Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biab094","usgsCitation":"Myers, B., Weiskopf, S.R., Shiklomanov, A.N., Ferrier, S., Weng, E., Casey, K.A., Harfoot, M., Jackson, S., Leidner, A.K., Lenton, T.M., Luikart, G., Matsuda, H., Pettorelli, N., Rosa, I.M., Ruane, A.C., Senay, G.B., Serbin, S.P., Tittensor, D.P., and Beard, 2021, A new approach to evaluate and reduce uncertainty of model-based biodiversity projections for conservation policy formulation: BioScience, v. 71, no. 12, p. 1261-1273, https://doi.org/10.1093/biosci/biab094.","productDescription":"13 p.","startPage":"1261","endPage":"1273","ipdsId":"IP-101564","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":450474,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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As part of the U.S. Geological Survey Integrated hyperspectral, geophysical and geochemical studies of Yellowstone National Park hydrothermal systems project, we have collected multiscale imaging spectrometer data including borehole core, field, and airborne data. These data give us the unique opportunity to map subsurface and surface alteration of shallow epithermal systems at scales ranging from microns to meters per pixel. Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), Corescan HCI-3, HySpex VNIR-1800 and SWIR-384 imaging spectrometers, and a Riegl VZ-2000i terrestrial lidar system were used in this study. Maps utilizing spectral analysis of multiscale hyperspectral data indicate the presence of mineral assemblages consistent with epithermal deposits. Minerals such as alunite, hydrated silica, and kaolinite typically form in steam-heated cap systems and can be found in core and AVIRIS data. At depth, higher temperature fluids that are less acidic produce mixed-layer kaolinite with clay/muscovite and then montmorillonite and muscovite towards the bottom of the borehole. This transition can be seen in AVIRIS and field data at the Grand Canyon of the Yellowstone and in borehole Corescan data at Y-12 Norris-Ii R874. Instrument specifications, collection parameters, and setup for data acquisition will be discussed as well as mapping software and preliminary results.
","largerWorkTitle":"2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Proceedings","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS47720.2021.9553654","usgsCitation":"Hoefen, T.M., Kokaly, R.F., Keith Eric Livo, Meyer, J.M., and Holloway, J.M., 2021, Multiscale hyperspectral imaging of hydrothermal alteration in Yellowstone National Park, USA, in 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Proceedings, p. 132-135, https://doi.org/10.1109/IGARSS47720.2021.9553654.","productDescription":"4 p.","startPage":"132","endPage":"135","ipdsId":"IP-130036","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":408493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.11572265625,\n 44.13885576756881\n ],\n [\n -109.8,\n 44.13885576756881\n ],\n [\n -109.8,\n 45.108423337694084\n ],\n [\n -111.11572265625,\n 45.108423337694084\n ],\n [\n -111.11572265625,\n 44.13885576756881\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":854933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":205165,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond","email":"","middleInitial":"F.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":854934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keith Eric Livo 0000-0001-7331-8130","orcid":"https://orcid.org/0000-0001-7331-8130","contributorId":298035,"corporation":false,"usgs":false,"family":"Keith Eric Livo","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":854935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, John Michael 0000-0003-2810-9414","orcid":"https://orcid.org/0000-0003-2810-9414","contributorId":298036,"corporation":false,"usgs":false,"family":"Meyer","given":"John","email":"","middleInitial":"Michael","affiliations":[{"id":64484,"text":"Colorado School of Mines, USGS Intern","active":true,"usgs":false}],"preferred":false,"id":854936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holloway, JoAnn M. 0000-0003-3603-7668","orcid":"https://orcid.org/0000-0003-3603-7668","contributorId":201855,"corporation":false,"usgs":true,"family":"Holloway","given":"JoAnn","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":854937,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269698,"text":"70269698 - 2021 - Integrating satellite thermal imagery and global weather datasets for operational actual evapotranspiration mapping and drought early warning applications","interactions":[],"lastModifiedDate":"2025-08-01T13:59:56.951292","indexId":"70269698","displayToPublicDate":"2021-10-12T08:55:52","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Integrating satellite thermal imagery and global weather datasets for operational actual evapotranspiration mapping and drought early warning applications","docAbstract":"The development and online access to an operational global actual evapotranspiration (ETa) is described. The global ETa is generated using the Operational Simplified Surface Energy Balance (SSEBop) model with inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and gridded weather datasets. Global and regional ETa, as well as anomaly graphics and data, are posted at https://earlywarning.usgs.gov/fews at dekadal, monthly, and annual aggregation periods at 1 km spatial resolution since 2003. As part of the convergence of evidence, the Famine Early Warning Systems Network (FEWS NET) consults these products along with precipitation- and vegetation-derived products for drought monitoring and early warning applications to avert food insecurity crises around the world.
","conferenceTitle":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","conferenceDate":"July 11-16, 2021","conferenceLocation":"Brussels, Belgium","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS47720.2021.9553963","usgsCitation":"Senay, G.B., Bohms, S., Young, C., Holen, C.L., Mcelhone, M., Budde, M., and Rowland, J., 2021, Integrating satellite thermal imagery and global weather datasets for operational actual evapotranspiration mapping and drought early warning applications, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, v. 2021, Brussels, Belgium, July 11-16, 2021, p. 1769-1772, https://doi.org/10.1109/IGARSS47720.2021.9553963.","productDescription":"4 p.","startPage":"1769","endPage":"1772","ipdsId":"IP-126214","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2021","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohms, Stefanie 0000-0002-2979-4655 sbohms@usgs.gov","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":3148,"corporation":false,"usgs":true,"family":"Bohms","given":"Stefanie","email":"sbohms@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Claudia 0000-0002-0859-7206 claudia.young.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-0859-7206","contributorId":192363,"corporation":false,"usgs":true,"family":"Young","given":"Claudia","email":"claudia.young.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holen, Cheryl L. 0000-0003-2200-809X","orcid":"https://orcid.org/0000-0003-2200-809X","contributorId":358915,"corporation":false,"usgs":true,"family":"Holen","given":"Cheryl","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mcelhone, Maxwell Thomas 0000-0002-3473-733X","orcid":"https://orcid.org/0000-0002-3473-733X","contributorId":358918,"corporation":false,"usgs":true,"family":"Mcelhone","given":"Maxwell Thomas","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944475,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270573,"text":"70270573 - 2021 - USGS CEOS analysis ready data for land achievements and future plans","interactions":[],"lastModifiedDate":"2025-08-20T14:55:12.018563","indexId":"70270573","displayToPublicDate":"2021-10-12T08:40:17","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"USGS CEOS analysis ready data for land achievements and future plans","docAbstract":"The efforts of the Committee on Earth Observation Satellites (CEOS) to bring CEOS Analysis Ready Data for Land (CARD4L) products to countries and international organizations quickly and easily continues to receive important support from the U.S. Geological Survey (USGS). As part of its engagement with CARD4L, the USGS worked to address specific Threshold and Target Product Family Specification (PFS) requirements for its Landsat Collection 2 Level-2 science products and in July 2020, received formal CEOS endorsement for 100 percent CARD4L-compliance at the Threshold level for Collection 2 surface reflectance and surface temperature. This endorsement ensures these products meet a level of interoperability with data from other Earth-observing platforms, such as Europe's Sentinel-2 satellites, as the European Space Agency also works toward CARD4L-compliant products. In addition to the Collection 2 Level-2 land surface data products, the USGS recognizes Landsat's potential to make a valuable contribution to aquatic science and environmental monitoring capabilities for aquatic ecosystems, especially in coastal and inland waters. Working with subject matter experts, the USGS has been coordinating an international agency effort to establish a new CARD4L PFS for aquatic reflectance to be considered for CEOS endorsement in 2021.
