Research - Oceans, v. 126, no. 9, e2021JC017363, 27 p., https://doi.org/10.1029/2021JC017363.","productDescription":"e2021JC017363, 27 p.","ipdsId":"IP-127864","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450995,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jc017363","text":"Publisher Index Page"},{"id":389330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.12304687500001,\n 38.9594087924542\n ],\n [\n -121.37695312499999,\n 38.9594087924542\n ],\n [\n -121.37695312499999,\n 49.095452162534826\n ],\n [\n -126.12304687500001,\n 49.095452162534826\n ],\n [\n -126.12304687500001,\n 38.9594087924542\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.20703125,\n 25.24469595130604\n ],\n [\n -82.529296875,\n 25.24469595130604\n ],\n [\n -82.529296875,\n 31.27855085894653\n ],\n [\n -97.20703125,\n 31.27855085894653\n ],\n [\n -97.20703125,\n 25.24469595130604\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.83984375,\n 42.032974332441405\n ],\n [\n -77.607421875,\n 40.91351257612758\n ],\n [\n -79.89257812499999,\n 35.460669951495305\n ],\n [\n -78.75,\n 33.65120829920497\n ],\n [\n -76.025390625,\n 33.137551192346145\n ],\n [\n -70.83984375,\n 42.032974332441405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fu, Xiaojing 0000-0001-7120-704X","orcid":"https://orcid.org/0000-0001-7120-704X","contributorId":216142,"corporation":false,"usgs":false,"family":"Fu","given":"Xiaojing","email":"","affiliations":[],"preferred":false,"id":823377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":823378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":823379,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229103,"text":"70229103 - 2021 - Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems","interactions":[],"lastModifiedDate":"2022-03-02T12:14:23.513284","indexId":"70229103","displayToPublicDate":"2021-08-31T17:56:04","publicationYear":"2021","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":"Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems","docAbstract":"Dimethyl sulfide (DMS) serves as an anti-greenhouse gas, plays multiple roles\n7 in aquatic ecosystems, and contributes to the global sulfur cycle. The chlorophyll\n8 a (CHL, an indicator of phytoplankton biomass)-DMS relationship is critical for\n9 estimating DMS emissions from aquatic ecosystems. Importantly, recent research has\n10 identified that the CHL-DMS relationship has a breakpoint, where the relationship\n11 is positive below a CHL threshold and negative at higher CHL concentrations.\n12 Conventionally, mean regression methods are employed to characterize the CHL-DMS\n13 relationship. However, these approaches focus on the response of mean conditions\n14 and cannot illustrate responses of other parts of the DMS distribution, which could\n15 be important in order to obtain a complete view of the CHL-DMS relationship. In\n16 this study, for the first time, we proposed a novel Bayesian change point quantile\n17 regression (BCPQR) model that integrates and inherits advantages of Bayesian change\n18 point models and Bayesian quantile regression models. Our objective was to examine\n19 whether or not the BCPQR approach could enhance the understanding of shifting\n20 CHL-DMS relationships in aquatic ecosystems. We fitted BCPQR models at five\n21 regression quantiles for freshwater lakes and for seas. We found that BCPQR models\n22 could provide a relatively complete view on the CHL-DMS relationship. In particular,\n23 it quantified the upper boundary of the relationship, representing the limiting effect of\n24 CHL on DMS. Based on the results of paired parameter comparisons, we revealed the\n25 inequality of regression slopes in BCPQR models for seas, indicating that applying\n26 the mean regression method to develop the CHL-DMS relationship in seas might not\n27 be appropriate. We also confirmed relationship differences between lakes and seas at\n28 multiple regression quantiles. Further, by introducing the concept of DMS emission\n29 potential, we found that pH was not likely a key factor leading to the change of the\n30 CHL-DMS relationship in lakes. These findings cannot be revealed using piecewise\n31 linear regression. We thereby concluded that the BCPQR model does indeed enhance\n \n32 the understanding of shifting CHL-DMS relationships in aquatic ecosystems and is\n33 expected to benefit efforts aimed at estimating DMS emissions. Considering that\n34 shifting (threshold) relationships are not rare and that the BCPQR model can easily\n35 be adapted to different systems, the BCPQR approach is expected to have great\n36 potential for generalization in other environmental and ecological studies.","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2021.117287","usgsCitation":"Liang, Z., Liu, Y., Xu, Y., and Wagner, T., 2021, Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems: Water Research, v. 201, 117287, 13 p., https://doi.org/10.1016/j.watres.2021.117287.","productDescription":"117287, 13 p.","ipdsId":"IP-122304","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451004,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2021.117287","text":"Publisher Index Page"},{"id":396613,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"201","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liang, Zhongyao","contributorId":287143,"corporation":false,"usgs":false,"family":"Liang","given":"Zhongyao","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":836518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Yong","contributorId":287144,"corporation":false,"usgs":false,"family":"Liu","given":"Yong","email":"","affiliations":[{"id":57409,"text":"Peking University","active":true,"usgs":false}],"preferred":false,"id":836519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xu, Yaoyang","contributorId":287145,"corporation":false,"usgs":false,"family":"Xu","given":"Yaoyang","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":836520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836517,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230258,"text":"70230258 - 2021 - An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA","interactions":[],"lastModifiedDate":"2022-04-06T14:19:46.516429","indexId":"70230258","displayToPublicDate":"2021-08-31T09:11:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1894,"text":"Herpetological Conservation and Biology","onlineIssn":"2151-0733","printIssn":"1931-7603","active":true,"publicationSubtype":{"id":10}},"displayTitle":"An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA","title":"An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA","docAbstract":"

Conservation efforts need reliable information concerning the status of a species and their trends to help identify which species are in most need of assistance. We completed a comparative evaluation of the occurrence of breeding for Cascades Frog (Rana cascadae), an amphibian that is being considered for federal protection under the U.S. Endangered Species Act. Specifically, in 2018–2019 we resurveyed 67 sites that were surveyed approximately 15 y prior and fit occupancy models to quantify the distribution of R. cascadae breeding in the Cascade Range, Oregon, USA. Furthermore, we conducted a simulation exercise to assess the power of sampling designs to detect declines in R. cascadae breeding at these sites. Our analysis of field data combined with our simulation results suggests that if there was a decline in the proportion of sites used for R. cascadae breeding in Oregon, it was likely a < 20% decline across our study period. Our results confirm that while R. cascadae detection probabilities are high, methods that allow the sampling process to be explicitly modeled are necessary to reliably track the status of the species. This study demonstrates the usefulness of investing in baseline information and data quality standards to increase capacity to make similar comparisons for other species in a timeframe that meet the needs of land managers and policy makers.

","language":"English","publisher":"Herpetological Conservation and Biology","usgsCitation":"Duarte, A., Pearl, C., McCreary, B., Rowe, J., and Adams, M.J., 2021, An updated assessment of status and trend in the distribution of the Cascades frog (Rana cascadae) in Oregon, USA: Herpetological Conservation and Biology, v. 16, no. 2, p. 361-373.","productDescription":"13 p.","startPage":"361","endPage":"373","ipdsId":"IP-127196","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":398216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398175,"type":{"id":15,"text":"Index Page"},"url":"https://www.herpconbio.org/contents_vol16_issue2.html"}],"country":"United States","state":"Oregon","otherGeospatial":"Cascade Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.398681640625,\n 45.537136680398596\n ],\n [\n -122.82714843749999,\n 44.88701247981298\n ],\n [\n -123.07983398437499,\n 44.07969327425713\n ],\n [\n -123.26660156249999,\n 42.58544425738491\n ],\n [\n -123.145751953125,\n 42.00848901572399\n ],\n [\n -121.61865234375,\n 42.00032514831621\n ],\n [\n -121.77246093750001,\n 42.98053954751642\n ],\n [\n -121.278076171875,\n 44.134913443750726\n ],\n [\n -121.025390625,\n 45.034714778688624\n ],\n [\n -121.124267578125,\n 45.68315803253308\n ],\n [\n -121.57470703125,\n 45.744526980468436\n ],\n [\n -121.871337890625,\n 45.729191061299915\n ],\n [\n -122.398681640625,\n 45.537136680398596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":28492,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":839736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearl, Christopher 0000-0003-2943-7321 christopher_pearl@usgs.gov","orcid":"https://orcid.org/0000-0003-2943-7321","contributorId":172669,"corporation":false,"usgs":true,"family":"Pearl","given":"Christopher","email":"christopher_pearl@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":839737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCreary, Brome 0000-0002-0313-7796 brome_mccreary@usgs.gov","orcid":"https://orcid.org/0000-0002-0313-7796","contributorId":3130,"corporation":false,"usgs":true,"family":"McCreary","given":"Brome","email":"brome_mccreary@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rowe, Jennifer 0000-0002-5253-2223 jrowe@usgs.gov","orcid":"https://orcid.org/0000-0002-5253-2223","contributorId":172670,"corporation":false,"usgs":true,"family":"Rowe","given":"Jennifer","email":"jrowe@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839739,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839740,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224304,"text":"70224304 - 2021 - Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections","interactions":[],"lastModifiedDate":"2021-11-16T15:44:27.13098","indexId":"70224304","displayToPublicDate":"2021-08-31T07:54:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections","docAbstract":"

