The U.S. Geological Survey (USGS) 21st-century science strategy 2020–30 promotes a bureau-wide strategy to develop and deliver an integrated, predictive science capability that works at the scales and timelines needed to inform societally relevant resource management and protection and public safety and environmental health decisions (U.S. Geological Survey, 2021). This is the overarching goal of the USGS Earth Monitoring, Analysis, and Prediction (EarthMAP) vision, which consists of three components: (1) integrated data and information, (2) integrated predictive science, and (3) actionable information—all designed and delivered to respond to user needs. To launch this vision and help shape the design and implementation of integrated predictive science, the USGS Regional Offices each developed a set of use cases (hereafter Use Cases)—short descriptions of potential science applications that could clearly address high priority decision-making needs of our stakeholders and that align with an integrated science focus. Use Cases are not actionable science planning documents, nor stand-alone scholarly works, but should be considered as innovative, next-generation science ideas that can be considered as potential components of science plans still under development. The goal of Use Case development was to (1) identify and characterize existing USGS scientific capacities and expertise that can support science goals and products, (2) identify opportunities to leverage current capacities for next-generation science, and (3) foster engagement across the entire Bureau to further refine the USGS strategy for EarthMAP and integrated predictive science.
The Use Case development effort documented in this report was coordinated by the Use Case Development Team (UCDT), consisting of representatives from each region. The UCDT undertook five tasks: (1) develop a unified approach to engage bureau scientists consistently across all regions in aspirational thinking about what can be accomplished; (2) work with the regions and their Science Centers to generate an initial set of Use Cases, authored directly by scientists; (3) characterize, summarize, and document the initial set of Use Case submissions from authors to illuminate bureau-level demand for integrated science; (4) compare existing and needed capacities from the Use Case descriptions with preliminary results of the EarthMAP Capacity Assessment (Keisman and others, 2021); and (5) describe lessons learned from the Use Case development process and provide recommendations to inform future efforts to generate integrated science activities. This report outlines the approach the UCDT developed to solicit Use Cases from the regions and summarizes the high-level qualitative findings from this first-round effort.
The UCDT received 36 Use Cases from the regions and identified potential points of convergence and commonalities considered useful in making connections among the participating scientists. The Southwest (SW) Region and the Rocky Mountain (RM) Region asked scientists to give special consideration to Use Cases with applicability to the Colorado River Basin, and seven of the Use Cases specifically named that geographic area as a focus. Coastal hazards and coastal resilience were identified in Use Cases from the Alaska (AK), Northeast (NE), and Southeast (SE) Regions. Aspects of wildfire and post-wildfire response were part of Uses Cases from AK, RM, and SW Regions. The greatest convergence of Use Case themes was related to conservation of public lands and waters, which is a powerful linkage lending strength to future collaborative efforts.
The most common type of stakeholder decisions that would be informed by the Use Case science applications were related to adaptation, mitigation, and response (for example, how to increase the resilience of coastal communities to climate-related stressors and how to prevent or respond to harmful algal blooms). Other common types of decisions included water and land management decisions (including operational water management decisions such as reservoir operations and land use planning in the sagebrush biome), decisions about how to manage and conserve habitats and species, and risk management decisions (such as managing the post-wildfire flood risks). These decision types are not exclusive because many Use Cases cross categories.
Use Case authors identified existing and needed science and technology capabilities required for Use Case implementation, which were then aligned to capabilities assessed in the EarthMAP Capacity Assessment (Keisman and others, 2021). Strong alignment was found for data and information integration approaches, modeling and prediction approaches, and capabilities related to delivery of actionable information. A majority of Use Cases indicated insufficient current capacity for needed data collection methods, data integration, and modeling and prediction approaches, whereas only 25 percent indicated insufficient capacity for actionable information delivery. Overall, many Use Case capacity demand gaps could potentially be met by existing bureau-wide capacity. In addition, nearly half of the Use Cases could potentially be implemented within 3 years if funding, capabilities, and personnel impediments were removed and science priorities were realigned.
Several challenges emerged during the Use Case development process. The first challenge was developing an approach that was flexible enough to accommodate regional differences in planning and implementation, while also ensuring enough guidance to promote meaningful summary analyses. The UCDT encountered a strong demand for continuous communication and education to improve overall understanding of the integrated predictive science strategy. Another challenge was managing expectations about EarthMAP activities as a design effort that was not aligned to an immediate funding opportunity. Connecting the Use Cases to stakeholder needs without the opportunity for direct stakeholder engagement was also challenging. The last notable challenge was in obtaining consistent interpretation and characterization of the qualitative data housed in the narrative descriptions of Use Cases, written in different styles.
Overall, the 36 Use Cases can serve as components of a road map for advancing integrated monitoring and predictive science throughout the USGS by revealing opportunities to (1) encourage cross-region initiatives that address shared interests in common themes by integrating similar Use Cases and through direct involvement of stakeholders in identifying needs and designing effective responses, (2) leverage the Use Cases to target investments that are aligned with the Bureau and Department of the Interior (DOI) priorities, (3) connect Use Cases and the results of the companion EarthMAP Capacity Assessment (Keisman and others, 2021) to identify potential priorities for capacity building investments, and (4) raise awareness of common integrated and interdisciplinary science interests within and across the regions through Use Case and Capacity Assessment summary outreach activities.
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Few wells have logged or sampled the gas hydrate zone on this margin, meaning that the presence of gas hydrates is inferred primarily based on seismic data that reveal bottom simulating reflections, mostly at water depths greater than 2000 m. The highest hydrate saturations most likely exist in sandy sediments of the Whale Prospect offshore New Jersey, New York, and the western part of Cape Cod, an area characterized by strong bottom simulating reflections. Such reflections are also imaged on the well-studied Blake Ridge, where fine-grained sediments host lower hydrate saturations that have been constrained by drilling. Within the section of the margin stretching from south of Cape Hatteras to nearly Hudson Canyon, the diagnostic seismic reflections are hard to discern, making inferences about gas hydrate distributions more uncertain. The recognition of as-yet unmapped bottom simulating reflections or top of gas features seaward of the 2000 m bathymetric contour (e.g., Cape Fear Slide, Currituck slide, beneath deepwater gas seeps) within the Mid-Atlantic Bight expands the area of probable gas hydrates on this margin.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"World atlas of submarine gas hydrates in continental margins","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-81186-0_24","usgsCitation":"Ruppel, C.D., Shedd, W., Miller, N.C., Kluesner, J.W., Frye, M., and Hutchinson, D., 2022, U.S. Atlantic margin gas hydrates, chap. of World atlas of submarine gas hydrates in continental margins, p. 287-302, https://doi.org/10.1007/978-3-030-81186-0_24.","productDescription":"16 p.","startPage":"287","endPage":"302","ipdsId":"IP-122469","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science 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Here, we provide a comprehensive set of water chemistry measurements and ecohydrological metrics describing the biogeochemical conditions of permafrost-affected Arctic watersheds. These data were collected in watershed-wide synoptic campaigns in six stream networks across northern Alaska. Three watersheds are associated with the Arctic Long-Term Ecological Research site at Toolik Field Station (TFS), which were sampled seasonally each June and August from 2016 to 2018. Three watersheds were associated with the National Park Service (NPS) of Alaska and the U.S. Geological Survey (USGS) and were sampled annually from 2015 to 2019. Extensive water chemistry characterization included carbon species, dissolved nutrients, and major ions. The objective of the sampling designs and data acquisition was to characterize terrestrial–aquatic linkages and processing of material in stream networks. The data allow estimation of novel ecohydrological metrics that describe the dominant location, scale, and overall persistence of ecosystem processes in continuous permafrost. These metrics are (1) subcatchment leverage, (2) variance collapse, and (3) spatial persistence. Raw data are available at the National Park Service Integrated Resource Management Applications portal (O'Donnell et al., 2021, https://doi.org/10.5066/P9SBK2DZ) and within the Environmental Data Initiative (Abbott, 2021, https://doi.org/10.6073/pasta/258a44fb9055163dd4dd4371b9dce945).
