Inland lakes face unprecedented pressures from climatic and anthropogenic stresses, causing their recession and desiccation globally. Climate change is increasingly blamed for such environmental degradation, but in many regions, direct anthropogenic pressures compound, and sometimes supersede, climatic factors. This study examined a human-environmental system – the terminal Hamun Lakes on the Iran-Afghanistan border – that embodies amplified challenges of inland waters. Satellite and climatic data from 1984 to 2019 were fused, which documented that the Hamun Lakes lost 89% of their surface area between 1999 and 2001 (3809 km2 versus 410 km2), coincident with a basin-wide, multi-year meteorological drought. The lakes continued to shrink afterwards and desiccated in 2012, despite the above-average precipitation in the upstream basin. Rapid growth in irrigated agricultural lands occurred in upstream Afghanistan in the recent decade, consuming water that otherwise would have fed the Hamun Lakes. Compounding upstream anthropogenic stressors, Iran began storing flood water that would have otherwise drained to the lakes, for urban and agricultural consumption in 2009. Results from a deep Learning model of Hamun Lakes' dynamics indicate that the average lakes' surface area from 2010 to 2019 would have been 2.5 times larger without increasing anthropogenic stresses across the basin. The Hamun Lakes' desiccation had major socio-environmental consequences, including loss of livelihood, out-migration, dust-storms, and loss of important species in the region.
Climate change is thawing and potentially mobilizing vast quantities of organic carbon (OC) previously stored for millennia in permafrost soils of northern circumpolar landscapes. Climate-driven increases in fire and thermokarst may play a key role in OC mobilization by thawing permafrost and promoting transport of OC. Yet, the extent of OC mobilization and mechanisms controlling terrestrial-aquatic transfer are unclear. We demonstrate that hydrologic transport of soil dissolved OC (DOC) from the active layer and thawing permafrost to headwater streams is extremely heterogeneous and regulated by the interactions of soils, seasonal thaw, fire, and thermokarst. Repeated sampling of streams in eight headwater catchments of interior Alaska showed that the mean age of DOC for each stream ranges widely from modern to ∼2,000 years B.P. Together, an endmember mixing model and nonlinear, generalized additive models demonstrated that Δ14C-DOC signature (and mean age) increased from spring to fall, and was proportional to hydrologic contributions from a solute-rich water source, related to presumed deeper flow paths found predominantly in silty catchments. This relationship was correlated with and mediated by catchment properties. Mean DOC ages were older in catchments with >50% burned area, indicating that fire is also an important explanatory variable. These observations underscore the high heterogeneity in aged C export and difficulty of extrapolating estimates of permafrost-derived DOC export from watersheds to larger scales. Our results provide the foundation for developing a conceptual model of permafrost DOC export necessary for advancing understanding and prediction of land-water C exchange in changing boreal landscapes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GB007242","usgsCitation":"Koch, J.C., Bogard, M., Butman, D., Finlay, K., Ebel, B., James, J., Johnston, S.E., Jorgenson, M., Pastick, N., Spencer, R., Striegl, R., Walvoord, M.A., and Wickland, K., 2022, Heterogeneous patterns of aged organic carbon export driven by hydrologic flow paths, soil texture, fire, and thaw in discontinuous permafrost headwaters: Global Biogeochemical Cycles, v. 36, no. 4, e2021GB007242, 16 p., https://doi.org/10.1029/2021GB007242.","productDescription":"e2021GB007242, 16 p.","ipdsId":"IP-134558","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448441,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gb007242","text":"Publisher Index Page"},{"id":398213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151,\n 65\n ],\n [\n -147,\n 65\n ],\n [\n -147,\n 67\n ],\n [\n -151,\n 67\n ],\n [\n -151,\n 65\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Koch, Joshua C. 0000-0001-7180-6982 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":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":839768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bogard, Matthew","contributorId":272635,"corporation":false,"usgs":false,"family":"Bogard","given":"Matthew","affiliations":[{"id":16962,"text":"U. 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We developed and used an environmental DNA (eDNA) assay for longnose darter paired with visual surveys to better determine the species’ range and compare detection probability between sampling approaches in an occupancy modeling framework. We detected longnose darter eDNA further upstream in the mainstem St. Francis River than previously reported and in a tributary for the first time. Our multi-scale occupancy approach compared models where detection was constant against a model that allowed detection to vary by survey method. The constant model received the most support indicating survey method was not a strong predictor and detection was estimated at 0.70 (0.45–0.86; 95% CI) across both methods. Our study produced effective longnose darter eDNA primers and demonstrated the application of eDNA for sampling small-bodied, cryptic fish. We detected longnose darter eDNA 27 km upstream of their known range and determined that snorkel surveys are the most efficient sampling method if water clarity allows. We recommend target sample sizes to achieve various detection goals for both sample methods and our results inform future design of distributional and monitoring efforts.
","language":"English","publisher":"MPDI","doi":"10.3390/fishes7020070","usgsCitation":"Westhoff, J.T., Berkman, L.K., Klymus, K.E., Thompson, N., and Richter, C.A., 2022, A comparison of eDNA and visual survey methods for detection of longnose darter Percina nasuta in Missouri: Fishes, v. 7, no. 2, 70, 16 p., https://doi.org/10.3390/fishes7020070.","productDescription":"70, 16 p.","ipdsId":"IP-121698","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":448443,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes7020070","text":"Publisher Index Page"},{"id":435919,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z597LN","text":"USGS data release","linkHelpText":"Longnose darter (Percina nasuta) eDNA survey results from the St. Francis River, Missouri 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\"}}]}","volume":"7","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Westhoff, Jacob Thomas 0000-0002-2347-5098","orcid":"https://orcid.org/0000-0002-2347-5098","contributorId":288958,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"Thomas","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berkman, Leah K.","contributorId":275349,"corporation":false,"usgs":false,"family":"Berkman","given":"Leah","email":"","middleInitial":"K.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":838494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838496,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Cathy A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":1878,"corporation":false,"usgs":true,"family":"Richter","given":"Cathy","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":838497,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229838,"text":"70229838 - 2022 - Errors in aerial survey count data: Identifying pitfalls and solutions","interactions":[],"lastModifiedDate":"2022-03-21T13:41:26.57231","indexId":"70229838","displayToPublicDate":"2022-03-18T08:34:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Errors in aerial survey count data: Identifying pitfalls and solutions","docAbstract":"Accurate estimates of animal abundance are essential for guiding effective management, and poor survey data can produce misleading inferences. Aerial surveys are an efficient survey platform, capable of collecting wildlife data across large spatial extents in short timeframes. However, these surveys can yield unreliable data if not carefully executed. Despite a long history of aerial survey use in ecological research, problems common to aerial surveys have not yet been adequately resolved. Through an extensive review of the aerial survey literature over the last 50 years, we evaluated how common problems encountered in the data (including nondetection, counting error, and species misidentification) can manifest, the potential difficulties conferred, and the history of how these challenges have been addressed. Additionally, we used a double-observer case study focused on waterbird data collected via aerial surveys and an online group (flock) counting quiz to explore the potential extent of each challenge and possible resolutions. We found that nearly three quarters of the aerial survey methodology literature focused on accounting for nondetection errors, while issues of counting error and misidentification were less commonly addressed. Through our case study, we demonstrated how these challenges can prove problematic by detailing the extent and magnitude of potential errors. Using our online quiz, we showed that aerial observers typically undercount group size and that the magnitude of counting errors increases with group size. Our results illustrate how each issue can act to bias inferences, highlighting the importance of considering individual methods for mitigating potential problems separately during survey design and analysis. We synthesized the information gained from our analyses to evaluate strategies for overcoming the challenges of using aerial survey data to estimate wildlife abundance, such as digital data collection methods, pooling species records by family, and ordinal modeling using binned data. Recognizing conditions that can lead to data collection errors and having reasonable solutions for addressing errors can allow researchers to allocate resources effectively to mitigate the most significant challenges for obtaining reliable aerial survey data.