Land use and climate change are anticipated to affect phytoplankton of lakes worldwide. The effects will depend on the magnitude of projected land use and climate changes and lake sensitivity to these factors. We used random forests fit with long-term (1971–2016) phytoplankton and cyanobacteria abundance time series, climate observations (1971–2016), and upstream catchment land use (global Clumondo models for the year 2000) data from 14 European and 15 North American lakes basins. We projected future phytoplankton and cyanobacteria abundance in the 29 focal lake basins and 1567 lakes across focal regions based on three land use (sustainability, middle of the road, and regional rivalry) and two climate (RCP 2.6 and 8.5) scenarios to mid-21st century. On average, lakes are expected to have higher phytoplankton and cyanobacteria due to increases in both urban land use and temperature, and decreases in forest habitat. However, the relative importance of land use and climate effects varied substantially among regions and lakes. Accounting for land use and climate changes in a combined way based on extensive data allowed us to identify urbanization as the major driver of phytoplankton development in lakes located in urban areas, and climate as major driver in lakes located in remote areas where past and future land use changes were minimal. For approximately one-third of the studied lakes, both drivers were relatively important. The results of this large scale study suggest the best approaches for mitigating the effects of human activity on lake phytoplankton and cyanobacteria will depend strongly on lake sensitivity to long-term change and the magnitude of projected land use and climate changes at a given location. Our quantitative analyses suggest local management measures should focus on retaining nutrients in urban landscapes to prevent nutrient pollution from exacerbating ongoing changes to lake ecosystems from climate change.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15866","usgsCitation":"Kakouei, K., Kraemer, B., Anneville, O., Carvalho, L., Feuchtmayr, H., Graham, J.L., Higgins, S., Pomati, F., Rudstam, L., Stockwell, J., Thackeray, S., Vanni, M., and Adrian, R., 2021, Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections: Global Change Biology, v. 27, no. 24, p. 6409-6422, https://doi.org/10.1111/gcb.15866.","productDescription":"14 p.","startPage":"6409","endPage":"6422","ipdsId":"IP-130740","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":451019,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.15866","text":"External Repository"},{"id":389540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"24","noUsgsAuthors":false,"publicationDate":"2021-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kakouei, Karan 0000-0001-8665-6841","orcid":"https://orcid.org/0000-0001-8665-6841","contributorId":211859,"corporation":false,"usgs":false,"family":"Kakouei","given":"Karan","email":"","affiliations":[{"id":38332,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":823640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraemer, B.M.","contributorId":265877,"corporation":false,"usgs":false,"family":"Kraemer","given":"B.M.","email":"","affiliations":[{"id":34275,"text":"Freie Universitat Berlin","active":true,"usgs":false}],"preferred":false,"id":823641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anneville, O.","contributorId":243525,"corporation":false,"usgs":false,"family":"Anneville","given":"O.","affiliations":[{"id":48714,"text":"Université Savoie","active":true,"usgs":false}],"preferred":false,"id":823642,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carvalho, L.","contributorId":265878,"corporation":false,"usgs":false,"family":"Carvalho","given":"L.","email":"","affiliations":[{"id":33563,"text":"Lancaster University","active":true,"usgs":false}],"preferred":false,"id":823643,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feuchtmayr, H.","contributorId":265879,"corporation":false,"usgs":false,"family":"Feuchtmayr","given":"H.","affiliations":[{"id":33563,"text":"Lancaster University","active":true,"usgs":false}],"preferred":false,"id":823644,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823645,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Higgins, S.","contributorId":265880,"corporation":false,"usgs":false,"family":"Higgins","given":"S.","email":"","affiliations":[{"id":54814,"text":"IISD Experimental Lakes Area","active":true,"usgs":false}],"preferred":false,"id":823646,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pomati, F.","contributorId":265881,"corporation":false,"usgs":false,"family":"Pomati","given":"F.","affiliations":[{"id":54815,"text":"Swiss Federal Institute of Water Science and Technology","active":true,"usgs":false}],"preferred":false,"id":823647,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rudstam, L.G.","contributorId":243538,"corporation":false,"usgs":false,"family":"Rudstam","given":"L.G.","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":823648,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stockwell, J.D.","contributorId":265882,"corporation":false,"usgs":false,"family":"Stockwell","given":"J.D.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":823649,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thackeray, S.J.","contributorId":265883,"corporation":false,"usgs":false,"family":"Thackeray","given":"S.J.","affiliations":[{"id":33563,"text":"Lancaster University","active":true,"usgs":false}],"preferred":false,"id":823650,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Vanni, M.","contributorId":265884,"corporation":false,"usgs":false,"family":"Vanni","given":"M.","email":"","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":823651,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Adrian, R.","contributorId":265885,"corporation":false,"usgs":false,"family":"Adrian","given":"R.","email":"","affiliations":[{"id":54816,"text":"Leibniz Institute of Freshwater Ecology and Inland Fisheries, Freie Universitat Berlin","active":true,"usgs":false}],"preferred":false,"id":823652,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70223710,"text":"70223710 - 2021 - Predicting non-native insect impact: Focusing on the trees to see the forest","interactions":[],"lastModifiedDate":"2021-11-16T15:38:38.433883","indexId":"70223710","displayToPublicDate":"2021-08-31T07:36:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting non-native insect impact: Focusing on the trees to see the forest","docAbstract":"

Non-native organisms have invaded novel ecosystems for centuries, yet we have only a limited understanding of why their impacts vary widely from minor to severe. Predicting the impact of non-established or newly detected species could help focus biosecurity measures on species with the highest potential to cause widespread damage. However, predictive models require an understanding of potential drivers of impact and the appropriate level at which these drivers should be evaluated. Here, we used non-native, specialist herbivorous insects of forest ecosystems to test which factors drive impact and if there were differences based on whether they used woody angiosperms or conifers as hosts. We identified convergent and divergent patterns between the two host types indicating fundamental similarities and differences in their interactions with non-native insects. Evolutionary divergence time between native and novel hosts was a significant driver of insect impact for both host types but was modulated by different factors in the two systems. Beetles in the subfamily Scolytinae posed the highest risk to woody angiosperms, and different host traits influenced impact of specialists on conifers and woody angiosperms. Tree wood density was a significant predictor of host impact for woody angiosperms with intermediate densities (0.5–0.6 mg/mm3) associated with highest risk, whereas risk of impact was highest for conifers that coupled shade tolerance with drought intolerance. These results underscore the importance of identifying the relevant levels of biological organization and ecological interactions needed to develop accurate risk models for species that may arrive in novel ecosystems.

","language":"English","publisher":"Springer","doi":"10.1007/s10530-021-02621-5","usgsCitation":"Schulz, A.N., Mech, A.M., Ayres, M.P., Gandhi, K., Havill, N.P., Herms, D.A., Hoover, A.M., Hufbauer, R.A., Liebhold, A.M., Marsico, T.D., Raffa, K.F., Tobin, P.C., Uden, D.R., and Thomas, K.A., 2021, Predicting non-native insect impact: Focusing on the trees to see the forest: Biological Invasions, v. 23, p. 3921-3936, https://doi.org/10.1007/s10530-021-02621-5.","productDescription":"16 p.","startPage":"3921","endPage":"3936","ipdsId":"IP-124152","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436222,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FT7C1O","text":"USGS data release","linkHelpText":"Traits and Factors Catalog (TRAFAC): Hardwood specialists of North America"},{"id":388798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2021-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Schulz, Ashley N.","contributorId":219894,"corporation":false,"usgs":false,"family":"Schulz","given":"Ashley","email":"","middleInitial":"N.","affiliations":[{"id":40088,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR","active":true,"usgs":false}],"preferred":false,"id":822409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mech, Angela M.","contributorId":219892,"corporation":false,"usgs":false,"family":"Mech","given":"Angela","email":"","middleInitial":"M.","affiliations":[{"id":40087,"text":"School of Environmental and Forest Sciences, University of Washington, Seattle, WA. Corresponding email: ammech@wcu.edu. Present address: Department of Geosciences and Natural Resources, Western Carolina University, Cullowhee, NC","active":true,"usgs":false}],"preferred":false,"id":822410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ayres, Matthew P.","contributorId":219897,"corporation":false,"usgs":false,"family":"Ayres","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":35787,"text":"Department of Biological Sciences, Dartmouth College, Hanover, NH","active":true,"usgs":false}],"preferred":false,"id":822411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gandhi, Kamal J.K.","contributorId":219898,"corporation":false,"usgs":false,"family":"Gandhi","given":"Kamal J.K.","affiliations":[{"id":40090,"text":"D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA","active":true,"usgs":false}],"preferred":false,"id":822412,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Havill, Nathan P.","contributorId":219900,"corporation":false,"usgs":false,"family":"Havill","given":"Nathan","email":"","middleInitial":"P.","affiliations":[{"id":40091,"text":"Northern Research Station, USDA Forest Service, Hamden, CT","active":true,"usgs":false}],"preferred":false,"id":822413,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herms, Daniel A.","contributorId":219895,"corporation":false,"usgs":false,"family":"Herms","given":"Daniel","email":"","middleInitial":"A.","affiliations":[{"id":40089,"text":"The Davey Tree Expert Company, Kent, OH","active":true,"usgs":false}],"preferred":false,"id":822414,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hoover, Angela Marie 0000-0003-0401-5587","orcid":"https://orcid.org/0000-0003-0401-5587","contributorId":265174,"corporation":false,"usgs":true,"family":"Hoover","given":"Angela","email":"","middleInitial":"Marie","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822415,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hufbauer, Ruth A.","contributorId":219901,"corporation":false,"usgs":false,"family":"Hufbauer","given":"Ruth","email":"","middleInitial":"A.","affiliations":[{"id":40092,"text":"Department of Bioagricultural Science and Pest Management, Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":822416,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Liebhold, Andrew M.","contributorId":219902,"corporation":false,"usgs":false,"family":"Liebhold","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":40093,"text":"USDA Forest Service Northern Research Station, Morgantown, WV","active":true,"usgs":false}],"preferred":false,"id":822417,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marsico, Travis D.","contributorId":219893,"corporation":false,"usgs":false,"family":"Marsico","given":"Travis","email":"","middleInitial":"D.","affiliations":[{"id":40088,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR","active":true,"usgs":false}],"preferred":false,"id":822418,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Raffa, Kenneth F.","contributorId":219903,"corporation":false,"usgs":false,"family":"Raffa","given":"Kenneth","email":"","middleInitial":"F.","affiliations":[{"id":40094,"text":"Department of Entomology, University of Wisconsin, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":822419,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tobin, Patrick C.","contributorId":200172,"corporation":false,"usgs":false,"family":"Tobin","given":"Patrick","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":822420,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Uden, Daniel R.","contributorId":219904,"corporation":false,"usgs":false,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":40095,"text":"Nebraska Cooperative Fish and Wildlife Unit, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE","active":true,"usgs":false}],"preferred":false,"id":822421,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Thomas, Kathryn A. 0000-0002-7131-8564 kathryn_a_thomas@usgs.gov","orcid":"https://orcid.org/0000-0002-7131-8564","contributorId":167,"corporation":false,"usgs":true,"family":"Thomas","given":"Kathryn","email":"kathryn_a_thomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":822422,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70227995,"text":"70227995 - 2021 - Flow dynamics influence fish recruitment in hydrologically connected river-reservoir landscapes","interactions":[],"lastModifiedDate":"2022-02-03T17:28:18.338559","indexId":"70227995","displayToPublicDate":"2021-08-30T11:23:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Flow dynamics influence fish recruitment in hydrologically connected river-reservoir landscapes","docAbstract":"