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jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855633,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Medvedeff, Alex","contributorId":298453,"corporation":false,"usgs":false,"family":"Medvedeff","given":"Alex","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":855634,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"O’Donnell, Jonathan A. 0000-0001-7031-9808","orcid":"https://orcid.org/0000-0001-7031-9808","contributorId":191423,"corporation":false,"usgs":false,"family":"O’Donnell","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":855635,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Patch, Leika","contributorId":298455,"corporation":false,"usgs":false,"family":"Patch","given":"Leika","email":"","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":855636,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Poulin, Brett 0000-0002-5555-7733","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":260893,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett","affiliations":[{"id":52706,"text":"Department of Environmental Toxicology, University of California Davis, Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":855637,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Williamson, Tanner J.","contributorId":223165,"corporation":false,"usgs":false,"family":"Williamson","given":"Tanner","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":855638,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Bowden, William B.","contributorId":169388,"corporation":false,"usgs":false,"family":"Bowden","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":6735,"text":"University of Vermont, Rubenstein School of Environment and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":855639,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70227459,"text":"70227459 - 2022 - Response to comment on “Evidence of humans in North America during the Last Glacial Maximum”","interactions":[],"lastModifiedDate":"2022-01-18T13:25:51.653562","indexId":"70227459","displayToPublicDate":"2022-01-14T07:24:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Response to comment on “Evidence of humans in North America during the Last Glacial Maximum”","docAbstract":"Dry conditions during 2020 and 2021 affected the water supply within the Souris River Basin and highlighted the need for better understanding of the streamflow dynamics for managing the resource during low-flow conditions. In June 2021, a loss of streamflow was observed on the Souris River between U.S. Geological Survey streamgages on the Souris River near Foxholm, North Dakota (site 1), and near Verendrye, N. Dak. (site 22). The largest loss was upstream from the Souris River above Minot, N. Dak. (site 7). On June 6, 2021, the daily mean streamflow decreased from 33.8 cubic feet per second at site 1 to 16.3 cubic feet per second at site 7, a loss of 17.5 cubic feet per second. To better understand where streamflow losses occurred in the reach from site 1 to site 22, multiple sites were selected for streamflow measurements between the three streamgages (sites 1, 7, and 22). Streamflow measurements made at 22 selected sites on the Souris River on August 31 and September 1, 2021, did not indicate the loss in streamflow that was observed at the three streamgages (sites 1, 7, and 22) in June 2021. Measurements made at the three streamgages (sites 1, 7, and 22) on August 31 had streamflows of 44.2, 45.9, and 46.8 cubic feet per second, respectively. Streamflow measured at all 22 sites on August 31 and September 1 on the Souris River ranged from 38.4 (site 9) to 49.8 cubic feet per second (site 12). In general, the largest change in streamflow was measured among sites on the Souris River in or near the city of Minot, N. Dak.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221005","collaboration":"Prepared in cooperation with the North Dakota Department of Water Resources, the U.S. Fish and Wildlife Service, and the U.S. Army Corps of Engineers, St. Paul District","usgsCitation":"Galloway, J.M., and Hanson, B.R., 2022, Measurements of streamflow gain and loss on the Souris River between Lake Darling and Verendrye, North Dakota, August 31 and September 1, 2021: U.S. Geological Survey Open-File Report 2022–1005, 10 p., https://doi.org/10.3133/ofr20221005.","productDescription":"Report: vi, 10 p.; Dataset","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-135605","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":394315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1005/coverthb.jpg"},{"id":394317,"rank":3,"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":394316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1005/ofr20221005.pdf","text":"Report","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1005"},{"id":501538,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112123.htm","linkFileType":{"id":5,"text":"html"}},{"id":394329,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1005/images","description":"OFR 2022–1005 images"},{"id":394328,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1005/ofr20221005.XML","description":"OFR 2022–1005 XML"}],"country":"United States","state":"North Dakota","otherGeospatial":"Souris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.6667,\n 48\n ],\n [\n -100.5,\n 48\n ],\n [\n -100.5,\n 48.50\n ],\n [\n -101.6667,\n 48.50\n ],\n [\n -101.6667,\n 48\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
We present a new field measurement and numerical interpretation method (combined termed ‘test’) to parameterize the diffusion of trichloroethene (TCE) and its biodegradation products (DPs) from the matrix of sedimentary rock. The method uses a dual-packer system to interrogate a low-permeability section of the rock matrix adjacent to a previously contaminated borehole and uses the borehole monitoring history to establish the pre-test condition. TCE and its DPs are removed from the groundwater between the packers at the onset of the testing. The parameters estimated by fitting a radial diffusion model to the concentration history and borehole concentration data, also termed back-diffusion, are the tortuosity factor and sorption coefficients of TCE and DPs in the rock matrix and the TCE and DP biodegradation rate coefficients in the borehole. We demonstrate the equipment design and the interpretive method using a borehole accessing the grey mudstone at a TCE contaminated site in the Newark Basin. In this test, both nonreactive (bromide) and reactive (trichlorofluoroethene) tracers are used to constrain the estimated parameters; however, the bromide tracer was not needed to estimate the parameters in this test. The parameters estimated from the field test are consistent with values measured independently in laboratory experiments using field samples of similar lithology. From the interpretation, we compute the TCE and DP concentration distributions in the rock matrix prior to the test to illustrate how the results can be used to enhance understanding of contaminant distribution in the rock matrix.