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Kerr Reservoir and Lake Gaston. The Raleigh terrane (located on the eastern side of the map) contains Neoproterozoic to early Paleozoic(?) polydeformed, amphibolite-facies gneisses and schists. The Carolina slate belt of the Carolina terrane (located in the central part of the map) contains Neoproterozoic metavolcanic and metasedimentary rocks at greenschist facies. Although locally complicated, the slate-belt structure mapped across the South Boston map area is generally a broad, complex anticlinorium of the Hyco Formation (here called the Chase City anticlinorium) and is flanked to the west and east by synclinoria, which are cored by the overlying Aaron and Virgilina Formations. The western flank of the Carolina terrane (located in the western-central part of the map) contains similar rocks at higher metamorphic grade. This terrane includes epidote-amphibolite-facies to amphibolite-facies gneisses of the Neoproterozoic Country Line complex, which extends north-northeastward across the map. The Milton terrane (located on the western side of the map) contains Ordovician amphibolite-facies metavolcanic and metasedimentary gneisses of the Cunningham complex.
Crosscutting relations and fabrics in mafic to felsic plutonic rocks constrain the timing of Neoproterozoic to late Paleozoic deformations across the Piedmont. In the eastern part of the map, a 5- to 9-kilometer-wide band of tectonic elements that contains two late Paleozoic mylonite zones (Nutbush Creek and Lake Gordon) and syntectonic granite (Buggs Island pluton) separates the Raleigh and Carolina terranes. Amphibolite-facies, infrastructural metaigneous and metasedimentary rocks east of the Lake Gordon mylonite zone are generally assigned to the Raleigh terrane. In the western part of the map area, a 5- to 8-kilometer-wide band of late Paleozoic tectonic elements includes the Hyco and Clover shear zones, syntectonic granitic sheets, and amphibolite-facies gneisses along the western margin of the Carolina terrane at its boundary with the Milton terrane. This band of tectonic elements is also the locus for early Mesozoic extensional faults associated with the early Mesozoic Scottsburg, Randolph, and Roanoke Creek rift basins.
The map shows fluvial terrace deposits of sand and gravel on hills and slopes near the Roanoke and Dan Rivers. The terrace deposits that are highest in altitude are the oldest. Saprolite regolith is spatially associated with geologic source units and is not shown separately on the map.
Mineral resources in the area include gneiss and granite quarried for crushed stone, tungsten-bearing vein deposits of the Hamme district, and copper and gold deposits of the Virgilina district. Surface-water resources are abundant and include rivers, tributaries, the John H. Kerr Reservoir, and Lake Gaston. Groundwater flow is concentrated in saprolite regolith, along fractures in the crystalline bedrock, and along fractures and bedding-plane partings in the Mesozoic rift basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3483","usgsCitation":"Horton, J.W., Jr., Peper, J.D., Burton, W.C., Weems, R.E., and Sacks, P.E., 2022, Geologic map of the South Boston 30' × 60' quadrangle, Virginia and North Carolina: U.S. Geological Survey Scientific Investigations Map 3483, 1 sheet, scale 1:100,000, 46-p. pamphlet, https://doi.org/10.3133/sim3483. [Supersedes USGS Open-File Report 93–244.]","productDescription":"Pamphlet: vi, 46 p.; 1 Sheet: 62.00 x 35.00 inches; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112223","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501889,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112691.htm","linkFileType":{"id":5,"text":"html"}},{"id":396136,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3483/sim3483_map.pdf","text":"Map","size":"38.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3483 map"},{"id":396134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3483/coverthb2.jpg"},{"id":396135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3483/sim3483_pamphlet.pdf","text":"Pamphlet","size":"871 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3483 pamphlet"},{"id":396935,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98AQDR7","text":"USGS data release","linkHelpText":"Database for the Geologic Map of the South Boston 30' × 60' Quadrangle, Virginia and North Carolina"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"South Boston 30 x 60 minute quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79,\n 36.5\n ],\n [\n -78,\n 36.5\n ],\n [\n -78,\n 37\n ],\n [\n -79,\n 37\n ],\n [\n -79,\n 36.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
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Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high-frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady-state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub-daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation-based approach to estimate the sub-daily flux under highly dynamic flow conditions using multi-depth temperature observations at a 5-min resolution. If the hydraulic gradient is measured, Darcy's law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi-depth temperature data at 1- or 2-hr time intervals assuming one-dimensional flow and heat transport governing equation. By comparing estimated fluxes with model-generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation-based flux estimation method was able to capture the vertical sub-daily fluxes using multi-depth high-resolution temperature data alone, even in the presence of multi-dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1-D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.
Missouri, one of only two States that borders eight different States, lies in the heart of the United States. Distinguished by its farm fields and forests, substantial rivers and lakes, and cities filled with culture and industry, the “Show Me State” has abundant beauty and a long history of connecting the East and the West. The Pony Express, Oregon Trail, Santa Fe Trail, and California Trail all began in Missouri.
The land and the people of Missouri contribute to its resiliency. Landsat data provide important tools for Missourians to protect their landscapes and waterways and enhance their economy under a variety of circumstances, from fast-arising natural disasters to longer-term environmental phenomena.
Here are several ways that Landsat data benefit Missouri.