Hydrologic processes are often important determinants of successful recruitment of native fishes. However, water management practices can result in abnormal changes in daily and seasonal hydrology patterns. Rarely has fish recruitment across river–reservoir landscapes been considered in relation to flow management, despite the direct relationship between reservoir water management and the resulting upstream and downstream hydrology. We evaluated the relationships between lotic and lentic hydrology and recruitment of two native broadcast-spawning fishes, Freshwater Drum Aplodinotus grunniens and Gizzard Shad Dorosoma cepedianum. Four seasonal periods for each species were identified that related to the species’ spawning biology, from which we derived our remaining hydrology variables. Annual hydrology variables were also considered in our analysis. We developed regression models in conjunction with a model-selection procedure for each species and habitat type based on the catch-curve residuals from fish populations in hydrologically connected river–reservoir systems in the Ozark Highland and Ouachita Mountain ecoregions, USA. Our results indicated that recruitment of reservoir Freshwater Drum was negatively correlated to annual reservoir retention time. In lotic habitats, Freshwater Drum recruitment was positively correlated with prespawn discharge conditions and negatively correlated with annual flow variability. Similarly, riverine Gizzard Shad recruitment was positively correlated to the frequency of high-flow pulses during the spawning period. Our results indicate that releasing reservoir water to best mimic relatively natural flow patterns may benefit some broadcast-spawning species that occupy both lentic and downstream lotic environments, especially during the spring. This information, combined with future efforts on additional spawning guilds, will provide a foundation for developing holistic river–reservoir water-allocation plans.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10692","usgsCitation":"Dattilo, J., Brewer, S.K., and Shoup, D., 2021, Flow dynamics influence fish recruitment in hydrologically connected river-reservoir landscapes: North American Journal of Fisheries Management, v. 41, no. 6, p. 1752-1763, https://doi.org/10.1002/nafm.10692.","productDescription":"12 p.","startPage":"1752","endPage":"1763","ipdsId":"IP-096322","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Oklahoma","otherGeospatial":"Elk River, Grand Lake O’ the Cherokee, Kiamichi River, Sardis Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.372314453125,\n 36.43012234551576\n ],\n [\n -93.80126953124999,\n 36.43012234551576\n ],\n [\n -93.80126953124999,\n 37.142803443716836\n ],\n [\n -95.372314453125,\n 37.142803443716836\n ],\n [\n -95.372314453125,\n 36.43012234551576\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.95458984375,\n 33.779147331286474\n ],\n [\n -94.493408203125,\n 33.779147331286474\n ],\n [\n -94.493408203125,\n 34.488447837809304\n ],\n [\n -95.95458984375,\n 34.488447837809304\n ],\n [\n -95.95458984375,\n 33.779147331286474\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Dattilo, J.","contributorId":274267,"corporation":false,"usgs":false,"family":"Dattilo","given":"J.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":832865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoup, D. E.","contributorId":242905,"corporation":false,"usgs":false,"family":"Shoup","given":"D. E.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":832864,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223282,"text":"sir20215064 - 2021 - Geohydrology and water quality of the stratified-drift aquifers in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York","interactions":[],"lastModifiedDate":"2021-08-30T15:07:53.555341","indexId":"sir20215064","displayToPublicDate":"2021-08-30T10:00:00","publicationYear":"2021","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":"2021-5064","displayTitle":"Geohydrology and Water Quality of the Stratified-Drift Aquifers in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York","title":"Geohydrology and water quality of the stratified-drift aquifers in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York","docAbstract":"

From 2011 to 2016, the U.S. Geological Survey, in cooperation with the Town of Newfield and the Tompkins County Planning Department, performed a study of the stratified-drift aquifers in the West Branch Cayuga Inlet and Fish Kill Valleys in Newfield, Tompkins County, New York. Both confined and unconfined aquifers were identified, mostly in the valleys. The confined aquifer consists of a discontinuous sand and gravel layer that overlies bedrock and is commonly confined by overlying fine-grained sediments. The unconfined aquifer consists of surficial ice contact sand and gravel, alluvial silt, sand and gravel, and areas where several large tributary streams deposited alluvial fans in the valley, all of which were deposited during and after the last glacial recession.

The unconfined aquifers are primarily recharged by direct infiltration of precipitation at the land surface, by surface runoff and shallow subsurface flow from adjacent hillsides, and by seepage loss from streams crossing the aquifer, especially on alluvial fans. The confined aquifers are primarily recharged by groundwater stored in the overlying sand and gravel aquifer that slowly seeps downward through the underlying confining layer. Other sources of recharge are precipitation that falls directly on the surficial confining unit and adjacent valley walls, which then slowly seeps downward and enters the confined aquifer, and groundwater flow from bordering till and bedrock and from bedrock below the valley. There may also be some recharge where confining units are absent or where parts of the confining units contain sediments with moderate permeability.

The groundwater naturally discharges to the Fish Kill and West Branch Cayuga Inlet streams and to wetlands overlying the aquifer boundaries, with additional losses due to evapotranspiration. Groundwater is pumped from the aquifers by domestic, municipal, and agricultural wells. Approximately 57.9 million gallons per year was withdrawn from the stratified-drift (sand and gravel) aquifers.

Groundwater samples were collected from 11 wells, and surface water samples were collected at 2 sites, one each from Fish Kill and West Branch Cayuga Inlet. None of the common ions (for example, sodium, chloride, and magnesium) exceeded existing drinking water standards at either surface water site. The concentration of nitrate plus nitrite detected was 0.4 milligram per liter as nitrogen in the West Branch Cayuga Inlet site. Total phosphorus was detected at 0.01 milligram per liter as phosphate for both sites. Of the 11 wells sampled, 8 were finished in confined sand and gravel aquifers, 1 was finished in unconfined sand and gravel, and 2 were finished in shale bedrock. Groundwater quality in the study area generally met Federal and State drinking-water standards. However, of the 11 samples taken, 2 exceeded the U.S. Environmental Protection Agency drinking water advisory taste threshold of 20 milligrams per liter for sodium, 8 exceeded the secondary maximum contaminant level of 300 micrograms per liter for iron, and 9 exceeded the secondary maximum contaminant level of 50 micrograms per liter for manganese.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215064","collaboration":"Prepared in cooperation with the Town of Newfield and the Tompkins County Planning Department","usgsCitation":"Fisher, B.N., Heisig, P.M., and Kappel, W.M., 2021, Geohydrology and water quality of the stratified-drift aquifers in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York: U.S. Geological Survey Scientific Investigations Report 2021–5064, 42 p., https://doi.org/10.3133/sir20215064.","productDescription":"Report: vii, 42 p.; 2 Tables; 2 Data Releases","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103464","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":388165,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5064/coverthb.jpg"},{"id":388166,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5064/sir20215064.pdf","text":"Report","size":"5.46 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5064"},{"id":388167,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Y3E81","text":"USGS data release","linkHelpText":"Geospatial datasets for the geohydrology and water quality of the stratified-drift aquifers in West Branch Cayuga Inlet/Fish Kill aquifers in Newfield, Tompkins County, New York"},{"id":388169,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5064/sir20215064_table03.01.csv","text":"Table 3.1","size":"4.74 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Physical properties and concentrations of common ions, nutrients, radiochemical properties, and dissolved gases in groundwater samples from confined aquifers in the West Branch Cayuga Inlet and Fish Kill Creek Valleys, Newfield, Tompkins County, New York"},{"id":388217,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5064/images/"},{"id":388218,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5064/sir20215064.XML"},{"id":388170,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5064/sir20215064_table03.02.csv","text":"Table 3.2","size":"2.98 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Concentrations of trace elements in groundwater samples from confined aquifers in the West Branch Cayuga Inlet and Fish Kill Creek Valleys, Newfield, Tompkins County, New York"},{"id":388168,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N6AZ4E","text":"USGS data release","linkHelpText":"Horizontal-to-vertical spectral ratio and depth-to-bedrock for geohydrology and water quality of the stratified-drift aquifer in West Branch Cayuga Inlet and Fish Kill Valleys, Newfield, Tompkins County, New York, July 2011–November 2016"}],"country":"United States","state":"New York","otherGeospatial":"West Branch Cayuga Inlet and Fish Kill Valleys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.83333333,\n 42.1666\n ],\n [\n -76.83333333,\n 42.8333\n ],\n [\n -76.00,\n 42.83333333\n ],\n [\n -76.00,\n 42.1666\n ],\n [\n -76.83333333,\n 42.1666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-08-30","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Fisher, Benjamin N. 0000-0003-1308-1906","orcid":"https://orcid.org/0000-0003-1308-1906","contributorId":220916,"corporation":false,"usgs":true,"family":"Fisher","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heisig, Paul M. 0000-0003-0338-4970 pmheisig@usgs.gov","orcid":"https://orcid.org/0000-0003-0338-4970","contributorId":793,"corporation":false,"usgs":true,"family":"Heisig","given":"Paul","email":"pmheisig@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kappel, William M. 0000-0002-2382-9757 wkappel@usgs.gov","orcid":"https://orcid.org/0000-0002-2382-9757","contributorId":1074,"corporation":false,"usgs":true,"family":"Kappel","given":"William","email":"wkappel@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821597,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232171,"text":"70232171 - 2021 - The role of genome duplication in big sagebrush growth and fecundity","interactions":[],"lastModifiedDate":"2022-06-09T12:27:29.793763","indexId":"70232171","displayToPublicDate":"2021-08-30T07:26:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"The role of genome duplication in big sagebrush growth and fecundity","docAbstract":"

Premise

Adaptive traits can be dramatically altered by genome duplication. The study of interactions among traits, ploidy, and the environment are necessary to develop an understanding of how polyploidy affects niche differentiation and to develop restoration strategies for resilient native ecosystems.

Methods

Growth and fecundity were measured in common gardens for 39 populations of big sagebrush (Artemisia tridentata) containing two subspecies and two ploidy levels. General linear mixed-effect models assessed how much of the trait variation could be attributed to genetics (i.e., ploidy and climatic adaptation), environment, and gene–environment interactions.

Results

Growth and fecundity variation were explained well by the mixed models (80% and 91%, respectively). Much of the trait variation was attributed to environment, and 15% of variation in growth and 34% of variation in seed yield were attributed to genetics. Genetic trait variation was mostly attributable to ploidy, with much higher growth and seed production in diploids, even in a warm-dry environment typically dominated by tetraploids. Population-level genetic variation was also evident and was related to the climate of each population's origin.

Conclusions

Ploidy is a strong predictor growth and seed yield, regardless of common-garden environment. The superior growth and fecundity of diploids across environments raises the question as to how tetraploids can be more prevalent than diploids, especially in warm-dry environments. Two hypotheses that may explain the abundance of tetraploids on the landscape include selection for drought resistance at the seedling stage, and greater competitive ability in water uptake in the upper soil horizon.