","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwmr.12495","usgsCitation":"Allen-King, R.M., Kiekhaefer, R.L., Goode, D.J., Hsieh, P.A., Lorah, M.M., and Imbrigiotta, T.E., 2022, A borehole test for chlorinated solvent diffusion and degradation rates in sedimentary rock: Groundwater Monitoring and Remediation, v. 42, no. 2, p. 23-34, https://doi.org/10.1111/gwmr.12495.","productDescription":"12 p.","startPage":"23","endPage":"34","ipdsId":"IP-122580","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":394652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394804,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99I50JE","text":"USGS data release","linkHelpText":"A finite-difference algorithm used to simulate radial diffusion, adsorption, and reactions of chlorinated ethenes in porous media"}],"volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen-King, Richelle M. 0000-0001-5559-1213","orcid":"https://orcid.org/0000-0001-5559-1213","contributorId":272047,"corporation":false,"usgs":false,"family":"Allen-King","given":"Richelle","email":"","middleInitial":"M.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiekhaefer, Rebecca L.","contributorId":272048,"corporation":false,"usgs":false,"family":"Kiekhaefer","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":56340,"text":"University at Buffalo, SUNY","active":true,"usgs":false}],"preferred":false,"id":831400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goode, Daniel J. 0000-0002-8527-2456","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":216750,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true}],"preferred":true,"id":831402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorah, Michelle M. 0000-0002-9236-587X","orcid":"https://orcid.org/0000-0002-9236-587X","contributorId":224040,"corporation":false,"usgs":true,"family":"Lorah","given":"Michelle","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":152114,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","email":"timbrig@usgs.gov","middleInitial":"E.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227867,"text":"70227867 - 2022 - Quantifying regional effects of best management practices on nutrient losses from agricultural lands","interactions":[],"lastModifiedDate":"2022-02-01T18:12:27.328917","indexId":"70227867","displayToPublicDate":"2022-01-12T13:12:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying regional effects of best management practices on nutrient losses from agricultural lands","docAbstract":"Nitrogen (N) and phosphorus (P) losses from agricultural areas have degraded the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown, but assessments of their effectiveness at large spatial scales have lagged. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast United States to quantify potential regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. The BMPs included in the models were cover crops, no-till, and conservation tillage. Sensitivity testing for the BMPs on agricultural nutrient loads was done using simulations that varied the intensity of BMPs specified in each region. When the BMP intensity was increased 50% relative to the 2012 intensity, the predicted agricultural load of total P decreased across all regions (4% to 14%). The predicted reduction in average P yields in the Midwest, Northeast, and Southeast was 706, 544, and 26 kg km–2, respectively. Increasing BMPs by 50% decreased predicted agricultural total N loads by 3.5% in the Southeast but increased predicted N loads in the Midwest and Northeast by 4.7% and 1.8%, respectively. Model-predicted average N yields increased by 402 kg km–2 and 302 kg km–2 in the Midwest and Northeast, respectively, and decreased in the Southeast by 329 kg km–2. In model simulations, cover crops were more effective at reducing N and P loads than the tillage BMPs despite lower intensity of implementation in 2012. However, at the regional scale of this investigation, implementation of BMPs result in only moderate predicted effects on agricultural nutrient loads
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.2022.00162","usgsCitation":"Roland, V.L., Garcia, A.M., Saad, D.A., Ator, S., Robertson, D., and Schwarz, G.E., 2022, Quantifying regional effects of best management practices on nutrient losses from agricultural lands: Journal of Soil and Water Conservation, v. 77, no. 1, p. 15-29, https://doi.org/10.2489/jswc.2022.00162.","productDescription":"15 p.","startPage":"15","endPage":"29","ipdsId":"IP-119875","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":449184,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.2022.00162","text":"Publisher Index Page"},{"id":435998,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H2NDWU","text":"USGS data release","linkHelpText":"Nutrient Load Data used to Quantify Regional Effects of Agricultural Best Management Practices: An application of the 2012 SPARROW models for the Midwest, Northeast, and Southeast United States"},{"id":395226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Roland, Victor L. II 0000-0002-6260-9351 vroland@usgs.gov","orcid":"https://orcid.org/0000-0002-6260-9351","contributorId":212248,"corporation":false,"usgs":true,"family":"Roland","given":"Victor","suffix":"II","email":"vroland@usgs.gov","middleInitial":"L.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Ana Maria 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":2035,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"Maria","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saad, David A. 0000-0001-6559-6181 dasaad@usgs.gov","orcid":"https://orcid.org/0000-0001-6559-6181","contributorId":204667,"corporation":false,"usgs":true,"family":"Saad","given":"David","email":"dasaad@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ator, Scott W. 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":220504,"corporation":false,"usgs":true,"family":"Ator","given":"Scott W.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832444,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":832445,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229185,"text":"70229185 - 2022 - The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2022-03-03T15:34:56.28404","indexId":"70229185","displayToPublicDate":"2022-01-12T09:27:17","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"title":"The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","docAbstract":"Depressional wetlands are extremely sensitive to changes in temperature and precipitation, so understanding how wetland inundation dynamics respond to changes in climate is essential for describing potential effects on wildlife breeding habitat. Millions of depressional basins make up the largest wetland complex in North America known as the Prairie Pothole Region (PPR). The wetland ecosystems that have formed in these basins provide important migratory-bird breeding habitat. The southeast portion of the U.S. PPR in Minnesota and Iowa has faced some of the greatest challenges in wetland conservation. Many existing prairie-pothole wetlands are small (<1 ha) and shallow (<2 m) and are typically not inundated with surface water year-round. Our goal with this project is to increase the efficacy of mapping tools used by management agencies to predict future changes in water levels in the PPR. We accomplish this goal by improving the link between existing data (about wetland water characteristics) and existing tools (mapping products). Our results successfully validated (2009-2021) the current mapping tool (a wetland hydrology model) used by the U.S. Fish and Wildlife Service (USFWS) to manage 22 wetlands in Minnesota. We were able to hindcast wetland water levels to 1984 and assess the accuracy of a satellite-derived surface water product and forecast water levels through 2099 using a suite of modeled climate data. This newly refined link between monitoring data and remote sensing tools will increase understanding and prediction for other wetlands beyond our study sites and through the Minnesota and Iowa portions of the PPR. Through conference presentations, publications, and development of an interactive climate change dashboard we are now working with managers to determine how we can help incorporate these predicted changes to waterfowl breeding habitat into their future management, acquisition, and restoration strategy.