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\"}}]}","edition":"Version 1.0: March 16, 2022; Version 1.1: January 19, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The estuarine habitat mosaic supports the reproduction, growth, and survival of resident and migratory fish species by providing a diverse portfolio of unique habitats with varying physical and biological features. Global climate change is expected to result in increasing temperatures, rising sea levels, and changes in riverine hydrology, which will have profound effects on the extent and composition of the estuarine habitat mosaic and its associated nursery quality for juvenile fish. We used a spatially explicit bioenergetics model to assess how different climate change scenarios might affect juvenile salmon growth rate potential relative to present day conditions in the Nisqually River Delta, WA, USA. The model indicated that prey-rich habitats such as emergent salt marshes and eelgrass meadows were most likely to facilitate growth, and that reductions in their areal extent and accessibility could have severe consequences for salmon. For instance, unmitigated sea-level rise halved the predicted extent of low- and high-elevation emergent salt marsh, leading to a 30% reduction in end-of-season weights. Increasing water temperatures compounded these effects during the late spring and summer such that the average daily growth rate of an individual fish decreased by an additional 5–50% when compared to the effects of sea-level rise alone. Lethal temperatures (> 24 °C) were infrequently observed, but they were more likely to occur during summer low tides in the mudflat and eelgrass habitats when accessibility to prey-rich marsh was minimal, thereby limiting foraging capacity and the availability of thermal refugia. Our findings indicate that, barring the enactment of targeted management strategies, rising tidal levels and increasing ocean temperatures may reduce the quality of the estuarine habitat mosaic for out-migrating salmon and other sensitive fish species.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-021-01003-3","usgsCitation":"Davis, M.J., Woo, I., Ellings, C.S., Hodgson, S., Beauchamp, D., Nakai, G., and De La Cruz, S.E., 2022, A climate-mediated shift in the estuarine habitat mosaic limits prey availability and reduces nursery quality for juvenile salmon: Estuaries and Coasts, v. 45, p. 1445-1464, https://doi.org/10.1007/s12237-021-01003-3.","productDescription":"20 p.","startPage":"1445","endPage":"1464","ipdsId":"IP-129415","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":397156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta, Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.7385711669922,\n 47.06731569299121\n ],\n [\n -122.73273468017578,\n 47.067900315766245\n ],\n [\n -122.71350860595702,\n 47.06836800936954\n ],\n [\n -122.69496917724608,\n 47.07526601334617\n ],\n [\n -122.67488479614258,\n 47.08239690925263\n ],\n [\n -122.67454147338866,\n 47.11172875008271\n ],\n [\n -122.73822784423828,\n 47.11137826571562\n ],\n [\n -122.7385711669922,\n 47.06731569299121\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838145,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838146,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellings, Christopher S.","contributorId":149343,"corporation":false,"usgs":false,"family":"Ellings","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":17711,"text":"Dep't Natural Resources, Nisqually Indian Tribe, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":838147,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hodgson, Sayre","contributorId":172121,"corporation":false,"usgs":false,"family":"Hodgson","given":"Sayre","email":"","affiliations":[{"id":26985,"text":"Nisqually Indian Tribe, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":838148,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beauchamp, David 0000-0002-3592-8381","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":217816,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":838149,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nakai, Glynnis","contributorId":172123,"corporation":false,"usgs":false,"family":"Nakai","given":"Glynnis","email":"","affiliations":[{"id":26986,"text":"US Fish and Wildlife Service, Nisqually Nat'l Wildlife Refuge, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":838150,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838151,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70231906,"text":"70231906 - 2022 - GW/SW-MST: A groundwater/surface-water method selection tool","interactions":[],"lastModifiedDate":"2022-11-16T16:51:50.333401","indexId":"70231906","displayToPublicDate":"2022-03-16T09:48:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"GW/SW-MST: A groundwater/surface-water method selection tool","docAbstract":"Groundwater/surface-water (GW/SW) exchange and hyporheic processes are topics receiving increasing attention from the hydrologic community. Hydraulic, chemical, temperature, geophysical, and remote sensing methods are used to achieve various goals (e.g., inference of GW/SW exchange, mapping of bed materials, etc.), but the application of these methods is constrained by site conditions such as water depth, specific conductance, bed material, and other factors. Researchers and environmental professionals working on GW/SW problems come from diverse fields and rarely have expertise in all available field methods; hence there is a need for guidance to design field campaigns and select methods that both contribute to study goals and are likely to work under site-specific conditions. Here, we present the spreadsheet-based GW/SW-Method Selection Tool (GW/SW-MST) to help practitioners identify methods for use in GW/SW and hyporheic studies. The GW/SW-MST is a Microsoft Excel-based decision support tool in which the user selects answers to questions about GW/SW-related study goals and site parameters and characteristics. Based on user input, the tool indicates which methods from a toolbox of 32 methods could potentially contribute to achieving the specified goals at the site described.
","language":"English","publisher":"National Groundwater Association (NGWA)","doi":"10.1111/gwat.13194","usgsCitation":"Hammett, S., Day-Lewis, F., Trottier, B.R., Barlow, P.M., Briggs, M., Delin, G.N., Harvey, J., Johnson, C., Lane, J., Rosenberry, D., and Werkema, D.D., 2022, GW/SW-MST: A groundwater/surface-water method selection tool: Groundwater, v. 60, no. 6, p. 784-791, https://doi.org/10.1111/gwat.13194.","productDescription":"8 p.","startPage":"784","endPage":"791","ipdsId":"IP-128682","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448467,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9477975","text":"Publisher Index Page"},{"id":435922,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YFJALF","text":"USGS data release","linkHelpText":"GW/SW-MST: A Groundwater/Surface-Water Method Selection Tool"},{"id":401640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hammett, Steven 0000-0002-6051-966X","orcid":"https://orcid.org/0000-0002-6051-966X","contributorId":292207,"corporation":false,"usgs":false,"family":"Hammett","given":"Steven","email":"","affiliations":[{"id":38050,"text":"Contractor","active":true,"usgs":false}],"preferred":false,"id":844072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844073,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trottier, Brett Russell 0000-0002-6148-0875","orcid":"https://orcid.org/0000-0002-6148-0875","contributorId":291383,"corporation":false,"usgs":true,"family":"Trottier","given":"Brett","email":"","middleInitial":"Russell","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":844080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barlow, Paul M. 0000-0003-4247-6456 pbarlow@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6456","contributorId":1200,"corporation":false,"usgs":true,"family":"Barlow","given":"Paul","email":"pbarlow@usgs.gov","middleInitial":"M.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":844074,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":844075,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delin, Geoffrey N. 0000-0001-7991-6158","orcid":"https://orcid.org/0000-0001-7991-6158","contributorId":224981,"corporation":false,"usgs":true,"family":"Delin","given":"Geoffrey","email":"","middleInitial":"N.","affiliations":[{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":844076,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844077,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Carole D. 0000-0001-6941-1578","orcid":"https://orcid.org/0000-0001-6941-1578","contributorId":245365,"corporation":false,"usgs":true,"family":"Johnson","given":"Carole D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844082,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":844078,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rosenberry, D.O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":38500,"corporation":false,"usgs":true,"family":"Rosenberry","given":"D.O.","affiliations":[],"preferred":true,"id":844079,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Werkema, Dale D.","contributorId":40488,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","email":"","middleInitial":"D.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":844081,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70232522,"text":"70232522 - 2022 - Atmospheric river storm flooding influences tidal marsh elevation building processes","interactions":[],"lastModifiedDate":"2022-07-06T11:56:06.295944","indexId":"70232522","displayToPublicDate":"2022-03-16T06:52:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Atmospheric river storm flooding influences tidal marsh elevation building processes","docAbstract":"Disturbances are a key component of ecological processes in coastal ecosystems. Investigating factors that affect tidal marsh accretion and elevation change is important, largely due to accelerating sea-level rise and the ecological and economic value of wetlands. Sediment accumulation rates, elevation change, and flooding were examined at five marshes along a riverine-tidal gradient in the northern San Francisco Bay-Delta, California, USA during an Atmospheric River storm event in 2017 using Surface Elevation Tables (SETs), feldspar marker horizons (MH), and continuous water-level sensors. Our results showed that localized marsh flooding increased during the storm event, but not evenly across sites. Marsh surface elevation increased the most at the tidal freshwater marsh site in response to the storms, with an average surface elevation gain of 45.6 ± 13.1 mm, and the least at a tidal saline marsh with an average surface elevation gain of 4.0 ± 1.2 mm. A marsh located on the large embayment did not exhibit an immediate response to the storm but had a surface elevation gain of 21.5 ± 13.7 mm 6 months after the storm. During the storm period, marsh distance to the bay was the strongest predictor of elevation change, followed by SET-MH elevations. Conversely, during non-storm periods, SET-MH elevation was a relatively strong predictor of elevation change. Atmospheric Rivers appear to be a major factor affecting short-term spatial and temporal variability in flooding and sedimentation rates in tidal marsh systems. Incorporating information about storms into monitoring could increase our understanding of how episodic storms can impact marshes.