","language":"English","publisher":"Botanical Society of America","doi":"10.1002/ajb2.1714","usgsCitation":"Richardson, B., Germino, M., Warwell, M.V., and Buerki, S., 2021, The role of genome duplication in big sagebrush growth and fecundity: American Journal of Botany, v. 108, no. 8, p. 1405-1416, https://doi.org/10.1002/ajb2.1714.","productDescription":"12 p.","startPage":"1405","endPage":"1416","ipdsId":"IP-121824","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":451041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ajb2.1714","text":"Publisher Index Page"},{"id":401968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"108","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Bryce 0000-0001-9521-4367","orcid":"https://orcid.org/0000-0001-9521-4367","contributorId":195702,"corporation":false,"usgs":false,"family":"Richardson","given":"Bryce","email":"","affiliations":[],"preferred":false,"id":844436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew","contributorId":292386,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warwell, Marcus V","contributorId":292387,"corporation":false,"usgs":false,"family":"Warwell","given":"Marcus","email":"","middleInitial":"V","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":844438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buerki, Sven","contributorId":257075,"corporation":false,"usgs":false,"family":"Buerki","given":"Sven","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844439,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238839,"text":"70238839 - 2021 - Surface energy balance of sub-Arctic roads with varying snow regimes and properties in permafrost regions","interactions":[],"lastModifiedDate":"2022-12-14T14:01:54.669694","indexId":"70238839","displayToPublicDate":"2021-08-30T07:25:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3032,"text":"Permafrost and Periglacial Processes","active":true,"publicationSubtype":{"id":10}},"title":"Surface energy balance of sub-Arctic roads with varying snow regimes and properties in permafrost regions","docAbstract":"

Surface energy balance (SEB) strongly influences the thermal state of permafrost, cryohydrological processes, and infrastructure stability. Road construction and snow accumulation affect the energy balance of underlying permafrost. Herein, we use an experimental road section of the Alaska Highway to develop a SEB model to quantify the surface energy components and ground surface temperature (GST) for different land cover types with varying snow regimes and properties. Simulated and measured ground temperatures are in good agreement, and our results show that the quantity of heat entering the embankment center and slope is mainly controlled by net radiation, and less by the sensible heat flux. In spring, lateral heat flux from the embankment center leads to earlier disappearance of snowpack on the embankment slope. In winter, the insulation created by the snow cover on the embankment slope reduces heat loss by a factor of three compared with the embankment center where the snow is plowed. The surface temperature offsets are 5.0°C and 7.8°C for the embankment center and slope, respectively. Furthermore, the heat flux released on the embankment slope exponentially decreases with increasing snow depth, and linearly decreases with earlier snow cover in fall and shorter snow-covered period in spring.

","language":"English","publisher":"Wiley","doi":"10.1002/ppp.2129","usgsCitation":"Chen, L., Voss, C., Fortier, D., and McKenzie, J.M., 2021, Surface energy balance of sub-Arctic roads with varying snow regimes and properties in permafrost regions: Permafrost and Periglacial Processes, v. 32, no. 4, p. 681-701, https://doi.org/10.1002/ppp.2129.","productDescription":"21 p.","startPage":"681","endPage":"701","ipdsId":"IP-121759","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":410466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Yukon","otherGeospatial":"Beaver Creek area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -140.9,\n 62.4\n ],\n [\n -140.9,\n 62.3\n ],\n [\n -140.85,\n 62.3\n ],\n [\n -140.85,\n 62.4\n ],\n [\n -140.9,\n 62.4\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Lin","contributorId":299914,"corporation":false,"usgs":false,"family":"Chen","given":"Lin","email":"","affiliations":[],"preferred":false,"id":858867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voss, Clifford I. 0000-0001-5923-2752","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":211844,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":858868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fortier, Daniel","contributorId":194641,"corporation":false,"usgs":false,"family":"Fortier","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":858869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKenzie, Jeffrey M.","contributorId":176299,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":858870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70253092,"text":"70253092 - 2021 - GeoAI in the US Geological Survey for topographic mapping","interactions":[],"lastModifiedDate":"2024-04-18T12:16:33.943595","indexId":"70253092","displayToPublicDate":"2021-08-30T07:14:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3618,"text":"Transactions in GIS","active":true,"publicationSubtype":{"id":10}},"title":"GeoAI in the US Geological Survey for topographic mapping","docAbstract":"

Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in terrain feature extraction, hydrographic network extraction, and semantic modeling. This article is a documentation of the recent work and current state of research and development. The article helps define the accomplishments and directions of research and applications in fields of GeoAI for topographic mapping within the USGS and more broadly.

","language":"English","publisher":"Wiley","doi":"10.1111/tgis.12830","usgsCitation":"Usery, E., Arundel, S., Shavers, E.J., Stanislawski, L., Thiem, P.T., and Varanka, D.E., 2021, GeoAI in the US Geological Survey for topographic mapping: Transactions in GIS, v. 26, no. 1, p. 25-40, https://doi.org/10.1111/tgis.12830.","productDescription":"16 p.","startPage":"25","endPage":"40","ipdsId":"IP-126887","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":427902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":899123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":899124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":899125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":210088,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":899126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":899127,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":899128,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229529,"text":"70229529 - 2021 - American eel personality and body length influence passage success in an experimental fishway","interactions":[],"lastModifiedDate":"2022-03-11T12:32:47.498359","indexId":"70229529","displayToPublicDate":"2021-08-28T10:51:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"American eel personality and body length influence passage success in an experimental fishway","docAbstract":"
  1. Millions of dams impair watershed connectivity across the globe and have severely affected migratory fish populations. Fishways offer upstream passage opportunities, but artificial selection may be imposed by these structures. Using juvenile American eel Anguilla rostrata as a model species, we consider whether individual differences in behaviour (i.e. personality) and fish size can predict passage success.
  2. We evaluated the expression of bold and exploratory behaviours using open field and emergence assays in the laboratory. Then we assessed the propensity for individuals to volitionally climb through an experimental fishway to understand if personality and fish size could predict climbing success.
  3. We demonstrate personality in juvenile eels, and swimming speed in the open field was negatively associated with climbing propensity. Slower swimmers were up to 60% more likely to use the passage device suggesting that more exploratory eels incurred greater passage success. For successful climbers, climbing time was negatively associated with fish length.
  4. Synthesis and applications. Our results suggest fish may segregate at barriers based on personality and size. Preventing a subset of individuals from accessing upstream habitat is likely to have negative consequences for fish populations and aquatic ecosystems. Selection may be alleviated by increasing passage opportunities, maximizing fishway attraction and avoiding inefficient passage solutions.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14009","usgsCitation":"Mensinger, M.A., Brehm, A.M., Mortelliti, A., Blomberg, E., and Zydlewski, J.D., 2021, American eel personality and body length influence passage success in an experimental fishway: Journal of Applied Ecology, v. 58, no. 12, p. 2760-2769, https://doi.org/10.1111/1365-2664.14009.","productDescription":"10 p.","startPage":"2760","endPage":"2769","ipdsId":"IP-126473","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Mensinger, Matthew A.","contributorId":288336,"corporation":false,"usgs":false,"family":"Mensinger","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brehm, Allison M.","contributorId":288337,"corporation":false,"usgs":false,"family":"Brehm","given":"Allison","email":"","middleInitial":"M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mortelliti, Alessio","contributorId":288338,"corporation":false,"usgs":false,"family":"Mortelliti","given":"Alessio","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blomberg, Erik J.","contributorId":288339,"corporation":false,"usgs":false,"family":"Blomberg","given":"Erik J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":837771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":837767,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224565,"text":"70224565 - 2021 - Groundwater, biodiversity, and the role of flow system scale","interactions":[],"lastModifiedDate":"2022-01-06T17:22:41.248645","indexId":"70224565","displayToPublicDate":"2021-08-28T07:33:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater, biodiversity, and the role of flow system scale","docAbstract":"

Groundwater-dependent ecosystems and species (GDEs) are found throughout watersheds at locations of groundwater discharge, yet not all GDEs are the same, nor are the groundwater systems supporting them. Groundwater moves along a variety of flow paths of different lengths and with different contributing areas, ranging from shorter local flow paths with low discharge and large seasonal variability to streams, springs and wetlands to longer regional flow paths with potentially larger discharge and low seasonal variability, commonly at low basin elevations. How does this variation in physical hydrology affect the type and distribution of GDEs? Using data on hypsographic position, groundwater-dependent species distributions, groundwater pumping and streamflow from Oregon, USA, we provide a conceptual model and initial supporting evidence demonstrating that spatial variation in groundwater flow path scales, illustrated using basin hypsography, is a driver of non-random distribution of GDEs across watersheds. Further, we posit that the spatial variation in primary stressors to groundwater (e.g. pumping and climate change) will differentially affect GDEs depending on their hypsographic position. Furthermore, because of their use for irrigation and municipal water supply, regional groundwater systems and associated species are more likely to be studied and receive regulatory protection. Our initial data point to a disproportionate focus on larger discharge, lower elevation GDEs, which leads to a bias in our understanding of the full suite of biodiversity associated with groundwater discharge as well as their stressors and potential mechanisms for protection.