Hydrous ferric-oxide (HFO) coatings on streambed sediments may attenuate dissolved phosphate (PO4) concentrations at acidic to neutral pH conditions, limiting phosphorus (P) transport and availability in aquatic ecosystems. Mesh-covered tiles on which “natural” HFO from abandoned mine drainage (AMD) had precipitated were exposed to treated municipal wastewater (MWW) effluent or a mixture of stream water and effluent. Between 42 and 99% of the dissolved P in effluent was removed from the water to a thin coating (~2 μm) of HFO on the mesh. Geochemical equilibrium model results predicted the removal of 76 to 99% of PO4 from the water by adsorption to the HFO, depending on the HFO quantity, initial PO4 concentration, and pH. The measurements and model results indicated the capacity for P removal decreased as the concentration of P associated with the HFO increased. Continuing accumulation of HFO from upstream AMD sources replenish the in-stream capacity for P attenuation below the MWW discharge. This indicates AMD pollution may conceal P inputs and limit the amount of dissolved P transported to downstream ecosystems. However, HFO-rich sediments also represent a potential source of “legacy” P that could confound management practices intended to decrease nutrient and metal loadings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.152672","usgsCitation":"Smyntek, P.M., Lamagna, N., Cravotta, C., and Strosnider, W., 2022, Mine drainage precipitates attenuate and conceal wastewater-derived phosphate pollution in stream water: Science of the Total Environment, v. 815, 152672, 8 p., https://doi.org/10.1016/j.scitotenv.2021.152672.","productDescription":"152672, 8 p.","ipdsId":"IP-132530","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":436002,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D8VQDV","text":"USGS data release","linkHelpText":"Interactive PHREEQ-N-Titration-PO4-Adsorption water-quality modeling tools to evaluate potential attenuation of phosphate and associated dissolved constituents by aqueous-solid equilibrium processes (software download)"},{"id":400574,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Four Mile Run","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.44746017456055,\n 40.28279959861071\n ],\n [\n -79.39699172973633,\n 40.28279959861071\n ],\n [\n -79.39699172973633,\n 40.32050383546901\n ],\n [\n -79.44746017456055,\n 40.32050383546901\n ],\n [\n -79.44746017456055,\n 40.28279959861071\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"815","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smyntek, Peter M.","contributorId":291642,"corporation":false,"usgs":false,"family":"Smyntek","given":"Peter","email":"","middleInitial":"M.","affiliations":[{"id":62738,"text":"Saint Vincent College","active":true,"usgs":false}],"preferred":false,"id":842810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamagna, Natalie","contributorId":291643,"corporation":false,"usgs":false,"family":"Lamagna","given":"Natalie","email":"","affiliations":[{"id":62738,"text":"Saint Vincent College","active":true,"usgs":false}],"preferred":false,"id":842811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":842812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Strosnider, William H. J.","contributorId":291644,"corporation":false,"usgs":false,"family":"Strosnider","given":"William H. J.","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":842813,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236989,"text":"70236989 - 2022 - Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale","interactions":[],"lastModifiedDate":"2022-09-27T11:54:09.921852","indexId":"70236989","displayToPublicDate":"2022-01-10T06:50:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":664,"text":"Advances in Water Resources","active":true,"publicationSubtype":{"id":10}},"title":"Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale","docAbstract":"Predicting baseflow dynamics, protecting aquatic habitat, and managing legacy contaminants requires explicit characterization and prediction of groundwater discharge patterns throughout river networks. Using handheld thermal infrared (TIR) cameras, we surveyed 47 km of stream length across the Farmington River watershed (1,570 km2; CT and MA, USA), mapping locations of bank and waterline groundwater discharges based on their thermal signature. Using the observed groundwater discharge locations and predicted groundwater discharge rates from 6 variations of a numerical groundwater-flow model (MODFLOW-NWT), we compared 1) predicted groundwater-discharge rates in areas with and without observed groundwater discharge, 2) spatial patterns of observed and predicted groundwater discharge locations, and 3) density of observed groundwater discharge locations with predicted discharge rates. Five of six models reasonably predicted the spatial patterns of discharge locations along the 5th order mainstem, but fewer models predicted groundwater discharge patterns in smaller streams. Our results highlight 1) the feasibility of using TIR observations to evaluate groundwater models, 2) model parameters that influence discharge prediction accuracy (riverbed sediment and bedrock hydraulic conductivity and river-aquifer connections), and 3) current strengths and future opportunities for improved modeling of groundwater-discharge patterns.
California’s Central Valley provides critical habitat for migratory waterbirds, yet only 10% of naturally occurring wetlands remain. Competition for limited water supplies and climate change will impact the long-term viability of these intensively managed habitats.
Forecast the distribution, abundance, and connectivity of surface water and managed wetland habitats, using 5 spatially explicit (270 m2) climate/land use/water prioritization scenarios. Mapping potential future dynamic flooded habitat used by waterbirds and other wetland-dependent wildlife to inform management decisions.
We integrated a climate-driven hydrologic water use model with a spatially explicit land change model, to examine stakeholder-driven scenarios of future land change, climate, and water use and their impacts on future habitat availability.
Declining water availability is the dominant driver of habitat loss across scenarios. The hot/dry scenarios showed the greatest declines in January flooded area by 2101—an important month for overwintering waterbirds. In contrast, higher water supplies in wet climates drive perennial cropland conversion and loss of potential habitat. Potential flooded cropland declined (25 and 33%) under warmer/wetter climate conditions due to this conversion to perennial crops, exposing habitat vulnerability.