","conferenceTitle":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","conferenceDate":"July 11-16, 2021","conferenceLocation":"Brussels, Blegium","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS47720.2021.9554440","usgsCitation":"Barnes, C., Siqueira, A., and Labahn, S., 2021, USGS CEOS analysis ready data for land achievements and future plans, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Blegium, July 11-16, 2021, p. 1785-1788, https://doi.org/10.1109/IGARSS47720.2021.9554440.","productDescription":"4 p.","startPage":"1785","endPage":"1788","ipdsId":"IP-129380","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnes, Christopher","contributorId":359950,"corporation":false,"usgs":false,"family":"Barnes","given":"Christopher","affiliations":[{"id":68993,"text":"KBR Inc., Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":946557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siqueira, Andreia","contributorId":359951,"corporation":false,"usgs":false,"family":"Siqueira","given":"Andreia","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":946558,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Labahn, Steven T. 0000-0002-9258-2890 labahn@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2890","contributorId":3994,"corporation":false,"usgs":true,"family":"Labahn","given":"Steven T.","email":"labahn@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":946559,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70262548,"text":"70262548 - 2021 - Characterizing downstream migration timing of American Eels using commercial catch data in the Penobscot and Delaware rivers","interactions":[],"lastModifiedDate":"2025-01-22T16:13:58.136579","indexId":"70262548","displayToPublicDate":"2021-10-12T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing downstream migration timing of American Eels using commercial catch data in the Penobscot and Delaware rivers","docAbstract":"Adult “silver-phase” American Eels Anguilla rostrata were a focus of commercial fisheries in the 1970s and 1980s, but stocks have been depleted due to many anthropogenic factors. One significant source of mortality occurs during the downstream migration of eels when passing through turbines at hydroelectric facilities. We sought to construct a model to predict eel migration timing to inform optimization of mitigation actions that might reduce mortality. We utilized commercial catch collected from 16 tributaries in the Penobscot River watershed, Maine (2–10 years), and the Delaware River, New York (31 years). A Bayesian hierarchical approach was used to model the relationship between the timing of silver eel capture and environmental conditions that are known to be related to their movements (i.e., river discharge, water temperature, and lunar cycle). Among river systems, daily catch was associated with higher-than-average flows, temperatures of 7–22°C, and new lunar phase cycles. A cross-validation approach to evaluate the ability of the models to make predictions for new data demonstrated a greater ability (higher R2 values) to predict weekly eel catch (0.01–0.92) compared to daily eel catch (0.00–0.42). In addition, we examined the model’s ability to forecast migration events by applying posterior simulations to make predictions of eel catch by ordinal date. Predicted daily eel catch generally followed the trend of observed daily catch and was stronger for the Delaware River (R2 = 0.67) than for Souadabscook Stream, Maine (R2 = 0.07). Sharp pulses in observed catch were not reflected by the predicted catch. Additionally, variability observed among rivers suggests that site-specific modeling may be advantageous (and necessary) to capture local conditions, thereby improving predictive power. More broadly, our work highlights a novel use of fishery-dependent data in a Bayesian modeling framework to predict intervals of risk for migrating fish.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10182","usgsCitation":"Weaver, D., Sigourney, D., Delucia, M., and Zydlewski, J.D., 2021, Characterizing downstream migration timing of American Eels using commercial catch data in the Penobscot and Delaware rivers: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 13, no. 5, p. 534-547, https://doi.org/10.1002/mcf2.10182.","productDescription":"14 p.","startPage":"534","endPage":"547","ipdsId":"IP-119530","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10182","text":"Publisher Index Page"},{"id":480929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, New York","otherGeospatial":"Delaware River, Penobscot 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\"}}]}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Weaver, Daniel M.","contributorId":349624,"corporation":false,"usgs":false,"family":"Weaver","given":"Daniel M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":924524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sigourney, Douglas B.","contributorId":349625,"corporation":false,"usgs":false,"family":"Sigourney","given":"Douglas B.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":924525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Delucia, Mari-Beth","contributorId":349627,"corporation":false,"usgs":false,"family":"Delucia","given":"Mari-Beth","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":924526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924523,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227461,"text":"70227461 - 2021 - A new analysis of caldera unrest through the integration of geophysical data and FEM modeling: The Long Valley caldera case study","interactions":[],"lastModifiedDate":"2022-01-18T13:17:45.229897","indexId":"70227461","displayToPublicDate":"2021-10-11T07:14:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A new analysis of caldera unrest through the integration of geophysical data and FEM modeling: The Long Valley caldera case study","docAbstract":"We develop a spatio-temporal geostatistical interpolation framework to estimate hypoxia extent (dissolved oxygen [DO] concentrations below 2 mg/L) with data from a network of DO loggers. The framework uses empirical orthogonal functions and Bayesian kriging to identify the spatially varying temporal pattern and estimate the distribution of hypoxia, including estimation uncertainty. A prototype web application is also developed in R. The framework is applied to analyze spatio-temporal dynamics of DO in the central basin of Lake Erie in North America using data sampled from a logger network placed on the lake bottom during the summers of 2014, 2015, and 2016. Cross-validation results demonstrate that the framework is capable of capturing the dynamic nature of bottom hypoxia over offshore areas, but nearshore areas have poor interpolation performance due to the impacts of complex physical processes such as seiche events. The findings showed that in the central basin, hypoxia started to emerge in early August of 2014, while in 2015 and 2016 hypoxia began in July. The peak hypoxia extent occurred in late September 2014, mid-August 2015, and early September 2016. The prediction error of the overall spatial extent of hypoxia was as large as 25% of the interpolation area based on current logger deployment. Based on the cross-validation and interpolation error, we suggest placing more loggers in nearshore areas to reduce prediction error near the margins of the hypoxic zone.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027676","usgsCitation":"Xu, W., Collingsworth, P.D., Kraus, R., and Minsker, B., 2021, Spatio-temporal analysis of hypoxia in the Central Basin of Lake Erie of North America: Water Resources Research, e2020WR027676, 21 p., https://doi.org/10.1029/2020WR027676.","productDescription":"e2020WR027676, 21 p.","ipdsId":"IP-118196","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":488149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr027676","text":"Publisher Index Page"},{"id":485521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"central Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.42336336234484,\n 42.064125981872365\n ],\n [\n -80.63612291121615,\n 42.61061044439231\n ],\n [\n -81.33119453643565,\n 42.65041782076645\n ],\n [\n -82.23999909377955,\n 42.12924860730712\n ],\n [\n -82.58547021684097,\n 41.98346357854376\n ],\n [\n -82.60591624431427,\n 41.37891831441843\n ],\n [\n -82.04907153916253,\n 41.448543421544855\n ],\n [\n -81.71420631389763,\n 41.45777327935954\n ],\n [\n -81.26435037076813,\n 41.717583757728164\n ],\n [\n -80.42336336234484,\n 42.064125981872365\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Wenzhao","contributorId":200526,"corporation":false,"usgs":false,"family":"Xu","given":"Wenzhao","email":"","affiliations":[],"preferred":false,"id":935976,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collingsworth, Paris D.","contributorId":145526,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":935977,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":935978,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Minsker, Barbara","contributorId":200528,"corporation":false,"usgs":false,"family":"Minsker","given":"Barbara","email":"","affiliations":[],"preferred":false,"id":935979,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249430,"text":"70249430 - 2021 - Impact of precipitation and increasing temperatures on drought trends in eastern Africa","interactions":[],"lastModifiedDate":"2023-10-10T11:47:06.220865","indexId":"70249430","displayToPublicDate":"2021-10-10T06:43:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17049,"text":"Earth Systems Science Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Impact of precipitation and increasing temperatures on drought trends in eastern Africa","docAbstract":"In eastern Africa droughts can cause crop failure and lead to food insecurity. With increasing temperatures, there is an a priori assumption that droughts are becoming more severe. However, the link between droughts and climate change is not sufficiently understood. Here we investigate trends in long-term agricultural drought and the influence of increasing temperatures and precipitation deficits.
Using a combination of models and observational datasets, we studied trends, spanning the period from 1900 (to approximate pre-industrial conditions) to 2018, for six regions in eastern Africa in four drought-related annually averaged variables: soil moisture, precipitation, temperature, and evaporative demand (E0). In standardized soil moisture data, we found no discernible trends. The strongest influence on soil moisture variability was from precipitation, especially in the drier or water-limited study regions; temperature and E0 did not demonstrate strong relations to soil moisture. However, the error margins on precipitation trend estimates are large and no clear trend is evident, whereas significant positive trends were observed in local temperatures. The trends in E0 are predominantly positive, but we do not find strong relations between E0 and soil moisture trends. Nevertheless, the E0 trend results can still be of interest for irrigation purposes because it is E0 that determines the maximum evaporation rate.