","language":"English","publisher":"Wiley","doi":"10.1002/eco.2342","usgsCitation":"Aldous, A.R., and Gannett, M.W., 2021, Groundwater, biodiversity, and the role of flow system scale: Ecohydrology, v. 14, no. 8, e2342, 14 p., https://doi.org/10.1002/eco.2342.","productDescription":"e2342, 14 p.","ipdsId":"IP-117907","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":451049,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2342","text":"Publisher Index Page"},{"id":389865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Aldous, Allison R 0000-0002-8670-6017","orcid":"https://orcid.org/0000-0002-8670-6017","contributorId":266015,"corporation":false,"usgs":false,"family":"Aldous","given":"Allison","email":"","middleInitial":"R","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":824080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gannett, Marshall W. 0000-0003-2498-2427 mgannett@usgs.gov","orcid":"https://orcid.org/0000-0003-2498-2427","contributorId":2942,"corporation":false,"usgs":true,"family":"Gannett","given":"Marshall","email":"mgannett@usgs.gov","middleInitial":"W.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824081,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223457,"text":"ofr20181094 - 2021 - Development of demographic models to analyze populations with multi-year data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a case study","interactions":[],"lastModifiedDate":"2021-08-30T11:40:21.500348","indexId":"ofr20181094","displayToPublicDate":"2021-08-27T08:20:51","publicationYear":"2021","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":"2018-1094","displayTitle":"Development of Demographic Models to Analyze Populations with Multi-Year Data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a Case Study","title":"Development of demographic models to analyze populations with multi-year data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a case study","docAbstract":"

We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (Gopherus agassizii) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during seven annual surveys consisting of two censuses each over a 34-year period. Additional data were collected when marked tortoises were recovered dead and removed between survey years. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. Three size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary), and three survival states (not-yet-entered, entered/alive, and dead/removed) were incorporated into the model. We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181094","usgsCitation":"Berry, K.H., and Yee, J.L., 2021, Development of demographic models to analyze populations with multi-year data—Using Agassiz’s Desert Tortoise (Gopherus agassizii) as a case study: U.S. Geological Survey Open-File Report 2018–1094, 55 p., https://doi.org/10.3133/ofr20181094.","productDescription":"vi, 55 p.","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-086643","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":388564,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2018/1094/images"},{"id":388563,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2018/1094/ofr20181094.xml"},{"id":388562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1094/ofr20181094.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1094/covrthb.jpg"}],"contact":"

Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2021-08-27","noUsgsAuthors":false,"publicationDate":"2021-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":822069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":822070,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224524,"text":"70224524 - 2021 - An efficient Bayesian framework for updating PAGER loss estimates","interactions":[],"lastModifiedDate":"2021-09-27T11:01:08.524796","indexId":"70224524","displayToPublicDate":"2021-08-27T08:03:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7565,"text":"Earthquake Spectra Journal","active":true,"publicationSubtype":{"id":10}},"title":"An efficient Bayesian framework for updating PAGER loss estimates","docAbstract":"

We introduce a Bayesian framework for incorporating time-varying noisy reported data on damage and loss information to update near real-time loss estimates/alerts for the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) system. Initial loss estimation by PAGER immediately following an earthquake includes several uncertainties. Historically, the PAGER’s alerting on fatality and economic losses has not incorporated location-specific reported data on physical damage or casualties for a given earthquake. The proposed framework provides the ability to include early reports on fatalities at any given time and improve the overall impact forecast for the earthquake. The reported data on fatalities or damage are generally incomplete and noisy, especially in the early hours of the disaster. To address these challenges, we develop a recursive Bayesian updating framework that takes into account the loss projection model and the measurement and model uncertainties. The framework is applied to loss data for three example earthquakes, and the results show that the proposed updating improves the loss estimates and alert level to the correct level within the first day of the earthquake.

","language":"English","publisher":"Sage Journals","doi":"10.1177/8755293020944177","usgsCitation":"Noh, H.Y., Jaiswal, K.S., Engler, D.T., and Wald, D.J., 2021, An efficient Bayesian framework for updating PAGER loss estimates: Earthquake Spectra Journal, v. 36, no. 4, p. 1719-1742, https://doi.org/10.1177/8755293020944177.","productDescription":"24 p.","startPage":"1719","endPage":"1742","ipdsId":"IP-118585","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":389706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":823863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engler, Davis T. 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265962,"corporation":false,"usgs":true,"family":"Engler","given":"Davis","email":"","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823865,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823866,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224924,"text":"70224924 - 2021 - Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change","interactions":[],"lastModifiedDate":"2022-01-06T17:24:33.238441","indexId":"70224924","displayToPublicDate":"2021-08-27T07:22:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change","docAbstract":"

Floods play a critical role in geomorphic change, but whether peak magnitude, duration, volume, or frequency determines the resulting magnitude of erosion and deposition is a question often proposed in geomorphic effectiveness studies. This study investigated that question using digital elevation model differencing to compare and contrast three hydrologically distinct epochs of topographic change spanning 18 years in the 37-km gravel–cobble lower Yuba River in northern California, USA. Scour and fill were analysed by volume at segment and geomorphic reach scales. Each epoch's hydrology was characterized using 15-min and daily averaged flow to obtain distinct peak and recurrence, duration, and volume metrics. Epochs 1 (1999–2008) and 3 (2014–2017) were wetter than average with large floods reaching 3206 and 2466 m3/s, respectively, though of different flood durations. Epoch 2 (2008–2014) was a drought period with only four brief moderate floods (peak of 1245 m3/s). Total volumetric changes showed that major geomorphic response occurred primarily during large flood events; however, total scour and net export of sediment varied greatly, with 20 times more export in epoch 3 compared to epoch 1. The key finding was that greater peak discharge was not correlated with greater net and total erosion; differences were better explained by duration and volume above floodway-filling stage. This finding highlights the importance of considering flood duration and volume, along with peak, to assess flood magnitude in the context of flood management, frequency analysis, and resulting geomorphic changes.

","language":"English","publisher":"Wiley","doi":"10.1002/esp.5230","usgsCitation":"Gervasi, A., Pasternack, G., and East, A.E., 2021, Flooding duration and volume more important than peak discharge in explaining 18 years of gravel–cobble river change: Earth Surface Processes and Landforms, v. 46, no. 15, p. 3194-3212, https://doi.org/10.1002/esp.5230.","productDescription":"9 p.","startPage":"3194","endPage":"3212","ipdsId":"IP-129882","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":390233,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"lower Yuba River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.695556640625,\n 38.78406349514289\n ],\n [\n -120.17944335937499,\n 38.78406349514289\n ],\n [\n -120.17944335937499,\n 39.6606850221923\n ],\n [\n -121.695556640625,\n 39.6606850221923\n ],\n [\n -121.695556640625,\n 38.78406349514289\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"15","noUsgsAuthors":false,"publicationDate":"2021-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Gervasi, Arielle","contributorId":267178,"corporation":false,"usgs":false,"family":"Gervasi","given":"Arielle","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":824622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pasternack, Gregory","contributorId":267179,"corporation":false,"usgs":false,"family":"Pasternack","given":"Gregory","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":824623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":824624,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223431,"text":"70223431 - 2021 - Pollinator communities vary with vegetation structure and time since management within regenerating timber harvests of the Central Appalachian Mountains","interactions":[],"lastModifiedDate":"2021-08-27T13:15:05.97235","indexId":"70223431","displayToPublicDate":"2021-08-26T11:08:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Pollinator communities vary with vegetation structure and time since management within regenerating timber harvests of the Central Appalachian Mountains","docAbstract":"Native pollinator populations across the United States are increasingly threatened by a multitude of ecological stressors. Although the drivers behind pollinator population declines are varied, habitat loss/degradation remains one of the most important threats. Forested landscapes, where the impacts of habitat loss/degradation are minimized, are known to support robust pollinator populations in eastern North America. Within heavily forested landscapes, timber management is already implemented as a means for improving forest health and enhancing wildlife habitat, however, little is known regarding the characteristics within regenerating timber harvests that affect forest pollinator populations. In 2018-19, we monitored insect pollinators in 143 regenerating (≤ 9 growing seasons post-harvest) timber harvest sites across Pennsylvania. During 1,129 survey events, we observed over 9,100 bees and butterflies, 220 blooming plant taxa, and collected over 2,200 pollinator specimens. Bee and butterfly abundance were positively associated with season-wide floral abundance and negatively associated with dense vegetation that inhibits the growth of understory floral resources. Particularly in late summer, few pollinators were observed in stands > 6 years post-harvest, with models predicting five times more bees in 1-year-old harvests than in 9-year-old harvests. Pollinator species diversity was positively associated with floral diversity and percent forb cover, and negatively associated with percent tall (>1m) sapling cover. These results suggest that regenerating timber harvests promote abundant and diverse pollinator communities in the Appalachian Mountains, though pollinator abundance declined quickly as woody stems regenerated. Ultimately, our findings contribute to a growing body of literature suggesting that dynamic forest management producing an even mix of age classes would benefit forest pollinator populations in the Central Appalachian Mountains.","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119373","usgsCitation":"Mathis, C.L., McNeil, D.J., Lee, M.R., Grozinger, C.M., King, D.I., Otto, C., and Larkin, J., 2021, Pollinator communities vary with vegetation structure and time since management within regenerating timber harvests of the Central Appalachian Mountains: Forest Ecology and Management, v. 495, 119373, 12 p., https://doi.org/10.1016/j.foreco.2021.119373.","productDescription":"119373, 12 p.","onlineOnly":"N","ipdsId":"IP-127927","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":451052,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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\"}}]}","volume":"495","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mathis, Codey L.","contributorId":264822,"corporation":false,"usgs":false,"family":"Mathis","given":"Codey","email":"","middleInitial":"L.","affiliations":[{"id":54565,"text":"Indiana Un of Penns","active":true,"usgs":false}],"preferred":false,"id":822061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McNeil, Darin J. Jr.","contributorId":37620,"corporation":false,"usgs":false,"family":"McNeil","given":"Darin","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":822062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Monica R.","contributorId":264824,"corporation":false,"usgs":false,"family":"Lee","given":"Monica","email":"","middleInitial":"R.","affiliations":[{"id":54565,"text":"Indiana Un of Penns","active":true,"usgs":false}],"preferred":false,"id":822063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grozinger, Christina M.","contributorId":214374,"corporation":false,"usgs":false,"family":"Grozinger","given":"Christina","email":"","middleInitial":"M.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":822064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"King, David I.","contributorId":34390,"corporation":false,"usgs":false,"family":"King","given":"David","email":"","middleInitial":"I.","affiliations":[{"id":13259,"text":"USDA Forest Service Northern Research Station","active":true,"usgs":false},{"id":18918,"text":"Department of Environmental Conservation, University of Massachusetts, Amherst, MA, 01003, USA","active":true,"usgs":false}],"preferred":false,"id":822065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":822066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larkin, Jeffery A.","contributorId":210725,"corporation":false,"usgs":false,"family":"Larkin","given":"Jeffery A.","affiliations":[{"id":38140,"text":"Department of Biology, Indiana University of Pennsylvania, Indiana, PA 15705, US","active":true,"usgs":false}],"preferred":false,"id":822067,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223403,"text":"sir20215090 - 2021 - Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19","interactions":[],"lastModifiedDate":"2021-12-14T12:26:17.570498","indexId":"sir20215090","displayToPublicDate":"2021-08-26T10:24:57","publicationYear":"2021","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":"2021-5090","displayTitle":"Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–19","title":"Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19","docAbstract":"

In 2015, the U.S. Geological Survey started a topical study to quantify water use in areas of continuous oil and gas (COG) development. The first phase of the study was completed in 2019 and analyzed the Williston Basin. The second phase of the study analyzed the Permian Basin using the same techniques and approaches used for the Williston Basin analysis. The Permian Basin was selected for the second phase of water-use analysis for the following reasons: (1) the basin has the largest undiscovered technically recoverable oil and gas resource in the United States, (2) the basin has a continuous resource in tight shale that primarily produces oil, and (3) the basin is within the contiguous United States. This study used data from 60 counties in Texas and New Mexico with spatial coverage based on the Permian Basin extent defined by the U.S. Energy Information Administration, a representation of the geologically defined Permian Basin.