Climate-driven loss of water availability had a greater impact on flooded habitat availability than land-use change. When combined, climate change and the conversion of potentially flooded cropland to perennial cropland will threaten future waterbird habitat particularly in January, the peak of the migratory bird season, even when habitat restoration goals are met. Stakeholder-informed scenario analysis can identify target areas for potential habitat change, vulnerability, and conservation.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01398-1","usgsCitation":"Wilson, T., Matchett, E., Byrd, K.B., Conlisk, E., Reiter, M.E., Wallace, C., Flint, L.E., Flint, A.L., and Moritsch, M.M., 2022, Climate and land change impacts on future managed wetland habitat: A case study from California’s Central Valley: Landscape Ecology, v. 37, p. 861-881, https://doi.org/10.1007/s10980-021-01398-1.","productDescription":"21 p.","startPage":"861","endPage":"881","ipdsId":"IP-127595","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":394093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":830455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":830456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":830457,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conlisk, Erin","contributorId":270185,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":830458,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reiter, Matthew E. 0000-0002-0587-786X","orcid":"https://orcid.org/0000-0002-0587-786X","contributorId":271031,"corporation":false,"usgs":false,"family":"Reiter","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":56258,"text":"Point Blue","active":true,"usgs":false}],"preferred":false,"id":830459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wallace, Cynthia 0000-0003-0001-8828 cwallace@usgs.gov","orcid":"https://orcid.org/0000-0003-0001-8828","contributorId":149179,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia","email":"cwallace@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":830460,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830461,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830462,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moritsch, Monica Mei Jeen 0000-0002-3890-1264","orcid":"https://orcid.org/0000-0002-3890-1264","contributorId":225210,"corporation":false,"usgs":true,"family":"Moritsch","given":"Monica","email":"","middleInitial":"Mei Jeen","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":830463,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70227381,"text":"70227381 - 2022 - Stoneflies in the genus Lednia (Plecoptera: Nemouridae): Sentinels of climate change impacts on mountain stream biodiversity","interactions":[],"lastModifiedDate":"2022-04-26T12:00:22.972891","indexId":"70227381","displayToPublicDate":"2022-01-07T06:39:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1006,"text":"Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Stoneflies in the genus Lednia (Plecoptera: Nemouridae): Sentinels of climate change impacts on mountain stream biodiversity","docAbstract":"Rapid recession of glaciers and snowfields is threatening the habitats of cold-water biodiversity worldwide. In many ice-sourced headwaters of western North America, stoneflies in the genus Lednia (Plecoptera: Nemouridae) are a prominent member of the invertebrate community. With a broad distribution in mountain streams and close ties to declining glacier cover, Lednia has emerged as a sentinel of climate change threats to high-elevation aquatic biodiversity. Lednia tumana, which is endemic to Glacier National Park, USA and the surrounding mountains, is the most well-studied species in the genus and in 2019 became federally protected under the U.S. Endangered Species Act (ESA) due to climate-induced loss of meltwater habitats. Three other Lednia species have also been described, and like L. tumana, each is endemic to a mountain region of western North America: Lednia sierra in the Sierra Nevada, Lednia borealis in the Cascade Range, and Lednia tetonica in the Teton Range. In this review, we provide a comprehensive overview of Lednia ecology, genetics, and physiology, with an emphasis on the conservation outlook for the group and species with similar headwater distributions. We highlight substantial progress made in the last decade to better understand the ecology and evolution of Lednia, including the identification of 140 Lednia-containing streams (an increase from 12 streams in 2010), and a more complete understanding of the degree to which warming streams may imperil species in the genus. In light of the ESA listing of L. tumana, we show that similar conservation threats likely face all extant Lednia species. However, substantial gaps in our knowledge remain, primarily centering around their distributions (and the potential for as yet undescribed species), life history, ecophysiology, and trophic ecology. We conclude by describing pressing questions for Lednia that when addressed will expand knowledge of the genus and its conservation as well as broader understanding of climate risks to mountain stream biodiversity worldwide.
Neurotoxic methylmercury (MeHg) accumulates in rice grain from paddy soil, where its concentration is controlled by microbial mercury methylation and demethylation. Both up- and down-regulation of methylation is known to occur in the presence of rice plants in comparison to non-vegetated paddy soils; the influence of rice plant presence/absence on demethylation is unknown. To assess the concurrent influence of rice plant presence/absence on methylation and demethylation, and to determine which process was more dominant in controlling soil MeHg concentrations, we maintained six rhizoboxes of paddy soil with and without rice plants. At the peak of plant growth, we simultaneously measured ambient MeHg, ambient inorganic mercury (IHg), and potential rate constants of methylation and demethylation (Kmeth and Kdemeth) in soil using stable isotope tracers and ID-GC-ICPMS. We also measured organic matter content, elemental S, and water-extractable sulfate. We found MeHg concentrations were differentially controlled by MeHg production and degradation processes, depending on whether plants were present. In non-vegetated boxes, MeHg concentration was controlled by Kmeth, as evidenced by a strong and positive correlation, while Kdemeth had no relation to MeHg concentration. These results indicate methylation was the dominant driver of MeHg concentration in non-vegetated soil. In vegetated boxes, Kdemeth strongly and negatively predicted MeHg concentration, indicating that demethylation was the dominant control in soil with plants. MeHg concentration, Kmeth, and % MeHg all had significantly less variance in vegetated than in non-vegetated soils due to a consistent elimination of greater values. This pattern suggests that reduced MeHg production capacity was a secondary control on MeHg concentrations in vegetated soils. We observed no difference in the magnitude or variance of Kdemeth between treatments, suggesting that demethylation was robust to soil chemical conditions influenced by the plant, perhaps because of a wider taxonomic diversity of demethylators. Our results suggest that methylation and demethylation processes could both be leveraged to alter MeHg concentrations in rice paddy soil.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoenv.2021.113143","usgsCitation":"Strickman, R.J., Larson, S.M., Huang, H., Kakouros, E., Marvin-DiPasquale, M.C., Mitchell, C.P., and Neumann, R.B., 2022, The relative importance of mercury methylation and demethylation in rice paddy soil varies depending on the presence of rice plants: Ecotoxicology and Environmental Safety, v. 230, 113143, 10 p., https://doi.org/10.1016/j.ecoenv.2021.113143.","productDescription":"113143, 10 p.","ipdsId":"IP-130664","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":449239,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoenv.2021.113143","text":"Publisher Index Page"},{"id":396427,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"230","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Strickman, R. J.","contributorId":280064,"corporation":false,"usgs":false,"family":"Strickman","given":"R.","email":"","middleInitial":"J.","affiliations":[{"id":27967,"text":"University of Washington, Seattle WA","active":true,"usgs":false}],"preferred":false,"id":835974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, S. M.","contributorId":36309,"corporation":false,"usgs":false,"family":"Larson","given":"S.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":835975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, H.","contributorId":280065,"corporation":false,"usgs":false,"family":"Huang","given":"H.","affiliations":[{"id":57416,"text":"University of Toronto Scarborough, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":835976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kakouros, Evangelos 0000-0002-4778-4039 kakouros@usgs.gov","orcid":"https://orcid.org/0000-0002-4778-4039","contributorId":2587,"corporation":false,"usgs":true,"family":"Kakouros","given":"Evangelos","email":"kakouros@usgs.gov","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":835977,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":835978,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mitchell, C. P. J.","contributorId":280066,"corporation":false,"usgs":false,"family":"Mitchell","given":"C.","email":"","middleInitial":"P. J.","affiliations":[{"id":57416,"text":"University of Toronto Scarborough, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":835979,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Neumann, R. B.","contributorId":280067,"corporation":false,"usgs":false,"family":"Neumann","given":"R.","email":"","middleInitial":"B.","affiliations":[{"id":27967,"text":"University of Washington, Seattle WA","active":true,"usgs":false}],"preferred":false,"id":835980,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227289,"text":"70227289 - 2022 - Mesilla / Conejos-Médanos Basin: U.S.-Mexico transboundary water resources and research needs","interactions":[],"lastModifiedDate":"2022-01-25T17:42:14.142072","indexId":"70227289","displayToPublicDate":"2022-01-06T07:16:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Mesilla / Conejos-Médanos Basin: U.S.-Mexico transboundary water resources and research needs","docAbstract":"The U.S. Geological Survey, in cooperation with DeKalb County Department of Watershed Management, established a long-term water-quantity and water-quality monitoring program in 2012 to monitor and analyze the hydrologic and water-quality conditions of 15 watersheds in DeKalb County, Georgia—an urban and suburban area located in north-central Georgia that includes the easternmost part of the City of Atlanta. This report synthesizes the watershed characteristics and monitoring data collected for the first 5 years of the program, 2012 through 2016. The study area was predominantly medium-density residential (43.9 percent), commercial/industrial/institutional (21.4 percent), forest/park/agriculture (13.6 percent), and high-density residential (11.5 percent) land uses. Land-surface slope averaged 8.7 percent, imperviousness averaged 25.3 percent, and population density averaged 2,936 people per square mile. Watershed imperviousness ranged from 8.7 to 36.6 percent.