Salmonella enterica serovar Typhimurium is typically considered a host generalist; however, certain isolates are associated with specific hosts and show genetic features of host adaptation. Here, we sequenced 131 S. Typhimurium isolates from wild birds collected in 30 U.S. states during 1978-2019. We found that isolates from broad taxonomic host groups including passerine birds, water birds (Aequornithes), and larids (gulls and terns) represented three distinct lineages and certain S. Typhimurium CRISPR types presented in individual lineages. We also showed that lineages formed by wild bird isolates differed from most isolates originating from domestic animal sources, and genomes from these lineages substantially improved source attribution of Typhimurium genomes to wild birds by a machine learning classifier. Furthermore, virulence gene signatures that differentiated S. Typhimurium from passerines, water birds, and larids were detected. Passerine isolates tended to lack S. Typhimurium-specific virulence plasmids. Isolates from the passerine, water bird, and larid lineages had close genetic relatedness with human clinical isolates, including those from a 2021 U.S. outbreak linked to passerine birds. These observations indicate that S. Typhimurium from wild birds in the United States are likely host-adapted, and the representative genomic dataset examined in this study can improve source prediction and facilitate outbreak investigation.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/aem.01979-21","usgsCitation":"Fu, Y., M’ikanatha, N.M., Lorch, J., Blehert, D.S., Berlowski-Zier, B.M., Whitehouse, C.A., Li, S., Deng, X., Smith, J., Shariat, N.W., Nawrocki, E.M., and Dudley, E.G., 2022, Salmonella enterica serovar Typhimurium from wild birds in the United States represent distinct lineages defined by bird type: Applied and Environmental Microbiology, v. 88, no. 6, e01979-21, 16 p., https://doi.org/10.1128/aem.01979-21.","productDescription":"e01979-21, 16 p.","ipdsId":"IP-131992","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":448497,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/aem.01979-21","text":"Publisher Index Page"},{"id":397112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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headwater stream permanence estimates from a monthly water balance model in the Pacific Northwest, USA","docAbstract":"Stream permanence classifications (i.e., perennial, intermittent, ephemeral) are a primary consideration to determine stream regulatory status in the United States (U.S.) and are an important indicator of environmental conditions and biodiversity. However, at present, no models or products adequately describe surface water presence for regulatory determinations. We modified the Thornthwaite monthly water balance model (MWBM) with a flow threshold parameter to estimate flow permanence and evaluated the model’s accuracy and precision for more than 1.3 million headwater stream reaches in the U.S. Pacific Northwest (PNW). Stream reaches were assigned to one of eight calibration groups by unsupervised classification based on sensitivity to MWBM parameters. Suitable MWBM parameter sets were identified by comparing modeled stream permanence estimates to surface water presence observations (SWPO). Parameter sets with accuracies > 65% were considered suitable. The MWBM estimated stream permanence with high precision at 40% of reaches, with poor precision at 20% of reaches, and no suitable parameter sets were identified for 40% of reaches. Results highlight the need for increased SWPO collection to improve calibration and assessment of stream permanence models. Additionally, implementation of the MWBM to estimate surface water presence indicates potential for process-based models to predict stream permanence with future development.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/w14060895","usgsCitation":"Hafen, K., Blasch, K.W., Gessler, P.E., Sando, R., and Rea, A.H., 2022, Precision of headwater stream permanence estimates from a monthly water balance model in the Pacific Northwest, USA: Water, v. 14, no. 6, 895, 21 p., https://doi.org/10.3390/w14060895.","productDescription":"895, 21 p.","ipdsId":"IP-127173","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":448499,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14060895","text":"Publisher Index Page"},{"id":435925,"rank":2,"type":{"id":30,"text":"Data 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Processes","active":true,"publicationSubtype":{"id":10}},"title":"Forest cover lessens the impact of drought on streamflow in Puerto Rico","docAbstract":"Tropical regions are experiencing high rates of forest cover loss coupled with changes in the volume and timing of rainfall. These shifts can compromise streamflow and water provision, highlighting the need to identify how forest cover influences streamflow generation under variable rainfall conditions. Although rainfall is the key driver of streamflow regimes, the role of forests is less clear, particularly in tropical regions where forest loss is an ongoing risk. Forest cover loss alters evapotranspiration, rainfall infiltration and storage, and may increase stream ecosystem vulnerability to rainfall extremes. Puerto Rico, an island with spatially heterogenous forest cover and a marked geographic rainfall gradient, is projected to experience more frequent droughts and flash flooding. Using 15-minute streamflow data collected between 2005 and 2016 from 20 USGS stream gages and 3-hourly Multi-Source Weighted-Ensemble Precipitation rainfall estimates, we utilized flow-duration curves and linear mixed regression models to examine the role of forest cover in regulating the timing and volume of streamflow. The mixed model approach helps to account for differences in watershed characteristics. We determined the effects of rainfall and forest cover on low and peak flows in Puerto Rican streams, then evaluated changes in these relationships under dry and wet antecedent rainfall conditions. Watersheds with high forest cover had consistently greater low and peak streamflow than deforested ones under all rainfall conditions, although the effect was more marked during wet antecedent conditions, suggesting that peak flow is largely the result of saturation excess overland flow. During dry antecedent rainfall conditions, highly forested watersheds had higher streamflow than deforested ones, suggesting greater hillslope storage and release may also be at play. Our results demonstrate that forest cover generated a net increase in hillslope infiltration and storage and may lessen drought impacts on streamflow in Puerto Rico. Resilience to prolonged drought may be limited by finite water storage potential in this steep, mountainous setting, highlighting maintenance of forest cover as an important water management strategy to increase infiltration.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14551","usgsCitation":"Hall, J.S., Scholl, M.A., Gorokhovich, Y., and Uriarte, M., 2022, Forest cover lessens the impact of drought on streamflow in Puerto Rico: Hydrological Processes, v. 36, no. 5, e14551, 16 p., https://doi.org/10.1002/hyp.14551.","productDescription":"e14551, 16 p.","ipdsId":"IP-122081","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":399811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto 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Yuri","contributorId":290689,"corporation":false,"usgs":false,"family":"Gorokhovich","given":"Yuri","email":"","affiliations":[{"id":39562,"text":"City University of New York","active":true,"usgs":false}],"preferred":false,"id":841587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Uriarte, Maria","contributorId":287019,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":841588,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229658,"text":"sir20225013 - 2022 - Evaluation of salinity and nutrient conditions in the Heart River Basin, North Dakota, 1970–2020","interactions":[],"lastModifiedDate":"2026-04-08T17:28:23.084393","indexId":"sir20225013","displayToPublicDate":"2022-03-14T11:12:51","publicationYear":"2022","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":"2022-5013","displayTitle":"Evaluation of Salinity and Nutrient Conditions in the Heart River Basin, North Dakota, 1970–2020","title":"Evaluation of salinity and nutrient conditions in the Heart River Basin, North Dakota, 1970–2020","docAbstract":"The Heart River Basin is predominantly an agricultural basin in western North Dakota and is approximately 3,350 square miles. The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service and the Grant County Soil Conservation District, completed a study to assess spatial and temporal patterns of water quality in the Heart River Basin. The purpose of this report is to describe the methods and results of a study to evaluate salinity and nutrients in the Heart River Basin in western North Dakota. Water-quality and streamflow data used in the study were compiled from 1970 to 2020 using the National Water Quality Monitoring Council Water Quality Portal and National Water Information System.
Changes in streamflow characteristics were investigated at three sites from 1970 to 2020, and changes in water quality were investigated at four sites from 1974 to 2019. Streamflow analysis indicated decreasing streamflow from 1970 until the late 1990s followed by increasing streamflow through 2020, with the largest increase in the 7-day minimum streamflow or base flow. For the historical water-quality trend period (1974–2019), total dissolved solids, sulfate, sodium, chloride, and sodium adsorption ratio concentrations have increased since the mid-1970s through 2019. Potassium concentrations during the historical period remained mostly constant with some small fluctuations. Calcium and magnesium concentrations increased since the mid-1970s at all sites, except for a decrease at one site between 1974 and 1999. During the recent trend period (1999–2019), increasing concentrations in total dissolved solids, sulfate, sodium, chloride, calcium, magnesium, and sodium adsorption ratios were observed across the Heart River Basin. The magnitude of the increases was smaller at tributary sites compared to main-stem sites. During the recent period, potassium was mostly constant, although small (−0.9 milligram per liter or less) decreases on tributaries and minor (1.3 milligrams per liter) increases on the main-stem sites were detected. Unlike dissolved ion concentrations, significant increases in nutrient concentrations were not detected from 1999 to 2019, but nitrate plus nitrite concentrations most likely decreased upstream from Lake Tschida.