Data from several sources were used in the analysis of direct, indirect, and ancillary water use associated with COG development in the Permian Basin and are available in an associated data release. Hydraulic fracturing water-use data were used to determine the start of the recent (before 2019) COG development boom in oil production in the Permian Basin in the same way that the data were used for the Williston Basin study. Water-use data were aggregated by county and year, which were the sampling units used in the analysis.

The water-use analysis of the Permian Basin contained three elements: (1) estimates of water use, in million gallons, by county and year; (2) coefficients of water use from regression models, in million gallons per developed oil and gas well; and (3) performance (based on goodness-of-fit metrics) of the regression models in estimating the observed water use.

Coefficients from the linear and quantile regression models of direct, indirect, and ancillary water use in the Permian Basin were produced as aggregate values for the counties and years. The mean estimate of direct water use had a 95-percent confidence interval of 4.13–5.45 million gallons (Mgal) per developed oil and gas well. The coefficient from the linear regression model of indirect water use was 0.111 Mgal per well, with a 95-percent confidence interval of 0.104–0.117 Mgal per well. The mean estimate of ancillary water use in the Permian Basin was 1.09 Mgal per well, with a 95-percent confidence interval of 1.05–1.13 Mgal per well. Model performance was evaluated with goodness-of-fit metrics including coefficient of determination (R2), root mean square error, and the ratio of root mean square error to standard deviation of observations computed from leave-one-out cross validation of the linear and quantile regression models of direct, indirect, and ancillary water use. Model performance for direct water use was acceptable, with an R2 value of 0.91. The model performance of indirect water use was acceptable, with an R2 value of 0.89. Values of R2 for the ancillary water-use categories were at least 0.89.

Annual mean estimates for hydraulic fracturing, cementing, drilling, indirect, and ancillary water use per well for the years 2010–17 were comparable between the Permian and Williston Basins. Hydraulic fracturing water use increased similarly from 2010 to 2015 in the Permian Basin and the Williston Basin, increasing from 0.6 Mgal per well in 2010 to 5.4 Mgal per well in 2015 in the Permian Basin and from 1.4 Mgal per well in 2010 to 4.7 Mgal per well in 2015 in the Williston Basin.

By design, the Permian water-use assessment is a simplification of a complex and continually developing system and therefore has uncertainty and limitations in the interpretation of results. Despite the modeling limitations, the results summarized in the report, when compared to other studies, compare well with water-use estimations. The favorable comparison highlights the transferability of the water-use methodology to other areas of COG development.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215090","programNote":"Water Availability and Use Science Program","usgsCitation":"Valder, J.F., McShane, R.R., Thamke, J.N., McDowell, J.S., Ball, G.P., Houston, N.A., and Galanter, A.E., 2021, Estimates of water use associated with continuous oil and gas development in the Permian Basin, Texas and New Mexico, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5090, 27 p., https://doi.org/10.3133/sir20215090.","productDescription":"Report: vii, 27 p.; Data Releases: 3; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126972","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":388522,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JIOU3V","text":"USGS data release","description":"USGS data release","linkHelpText":"R scripts and results of estimated water use associated with continuous oil and gas development, Permian Basin, United States, 2010–19"},{"id":388521,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPKRLW","text":"USGS data release","description":"USGS data release","linkHelpText":"Data to estimate water use associated with continuous oil and gas development, Williston Basin, United States, 1980–2017 (ver. 2.0, September 2019)"},{"id":388520,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LAWIPH","text":"USGS data release","description":"USGS data release","linkHelpText":"Data to estimate water use associated with continuous oil and gas development, Permian Basin, United States, 1980–2019"},{"id":391022,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20213053","text":"FS 2021–3053","size":"4.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3053","linkHelpText":"— Estimates of Water Use Associated with Continuous Oil and Gas Development in the Permian Basin, Texas and New Mexico, 2010–19, with Comparisons to the Williston Basin, North Dakota and Montana"},{"id":388523,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":388518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5090/coverthb.jpg"},{"id":388519,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5090/sir20215090.pdf","text":"Report","size":"2.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5090"}],"country":"United States","state":"New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.18359375,\n 34.27083595165\n ],\n [\n -104.8974609375,\n 33.797408767572485\n ],\n [\n -105.0732421875,\n 32.43561304116276\n ],\n [\n -104.8974609375,\n 31.87755764334002\n ],\n [\n -103.447265625,\n 30.334953881988564\n ],\n [\n -102.0849609375,\n 30.259067203213018\n ],\n [\n -100.634765625,\n 29.916852233070173\n ],\n [\n -100.01953125,\n 29.305561325527698\n ],\n [\n -99.0966796875,\n 29.38217507514529\n ],\n [\n -98.701171875,\n 30.41078179084589\n ],\n [\n -99.0966796875,\n 31.353636941500987\n ],\n [\n -99.84374999999999,\n 32.39851580247402\n ],\n [\n -100.8544921875,\n 32.39851580247402\n ],\n [\n -100.986328125,\n 33.100745405144245\n ],\n [\n -101.0302734375,\n 33.65120829920497\n ],\n [\n -101.6455078125,\n 34.52466147177172\n ],\n [\n -103.18359375,\n 34.27083595165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503

1608 Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-26","noUsgsAuthors":false,"publicationDate":"2021-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Valder, Joshua F. 0000-0003-3733-8868","orcid":"https://orcid.org/0000-0003-3733-8868","contributorId":220912,"corporation":false,"usgs":true,"family":"Valder","given":"Joshua F.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McShane, Ryan R. 0000-0002-3128-0039 rmcshane@usgs.gov","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":195581,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan","email":"rmcshane@usgs.gov","middleInitial":"R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thamke, Joanna N. 0000-0002-6917-1946 jothamke@usgs.gov","orcid":"https://orcid.org/0000-0002-6917-1946","contributorId":1012,"corporation":false,"usgs":true,"family":"Thamke","given":"Joanna N.","email":"jothamke@usgs.gov","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDowell, Jeremy S. 0000-0002-8132-9806","orcid":"https://orcid.org/0000-0002-8132-9806","contributorId":205199,"corporation":false,"usgs":true,"family":"McDowell","given":"Jeremy S.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ball, Grady P. 0000-0003-3030-055X","orcid":"https://orcid.org/0000-0003-3030-055X","contributorId":221343,"corporation":false,"usgs":true,"family":"Ball","given":"Grady","email":"","middleInitial":"P.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Houston, Natalie A. 0000-0002-6071-4545 nhouston@usgs.gov","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":1682,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","email":"nhouston@usgs.gov","middleInitial":"A.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821960,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Galanter, Amy E. 0000-0002-2960-0136","orcid":"https://orcid.org/0000-0002-2960-0136","contributorId":205393,"corporation":false,"usgs":true,"family":"Galanter","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821961,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224579,"text":"70224579 - 2021 - Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow","interactions":[],"lastModifiedDate":"2021-09-29T13:45:54.460103","indexId":"70224579","displayToPublicDate":"2021-08-26T08:45:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow","title":"Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow","docAbstract":"

Marine spatial planning relies on detailed spatial information of marine areas to ensure effective conservation of species. To enhance our understanding of marine habitat use by the highly pelagic Bermuda petrel Pterodroma cahow, we deployed GPS tags on 6 chick-rearing adults in April 2019 and constructed a habitat suitability model using locations classified as foraging to explore functional responses to a selection of marine environmental variables. We defined 15 trips for 5 individuals, ranging from 1-6 trips per bird, that included both short and long foraging excursions indicative of a dual foraging strategy that optimizes chick feeding and self maintenance. The maximum distance birds flew from Bermuda during foraging trips ranged from 61 to 2513 km (total trip lengths: 186-14051 km). Behaviourally deduced foraging habitat was best predicted at shorter distances from the colony, under warmer sea surface temperature, greater sea surface height, and in deeper water compared to transiting locations; our model results indicated that suitable foraging habitat exists beyond the core home range of the population, as far north as the highly productive Gulf Stream frontal system, and within the territorial waters of both the USA and Canada. Our results are crucial to inform management decisions and international conservation efforts by better identifying potential threats encountered at sea by this globally rare seabird and highlighting jurisdictions potentially responsible for mitigating those threats.

","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01139","usgsCitation":"Raine, A., Gjerdrum, C., Pratte, I., Madeiros, J., Felis, J.J., and Adams, J., 2021, Marine distribution and foraging habitat highlight potential threats at sea for Endangered Bermuda Petrel Pterodroma cahow: Endangered Species Research, v. 45, p. 337-356, https://doi.org/10.3354/esr01139.","productDescription":"20 p.","startPage":"337","endPage":"356","ipdsId":"IP-124810","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":451059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01139","text":"Publisher Index Page"},{"id":389951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bermuda, Canada, United States","otherGeospatial":"Nonsuch Island, Horn Rock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -63.54492187500001,\n 31.39115752282472\n ],\n [\n -49.7021484375,\n 43.77109381775651\n ],\n [\n -46.8896484375,\n 48.86471476180277\n ],\n [\n -55.06347656249999,\n 45.182036837015886\n ],\n [\n -61.962890625,\n 43.004647127794435\n ],\n [\n -69.345703125,\n 40.613952441166596\n ],\n [\n -72.99316406249999,\n 38.34165619279595\n ],\n [\n -72.99316406249999,\n 34.34343606848294\n ],\n [\n -66.4013671875,\n 30.90222470517144\n ],\n [\n -63.54492187500001,\n 31.39115752282472\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Raine, André F","contributorId":266026,"corporation":false,"usgs":false,"family":"Raine","given":"André F","affiliations":[{"id":54862,"text":"Archipelago Research and Conservation, Kauai, Hawai’i 96716, USA","active":true,"usgs":false}],"preferred":false,"id":824149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gjerdrum, Carina","contributorId":266027,"corporation":false,"usgs":false,"family":"Gjerdrum","given":"Carina","email":"","affiliations":[{"id":54863,"text":"Canadian Wildlife Service, Dartmouth, Nova Scotia B2Y 2N6, Canada","active":true,"usgs":false}],"preferred":false,"id":824150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pratte, Isabeau","contributorId":266028,"corporation":false,"usgs":false,"family":"Pratte","given":"Isabeau","email":"","affiliations":[{"id":54863,"text":"Canadian Wildlife Service, Dartmouth, Nova Scotia B2Y 2N6, Canada","active":true,"usgs":false}],"preferred":false,"id":824151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Madeiros, Jeremy","contributorId":196171,"corporation":false,"usgs":false,"family":"Madeiros","given":"Jeremy","email":"","affiliations":[],"preferred":false,"id":824152,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824153,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":824154,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223401,"text":"ofr20211030J - 2021 - System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","interactions":[{"subject":{"id":70223401,"text":"ofr20211030J - 2021 - System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","indexId":"ofr20211030J","publicationYear":"2021","noYear":false,"chapter":"J","displayTitle":"System Characterization Report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","title":"System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2024-11-06T15:36:02.779518","indexId":"ofr20211030J","displayToPublicDate":"2021-08-26T08:13:17","publicationYear":"2021","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":"2021-1030","chapter":"J","displayTitle":"System Characterization Report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","title":"System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)","docAbstract":"

Executive Summary

This report addresses system characterization of the China-Brazil Earth Resources Satellite-4A (CBERS–4A) multispectral remote sensing satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.