In the study area for 2014 to 2016 (when streamflow data were available for all watersheds), runoff represented 40.9 percent of precipitation. Hydrograph separations indicated that 43 percent of runoff occurred as base flow, whereas the remainder occurred as stormflow. Higher watershed imperviousness was significantly related to higher amounts of runoff (Pearson product-moment correlation coefficient [r] = 0.517), higher runoff ratios (r = 0.646), and lower amounts (r = −0.637) and proportions (r = −0.898) of base-flow runoff. Stormwater best management practices have been implemented in the study watersheds; however, these practices do not appear to fully mitigate the effects of urban development and land use on stream hydrology.
Total copper, lead, and zinc concentrations in base-flow and stormflow samples exceeded the national recommended aquatic life criteria for chronic and acute conditions, respectively, to varying degrees. Escherichia coli density predictive regression models indicated that the U.S. Environmental Protection Agency’s Beach Action Value was exceeded at individual watersheds between 44.6 and 100 percent of the time. Exceedance of the Beach Action Value indicates possible unsafe conditions for primary contact recreation and could be used for timely notification of the potential health risks. Annual loads and yields were estimated for 15 constituents. Loads were typically higher for years with higher runoff while variations among watershed yields appear associated with watershed and land use characteristics. The lowest yields for almost all constituents occurred in the Stone Mountain Creek watershed—likely the result of the retention of sediment and reduction of nutrients in Stone Mountain Lake and two smaller downstream reservoirs within the watershed. The Little Stone Mountain Creek watershed also had some of the lowest yields for most constituents, likely due to the lack of many pollutant sources associated with its predominantly medium-density residential land use (95.5 percent), but had the highest total nitrate plus nitrite yields. The Intrenchment Creek watershed consistently had some of the highest yields across all constituents except for total nitrate plus nitrite. The high yields may be related to its high percentage of impervious area (36.0 percent) and high amount of heavily developed land use (high-density residential, 29.9 percent and commercial/industrial/institutional, 26.0 percent). Mean watershed constituent yields in this study were significantly higher than those from a similar analysis of 13 suburban to urban watersheds in adjacent Gwinnett County for 6 of the 10 constituents compared.
This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions of the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215126","collaboration":"Prepared in cooperation with DeKalb County Department of Watershed Management","usgsCitation":"Aulenbach, B.T., Kolb, K., Joiner, J.K., and Knaak, A.E., 2022, Hydrology and water quality in 15 watersheds in DeKalb County, Georgia, 2012–16: U.S. Geological Survey Scientific Investigations Report 2021–5126, 105 p., https://doi.org/10.3133/sir20215126.","productDescription":"Report: xii, 105 p.; Data Release; Database","numberOfPages":"105","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-117184","costCenters":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":393756,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M6WCRH","text":"USGS data release","linkHelpText":"Streamwater constituent load data, models, and estimates for 15 watersheds in DeKalb County, Georgia, 2012–2016"},{"id":393757,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5126/sir20215126.pdf","text":"Report","size":"7.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5126"},{"id":393754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5126/coverthb.jpg"},{"id":502127,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112069.htm","linkFileType":{"id":5,"text":"html"}},{"id":393759,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5126/sir20215126.XML"},{"id":393758,"rank":5,"type":{"id":34,"text":"Image 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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
The Niobrara River of northern Nebraska is a valuable water resource that sustains irrigated agriculture and recreation, as well as a diverse ecosystem. Large-quantity withdrawals from the source aquifer system have the potential to reduce the flow into the river and to adversely affect the free-flowing condition of the Niobrara National Scenic River (NSR). Therefore, to understand the magnitude and characteristics of those flows, the U.S. Geological Survey (USGS), in cooperation with the National Park Service, began a study to quantify seepage gains/losses along the eastern half of the Niobrara NSR and to create a map characterizing the base-flow recession time constant (tau) in the Niobrara NSR study area.
In 2016, a seepage study was completed to quantify seepage gains/losses along the eastern half of the Niobrara NSR. The seepage study results indicated that the main-stem streamflow on the Niobrara River increases 375 cubic feet per second (ft3/s) in the 39.9-mile study reach (river mile 119.3 to river mile 79.4). Although most of the streamflow increases are attributed to tributary inflows (297 ft3/s, 79 percent), 78 ft3/s are attributed to seepage gains within the reach. Seepage rates in the study reach ranged from 1.41 cubic feet per second per mile ([ft3/s]/mi) to 2.56 (ft3/s)/mi, with a mean seepage rate of 2 (ft3/s)/mi.
Tau values were calculated at 10 sites in the Niobrara NSR study area, and kriging geostatistical techniques were used to develop a contour map to estimate tau values at locations where streamflow was not measured. The minimum tau value was 12.1 days at Willow Creek at Atwood Road near Carns, Nebraska (USGS station 06463670), and the maximum value was 45.5 days at Tyler Falls at Fort Niobrara National Wildlife Refuge near Valentine, Nebr. (USGS station 06461150).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215102","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Strauch, K.R., and Soenksen, P.J., 2022, Main-stem seepage and base-flow recession time constants in the Niobrara National Scenic River Basin, Nebraska, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5102, 17 p., https://doi.org/10.3133/sir20215102.","productDescription":"Report: vi, 17 p.; Data Release; Dataset","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125025","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":393921,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PDP1BI","text":"USGS data release","linkHelpText":"Datasets used to map the base-flow recession time constants in the Niobrara National Scenic River in Nebraska, 2016–18"},{"id":502117,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112068.htm","linkFileType":{"id":5,"text":"html"}},{"id":393922,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5102/sir20215102.pdf","text":"Report","size":"2.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5102"},{"id":393919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5102/coverthb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Niobrara National Scenic River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.974609375,\n 41.64007838467894\n ],\n [\n -100.08544921874999,\n 41.64007838467894\n ],\n [\n -100.08544921874999,\n 42.956422511073335\n ],\n [\n -103.974609375,\n 42.956422511073335\n ],\n [\n -103.974609375,\n 41.64007838467894\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The Des Moines River alluvial aquifer is an important source of water for Des Moines Water Works, the municipal water utility that provides residential and commercial water resources to the residents of Des Moines, Iowa, and surrounding municipalities. As an initial step in developing a better understanding of the groundwater resources of the Des Moines River alluvial aquifer, the U.S. Geological Survey constructed a steady-state numerical groundwater flow model in cooperation with Des Moines Water Works to simulate water-table elevations in the Des Moines River alluvial aquifer near Prospect Park in Des Moines under winter low-flow conditions.