Inverse modeling for period 1 (1974–99) in model zone 1 (Heart River reach from site 5 to site 6) had eight reasonable models that indicated the clay mineral-water interactions and dissolution of evaporites control the geochemistry. Results of the inverse modeling for period 2 (1999–2019) in model zone 1 also had eight reasonable models that indicated that the dissolution of evaporites was the major geochemical control. Results of the geochemical modeling for period 1 (1974–99) in model zone 2 (Heart River and Sweetbriar Creek reach from sites 20 and 21 to site 22) produced seven reasonable models, and the geochemical control of the system was the dissolution of sulfate evaporite minerals. Geochemical modeling results for period 2 (1999–2019) in model zone 2 produced 11 reasonable models and was also controlled by the dissolution of sulfate evaporite minerals. Differences between the two model zones indicated that geology controls some of the water-quality changes in the Heart River Basin.
Loads were estimated for total dissolved solids, sulfate, sodium, and chloride and total phosphorus. Annual loads estimated for the Heart River from 2013 through 2020 at the Heart River site upstream from Lake Tschida (site 5) and near Mandan (site 22) were generally greatest in 2014 and least in 2016 for total dissolved solids, sulfate, sodium, and chloride. Most of the annual loads of total dissolved solids, sulfate, sodium, and chloride are delivered in March through July in the Heart River at these sites and are likely from snowmelt and spring and summer rains. The mean annual yields of total dissolved solids and sodium from 2013 to 2020 generally were largest in Big Muddy Creek (site 18), whereas yields of sulfate and chloride were largest at Sweetbriar Creek (site 21) compared to the other selected sites in the Heart River Basin. Larger yields of total dissolved solids, sulfate, sodium, and chloride at sites located on Big Muddy Creek and Sweet Briar Creek in the lower Heart River Basin were likely a result of differences in geology and soils upstream from the selected sites.
A mass balance of total dissolved solids, sulfate, sodium, and chloride was estimated for the lower Heart River Basin, specifically the reach below Lake Tschida to Mandan (site 7 to site 22). Intervening flow was the largest contributor to the dissolved ion loads in the lower Heart River Basin and is an important part of understanding the transport of dissolved ions in the basin. The intervening load can include groundwater discharge, irrigation return flow, local runoff, and input from smaller ephemeral tributaries. Tributaries in the lower Heart River Basin contributed portions of the total dissolved solids, sulfate, sodium, and chloride loads at the Heart River near Mandan (site 22) that generally were proportional to the streamflow contributions.
Annual loads for total phosphorus between 2013 and 2020 at the Heart River site upstream from Lake Tschida (site 5) and near Mandan (site 22) generally were largest in 2019 and smallest in 2016. Most of the total phosphorus loads for main-stem sites 5 and 22 were transported in March, April, and June, likely from snowmelt and early summer rains. The mean annual yields of total phosphorus for 2013–20 were largest on the main-stem site upstream from Lake Tschida (site 5) and Sweetbriar Creek (site 21), whereas the smallest yields were in Big Muddy Creek (site 18). Much of the phosphorus that enters Lake Tschida from the upper basin does not get transported downstream to the lower basin, and much of the phosphorus in the lower basin was attributed to intervening flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225013","collaboration":"Prepared in cooperation with the Department of Agriculture Natural Resources Conservation Service and Grant County Soil Conservation District","usgsCitation":"Tatge, W.S., Nustad, R.A., and Galloway, J.M., 2022, Evaluation of salinity and nutrient conditions in the Heart River Basin, North Dakota, 1970–2020: U.S. Geological Survey Scientific Investigations Report 2022–5013, 76 p., https://doi.org/10.3133/sir20225013.","productDescription":"Report: ix, 76; Data Release; Dataset","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-131163","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":397042,"rank":6,"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":397041,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P987APZ8","text":"USGS data release","linkHelpText":"Data and scripts used in water-quality trend and load analysis in the Heart River Basin, North Dakota, 1970–2020"},{"id":502299,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112690.htm","linkFileType":{"id":5,"text":"html"}},{"id":398527,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225013/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397040,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5013/images"},{"id":397039,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5013/sir20225013.XML"},{"id":397038,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5013/sir20225013.pdf","text":"Report","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5013"},{"id":397037,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5013/coverthb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Heart River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.2769775390625,\n 46.27863122156088\n ],\n [\n -100.8160400390625,\n 46.27863122156088\n ],\n [\n -100.8160400390625,\n 47.25\n ],\n [\n -103.2769775390625,\n 47.25\n ],\n [\n -103.2769775390625,\n 46.27863122156088\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
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You can learn a lot about rivers, lakes, estuaries, and oceans by looking down at them from the sky. Scientists use a technique called remote sensing to measure the amount of light or heat energy reflected and emitted from the Earth. Sensors can be on satellites or mounted on airplanes, helicopters, or drones. Scientists use this information to map the quality of water in the San Francisco Bay-Delta estuary. Remote sensing helps scientists see where and when there might be problems for human health or for the plants and animals living in the estuary.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frym.2022.619716","usgsCitation":"Mejia, F.H., Torgersen, C.E., and Fichot, C.G., 2022, Keeping an eye on water quality from the sky: Frontiers for Young Minds, HTML Document, https://doi.org/10.3389/frym.2022.619716.","productDescription":"HTML Document","ipdsId":"IP-123673","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448507,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frym.2022.619716","text":"Publisher Index Page"},{"id":398217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay-Delta estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.55249023437501,\n 37.32430451813815\n ],\n [\n -121.57470703125,\n 37.32430451813815\n ],\n [\n -121.57470703125,\n 38.244651696093634\n ],\n [\n -122.55249023437501,\n 38.244651696093634\n ],\n [\n -122.55249023437501,\n 37.32430451813815\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-03-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":839742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fichot, Cedric G","contributorId":289767,"corporation":false,"usgs":false,"family":"Fichot","given":"Cedric","email":"","middleInitial":"G","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":839743,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229663,"text":"70229663 - 2022 - Quantifying large-scale continental shelf margin growth and dynamics across mid-Cretaceous Arctic Alaska with detrital zircon U-Pb dating","interactions":[],"lastModifiedDate":"2022-04-26T12:10:04.272603","indexId":"70229663","displayToPublicDate":"2022-03-14T08:12:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying large-scale continental shelf margin growth and dynamics across mid-Cretaceous Arctic Alaska with detrital zircon U-Pb dating","docAbstract":"Sequence stratigraphy provides a unifying framework for integrating diverse observations to interpret sedimentary basin evolution; however, key time assumptions about stratigraphic elements spanning hundreds of kilometers are rarely quantified. 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","language":"English","publisher":"Geological Society of America","doi":"10.1130/G49118.1","usgsCitation":"Lease, R.O., Houseknecht, D.W., and Kylander-Clark, A.R., 2022, Quantifying large-scale continental shelf margin growth and dynamics across mid-Cretaceous Arctic Alaska with detrital zircon U-Pb dating: Geology, v. 50, no. 5, p. 620-625, https://doi.org/10.1130/G49118.1.","productDescription":"6 p.","startPage":"620","endPage":"625","ipdsId":"IP-135413","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":448513,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/g49118.1","text":"Publisher Index Page"},{"id":435927,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F8BHTN","text":"USGS data release","linkHelpText":"U-Pb Isotopic Data and Ages of Detrital Zircon and Volcanic Zircon Grains from the Torok and Nanushuk Formations, Arctic Alaska, 2021"},{"id":397055,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -140.99853515625,\n 69.65708627301174\n ],\n [\n -139.81201171874997,\n 73.23937702441908\n ],\n [\n -162.59765625,\n 73.02900629225599\n ],\n [\n -169.8046875,\n 69.17037257214531\n ],\n [\n -163.828125,\n 67.05887024878373\n ],\n [\n -160.6640625,\n 67.30597574414466\n ],\n [\n -157.58789062499997,\n 67.04173496919447\n ],\n [\n -154.95117187499997,\n 66.93866882358137\n ],\n [\n -151.7431640625,\n 67.12729044909526\n ],\n [\n -147.3046875,\n 67.53377157140451\n ],\n [\n -145.1513671875,\n 68.46379955520322\n ],\n [\n -141.0205078125,\n 68.86351700272681\n ],\n [\n -140.99853515625,\n 69.65708627301174\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-03-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Lease, Richard O. 0000-0003-2582-8966 rlease@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-8966","contributorId":5098,"corporation":false,"usgs":true,"family":"Lease","given":"Richard","email":"rlease@usgs.gov","middleInitial":"O.