CBERS–4A is a joint Chinese-Brazilian medium-resolution satellite launched in December 2019 by the China National Space Agency/National Institute for Space Research (Brazil) on a Chang Zheng 4B rocket from the Taiyuan Satellite Launch Center for Earth resources monitoring. The CBERS–4A mission continues the CBERS mission that has been in continual operation since the launch of CBERS–1 in 1999.

The CBERS–4A satellite was designed and built by Academia Chinesa de Tecnologia Espacial/National Institute for Space Research and uses the Phoenix-Eye bus. CBERS–4A carries the multispectral camera and wide field imager sensors for medium-resolution land imaging and the wide swath panchromatic and multispectral camera sensor for high-resolution land imaging. This assessment focused on the multispectral camera sensor only. More information on CBERS sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a.

The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that CBERS–4A provides an interior (band-to-band) geometric performance in the range of −0.02 to −0.16 pixel; an exterior geometric accuracy performance of −22.02 (−1.47 pixels) to −16.06 meters (−1.07 pixels); a radiometric accuracy performance of –0.006 to 0.925 (offset and slope); and a spatial performance for relative edge response in the range of 0.39 to 0.44, for full width at half maximum in the range of 2.38 to 2.56 pixels, and for a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.013.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030J","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., Park, S., and Cantrell, S., 2021, System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A), chap. J of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 35 p., https://doi.org/10.3133/ofr20211030J.","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130782","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":388510,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/j/ofr20211030j.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030J"},{"id":388509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/j/coverthb.jpg"}],"contact":"

Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-08-26","noUsgsAuthors":false,"publicationDate":"2021-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Vrabel, James C. 0000-0002-0120-4721","orcid":"https://orcid.org/0000-0002-0120-4721","contributorId":264751,"corporation":false,"usgs":false,"family":"Vrabel","given":"James C.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":821947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":821948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":821949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christopherson, Jon 0000-0002-2472-0059 jonchris@usgs.gov","orcid":"https://orcid.org/0000-0002-2472-0059","contributorId":2552,"corporation":false,"usgs":true,"family":"Christopherson","given":"Jon","email":"jonchris@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":821950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kim, Minsu 0000-0003-4472-0926 minsukim@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":216429,"corporation":false,"usgs":true,"family":"Kim","given":"Minsu","email":"minsukim@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":821951,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Park, Seonkyung 0000-0003-3203-1998","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":223182,"corporation":false,"usgs":true,"family":"Park","given":"Seonkyung","email":"","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":821952,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cantrell, Simon J. 0000-0001-6909-1973","orcid":"https://orcid.org/0000-0001-6909-1973","contributorId":259304,"corporation":false,"usgs":false,"family":"Cantrell","given":"Simon J.","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":821953,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223424,"text":"70223424 - 2021 - Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","interactions":[],"lastModifiedDate":"2022-01-07T15:57:22.646685","indexId":"70223424","displayToPublicDate":"2021-08-25T10:21:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","title":"Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","docAbstract":"

Fish population structure in previously glaciated regions is often influenced by natural colonization processes and human-mediated dispersal, including fish stocking. Endemic populations are of conservation interest because they may contain rare and unique genetic variation. While coregonines are native to certain Michigan inland lakes, some were stocked with fish from Great Lakes sources, calling into question the origin of extant populations. While most stocking targeted lake whitefish (Coregonus clupeaformis), cisco (C. artedi) were also stocked from the Great Lakes to inland waterbodies. We used population genetic data (microsatellite genotypes and mitochondrial (mt)DNA sequences), coalescent modeling, and approximate Bayesian computation to investigate the origins of 12 inland Michigan cisco populations. The spatial distribution of mtDNA haplotypes suggests Michigan is an introgression zone for two ancestral cisco lineages associated with separate glacial refugia. Low levels of genetic diversity and high levels of genetic divergence were observed for populations located well inland of the Great Lakes relative to populations occupying waterbodies near the Great Lakes. Estimates of recent Great Lakes gene flow ranged from 27 to 48% for populations near the Great Lakes shoreline but were substantially lower (under 8%) for populations further inland. Inland lakes with elevated recent gene flow estimates may have been recipients of stocked coregonine fry, including cisco. Low levels of genetic diversity paired with a high likelihood of endemism as indicated by strong genetic divergence and low Great Lakes population inputs suggest the analyzed cisco populations occupying southern Michigan kettle lakes are of elevated conservation interest.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.08.008","usgsCitation":"Homola, J.J., Robinson, J.D., Kanefsky, J., Stott, W., Whelan, G., and Scribner, K.T., 2021, Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi): Journal of Great Lakes Research, v. 47, no. 6, p. 1781-1792, https://doi.org/10.1016/j.jglr.2021.08.008.","productDescription":"12 p.","startPage":"1781","endPage":"1792","ipdsId":"IP-124168","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":388588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.505859375,\n 41.47566020027821\n ],\n [\n -81.38671875,\n 41.47566020027821\n ],\n [\n -81.38671875,\n 46.830133640447386\n ],\n [\n -88.505859375,\n 46.830133640447386\n ],\n [\n -88.505859375,\n 41.47566020027821\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Homola, Jared J.","contributorId":264547,"corporation":false,"usgs":false,"family":"Homola","given":"Jared","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":822012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, John D","contributorId":264810,"corporation":false,"usgs":false,"family":"Robinson","given":"John","email":"","middleInitial":"D","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kanefsky, Jeannette","contributorId":243198,"corporation":false,"usgs":false,"family":"Kanefsky","given":"Jeannette","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822015,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whelan, Gary","contributorId":146115,"corporation":false,"usgs":false,"family":"Whelan","given":"Gary","email":"","affiliations":[{"id":16584,"text":"Fisheries Division, Michigan Department of Natural Resources, P.O. Box 30446, Lansing, MI 48909","active":true,"usgs":false}],"preferred":false,"id":822016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scribner, Kim T","contributorId":264811,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822017,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223676,"text":"70223676 - 2021 - Discrete sample introduction module for quantitative and isotopic analysis of methane and other gases by cavity ring-down spectroscopy","interactions":[],"lastModifiedDate":"2021-09-14T16:59:23.317558","indexId":"70223676","displayToPublicDate":"2021-08-25T08:18:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Discrete sample introduction module for quantitative and isotopic analysis of methane and other gases by cavity ring-down spectroscopy","docAbstract":"

Carbon dioxide (CO2) and methane (CH4) are natural and anthropogenic products that play a central role in the global carbon cycle and regulating Earth’s climate. Applications utilizing laser absorption spectroscopy, which continuously measure concentrations and stable isotope ratios of these greenhouse gases, are routinely employed to measure the source and magnitude of atmospheric inputs. We developed a discrete sample introduction module (DSIM) to enable measurements of methane and CO2 concentrations and δ13C values from limited volume (5–100 mL) gas samples when interfaced with a commercially available cavity ring-down spectroscopy (CRDS) analyzer. The analysis has a dynamic range that spans six orders of magnitude from 100% analyte to the lower limit of instrument detection (2 ppm). We demonstrate system performance for methane by comparing concentrations and δ13C results from the DSIM-CRDS system and traditional methods for a variety of sample types, including low concentration (nanomolar CH4) seawater and high concentration (>90% CH4) natural gas. The expansive concentration range of the field-portable DSIM-CRDS system can measure enhances analytical performance for investigating methane and CO2 dynamics and, potentially, other gases measured by laser absorption spectroscopy.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.1c01386","usgsCitation":"Pohlman, J., Casso, M., Magen, C., and Bergeron, E., 2021, Discrete sample introduction module for quantitative and isotopic analysis of methane and other gases by cavity ring-down spectroscopy: Environmental Science & Technology, v. 55, no. 17, p. 12066-12074, https://doi.org/10.1021/acs.est.1c01386.","productDescription":"9 p.","startPage":"12066","endPage":"12074","ipdsId":"IP-130600","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451068,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.1c01386","text":"Publisher Index Page"},{"id":436224,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99B34V1","text":"USGS data release","linkHelpText":"Comparison of methane concentration and stable carbon isotope data for natural samples analyzed by discrete sample introduction module - cavity ring down spectroscopy (DSIM-CRDS) and traditional methods"},{"id":388724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"17","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Pohlman, John 0000-0002-3563-4586","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":220804,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":822288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casso, Michael 0000-0002-6990-9090 mcasso@usgs.gov","orcid":"https://orcid.org/0000-0002-6990-9090","contributorId":2904,"corporation":false,"usgs":true,"family":"Casso","given":"Michael","email":"mcasso@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":822289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magen, Cedric","contributorId":265132,"corporation":false,"usgs":false,"family":"Magen","given":"Cedric","email":"","affiliations":[{"id":54603,"text":"University of Maryland Center for Environmental Science, Chesapeake Biological Lab, Solomons MD","active":true,"usgs":false}],"preferred":false,"id":822290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergeron, Emile M. ebergeron@usgs.gov","contributorId":3449,"corporation":false,"usgs":true,"family":"Bergeron","given":"Emile M.","email":"ebergeron@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":822329,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223331,"text":"sir20215072 - 2021 - Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17","interactions":[],"lastModifiedDate":"2021-08-25T11:39:29.585628","indexId":"sir20215072","displayToPublicDate":"2021-08-24T14:28:01","publicationYear":"2021","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":"2021-5072","displayTitle":"Evaluation of Actual Evapotranspiration Rates from the Operational Simplified Surface Energy Balance (SSEBop) Model in Florida and Parts of Alabama and Georgia, 2000–17","title":"Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17","docAbstract":"