We conclude that until now the impact of increasing local temperatures on agricultural drought in eastern Africa is limited and we recommend that any soil moisture analysis be supplemented by an analysis of precipitation deficit.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/esd-12-17-2021","usgsCitation":"Kew, S.F., Philip, S.Y., Hauser, M., Hobbins, M., Wanders, N., Veldkamp, T., von Oldenburgh, G., van der Wiel, K., Veldkamp, T., Kimutai, J., Funk, C., and Otto, F., 2021, Impact of precipitation and increasing temperatures on drought trends in eastern Africa: Earth Systems Science Dynamics, v. 12, no. 1, p. 17-35, https://doi.org/10.5194/esd-12-17-2021.","productDescription":"19 p.","startPage":"17","endPage":"35","ipdsId":"IP-119262","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":450499,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/esd-12-17-2021","text":"Publisher Index Page"},{"id":421805,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Eastern Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 29.667968750000483,\n 27.215556209028804\n ],\n [\n 29.667968750000483,\n -11.178401873712374\n ],\n [\n 53.04687500000094,\n -11.178401873712374\n ],\n [\n 53.04687500000094,\n 27.215556209028804\n ],\n [\n 29.667968750000483,\n 27.215556209028804\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Kew, Sarah F.","contributorId":330669,"corporation":false,"usgs":false,"family":"Kew","given":"Sarah","email":"","middleInitial":"F.","affiliations":[{"id":16158,"text":"Royal Netherlands Meteorological Institute","active":true,"usgs":false}],"preferred":false,"id":885591,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philip, Sjoukje Y.","contributorId":330686,"corporation":false,"usgs":false,"family":"Philip","given":"Sjoukje","email":"","middleInitial":"Y.","affiliations":[{"id":78966,"text":"Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":885592,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hauser, Mathias","contributorId":330687,"corporation":false,"usgs":false,"family":"Hauser","given":"Mathias","email":"","affiliations":[{"id":32389,"text":"Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":885593,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hobbins, Michael","contributorId":127605,"corporation":false,"usgs":false,"family":"Hobbins","given":"Michael","email":"","affiliations":[{"id":7075,"text":"National Integrated Drought Information System, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":885594,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wanders, Niko","contributorId":330688,"corporation":false,"usgs":false,"family":"Wanders","given":"Niko","email":"","affiliations":[{"id":78968,"text":"Department of Physical Geography, Utrecht University, Utrecht, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":885595,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Veldkamp, Ted","contributorId":330689,"corporation":false,"usgs":false,"family":"Veldkamp","given":"Ted","email":"","affiliations":[{"id":78968,"text":"Department of Physical Geography, Utrecht University, Utrecht, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":885596,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"von Oldenburgh, Gert","contributorId":330690,"corporation":false,"usgs":false,"family":"von Oldenburgh","given":"Gert","email":"","affiliations":[{"id":78969,"text":"Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":885597,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"van der Wiel, Karin","contributorId":209883,"corporation":false,"usgs":false,"family":"van der Wiel","given":"Karin","email":"","affiliations":[{"id":16158,"text":"Royal Netherlands Meteorological Institute","active":true,"usgs":false}],"preferred":false,"id":885888,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Veldkamp, Ted I. E.","contributorId":330795,"corporation":false,"usgs":false,"family":"Veldkamp","given":"Ted I. E.","affiliations":[],"preferred":false,"id":885889,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kimutai, Joyce","contributorId":330796,"corporation":false,"usgs":false,"family":"Kimutai","given":"Joyce","email":"","affiliations":[],"preferred":false,"id":885890,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885598,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Otto, Friederike","contributorId":330671,"corporation":false,"usgs":false,"family":"Otto","given":"Friederike","email":"","affiliations":[{"id":78958,"text":"Environmental Change Institute","active":true,"usgs":false}],"preferred":false,"id":885599,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70224986,"text":"70224986 - 2021 - Acute oral toxicity and tissue residues of saxitoxin in the mallard (Anas platyrhynchos)","interactions":[],"lastModifiedDate":"2023-06-23T13:18:52.258429","indexId":"70224986","displayToPublicDate":"2021-10-09T07:35:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Acute oral toxicity and tissue residues of saxitoxin in the mallard (Anas platyrhynchos)","title":"Acute oral toxicity and tissue residues of saxitoxin in the mallard (Anas platyrhynchos)","docAbstract":"Since 2014, widespread, annual mortality events involving multiple species of seabirds have occurred in the Gulf of Alaska, Bering Sea, and Chukchi Sea. Among these die-offs, emaciation was a common finding with starvation often identified as the cause of death. However, saxitoxin (STX) was detected in many carcasses, indicating exposure of these seabirds to STX in the marine environment. Few data are available that describe the effects of STX in birds, thus presenting challenges for determining its contributions to specific mortality events. To address these knowledge gaps, we conducted an acute oral toxicity trial in mallards (Anas platyrhynchos), a common laboratory avian model, using an up-and-down method to estimate the median lethal dose (LD50) for STX. Using an enzyme-linked immunosorbent assay (ELISA), we tested select tissues from all birds and feces from those individuals that survived initial dosing. Samples with an ELISA result that exceeded approximately 10 µg 100 g−1 STX and randomly selected ELISA negative samples were further tested by high-performance liquid chromatography (HPLC). Tissues collected from mallards were also examined grossly at necropsy and then later by microscopy to identify lesions attributable to STX. The estimated LD50 was 167 µg kg−1 (95% CI = 69–275 µg kg−1). Saxitoxin was detected in fecal samples of all mallards tested for up to 48 h after dosing and at the end of the sampling period (7 d) in three birds. In those individuals that died or were euthanized <2 h after dosing, STX was readily detected throughout the gastrointestinal tract but only infrequently in heart, kidney, liver, lung, and breast muscle. No gross or microscopic lesions were observed that could be attributable to STX exposure. Given its acute toxicity, limited detectability, and frequent occurrence in the Alaska marine environment, additional research on STX in seabirds is warranted.
The United States and European Union (EU) face common challenges in managing natural capital and balancing conservation and resource use with consumption of other forms of capital. This paper synthesizes findings from 11 individual application papers from a special issue of Ecosystem Services on natural capital accounting (NCA) and their application to the public and private sectors in the EU and U.S. NCA is inherently a data-integration centered exercise, aiming to draw new insights by realigning environmental and economic data into a consistent framework. Drawing primarily on papers from the special issue and other key NCA literature, we identify lessons learned and gaps remaining for NCA’s development and application to decision making. In doing so, we identify eight key similarities and three major differences in NCA development, status, and application between the U.S. and EU. NCA can be highly policy relevant: special issue papers address critical issues including agriculture, water, conservation/land-use planning, climate, and corporate decision making. In both the U.S. and EU, further application is needed to drive demand for the accounts’ production. Based on these experiences, the U.S. and EU can be important leaders in cross-sector, international collaboration toward next-generation environmental economic accounts that advance global NCA practice.
Trace-metal concentrations in sediment and in the clam Limecola petalum (formerly reported as Macoma balthica and M. petalum), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in south San Francisco Bay, Calif. This report includes the data collected by the U.S. Geological Survey (USGS) for the period January 2019 to December 2019. These data append to long-term datasets extending back to 1974. A major focus of the report is an integrated description of the 2019 data within the context of the longer, multidecadal dataset. This dataset supports the City of Palo Alto’s Near-Field Receiving-Water Monitoring Program, initiated in 1994.
Significant reductions in silver and copper contamination occurred at the site in the 1980s following the implementation by PARWQCP of advanced wastewater treatment and source control measures. Since the 1990s, concentrations of these elements in surface sediments have continued to decrease, although more slowly. Silver appears to have stabilized at concentrations about twice the regional background concentration. Presently, sediment copper concentrations appear to be near the regional background level. Over the same period (1994–2019), sedimentary iron and zinc also exhibited modest declines. Sedimentary aluminum, chromium, mercury, nickel, and selenium have not exhibited any trend. Since 1994, concentrations of silver and copper in L. petalum have varied seasonally, apparently in response to a combination of site-specific metal exposures and cyclic growth and reproduction, as reported previously. Seasonal patterns for other elements, including chromium, mercury, nickel, selenium, and zinc, were generally similar in timing and magnitude as those for silver and copper. The annual growth and reproductive cycle explained a small amount of the variance in annual silver and zinc tissue metal concentrations. However, interannual trends are not apparent for any element.