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":837863,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houseknecht, David W. 0000-0002-9633-6910 dhouse@usgs.gov","orcid":"https://orcid.org/0000-0002-9633-6910","contributorId":645,"corporation":false,"usgs":true,"family":"Houseknecht","given":"David","email":"dhouse@usgs.gov","middleInitial":"W.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":837864,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kylander-Clark, Andrew R. C.","contributorId":212897,"corporation":false,"usgs":false,"family":"Kylander-Clark","given":"Andrew","email":"","middleInitial":"R. C.","affiliations":[],"preferred":false,"id":837865,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229614,"text":"fs20223009 - 2022 - Detecting algal toxins and organic contaminants of concern in the environment","interactions":[],"lastModifiedDate":"2022-03-14T10:46:43.542315","indexId":"fs20223009","displayToPublicDate":"2022-03-11T14:11:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3009","displayTitle":"Detecting Algal Toxins and Organic Contaminants of Concern in the Environment","title":"Detecting algal toxins and organic contaminants of concern in the environment","docAbstract":"The U.S. Geological Survey (USGS) Kansas Water Science Center Organic Geochemistry Research Laboratory (OGRL) was established in 1987. The OGRL is a multidisciplinary program that contributes knowledge about the distribution, fate, transport, and effects of new and understudied organic compounds that may affect human health and (or) ecosystems. The OGRL consists of two units: Algal and Other Environmental Toxins Unit and Environmental Organic Chemistry Unit. The OGRL does independent and collaborative research, develops robust analytical methods, and provides fee-for-service analytical laboratory analyses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223009","usgsCitation":"Dietze, J.E., Lane, R.F., Loftin, K.A., Tush, D.L., and Wilson, M.C., 2022, Detecting algal toxins and organic contaminants of concern in the environment: U.S. Geological Survey Fact Sheet 2022–3009, 2 p., https://doi.org/10.3133/fs20223009.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-120628","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":397001,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3009/fs20223009.XML"},{"id":397000,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3009/fs20223009.pdf","text":"Report","size":"9.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3009"},{"id":396999,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3009/coverthb.jpg"},{"id":397002,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3009/images"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
This systematic assessment of occurrence for 85 volatile organic compounds (VOCs) in raw (untreated) groundwater used for public supply across the United States (U.S.), which includes 43 compounds not previously monitored by national studies, relates VOC occurrence to explanatory factors and assesses VOC detections in a human-health context. Samples were collected in 2013 through 2019 from 1537 public-supply wells in aquifers representing 78% of the volume pumped for public drinking-water supply. Laboratory detection limits for VOCs generally were less than 0.1 μg/L. Detections were reported for 36% of the sampled principal-aquifer area (38% of sampled wells) and were most common in wells in shallow, unconfined aquifers in urban areas that produce high proportions of modern-age and oxic groundwater. The disinfection by-product trichloromethane (chloroform) was the most commonly detected VOC associated primarily with anthropogenic sources (24% of the sampled area, 25% of sampled wells), followed by the gasoline oxygenate methyl tert-butyl ether (8.4% of area, 11% of wells). Carbon disulfide (12% of area, 14% of wells) was examined separately because of likely substantial contributions from natural sources. Newly monitored VOCs were each detected in <1% of the sampled area. Although detections of 1,4-dioxane in this first national study of its occurrence in raw groundwater were rare, measured concentrations exceeded the most stringent (non-enforceable) human-health benchmark in 0.5% of the sampled area (9 wells). Two wells had exceedances of enforceable benchmarks for tetrachloroethylene and trichloroethylene, and 50 wells total (representing 2.0% of the sampled area, 3.3% of sampled wells) had combined VOC concentrations exceeding 10% of benchmarks of any type. Compared with previous national findings, this study reports lower rates of VOC detection, but confirms widespread anthropogenic influence on groundwater used for public supply, with relatively few concentrations of individual VOCs or mixtures that approach or exceed human-health benchmarks.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.154313","usgsCitation":"Bexfield, L.M., Belitz, K., Fram, M.S., and Lindsey, B.D., 2022, Volatile organic compounds in groundwater used for public supply across the United States: Occurrence, explanatory factors, and human-health context: Science of the Total Environment, v. 827, 154313, 12 p., https://doi.org/10.1016/j.scitotenv.2022.154313.","productDescription":"154313, 12 p.","ipdsId":"IP-132315","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467192,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.154313","text":"Publisher Index Page"},{"id":435930,"rank":0,"type":{"id":30,"text":"Data 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]\n}","volume":"827","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":837961,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229986,"text":"70229986 - 2022 - The use of continuous sediment-transport measurements to improve sand-load estimates in a large sand-bedded river: The Lower Chippewa River, WI","interactions":[],"lastModifiedDate":"2022-07-07T16:43:25.271928","indexId":"70229986","displayToPublicDate":"2022-03-11T08:49:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"The use of continuous sediment-transport measurements to improve sand-load estimates in a large sand-bedded river: The Lower Chippewa River, WI","docAbstract":"Accurately determining sediment loads is necessary for managing river environments but is difficult because multiple processes can lead to large discharge-independent changes in sediment transport. Thus, estimations of sediment load using discharge–sediment rating curves fit to sparse or historical sediment-transport measurements can be inaccurate, necessitating alternative approaches to reduce uncertainty. Continuous sediment-transport measurements reduce uncertainty because they can be used to detect discharge-independent changes in transport and are therefore unaffected by hysteresis. We used largely continuous approaches to measure sand transport in the lower Chippewa River, a large sand-supplying tributary to the Mississippi River. We used side-looking acoustic-Doppler profilers to continuously measure suspended-sand concentration, and bedform-tracking techniques to episodically measure bedload transport. Bedload transport was then continuously estimated using a discharge-dependent ratio of bedload to suspended-sand transport. This approach allowed determination of sand loads that were not estimated based only on water discharge. Our continuous suspended-sand measurements show that hysteresis between discharge and suspended-sand concentration occurs during most floods. Quasi-continuous bed-elevation measurements using a scour monitor show that lags between discharge and dune geometric adjustment is also common, causing hysteresis between discharge and bedload transport during floods. Furthermore, comparisons of our measurements with historical sediment-transport measurements indicate large discharge-independent declines in both suspended-sand and bedload transport since the 1980s. These findings indicate that sand transport is a non-stationary function of water discharge over timescales ranging from within individual floods to decades. Consequently, although our continuous-measurement approach yields only a ~20–30% improvement over rating-curve estimates of sand load over multi-year periods, our approach yields up to a factor-of-five improvement in sand-load estimates over the shorter, i.e., within a flood, timescales over which the largest discharge-independent changes in sand transport occur.