Evapotranspiration (ET) is the water-vapor flux transported from the surface of the Earth into the atmosphere and is the sum of surface water directly evaporated and subsurface water transpired by plants. ET rates are commonly estimated by using potential or reference ET, which might differ from actual ET rates. Actual evapotranspiration (ETa) rates can be estimated by using the Operational Simplified Surface Energy Balance (SSEBop) model. This report evaluates SSEBop ETa rates at the point and basin scales in Florida and parts of Alabama and Georgia for 2000–17. ETa rates computed by using data from 24 micrometeorological stations in Florida are referred to as mETa rates and were used to quantify biases in the SSEBop ETa rates, stratified by generalized land-use type. Bias was computed as mETa minus SSEBop ETa rates for given generalized land-use types, and bias-correction equations were computed by using least-squares regressions. In addition to mETa rates at station locations, annual average ETa rates calculated from the application of a water-balance method to 55 basins in Florida and parts of Alabama and Georgia were used to assess the accuracy of the annual SSEBop ETa rates at the basin scale. Another independent model used to simulate ETa rates was based on monthly reference ET from the statewide daily reference evapotranspiration (ETo) gridded dataset for Florida computed by using Geostationary Operational Environmental Satellite estimates of solar radiation (GOES ETo). ETa at grid points was computed as monthly GOES ETo multiplied by ratios of monthly mETa to GOES ETo, computed at micrometeorological stations and stratified by each generalized land-use type.

The coefficient of determination (R2) between monthly mETa and SSEBop ETa rates for all stations combined improved from 0.37 before bias correction of SSEBop ETa rates to 0.79 after the bias correction stratified by land-use type. For individual land-uses types, R2 varied from 0.59 for the monthly mETa at a station in the land-use type forest to 0.82 for the monthly mETa at stations in the land-use type shallow-water-table pasture. Root-mean-square error (RMSE) was computed as a function of the difference between SSEBop ETa rates and mETa rates. RMSE of monthly SSEBop ETa rates was 1.27 inches per month before the bias corrections improved to 0.73 inch per month after the bias corrections. RMSE for bias-corrected annual SSEBop ETa rates based on micrometeorological stations with complete years of records ranged from 2.01 inches per year (in/yr) for the land-use type of agriculture to 5.73 in/yr for the land-use type of deep water-table pasture, or 4.96 and 21.21 percent errors relative to annual mETa rates, respectively. Bias-corrected annual SSEBop ETa rates were also compared to annual ETa rates computed by using a water-balance method (wbETa) for 55 basins in Florida. Differences in bias-corrected average annual SSEBop ETa rates and average annual wbETa rates for the 55 basins ranged from −3.67 to 5.29 in/yr (−9.24 to 17.36 percent). RMSE when computed as a function of the differences between annual SSEBop ETa rates and wbETa rates decreased, on average, from 4.13 in/yr for the uncorrected bias SSEBop ETa rates to 1.95 in/yr for the bias-corrected SSEBop rates. The average annual bias-corrected SSEBop ETa rates, from all basins, was 36.46 in/yr or 3.41 percent lower than the average annual wbETa rate of 37.79 inches.

Bias in SSEBop ETa rates varies based on time step (monthly versus annual), scale (point, basin, statewide), and land-use type. Applications to hydrologic models should consider bias relative to the inherent error in models. Bias-corrected SSEBop ETa rates could be used as calibration targets in models of hydrologic processes, such as groundwater models. Annual bias in SSEBop ETa introduced to the model calibration is typically below the margin of error associated with typical residuals in model simulations, depending on scale. Surface-water and groundwater-flow models with RMSEs on the order of a few feet could benefit from bias-corrected SSEBop values of ETa.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215072","collaboration":"Prepared in cooperation with Northwest Florida Water Management District, Suwannee River Water Management District, St. Johns River Water Management District, South Florida Water Management District, Southwest Florida Water Management District, and Tampa Bay Water","usgsCitation":"Sepúlveda, N., 2021, Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17: U.S. Geological Survey Scientific Investigations Report 2021–5072, 66 p., https://doi.org/10.3133/sir20215072.","productDescription":"Report: x, 66 p.; Data Release","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-112971","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":388346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5072/coverthb.jpg"},{"id":388349,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5072/images"},{"id":388347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5072/sir20215072.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5072"},{"id":388348,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99AB3X4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets of actual evapotranspiration rates from 2000 to 2017 for basins in Florida and parts of Alabama and Georgia, calculated using the water-balance method, the bias-corrected Operational Simplified Surface Energy Balance (SSEBop) model, and the land-use crop coefficients model"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.71484375,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 25.005972656239187\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-08-24","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Nicasio 0000-0002-6333-1865 nsepul@usgs.gov","orcid":"https://orcid.org/0000-0002-6333-1865","contributorId":1454,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Nicasio","email":"nsepul@usgs.gov","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":821783,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70223361,"text":"sir20215080 - 2021 - Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019","interactions":[],"lastModifiedDate":"2021-08-25T11:44:55.7065","indexId":"sir20215080","displayToPublicDate":"2021-08-24T14:20:10","publicationYear":"2021","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":"2021-5080","displayTitle":"Estimation of Dissolved-Solids Concentrations Using Continuous Water-Quality Monitoring and Regression Models at Four Sites in the Yuma Area, Arizona and California, January 2017 through March 2019","title":"Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019","docAbstract":"

Multiple linear regression models were developed to estimate dissolved-solids concentrations in water at four sites in the Yuma area between Imperial Dam, Arizona and California and the southerly international boundary with Mexico at San Luis, Arizona. Continuous and discrete water-quality data were collected at gaging stations in the Colorado River upstream from Imperial Dam, Arizona-California, the Colorado River below Cooper wasteway near Yuma, Arizona, the Yuma Main Drain above Arizona–Sonora, Mexico boundary, and the 242 lateral above Main Drain at the Arizona–Sonora boundary. Continuous specific conductance and water temperature data were collected at each site between January 2017 and March 2019. Bi-weekly to monthly dissolved-solids water samples were collected during the same period. Continuous specific conductance data collected at the Colorado River below Cooper wasteway were affected by poorly mixed streamflow during periods when the Pilot Knob Hydro-electric Plant was releasing water to the river. The continuous specific conductance data for the site downstream from Cooper wasteway were corrected using mean specific conductance values computed from cross-section measurements collected during site visits. Continuous specific conductance data were affected by sensor fouling issues at the 242 lateral site, and continued operation at the site would require more frequent visits for cleaning and service to ensure data quality.

During the study, instream specific conductance readings ranged from 966 to 3,030 microsiemens per centimeter (μS/cm) at 25 degrees Celsius. Computed dissolved-solids concentrations from discrete samples ranged from 690 to 2,580 milligrams per liter (mg/L). Dissolved-solids concentrations were estimated from regression models using the optimal relation between dissolved solids and environmental factors, such as specific conductance, water temperature, dissolved oxygen, streamflow, and seasonality. Specific conductance was the primary factor at all four sites and explained 87.6 to 94 percent of variation in dissolved solids. Water temperature, as an indicator of seasonality, was determined to be a statistically significant secondary factor at both the Colorado River above Imperial Dam and Colorado River below Cooper wasteway sites explaining an additional 6.9 and 2.1 percent of variation in dissolved solids, respectively. Regression models explained 87.6 to 96.9 percent of the variation in dissolved solids; the root mean square error in the modeled data ranged between about 6 and 27 mg/L.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215080","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Cederberg, J.R., Paretti, N.V., Coes, A.L., Hermosillo, E., Andrade, L., 2021, Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019: U.S. Geological Survey Scientific Investigations Report 2021–5080, 26 p., https://doi.org/10.3133/sir20215080.","productDescription":"Report: vii, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-111110","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":436228,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SMK908","text":"USGS data release","linkHelpText":"Water-Quality Field Blank and Replicate Sample Data, Instantaneous and Mean Daily Discharge Data, and Dissolved-Solids Concentrations Data Collected in Four Waterways of Southwest Arizona, January 2017-March 2019"},{"id":388445,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9SMK908","linkHelpText":"Supplemental streamflow, quality-assurance, and dissolved-solids concentration datasets used for regression model development at four sites in the Yuma area, Arizona and California, January 2017 through March 2019"},{"id":388447,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5080/covrthb.jpg"},{"id":388448,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5080/sir20215080.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona, California","otherGeospatial":"Yuma area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.873046875,\n 32.58384932565662\n ],\n [\n -114.3896484375,\n 32.58384932565662\n ],\n [\n -114.3896484375,\n 32.88881315761995\n ],\n [\n -114.873046875,\n 32.88881315761995\n ],\n [\n -114.873046875,\n 32.58384932565662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-08-24","noUsgsAuthors":false,"publicationDate":"2021-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Cederberg, Jay R. 0000-0001-6649-7353 cederber@usgs.gov","orcid":"https://orcid.org/0000-0001-6649-7353","contributorId":964,"corporation":false,"usgs":true,"family":"Cederberg","given":"Jay","email":"cederber@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paretti, Nicholas V. 0000-0003-2178-4820 nparetti@usgs.gov","orcid":"https://orcid.org/0000-0003-2178-4820","contributorId":173412,"corporation":false,"usgs":true,"family":"Paretti","given":"Nicholas","email":"nparetti@usgs.gov","middleInitial":"V.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coes, Alissa L. 0000-0001-6682-5417 alcoes@usgs.gov","orcid":"https://orcid.org/0000-0001-6682-5417","contributorId":4231,"corporation":false,"usgs":true,"family":"Coes","given":"Alissa","email":"alcoes@usgs.gov","middleInitial":"L.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hermosillo, Edyth 0000-0003-1648-1016 ehermosillo@usgs.gov","orcid":"https://orcid.org/0000-0003-1648-1016","contributorId":175455,"corporation":false,"usgs":true,"family":"Hermosillo","given":"Edyth","email":"ehermosillo@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrade, Lucia 0000-0003-3741-1404","orcid":"https://orcid.org/0000-0003-3741-1404","contributorId":264674,"corporation":false,"usgs":true,"family":"Andrade","given":"Lucia","email":"","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":821861,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}