Biological effects of elevated silver and copper contamination at the Palo Alto site have been interpreted from data collected during and after the recession of these contaminants. Concentrations of both elements in the soft tissues of L. petalum declined with sedimentary copper and silver. This pattern was associated with changes in the reproductive activity of L. petalum, as well as the structure of the benthic invertebrate community. Reproductive activity of L. petalum increased as metal concentrations in L. petalum declined and presently is stable with almost all animals initiating reproduction in the fall and spawning the following spring. Analyses of the benthic community structure indicate that the infaunal invertebrate community has shifted from one dominated by several opportunistic species when silver and copper exposures were highest to one in which the species abundance is more evenly distributed, a pattern that indicates a more stable community that is subjected to fewer stressors. Importantly, this long-term change is unrelated to other metals and other measured environmental factors, including salinity and sediment composition. In addition, two of the opportunistic species (Ampelisca abdita and Streblospio benedicti) that brood their young and live on the surface of the sediment in tubes have shown a continual decline in dominance coincident with the decline in metals. Both species had short-lived rebounds in abundance in 2008, 2009, and 2010 and showed signs of increasing abundance in 2019. Heteromastus filiformis (a subsurface polychaete worm that lives in the sediment, consumes sediment and organic particles residing in the sediment, and reproduces by laying its eggs on or in the sediment) showed a concurrent increase in dominance and, in the last several years before 2008, showed a stable population. H. filiformis abundance increased slightly in 2011–2012 and returned to pre-2011 numbers in 2019.
An unidentified disturbance occurred on the mudflat in early 2008 that resulted in the loss of the benthic animals, except for deep-dwelling animals like L. petalum. However, within two months of this event, animals returned to the mudflat. The resilience of the community suggested that the disturbance was not caused by a persistent toxin or anoxia. The reproductive mode of most species that were present in 2019 was indicative of species that were available either as pelagic larvae or as mobile adults. Although oviparous species were lower in number in this group, the authors hypothesize that these species will return slowly as more species move back into the area. The use of functional ecology was highlighted in the 2019 benthic community data, which showed that the animals that have now returned to the mudflat are those that can respond successfully to a physical, nontoxic disturbance. Today, community data show a mix of species that consume the sediment, or filter feed, those that have pelagic larvae that must survive landing on the sediment, and those that brood their young. USGS scientists view the 2008 disturbance event as a response by the infaunal community to an episodic natural stressor (possibly sediment accretion or a pulse of freshwater), in contrast to the long-term recovery from metal contamination. We will compare this recovery to the long-term recovery observed after the 1970s when the decline in sediment pollutants was the dominating factor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211079","collaboration":"Prepared in cooperation with the City of Palo Alto, California","usgsCitation":"Cain, D.J., Croteau, M.-N., Thompson, J.K., Parchaso, F., Stewart, R., Shrader, K.H., Zierdt Smith, E.L., and Luoma, S.N., 2021, Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2019: U.S. Geological Survey Open-File Report 2021–1079, 59 p., https://doi.org/10.3133/ofr20211079.","productDescription":"Report: viii, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","ipdsId":"IP-119549","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":416178,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231017","text":"Open-File Report 2023-1017","description":"Cain, D.J., Croteau, M.-N., Thompson, J.K., Parchaso, F., Stewart, R., Zierdt Smith, E.L., Shrader, K.H., Kieu, L.H., and Luoma, S.N., 2023, Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2020: U.S. Geological Survey Open-File Report 2023–1017, 51 p., https://doi.org/10.3133/ofr20231017.","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2020"},{"id":390272,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20171135","text":"Open-File Report 2017-1135","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California; 2016"},{"id":390273,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161118","text":"Open-File Report 2016-1118","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California; 2015"},{"id":390267,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IBQ23S","linkHelpText":"Data for monitoring trace metal and benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California"},{"id":390268,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1079/covrthb.jpg"},{"id":390269,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1079/ofr20211079.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390270,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191084","text":"Open-File Report 2019-1084","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2018"},{"id":390271,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181107","text":"Open-File Report 2018-1107","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2017"}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.16728210449219,\n 37.385980767871416\n ],\n [\n -121.90361022949219,\n 37.385980767871416\n ],\n [\n -121.90361022949219,\n 37.496107562317064\n ],\n [\n -122.16728210449219,\n 37.496107562317064\n ],\n [\n -122.16728210449219,\n 37.385980767871416\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Water Resources, Earth System Processes Division
U.S. Geological Survey
411 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Walleye (Sander vitreus) is an economically and culturally important species in Lake Erie that has experienced large interannual variability in recruitment. We examined the importance of prey biomass during the larval period to walleye recruitment while also considering the importance of temperature. Using nine years of field data over a 22-year period (1994–2016) for larval walleye and zooplankton, we found that strong recruitment events occurred in years when the biomass (dry µg L-1) of copepods (e.g., calanoids, cyclopoids) was greater during the spring larval period. Conversely, the biomass of cladocerans and mean spring water temperatures were poor predictors of walleye recruitment. Our results highlight the need to consider zooplankton availability during the larval period when seeking to understand the recruitment dynamics of freshwater fish populations such as Lake Erie walleye.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.09.009","usgsCitation":"May, C.J., Budnik, R., Ludsin, S., O’Donnell, D., Hood, J.M., Roseman, E., and Marschall, E., 2021, Evidence that copepod biomass during the larval period regulates recruitment of Lake Erie walleye: Journal of Great Lakes Research, v. 47, no. 6, p. 1737-1745, https://doi.org/10.1016/j.jglr.2021.09.009.","productDescription":"9 p.","startPage":"1737","endPage":"1745","ipdsId":"IP-114665","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":390394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Ohio","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.5455322265625,\n 41.52091689636249\n ],\n [\n -82.8533935546875,\n 41.52091689636249\n ],\n [\n -82.8533935546875,\n 41.94314874732696\n ],\n [\n -83.5455322265625,\n 41.94314874732696\n ],\n [\n -83.5455322265625,\n 41.52091689636249\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"May, Cassandra J.","contributorId":150961,"corporation":false,"usgs":false,"family":"May","given":"Cassandra","email":"","middleInitial":"J.","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":824820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budnik, R.","contributorId":267239,"corporation":false,"usgs":false,"family":"Budnik","given":"R.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":824821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ludsin, S.","contributorId":267240,"corporation":false,"usgs":false,"family":"Ludsin","given":"S.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":824822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, D.","contributorId":267241,"corporation":false,"usgs":false,"family":"O’Donnell","given":"D.","email":"","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":824823,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hood, James M.","contributorId":267332,"corporation":false,"usgs":false,"family":"Hood","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":824824,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":824825,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marschall, E.","contributorId":267242,"corporation":false,"usgs":false,"family":"Marschall","given":"E.","email":"","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":824826,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224943,"text":"ofr20211086 - 2021 - Water-quality distributions in the East Branch Black River near the Chemical Recovery Systems site in Elyria, Ohio, 2021","interactions":[],"lastModifiedDate":"2021-10-11T11:44:55.648814","indexId":"ofr20211086","displayToPublicDate":"2021-10-06T17:39:53","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":"2021-1086","displayTitle":"Water-Quality Distributions in the East Branch Black River near the Chemical Recovery Systems Site in Elyria, Ohio, 2021","title":"Water-quality distributions in the East Branch Black River near the Chemical Recovery Systems site in Elyria, Ohio, 2021","docAbstract":"Autonomous underwater vehicles are uniquely designed to provide spatially dense water-quality data along with bathymetry and velocimetry. The U.S. Environmental Protection Agency Region 5 requested technical assistance from the U.S. Geological Survey in support of ongoing investigations at the Chemical Recovery Systems site to collect spatially dense water-quality and bathymetry data in the East Branch Black River in Elyria, Ohio. This report was prepared in cooperation with the U.S. Environmental Protection Agency to present the results of the autonomous underwater vehicle survey near the Chemical Recovery Systems site on March 22, 2021. Plots of distributions of water temperature, specific conductance, pH, and dissolved oxygen are presented that may help guide and focus future U.S. Environmental Protection Agency efforts at the site to determine the degree of groundwater/surface-water interaction.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211086","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Wilson, J.L., and Dobrowolski, E.G., 2021, Water-quality distributions in the East Branch Black River near the Chemical Recovery Systems site in Elyria, Ohio, 2021: U.S. Geological Survey Open-File Report 2021–1086, 10 p., https://doi.org/10.3133/ofr20211086.","productDescription":"Report: vii, 10 p.; Data Release; Dataset","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-128518","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390284,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://waterdata.usgs.gov/oh/nwis/uv?site_no=04200500","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS 04200500 Black River at Elyria OH, in USGS water data for the Nation"},{"id":390281,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1086/coverthb.jpg"},{"id":390282,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1086/ofr20211086.pdf","text":"Report","size":"4.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021"},{"id":390283,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FEBCBY","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Autonomous underwater vehicle water-quality and sonar measurements in the East Branch Black River near Elyria, Ohio, 2021"}],"country":"United States","state":"Ohio","city":"Elyria","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.23541259765625,\n 41.2509675141624\n ],\n [\n -81.9635009765625,\n 41.2509675141624\n ],\n [\n -81.9635009765625,\n 41.49623534616764\n ],\n [\n -82.23541259765625,\n 41.49623534616764\n ],\n [\n -82.23541259765625,\n 41.2509675141624\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