A simple conceptual model consisting of a hydrogeologic framework, water budget, and inferred water-table elevation map was developed for the model area. The inferred water-table elevation map was constructed based on general knowledge of hydrogeology within the model area and was used to set calibration targets for numerical model calibration. A steady-state numerical model was constructed based on the conceptual model using MODFLOW-NWT to simulate an area of about 15 square kilometers near Prospect Park in Des Moines. Parameter ESTimation software was used for model calibration to assess and optimize performance of the horizontal hydraulic conductivity and recharge parameters. The numerical groundwater flow model and supporting data are available in the USGS data release associated with this report, which contains the model archive.
Performance of the calibrated steady-state model was assessed by comparing observed and simulated water-table elevations, as well as estimated and simulated contributions to streamflow within the model area. The difference between observed water-table elevations and simulated water-table elevations was −0.1 meter at the majority of calibration targets, with the negative value indicating an overestimation of the simulated water-table elevation value compared to the observed water-table elevation value, and the root mean square error was 0.13 meter, which represents about 20 percent of the difference in observed water-table elevations. The simulated value of contributions to streamflow within the model area was considered similar to the estimated value, increasing confidence in the ability of the model to accurately represent the groundwater flow system in the Des Moines River alluvial aquifer in the model area during winter low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211110","collaboration":"Prepared in cooperation with Des Moines Water Works","usgsCitation":"FitzGerald, K.M., Ha, W.S., Haj, A.E., Gruhn, L.R., Bristow, E.L., and Weber, J.R., 2022, A steady-state groundwater flow model for the Des Moines River alluvial aquifer near Prospect Park, Des Moines, Iowa: U.S. Geological Survey Open-File Report 2021–1110, 20 p., https://doi.org/10.3133/ofr20211110.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130288","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501532,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112070.htm","linkFileType":{"id":5,"text":"html"}},{"id":393916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1110/ofr20211110.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1110"},{"id":393915,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1110/coverthb.jpg"},{"id":393917,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":393918,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Prospect Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.65175247192383,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.611463744813506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The Des Plaines River in southern Wisconsin and northern Illinois is the principal conduit for the discharge of wastewater effluent and stormwater runoff from the greater Chicago metropolitan area. In November 2017, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, installed a continuous monitoring station to measure water quality and streamflow in the Des Plaines River at Joliet, Illinois. Surrogate models encompassing continuous data and discrete water-quality samples were used to estimate loads of nitrate, total phosphorus, and suspended sediment. Comparisons to other major rivers in Illinois show that the Des Plaines River is a substantial contributor to statewide loading estimates for nitrate and total phosphorus but only a minor contributor to suspended sediment. Future loading estimates of total phosphorus could include more research into the effects of combined sewage overflows because these effects likely increased model uncertainty. The results in this report document current loadings and provide a baseline from which to assess future water-quality management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215125","collaboration":"Prepared in cooperation with Metropolitan Water Reclamation District of Greater Chicago","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"Peake, C.S., and Hodson, T.O., 2022, Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20 (ver. 1.1, February 2022): U.S. Geological Survey Scientific Investigations Report 2021–5125, 15 p., https://doi.org/10.3133/sir20215125.","productDescription":"Report: vii, 15 p.; Data Release; Database","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-129874","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":393780,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M4BH1C","text":"USGS data release","linkHelpText":"Modeled nutrient and sediment concentrations from the Des Plaines River at Route 53 at Joliet, Illinois, based on continuous monitoring from October 1, 2017, through September 30, 2020"},{"id":396575,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5125/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":393783,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5125/images/"},{"id":393782,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5125/sir20215125.XML"},{"id":393781,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5125/sir20215125.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5125"},{"id":393778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5125/coverthb2.jpg"},{"id":502126,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112067.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois","city":"Joliet","otherGeospatial":"Des Plaines River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.25,\n 41.5\n ],\n [\n -87.5,\n 41.5\n ],\n [\n -87.5,\n 42.25\n ],\n [\n -88.25,\n 42.25\n ],\n [\n -88.25,\n 41.5\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 5, 2022; Version 1.1: February 28, 2022","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
We use particle tracking to determine contributing areas (CAs) to wells for transient flow models that simulate cyclic domestic pumping and extreme recharge events in a small synthetic watershed underlain by dipping sedimentary rocks. The CAs consist of strike-oriented bands at locations where the water table intersects high-hydraulic conductivity beds, and from which groundwater flows to the pumping well. Factors that affect the size and location of the CAs include topographic flow directions, rock dip direction, cross-bed fracture density, and position of the well relative to streams. For an effective fracture porosity (ne) of 10-4, the fastest advective travel times from CAs to wells are only a few hours. These results indicate that wells in this type of geologic setting can be highly vulnerable to contaminants or pathogens flushed into the subsurface during extreme recharge events. Increasing ne to 10-3 results in modestly smaller CAs and delayed well vulnerability due to slower travel times. CAs determined for steady-state models of the same setting, but with long-term average recharge and pumping rates, are smaller than CAs in the models with extreme recharge. Also, the earliest-arriving particles arrive at the wells later in the steady-state models than in the extreme-recharge models. The results highlight the importance of characterizing geologic structure, simulating plausible effective porosities, and simulating pumping and recharge transience when determining CAs in fractured rock aquifers to assess well vulnerability under extreme precipitation events.