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5360","usgsCitation":"Dean, D.J., Topping, D.J., Buscombe, D., Groten, J.T., Ziegeweid, J.R., Fitzpatrick, F., Lund, J., and Coenen, E.N., 2022, The use of continuous sediment-transport measurements to improve sand-load estimates in a large sand-bedded river: The Lower Chippewa River, WI: Earth Surface Processes and Landforms, v. 47, no. 8, p. 2006-2023, https://doi.org/10.1002/esp.5360.","productDescription":"18 p.","startPage":"2006","endPage":"2023","ipdsId":"IP-132461","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":448528,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5360","text":"Publisher Index Page"},{"id":397392,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Lower Chippewa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.4169921875,\n 44.33956524809713\n ],\n [\n -91.263427734375,\n 44.33956524809713\n ],\n [\n -91.263427734375,\n 45.120052841530544\n ],\n [\n -92.4169921875,\n 45.120052841530544\n ],\n [\n -92.4169921875,\n 44.33956524809713\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":131047,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":838577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":838578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buscombe, D. 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William 0000-0002-8830-4468","orcid":"https://orcid.org/0000-0002-8830-4468","contributorId":289132,"corporation":false,"usgs":true,"family":"Lund","given":"J. William","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838583,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coenen, Erin Nicole 0000-0003-2470-3854","orcid":"https://orcid.org/0000-0003-2470-3854","contributorId":289133,"corporation":false,"usgs":true,"family":"Coenen","given":"Erin","email":"","middleInitial":"Nicole","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838584,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229512,"text":"sir20215141 - 2022 - Using microbial source tracking to identify contamination sources in Port Jefferson Harbor, Setauket Harbor, and Conscience Bay on Long Island, New York","interactions":[],"lastModifiedDate":"2026-04-08T16:35:05.255428","indexId":"sir20215141","displayToPublicDate":"2022-03-10T13:25:00","publicationYear":"2022","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":"2021-5141","displayTitle":"Using Microbial Source Tracking To Identify Contamination Sources in Port Jefferson Harbor, Setauket Harbor, and Conscience Bay on Long Island, New York","title":"Using microbial source tracking to identify contamination sources in Port Jefferson Harbor, Setauket Harbor, and Conscience Bay on Long Island, New York","docAbstract":"The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering Port Jefferson Harbor, Setauket Harbor, and Conscience Bay, an embayment complex on the northern shore of Suffolk County, Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the harbor and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Our analysis revealed that the most substantial source of fecal contamination to the Port Jefferson Harbor complex was discharge from sites draining ponds and wetlands, particularly during the summer months. Fecal coliform bacteria at sites where ponds and wetlands drain are increased by stormwater runoff, which is another substantial source of fecal contamination. Human markers, likely originating from the outfall of the Port Jefferson sewage treatment plant, were found at least once in every sample collected within Port Jefferson Harbor; however, fecal coliform concentrations were low, indicating that the sewage treatment plant is not a likely source of fecal contamination to the embayment. Canine markers detected in Conscience Bay were associated with high fecal coliform, particularly in wet summer samples. Waterfowl markers were most often detected in source water for Port Jefferson Harbor, but in Conscience Bay, waterfowl was frequently detected within the bay itself. Resuspension of bed sediment may contribute to fecal contamination in the harbor, but more targeted analyses are needed to support this finding. There was little evidence of groundwater-contributing fecal bacteria by direct discharge from the subsurface. A classification scheme was developed to convey the degree of fecal contamination to stakeholders and resource managers. Based on this classification scheme, the Culvert North of State Route 25A, Culvert North of Shore Road, Old Mill Creek Culvert, and Mill Pond Culvert to Conscience Bay sites were identified as locations that contribute substantial fecal contamination to the Port Jefferson Harbor complex.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215141","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2022, Using microbial source tracking to identify contamination sources in Port Jefferson Harbor, Setauket Harbor, and Conscience Bay on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2021–5141, 25 p., https://doi.org/10.3133/sir20215141.","productDescription":"Report: vi, 25 p.; Database","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127839","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":502286,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112546.htm","linkFileType":{"id":5,"text":"html"}},{"id":396998,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215141/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":396934,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York"},{"id":396933,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5141/images/"},{"id":396932,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5141/sir20215141.XML"},{"id":396929,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5141/coverthb.jpg"},{"id":396931,"rank":3,"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":396930,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5141/sir20215141.pdf","text":"Report","size":"1.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5141"}],"country":"United States","state":"New York","otherGeospatial":"Port Jefferson Harbor, Setauket Harbor, Conscience Bay, Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.18061828613281,\n 40.90936126702326\n ],\n [\n -72.95127868652344,\n 40.90936126702326\n ],\n [\n -72.95127868652344,\n 40.994410999439516\n ],\n [\n -73.18061828613281,\n 40.994410999439516\n ],\n [\n -73.18061828613281,\n 40.90936126702326\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
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The Rice Creek Watershed District (RCWD) cooperated with the U.S. Geological Survey to establish a 10-year suspended sediment and bedload monitoring and streamflow modeling study to evaluate the effects of two restored meander sections on middle Rice Creek in Arden Hills, Minnesota. The RCWD goals of this stream restoration were to reduce water quality impairments, improve aquatic habitat, and reduce associated costs of dredging a sedimentation pond. During the study there were several factors that introduced uncertainty in the sampling results; however, the sampling results indicated there was an increase in the post-stream restoration sediment data because of higher streamflows during the post-stream than the pre-stream restoration monitoring period. The negative relation between suspended fines and streamflow was explained by a reduction in the supply of fines with increasing streamflows. The positive relation among suspended sand, bedload, and streamflow was because of those constituents having a functional relation with the hydraulic properties of flow and a consistent supply of sand. Two-dimensional flow modeling simulations indicated the downstream restored section had less shear stress, more pools, and could access the floodplain at a lower streamflow than the original channel. Overall, the uncertainty of the sampling results indicates the complexity of sediment transport in a river and suggests a need for multisite, multifaceted, multiyear data, and tools to simulate those data to effectively evaluate river restorations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225004","collaboration":"Prepared in cooperation with Rice Creek Watershed District","usgsCitation":"Groten, J.T., Livdahl, C.T., DeLong, S.B., Lund, J.W., Nelson, J.M., Coenen, E.N., Ziegeweid, J.R., and Kocian, M.J., 2022, Sediment monitoring and streamflow modeling before and after a stream restoration in Rice Creek, Minnesota, 2010–2019: U.S. Geological Survey Scientific Investigations Report 2022–5004, 40 p., https://doi.org/10.3133/sir20225004.","productDescription":"Report: viii, 40 p.; Data Release; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-126710","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396980,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJIY32","text":"USGS data release","linkHelpText":"Suspended sediment and bedload data, simple linear regression models, loads, elevation data, and FaSTMECH models for Rice Creek, Minnesota, 2010-2019"},{"id":396979,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":396978,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5004/images"},{"id":502292,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112549.htm","linkFileType":{"id":5,"text":"html"}},{"id":396976,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5004/sir20225004.pdf","text":"Report","size":"19.