This 1:1,000,000-scale geologic map of the Athabasca Valles region of Mars places the best-preserved lavas on Mars into their geologic context. The map shows vigorous geologic activity in the most recent epoch of the geologic history of Mars, which is extremely unusual for the planet. In these atypically youthful terrains, the interpretations of geologic processes are exceptionally robust for planetary geologic mapping. The investigation relies heavily on images from the NASA Mars Reconnaissance Orbiter (MRO) High-Resolution Imaging Science Experiment (HiRISE) and Context (CTX) cameras, which have sub-meter and sub-decameter spatial resolution, respectively. Geospatial fidelity is provided by the photogrammetrically controlled 100-meter-per-pixel map base derived from Thermal Emission Imaging System (THEMIS) daytime infrared image data. The individual HiRISE and CTX images are available through the National Aeronautics and Space Administration (NASA) Planetary Data System Cartography and Imaging Sciences Node (https://pds-imaging.jpl.nasa.gov/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3477","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Keszthelyi, L.P., Huff, A.E., and Jaeger, W.L., 2021, Geologic map of the Athabasca Valles region, Mars: U.S. Geological Survey Scientific Investigations Map 3477, pamphlet 11 p., 1 sheet, scale 1,000,000, https://doi.org/10.3133/sim3477.","productDescription":"Report: iv, 11 p.; 1 Sheet: 44.19 x 36.43 inches; Metadata; Database; Read Me","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-101388","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436171,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JA21OB","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3477 Geologic Map of the Athabasca Valles Region, Mars"},{"id":390265,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3477/sim3477_pamphlet.pdf","text":"Pamphlet","size":"600 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":390259,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3477/covrthb.jpg"},{"id":400822,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9JA21OB","text":"Interactive map","linkHelpText":"- Geologic Map of the Athabasca Valles Region, Mars, 1:1M. Keszthelyi et al. (2021)"},{"id":390266,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3477/sim3477_sheet.pdf","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390264,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3477/sim3477_database.zip","size":"90 MB","linkFileType":{"id":6,"text":"zip"}},{"id":390263,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3477/sim3477_readme.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}},{"id":390262,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3477/sim3477_metadata.xml","size":"10 KB","linkFileType":{"id":8,"text":"xml"}},{"id":390261,"rank":2,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3477/sim3477_metadata.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}}],"otherGeospatial":"Mars","contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
A new data layer provides Coastal and Marine Ecological Classification Standard (CMECS) labels for global coastal segments at 1 km or shorter resolution. These characteristics are summarized for six US Marine Biodiversity Observation Network (MBON) sites and one MBON Pole to Pole of the Americas site in Argentina. The global coastlines CMECS classifications were produced from a partitioning of a 30 m Landsat-derived shoreline vector that was segmented into 4 million 1 km or shorter segments. Each segment was attributed with values from 10 variables that represent the ecological settings in which the coastline occurs, including properties of the adjacent water, adjacent land, and coastline itself. The 4 million segments were classified into 81,000 coastal segment units (CSUs) as unique combinations of variable classes. We summarize the process to develop the CSUs and derive summary descriptions for the seven MBON case study sites. We discuss the intended application of the new CSU data for research and management in coastal areas.
","language":"English","publisher":"The Oceanography Society","doi":"10.5670/oceanog.2021.219","usgsCitation":"Sayre, R., Butler, K., Van Graafeiland, K., Breyer, S., Wright, D., Frye, C., Karagulle, D., Martin, M.T., Cress, J.J., Allen, T., Allee, R., Parsons, R., Nyberg, B., Costello, M., Harris, P., and Muller-Karger, F., 2021, A global ecological classification of coastal segment units to complement marine biodiversity observation network assessments: Oceanography, v. 34, no. 2, 10 p., https://doi.org/10.5670/oceanog.2021.219.","productDescription":"10 p.","ipdsId":"IP-129029","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":450534,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5670/oceanog.2021.219","text":"Publisher Index Page"},{"id":436173,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HWHSPU","text":"USGS data release","linkHelpText":"Global Ecological Classification of Coastal Segment Units"},{"id":390515,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":213674,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":825178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Butler, Kevin","contributorId":267714,"corporation":false,"usgs":false,"family":"Butler","given":"Kevin","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":825179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van 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0000-0002-3148-8374","orcid":"https://orcid.org/0000-0002-3148-8374","contributorId":213682,"corporation":false,"usgs":true,"family":"Cress","given":"Jill","email":"","middleInitial":"Janene","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":825186,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Allen, Tom","contributorId":267720,"corporation":false,"usgs":false,"family":"Allen","given":"Tom","affiliations":[{"id":36518,"text":"Old Dominion University","active":true,"usgs":false}],"preferred":false,"id":825187,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Allee, Rebecca","contributorId":267721,"corporation":false,"usgs":false,"family":"Allee","given":"Rebecca","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":825188,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Parsons, 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Arendal","active":true,"usgs":false}],"preferred":false,"id":825193,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Muller-Karger, Frank","contributorId":267728,"corporation":false,"usgs":false,"family":"Muller-Karger","given":"Frank","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":825192,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70224917,"text":"sir20215073 - 2021 - Cimarron River alluvial aquifer hydrogeologic framework, water budget, and implications for future water availability in the Pawnee Nation Tribal jurisdictional area, Payne County, Oklahoma, 2016–18","interactions":[],"lastModifiedDate":"2021-10-06T11:55:47.832961","indexId":"sir20215073","displayToPublicDate":"2021-10-05T16:57:57","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":"2021-5073","displayTitle":"Cimarron River Alluvial Aquifer Hydrogeologic Framework, Water Budget, and Implications for Future Water Availability in the Pawnee Nation Tribal Jurisdictional Area, Payne County, Oklahoma, 2016–18","title":"Cimarron River alluvial aquifer hydrogeologic framework, water budget, and implications for future water availability in the Pawnee Nation Tribal jurisdictional area, Payne County, Oklahoma, 2016–18","docAbstract":"The Cimarron River is a free-flowing river and is a major source of water as it flows across Oklahoma. Increased demand for water resources within the Cimarron River alluvial aquifer in north-central Oklahoma (primarily in Payne County) has led to increases in groundwater withdrawals for agriculture, public, irrigation, industrial, and domestic supply purposes. The Pawnee Nation of Oklahoma (Pawnee Nation) is particularly concerned about the sustainability of the Cimarron River alluvial aquifer and whether the aquifer will continue to be a viable water resource for future generations of Tribal members and residents. To better understand current (2021) water resources and possible future water availability in the Pawnee Nation Tribal jurisdictional area, the U.S. Geological Survey, in cooperation with the Bureau of Indian Affairs and the Pawnee Nation of Oklahoma, compiled available hydrogeologic data and developed conceptual and numerical groundwater-flow models for the Cimarron River alluvial aquifer in Payne County, north-central Oklahoma, including a focus area in the Pawnee Nation Tribal jurisdictional area for the 2016–18 study period.
A conceptual water budget was created to establish estimates of groundwater fluxes into and out of the aquifer through hydrologic boundaries and groundwater withdrawals for use in the numerical groundwater-flow model. The conceptual water budget focuses on the alluvial aquifer, meaning that inflows include sources of water to the aquifer and that outflows include sources of water out of the aquifer, such as base-flow contributions to the Cimarron River. The conceptual water budget was constructed by using data from 2017 (the most complete year of record for each data type included in the model) for the Pawnee Nation subdomain of the Cimarron River alluvial aquifer model extent (Pawnee Nation subdomain).