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13169","usgsCitation":"Tiedeman, C.R., and Shapiro, A.M., 2022, Contributing areas to domestic wells in dipping sedimentary rocks under extreme recharge events: Groundwater, v. 60, no. 4, p. 460-476, https://doi.org/10.1111/gwat.13169.","productDescription":"17 p.","startPage":"460","endPage":"476","ipdsId":"IP-128281","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":449254,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13169","text":"Publisher Index Page"},{"id":436015,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93DZ84P","text":"USGS data release","linkHelpText":"MODFLOW-NWT and MODPATH6 models used to simulate contributing areas in hypothetical sedimentary rock aquifers under extreme recharge events"},{"id":394014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Tiedeman, Claire R. 0000-0002-0128-3685 tiedeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0128-3685","contributorId":196777,"corporation":false,"usgs":true,"family":"Tiedeman","given":"Claire","email":"tiedeman@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":830286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":830287,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227473,"text":"70227473 - 2022 - Regression of the Tethys Sea (central Asia) during middle to late Eocene: Evidence from calcareous nannofossils of western Tarim Basin, NW China","interactions":[],"lastModifiedDate":"2022-01-19T13:16:11.70088","indexId":"70227473","displayToPublicDate":"2022-01-05T07:13:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2673,"text":"Marine Micropaleontology","active":true,"publicationSubtype":{"id":10}},"title":"Regression of the Tethys Sea (central Asia) during middle to late Eocene: Evidence from calcareous nannofossils of western Tarim Basin, NW China","docAbstract":"Calcareous nannofossil assemblages from middle to upper Eocene sediments of the western Tarim Basin indicate two important episodes of marine incursion into the basin. The first episode represents a period of shallowing upward in the Wulagen Formation, which is dated as Zone CNE13 (Lutetian) by the co-occurrence of Discoaster bifax, Chiasmolithus solitus, and common Reticulofenestra umbilicus. The presence of a diverse assemblage of discoasters in the basal Wulagen suggests deposition occurred in oligotrophic, warm water with a connection to the open ocean. Progressive shallowing over time led to the formation of a restricted basin in which only Coccolithus pelagicus could survive. The second major episode of marine incursion is preserved in the middle part of the Bashibulake Formation, which is dated as Zone CNE17 (Bartonian/Priabonian) based on the presence of common Cribrocentrum erbae. The interval between the two marine incursions was dominated by subaerial exposure and evaporation, resulting in the deposition of gypsum at the top of the Wulagen Formation. Although maximum regression at the end of the Lutetian is followed by sea-level rise at the beginning of the Bartonian globally, it is clear that local tectonics played a crucial role in regional marine incursions into the Tarim Basin during deposition of the Wulagen Formation.
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.
Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2–7 metrics were compared for three measures of performance. After ranking the best-performing MMIs, we selected the final MMIs by evaluating stress-response relations in independent regional datasets of diatom samples paired with measures of several water-quality stressors, including herbicides and streamflow flashiness. Each regional MMI performed well at calibration sites and represented diverse aspects of the structure and function of diatom communities. Most metrics included in the best MMIs were modeled to account for natural variation including climate, topography, soil characteristics, lithology, and groundwater influence on streamflow. MMI performance improved with higher numbers of component metrics, but this effect diminished beyond six metrics. Component metrics of MMIs were associated with a broad suite of measured stressors in every region, including salinity, nutrients, herbicides, and streamflow flashiness. We provide a web-based software application that allows users in the conterminous United States to apply our MMIs to their own datasets and compare MMI scores from their sites to a broader regional context.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108513","usgsCitation":"Carlisle, D.M., Spaulding, S., Tyree, M., Schulte, N.O., Lee, S.S., Mitchell, R., and Pollard, A.A., 2022, A web-based tool for assessing the condition of benthic diatom assemblages in streams and rivers of the conterminous United States: Ecological Indicators, v. 135, 108512, 13 p., https://doi.org/10.1016/j.ecolind.2021.108513.","productDescription":"108512, 13 p.","ipdsId":"IP-123713","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":449280,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108513","text":"Publisher Index Page"},{"id":436017,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XD9398","text":"USGS data release","linkHelpText":"Data Release for: A Web-Based Tool for Assessing the Condition of Benthic Diatom 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S","contributorId":245621,"corporation":false,"usgs":false,"family":"Lee","given":"Sylvia","email":"","middleInitial":"S","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":854305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mitchell, Richard M.","contributorId":215406,"corporation":false,"usgs":false,"family":"Mitchell","given":"Richard M.","affiliations":[{"id":39239,"text":"USEPA, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":854306,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pollard, Amina A.","contributorId":297613,"corporation":false,"usgs":false,"family":"Pollard","given":"Amina","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":854307,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70262482,"text":"70262482 - 2022 - It’s complicated and it depends: A review of the effects of ecosystem changes on Walleye and Yellow Perch populations in North America","interactions":[],"lastModifiedDate":"2025-01-22T18:03:09.557191","indexId":"70262482","displayToPublicDate":"2022-01-04T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"It’s complicated and it depends: A review of the effects of ecosystem changes on Walleye and Yellow Perch populations in North America","docAbstract":"Walleye Sander vitreus and Yellow Perch Perca flavescens are culturally, economically, and ecologically significant fish species in North America that are affected by drivers of global change. Here, we review and synthesize the published literature documenting the effects of ecosystem changes on Walleye and Yellow Perch. We focus on four drivers: climate (including temperature and precipitation), aquatic invasive species, land use and nutrient loading, and water clarity. We identified 1,232 tests from 370 papers, split evenly between Walleye (n = 613) and Yellow Perch (n = 619). Climate was the most frequently studied driver (n = 572), and growth or condition was the most frequently studied response (n = 297). The most commonly reported relationship was “no effect” (42% of analyses), usually because multiple variables were tested and only a few were found to be significant. Overall responses varied among studies for most species-response–driver combinations. For example, the influence of invasive species on growth of both Walleye and Yellow Perch was approximately equally likely to be positive, negative, or have no effect. Even when results were variable, important patterns emerged; for example, growth responses of both species to temperature were variable, but very few negative responses were observed. A few relationships were relatively consistent across studies. Invasive species were negatively associated with Walleye recruitment and abundance, and higher water clarity was negatively associated with Walleye abundance, biomass, and production. Some variability in responses may be due to differences in methodology or the range of variables studied; others represent true context dependence, where the effect of a driver depends on the influence of other variables. Using common metrics of impact, publishing negative results, and robust analytical approaches could facilitate comparisons among systems and provide a more comprehensive understanding of the responses of Walleye and Yellow Perch to ecosystem change.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10741","usgsCitation":"Hansen, G., Ruzich, J., Krabbenhoft, C., Kundel, H., Mahlum, S., Rounds, C., Van Pelt, A., Eslinger, L., Logsdon, D., and Isermann, D.A., 2022, It’s complicated and it depends: A review of the effects of ecosystem changes on Walleye and Yellow Perch populations in North America: North American Journal of Fisheries Management, v. 42, no. 3, p. 484-506, https://doi.org/10.1002/nafm.10741.","productDescription":"23 p.","startPage":"484","endPage":"506","ipdsId":"IP-133285","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United 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