Groundwater withdrawals were estimated from groundwater-withdrawal rate information for permanent and temporary permitted wells that was obtained from the Oklahoma Water Resources Board. One-half of each annual permitted groundwater-withdrawal rate allotted was used as the estimated annual groundwater-withdrawal amount. Halving the permitted groundwater-withdrawal rate was done because permitted withdrawal rates are the maximum permitted rate and actual groundwater withdrawals are generally appreciably lower than the maximum permitted rate. Total groundwater withdrawals were estimated as 1,300 acre-feet per year for the Pawnee Nation subdomain. Various hydrogeologic data were measured to assist with model development, including depth to bedrock and water-table altitude data. In support of the model development, analyses pertaining to groundwater flow, groundwater/surface-water interactions, base flows in the Cimarron River, and lithological interpretations in the Pawnee Nation Tribal jurisdictional area were used to compute a conceptual water budget applicable to the 2016–18 study period. A numerical groundwater-flow model was developed using the hydrogeologic framework of the Cimarron River alluvial aquifer and the conceptual water budget. The numerical model consists of a single layer representing alluvium and terrace deposits within the alluvial aquifer model area. Hydraulic conductivities were estimated and modeled for the alluvium and terrace deposits in the alluvial aquifer. Base-flow values were estimated using the base-flow index from streamflow data collected at U.S. Geological Survey streamgages. Stream seepage values were derived from the mean 2017 base-flow index between certain streamgages. Hydraulic conductivities were specified an initial (before calibration) value of 120 feet per day for the alluvium deposits and 16 feet per day for the terrace deposits.
The simulated inflows in the numerical groundwater-flow model of the Pawnee Nation subdomain were higher than the inflows of conceptual water budget, and the simulated outflows were lower than the outflows of the conceptual water budget. Overall, simulated base flows matched closely to observed base flows for the 2016 and 2017 stress periods. Simulated streamflow tended to match better with the observed streamflow for 2017, which was the period with the most data for the Cimarron River alluvial aquifer model.
Streamflow capture analysis was applied to the steady-state simulation to identify areas of the aquifer where base flows in the Cimarron River were most sensitive to groundwater withdrawals. The initial base-flow value was assigned the value obtained from streamflow-routing software used to simulate stream outflow for the calibrated steady-state base model. Subsequent simulations were run in each active cell in the Pawnee Nation subdomain for a specified groundwater-withdrawal rate of 180,000 cubic feet per day. The study area that includes the Pawnee Nation subdomain is in the upper Arkansas River Basin. A groundwater-withdrawal rate of 180,000 cubic feet per second per day represents a 34 percent increase compared to the highest permitted groundwater-withdrawal rate for the study area, which corresponds to the estimated 34 percent increase in groundwater withdrawals predicted by 2060 for the upper Arkansas River Basin. Simulated streamflow capture was highest in the alluvium deposits adjacent to the Cimarron River; that is, base flow in the Cimarron River decreased the most for simulated groundwater withdrawals in the alluvium deposits adjacent to the Cimarron River. Streamflow capture increased as the distance of a well from the Cimarron River decreased in the simulation. The northeastern part of the Pawnee Nation subdomain showed greater streamflow capture in a broader area; streamflow in that part of the Pawnee Nation subdomain is likely more sensitive to groundwater withdrawals compared to other parts of the Pawnee Nation subdomain.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215073","collaboration":"Prepared in cooperation with the Bureau of Indian Affairs and the Pawnee Nation of Oklahoma","usgsCitation":"Paizis, N.C., and Trevisan, A.R., 2021, Cimarron River alluvial aquifer hydrogeologic framework, water budget, and implications for future water availability in the Pawnee Nation Tribal jurisdictional area, Payne County, Oklahoma, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5073, 49 p., https://doi.org/10.3133/sir20215073.","productDescription":"Report: x, 49 p.; Data Release; Dataset","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-119627","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":390181,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5073/coverthb.jpg"},{"id":390182,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5073/sir20215073.pdf","text":"Report","size":"7.46 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5073"},{"id":390184,"rank":4,"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":390183,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WZGYQF","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used for the simulation of the Cimarron River alluvial aquifer in the Pawnee Nation Tribal jurisdictional area in Payne County, Oklahoma, 2016–17"}],"country":"United States","state":"Oklahoma","county":"Payne County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-96.9277,36.246],[-96.8216,36.245],[-96.8212,36.1593],[-96.6245,36.1605],[-96.6228,35.9427],[-97.1428,35.9442],[-97.1423,35.9641],[-97.1557,35.9485],[-97.1729,35.9428],[-97.1872,35.9426],[-97.2013,35.9469],[-97.2163,35.9576],[-97.2252,35.9677],[-97.236,35.9683],[-97.2461,35.9721],[-97.2523,35.9744],[-97.2734,35.9734],[-97.2841,35.9767],[-97.2863,35.9795],[-97.2862,35.9835],[-97.2883,35.9931],[-97.2927,36.0004],[-97.2982,36.0091],[-97.3055,36.011],[-97.3203,36.0108],[-97.3329,36.0078],[-97.3359,36.0024],[-97.34,35.9947],[-97.3475,35.9885],[-97.3556,35.9841],[-97.3569,36.1583],[-97.1426,36.1588],[-97.1417,36.245],[-96.9277,36.246]]]},\"properties\":{\"name\":\"Payne\",\"state\":\"OK\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
School Branch in Hendricks County in central Indiana, is a small stream with a variety of agricultural and suburban land uses that drains into the Eagle Creek Reservoir, a major source of drinking water for Indianapolis, Indiana. The School Branch watershed has become the focus of a collaborative partnership of Federal, State, and local agencies; a university research center; and agricultural producers to understand the effects of land use and management practices on water quality and water quantity in the watershed. The U.S. Geological Survey, in cooperation with the Indiana Department of Environmental Management, contributed to the School Branch partnership with the operation of three streamgages (03353415 School Branch at Maloney Road near Brownsburg, Indiana; 03353420 School Branch at County Road 750 North at Brownsburg, Indiana; and 03353430 School Branch at Noble Drive at Brownsburg, Indiana) and the operation of a continuous water-quality gage (also known as a supergage) at County Road 750 North that measured dissolved oxygen, pH, temperature, specific conductance, turbidity, nitrate, and orthophosphate. Additional efforts included the use of passive samplers to identify wastewater indicators; assessment of fish and macroinvertebrate communities and stream habitat to identify ecological impairment; sampling for nutrients and sediment to estimate loads; and using major ions, stable isotopes and nested groundwater monitoring wells at County Road 750 North to determine hydrologic connectivity between the groundwater and surface water. The objectives of this study were to collect surface and groundwater data to analyze the hydrology and water quality within the watershed. Total nitrogen yields were highest at the upstream site, Maloney Road, and indicated a mixture of nitrogen sources in the watershed. Differences found in total nitrogen loading patterns throughout the watershed may be linked to differences in hydrology and land-use management from site to site. The groundwater and surface water were shown to be highly connected, and except for some low-flow periods, the water was flowing from groundwater to the stream for most of the study period. Fish and macroinvertebrate communities show improvement from upstream to downstream, with increases in diversity, richness, and species sensitive to poor water quality and habitat. These increases were most likely due to improved habitat quality at the downstream station.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215061","collaboration":"Prepared in cooperation with the Indiana Department of Environmental Management","usgsCitation":"Bunch, A.R., McCausland, D.R., and Bayless, E.R., 2021, Hydrologic and ecological investigations in the School Branch watershed, Hendricks County, Indiana—Water years 2016–2018: U.S. Geological Survey Scientific Investigations Report 2021–5061, 61 p., https://doi.org/10.3133/sir20215061.","productDescription":"Report: x, 61 p.; Data Release","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-114931","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":390195,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5061/coverthb.jpg"},{"id":390196,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5061/sir20215061.pdf","text":"Report","size":"4.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5061"},{"id":390197,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QCIDBV","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Data and rloadest models for daily total nitrogen load for the School Branch watershed, Hendricks County, Indiana—Water years 2016–2018"}],"country":"United States","state":"Indiana","county":"Hendricks County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-86.3267,39.9238],[-86.325,39.8662],[-86.328,39.8662],[-86.3281,39.8526],[-86.3268,39.6318],[-86.4648,39.6297],[-86.4642,39.6006],[-86.574,39.6002],[-86.6546,39.6001],[-86.6522,39.6087],[-86.6463,39.6128],[-86.6403,39.6201],[-86.6404,39.6305],[-86.6654,39.6305],[-86.6858,39.63],[-86.6853,39.6884],[-86.6849,39.7773],[-86.6845,39.8648],[-86.6929,39.8643],[-86.6937,39.9228],[-86.3267,39.9238]]]},\"properties\":{\"name\":\"Hendricks\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
On March 12, 2019, Congress passed the John D. Dingell, Jr., Conservation, Management, and Recreation Act (Public Law 116–9; 133 Stat. 580), in which Title V, §5001 (43 U.S.C. 31k) authorized the establishment of the National Volcano Early Warning and Monitoring System (NVEWS) within the U.S. Geological Survey (USGS). Conceived by the USGS Volcano Hazards Program in 2005, NVEWS is designed to be a proactive, fully integrated national-scale volcano monitoring system to ensure that the 161 potentially active volcanoes in the United States and its territories are monitored at levels commensurate with the threat they pose. The core of this report is the first USGS NVEWS five-year management plan, which was presented to Congress on March 12, 2020, and which details the principal elements of NVEWS that will be developed over the next five years, pending sufficient funding. These elements are improvements and enhancements to the monitoring network, a National Volcano Data Center, an external grants activity, an Advisory Committee, an Implementation Committee, and partnerships, with estimated cost projections and annual milestones.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211092","usgsCitation":"Cervelli, P.F., Mandeville, C.W., Avery, V.F., and Wilkins, A.M., 2021, Five-year management plan for establishing and operating NVEWS—The National Volcano Early Warning System: U.S. Geological Survey Open-File Report 2021–1092, 11 p., https://doi.org/10.3133/ofr20211092.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-117913","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":390179,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1092/coverthb.jpg"},{"id":390180,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1092/ofr20211092.pdf","text":"Report","size":"2.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1092"}],"country":"United States","otherGeospatial":"Northern Mariana Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 143.08593749999997,\n 13.410994034321702\n ],\n [\n 146.513671875,\n 13.410994034321702\n ],\n [\n 146.513671875,\n 16.720385051694\n ],\n [\n 143.08593749999997,\n 16.720385051694\n ],\n [\n 143.08593749999997,\n 13.410994034321702\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.55468749999997,\n 17.476432197195518\n ],\n [\n -153.28125,\n 17.476432197195518\n ],\n [\n -153.28125,\n 22.755920681486405\n ],\n [\n -158.55468749999997,\n 22.755920681486405\n ],\n [\n -158.55468749999997,\n 17.476432197195518\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.5078125,\n 31.20340495091737\n ],\n [\n -105.1171875,\n 31.20340495091737\n ],\n [\n -105.1171875,\n 48.922499263758255\n ],\n [\n -125.5078125,\n 48.922499263758255\n ],\n [\n -125.5078125,\n 31.20340495091737\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.328125,\n 69.83962194067463\n ],\n [\n -149.765625,\n 70.95969716686398\n ],\n [\n -155.91796874999997,\n 71.30079291637452\n ],\n [\n -162.94921875,\n 70.4367988185464\n ],\n [\n -166.640625,\n 68.52823492039876\n ],\n [\n -167.34375,\n 65.6582745198266\n ],\n [\n -165.41015625,\n 62.186013857194226\n ],\n [\n -165.41015625,\n 60.23981116999893\n ],\n [\n -164.00390625,\n 58.53959476664049\n ],\n [\n -164.53125,\n 56.07203547180089\n ],\n [\n -164.35546875,\n 53.85252660044951\n ],\n [\n -152.75390624999997,\n 56.84897198026975\n ],\n [\n -149.4140625,\n 59.62332522313024\n ],\n [\n -143.96484375,\n 59.62332522313024\n ],\n [\n -137.4609375,\n 57.42129439209407\n ],\n [\n -131.66015625,\n 52.3755991766591\n ],\n [\n -130.60546875,\n 53.4357192066942\n ],\n [\n -131.66015625,\n 57.326521225217064\n ],\n [\n -136.58203125,\n 59.88893689676585\n ],\n [\n -140.625,\n 60.50052541051131\n ],\n [\n -141.328125,\n 69.83962194067463\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Hazards Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Contact Volcano Hazards Program
","tableOfContents":"Land-use land-cover change (LULCC) has become an important topic of research for the central United States because of the extensive conversion of the natural prairie into agricultural land, especially in the northern Great Plains. As a result, shifts in the natural climate (minimum/maximum temperature, precipitation, etc.) across the north-central United States have been observed, as noted within the Fourth National Climate Assessment (NCA4) report. Thus, it is necessary to understand how further LULCC will affect the near-surface atmosphere, the lower troposphere, and the planetary boundary layer (PBL) atmosphere over this region. The goal of this work was to investigate the utility of a new future land-use land-cover (LULC) dataset within the Weather Research and Forecasting (WRF) modeling system. The present study utilizes a modeled future land-use dataset developed by the Forecasting Scenarios of Land-Use Change (FORE-SCE) model to investigate the influence of future (2050) land use on a simulated PBL development within the WRF Model. Three primary areas of LULCC were identified within the FORE-SCE future LULC dataset across Nebraska and South Dakota. Variations in LULC between the 2005 LULC control simulation and four FORE-SCE simulations affected near-surface temperature (0.5°–1°C) and specific humidity (0.3–0.5 g kg−1). The differences noted in the temperature and moisture fields affected the development of the simulated PBL, leading to variations in PBL height and convective available potential energy. Overall, utilizing the FORE-SCE dataset within WRF produced notable differences relative to the control simulation over areas of LULCC represented in the FORE-SCE dataset.
Groundwater depletion is a major threat to agricultural and municipal water supply in California's Central Valley. Recent droughts during 2007–2009 and 2012–2016 exacerbated chronic groundwater depletion. However, it is unclear how much groundwater storage recovered from drought-related overdrafts during post-drought years, and how climatic conditions and water management affected recovery times. We estimated groundwater storage change in the Central Valley for April 2002 through September 2019 using four methods: GRACE satellite data, a water balance approach, a hydrologic simulation model, and monitoring wells. We also evaluated the sensitivity of drought recovery to different climate scenarios (recent climate ± droughts and future climate change scenarios: 20 GCMs and 2 RCPs) using water balance method and statistical sampling of historical climate data. Estimated Central Valley groundwater loss during the two droughts ranged from 19 km3 (2007–2009) to 28 km3 (2012–2016) (median of four methods). Median aquifer storage recovery was 34% and 19% of the overdraft during the 2010–2011 and 2017–2019 post-drought years, respectively. Numerical experiments show that recovery times are sensitive to climate forcing, with longer recovery times for a future climate scenario that replicate historical climatology relative to historical forcing with no droughts. Overdraft recovery times decrease by ∼2× with implementation of pumping restrictions (30th to 50th percentiles of historical groundwater depletion) to constrain groundwater depletion relative to no restrictions with a no-drought future climatology. This study highlights the importance of considering water management implications for future drought recoveries within the context of climate change scenarios.
Successful wildlife management depends upon coordination and consultation with local communities. However, much of the research used to inform management is often derived solely from data collected directly from wildlife. Indigenous people living in the Arctic have a close connection to their environment, which provides unique opportunities to observe their environment and the ecology of Arctic species. Further, most northern Arctic communities occur within the range of polar bears (nanuq, Ursus maritimus) and have experienced significant climatic changes. Here, we used semi-structured interviews from 2017 to 2019 to document Iñupiaq knowledge of polar bears observed over four decades in four Alaskan communities in the range of the Southern Beaufort Sea polar bear subpopulation: Wainwright, Utqiaġvik, Nuiqsut, and Kaktovik. All but one of 47 participants described directional and notable changes in sea ice, including earlier ice breakup, later ice return, thinner ice, and less multiyear pack ice. These changes corresponded with observations of bears spending more time on land during the late summer and early fall in recent decades—observations consistent with scientific and Indigenous knowledge studies in Alaska, Canada, and Greenland. Participants noted that polar bear and seal body condition and local abundance either varied geographically or exhibited no patterns. However, participants described a recent phenomenon of bears being exhausted and lethargic when arriving on shore in the summer and fall after extensive swims from the pack ice. Further, several participants suggested that maternal denning is occurring more often on land than sea ice. Participants indicated that village and regional governments are increasingly challenged to obtain resources needed to keep their communities safe as polar bears spend more time on land, an issue that is likely to be exacerbated both in this region and elsewhere as sea ice loss continues.
Theodore Roosevelt National Park is in western North Dakota and was established in 1978 under the National Wilderness Preservation system to preserve and protect the qualities of the North Dakota Badlands, including the wildlife, scenery, and wilderness. The park is made up of three units (North, Elkhorn Ranch, and South) that are connected by the Little Missouri River, which was identified by the National Park Service as a significant resource essential to fulfilling the park's purpose. The development of oil and gas (OG) resources has expanded in the past two decades in the region surrounding Theodore Roosevelt National Park. This expansion of OG development outside park boundaries increases the potential for adverse environmental and economic effects inside the park boundaries, especially for the hydrologic processes within Theodore Roosevelt National Park.
This report assesses the vulnerability of critical components that contribute to supporting plants and wildlife of the Northwestern Great Plains ecological region and Theodore Roosevelt National Park’s mission of preservation. Critical components include land cover, slope, soil saturated hydraulic conductivity, distance to Ovis canadensis (Shaw, 1804) (bighorn sheep) critical habitat, distance to springs, distance to rivers and streams, and distance to surficial aquifers. The study area included all the 12-digit hydrologic units within the watershed boundary dataset that intersect Theodore Roosevelt National Park or are within the 12-digit hydrologic units for Little Missouri River tributaries that flow into the park. Critical components that had existing publicly available geographic data were assessed and assigned vulnerability index values. These values were then summed to develop a vulnerability score and mapped. OG development and associated transportation infrastructure, referred to as “stressors” in this report, with publicly available geographic data were mapped, and then flow paths were generated starting from the stressor locations to assess their likelihood to contaminate vulnerable areas within the study area.
The North Unit had the most area with moderate, high, and very high vulnerability. These areas occurred all across the southern and eastern parts of the North Unit where the Little Missouri River, surficial aquifer, wetland type land covers, and bighorn sheep critical habitat are present. Several stressor flow paths from pipelines and highways cross these areas and may pose the most risk to the vulnerable areas identified. In the Elkhorn Ranch Unit, areas with moderate, high, and very high vulnerability were in the southeastern part of the unit, where the Little Missouri River, surficial aquifer, wetland type land covers, and bighorn sheep critical habitat are present. The stressor flow paths in the Elkhorn Ranch Unit follow the length of the Little Missouri River and all its tributaries in the study area. The stressor flow paths originated from crude oil wells and pipelines. In the South Unit, one area had moderate, high, and very high vulnerability. This area is where the Little Missouri River and bighorn sheep critical range are present. The stressor flow paths in the South Unit follow the length of the Little Missouri River and nearly all its tributaries in the study area. Several stressor flow paths cross the one identified vulnerable area that originated from crude oil wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3479","collaboration":"Prepared in cooperation with the Inland Oil Spill Preparedness Project","usgsCitation":"Valseth, K.J., 2021, Vulnerability assessment in and near Theodore Roosevelt National Park, North Dakota: U.S. Geological Survey Scientific Investigations Map 3479, pamphlet 9 p., 1 sheet, https://doi.org/10.3133/sim3479.","productDescription":"Pamphlet: vi, 9 p.; 1 Sheet: 23.50 x 31.10 inches; Dataset","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-122274","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":390167,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3479/sim3479_sheet1.pdf","text":"Sheet 1","size":"9.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3479 Sheet 1"},{"id":390169,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3479/sim3479.xml","size":"53.7 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3479 Pamphlet xml"},{"id":390165,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3479/coverthb.jpg"},{"id":390168,"rank":4,"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":390166,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3479/sim3479_pamphlet.pdf","text":"Report","size":"2.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3479 Pamphlet"},{"id":390170,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3479/images"}],"country":"United States","state":"North Dakota","otherGeospatial":"Theodore Roosevelt National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.72467041015625,\n 46.751153008636884\n ],\n [\n -103.14788818359375,\n 46.751153008636884\n ],\n [\n -103.14788818359375,\n 47.11873795272715\n ],\n [\n -103.72467041015625,\n 47.11873795272715\n ],\n [\n -103.72467041015625,\n 46.751153008636884\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
In 1999, a jet-fuels release was discovered at the Bulk Fuels Facility on Kirtland Air Force Base, Albuquerque, New Mexico. Contaminants had reached the water table and migrated north-northeast toward water-supply wells. Monitoring wells were installed downgradient from the facility to determine the primary zones of groundwater production for water-supply wells and assess contaminant presence. The monitoring wells are screened within the Santa Fe Group aquifer system, which includes clay units, at depths as great as 445 meters below land surface, and were categorized as water table, shallow, middle, deep, and aquifer-test pumping wells. Water-supply wells are screened across multiple water-bearing units within the aquifer system. All wells were sampled for major ions, trace elements, nutrients, stable isotopes, dissolved gases, tritium, carbon isotopes, and chlorofluorocarbons. The deeper and water-supply wells have evidence of longer groundwater residence times, as much as thousands of years, and water from the shallower wells shows evidence of anthropogenic nutrient inputs. Aquifer recharge is derived from either the mountain front or seepage from the Rio Grande. Dissolved-gas data indicate that the middle, deep, and aquifer-test pumping, and water-supply wells have cooler recharge temperatures than the shallower wells. Inferred groundwater age varies by method but indicates that the deeper, aquifer-test pumping, and water-supply wells have older water, as much as 15,000 years before present. Results indicate that the water-supply wells draw primarily from the middle and deeper portions of the aquifer system below the clay units and have not been affected by the contaminant plume, although some data indicate a potential for modern water entering some of the deeper and water-supply wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215076","collaboration":"Prepared in cooperation with the Air Force Civil Engineer Center","usgsCitation":"Travis, R.E., Bell, M.T., Linhoff, B.S., and Beisner, K.R., 2021, Utilizing multiple hydrogeologic and anthropogenic indicators to understand zones of groundwater contribution to water-supply wells near Kirtland Air Force Base Bulk Fuels Facility in southeast Albuquerque, New Mexico: U.S. Geological Survey Scientific Investigations Report 2021–5076, 28 p., https://doi.org/10.3133/sir20215076.","productDescription":"Report: viii, 28 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-120223","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":390163,"rank":5,"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":389636,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5076/coverthb.jpg"},{"id":389637,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5076/sir20215076.pdf","text":"Report","size":"3.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5076"},{"id":389638,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NV9HHG","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Description of groundwater monitoring wells installed at and near Kirtland Air Force Base, Albuquerque, New Mexico, 2013–2016 (ver. 1.2, May 2019)"},{"id":389639,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5076/images/"}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Kirtland Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.69097900390625,\n 34.89156324823376\n ],\n [\n -106.43692016601562,\n 34.90170042871546\n ],\n [\n -106.4410400390625,\n 35.081707990840705\n ],\n [\n -106.68823242187499,\n 35.068221159859256\n ],\n [\n -106.69097900390625,\n 34.89156324823376\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The increasing frequency and severity of drought may exacerbate ongoing global amphibian declines. However, interactions between drought and coincident stressors, coupled with high interannual variability in amphibian abundances, can mask the extent and underlying mechanisms of drought impacts. We synthesized a decade (2009–2019) of regional-scale amphibian monitoring data (2273 surveys, 233 ponds, and seven species) from across California's Bay Area and used dynamic occupancy modeling to estimate trends and drivers of species occupancy. An extreme drought during the study period resulted in substantial habitat loss, with 51% of ponds drying in the worst year of drought, compared to <20% in pre-drought years. Nearly every species exhibited reduced breeding activity during the drought, with the occupancy of some species (American bullfrogs and California newts) declining by >25%. Invasive fishes and bullfrogs were also associated with reduced amphibian occupancy, and these taxa were locally extirpated from numerous sites during drought, without subsequent recovery– suggesting that drought may present an opportunity to remove invaders. Despite a historic, multi-year drought, native amphibians rebounded quickly to pre-drought occupancy levels, demonstrating evidence of resilience. Permanent waterbodies supported higher persistence of native species during drought years than did temporary waterbodies, and we therefore highlight the value of hydroperiod diversity in promoting amphibian stability.