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5004"},{"id":396975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5004/coverthb.jpg"},{"id":396977,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5004/sir20225004.XML"}],"country":"United States","state":"Minnesota","otherGeospatial":"Rice Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.21710586547852,\n 45.08236949749694\n ],\n [\n -93.18449020385742,\n 45.08236949749694\n ],\n [\n -93.18449020385742,\n 45.09957848291159\n ],\n [\n -93.21710586547852,\n 45.09957848291159\n ],\n [\n -93.21710586547852,\n 45.08236949749694\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Migratory waterbirds (i.e., shorebirds, wading birds, and waterfowl) rely on a diffuse continental network of wetland habitats to support annual life cycle needs. Emerging threats of climate and land-use change raise new concerns over the sustainability of these habitat networks as water scarcity triggers cascading ecological effects impacting wetland habitat availability. Here we use important waterbird regions in Oregon and California, United States, as a model system to examine patterns of landscape change impacting wetland habitat networks in western North America. Wetland hydrology and flooded agricultural habitats were monitored monthly from 1988 to 2020 using satellite imagery to quantify the timing and duration of inundation—a key delimiter of habitat niche values associated with waterbird use. Trends were binned by management practice and wetland hydroperiods (semi-permanent, seasonal, and temporary) to identify differences in their climate and land-use change sensitivity. Wetland results were assessed using 33 waterbird species to detect non-linear effects of network change across a diversity of life cycle and habitat needs. Pervasive loss of semi-permanent wetlands was an indicator of systemic functional decline. Shortened hydroperiods caused by excessive drying transitioned semi-permanent wetlands to seasonal and temporary hydrologies—a process that in part counterbalanced concurrent seasonal and temporary wetland losses. Expansion of seasonal and temporary wetlands associated with closed-basin lakes offset wetland declines on other public and private lands, including wildlife refuges. Diving ducks, black terns, and grebes exhibited the most significant risk of habitat decline due to semi-permanent wetland loss that overlapped important migration, breeding, molting, and wintering periods. Shorebirds and dabbling ducks were beneficiaries of stable agricultural practices and top-down processes of functional wetland declines that operated collectively to maintain habitat needs. Outcomes from this work provide a novel perspective of wetland ecosystem change affecting waterbirds and their migration networks. Understanding the complexity of these relationships will become increasingly important as water scarcity continues to restructure the timing and availability of wetland resources.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.844278","usgsCitation":"Donnelly, J., Moore, J.N., Casazza, M.L., and Coons, S.P., 2022, Functional wetland loss drives emerging risks to waterbird migration networks: Frontiers in Ecology and Evolution, v. 10, 844278, 18 p., https://doi.org/10.3389/fevo.2022.844278.","productDescription":"844278, 18 p.","ipdsId":"IP-137357","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448530,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.844278","text":"Publisher Index Page"},{"id":397863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.71826171875,\n 35.35321610123823\n ],\n [\n -118.71826171875,\n 36.06686213257888\n ],\n [\n -119.35546875000001,\n 36.87962060502676\n ],\n [\n -120.62988281249999,\n 38.151837403006766\n ],\n [\n -121.31103515625,\n 39.36827914916014\n ],\n [\n -121.86035156249999,\n 40.59727063442024\n ],\n [\n -122.36572265625,\n 40.730608477796636\n ],\n [\n -122.87109375,\n 40.38002840251183\n ],\n [\n -122.56347656249999,\n 39.33429742980725\n ],\n [\n -121.9921875,\n 38.54816542304656\n ],\n [\n -121.06933593749999,\n 37.579412513438385\n ],\n [\n -120.498046875,\n 36.61552763134925\n ],\n [\n -119.2236328125,\n 35.28150065789119\n ],\n [\n -118.71826171875,\n 35.35321610123823\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.47607421874999,\n 40.730608477796636\n ],\n [\n -119.46533203125,\n 40.697299008636755\n ],\n [\n -119.46533203125,\n 41.64007838467894\n ],\n [\n -118.740234375,\n 42.32606244456202\n ],\n [\n -117.99316406249999,\n 43.004647127794435\n ],\n [\n -118.89404296875,\n 44.15068115978094\n ],\n [\n -119.90478515625,\n 44.465151013519616\n ],\n [\n -121.1572265625,\n 44.071800467511565\n ],\n [\n -121.66259765625001,\n 43.27720532212024\n ],\n [\n -121.13525390625,\n 42.24478535602799\n ],\n [\n -121.2451171875,\n 41.672911819602085\n ],\n [\n -121.06933593749999,\n 41.244772343082076\n ],\n [\n -120.56396484375,\n 41.16211393939692\n ],\n [\n -120.47607421874999,\n 40.730608477796636\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Donnelly, J Patrick","contributorId":289526,"corporation":false,"usgs":false,"family":"Donnelly","given":"J Patrick","affiliations":[{"id":62169,"text":"Intermountain West Joint Venture - U.S. Fish and Wildlife Service, Migratory Bird Program, Missoula, MT, United States","active":true,"usgs":false}],"preferred":false,"id":839227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Johnnie N","contributorId":289527,"corporation":false,"usgs":false,"family":"Moore","given":"Johnnie","email":"","middleInitial":"N","affiliations":[{"id":62170,"text":"Group for Quantitative Study of Snow and Ice, Department of Geosciences, University of Montana, Missoula, MT, United States","active":true,"usgs":false}],"preferred":false,"id":839228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coons, Shea P","contributorId":289528,"corporation":false,"usgs":false,"family":"Coons","given":"Shea","email":"","middleInitial":"P","affiliations":[{"id":62172,"text":"Avian Science Center - University of Montana, Missoula, MT, United States","active":true,"usgs":false}],"preferred":false,"id":839230,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229524,"text":"70229524 - 2022 - Fire (plus) flood (equals) beach: Coastal response to an exceptional river sediment discharge event","interactions":[],"lastModifiedDate":"2022-03-10T21:53:37.028589","indexId":"70229524","displayToPublicDate":"2022-03-09T15:48:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Fire (plus) flood (equals) beach: Coastal response to an exceptional river sediment discharge event","docAbstract":"Wildfire and post-fire rainfall have resounding effects on hillslope processes and sediment yields of mountainous landscapes. Yet, it remains unclear how fire–flood sequences influence downstream coastal littoral systems. It is timely to examine terrestrial–coastal connections because climate change is increasing the frequency, size, and intensity of wildfires, altering precipitation rates, and accelerating sea-level rise; and these factors can be understood as contrasting accretionary and erosive agents for coastal systems. Here we provide new satellite-derived shoreline measurements of Big Sur, California and show how river sediment discharge significantly influenced shoreline positions during the past several decades. A 2016 wildfire followed by record precipitation increased sediment discharge in the Big Sur River and resulted in almost half of the total river sediment load of the past 50 years (~ 2.2 of ~ 4.8 Mt). Roughly 30% of this river sediment was inferred to be littoral-grade sand and was incorporated into the littoral cell, causing the widest beaches in the 37-year satellite record and spreading downcoast over timescales of years. Hence, the impact of fire–flood events on coastal sediment budgets may be substantial, and these impacts may increase with time considering projected intensification of wildfires and extreme rain events under global warming.
","language":"English","publisher":"Nature Pulications","doi":"10.1038/s41598-022-07209-0","usgsCitation":"Warrick, J.A., Vos, K., East, A.E., and Vitousek, S., 2022, Fire (plus) flood (equals) beach: Coastal response to an exceptional river sediment discharge event: Scientific Reports, v. 12, https://doi.org/10.1038/s41598-022-07209-0.","productDescription":"3848, 15 p.","startPage":"3848","ipdsId":"IP-133190","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":448535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-07209-0","text":"Publisher Index Page"},{"id":397006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Big Sur watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.87099456787108,\n 36.24898019141822\n ],\n [\n -121.77984237670898,\n 36.24898019141822\n ],\n [\n -121.77984237670898,\n 36.29257573938972\n ],\n [\n -121.87099456787108,\n 36.29257573938972\n ],\n [\n -121.87099456787108,\n 36.24898019141822\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vos, Kilian 0000-0002-9518-1582","orcid":"https://orcid.org/0000-0002-9518-1582","contributorId":229435,"corporation":false,"usgs":false,"family":"Vos","given":"Kilian","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":837746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837747,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837748,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229510,"text":"sir20225003 - 2022 - Response of Green Lake, Wisconsin, to changes in phosphorus loading, with special emphasis on near-surface total phosphorus concentrations and metalimnetic dissolved oxygen minima","interactions":[],"lastModifiedDate":"2026-04-08T17:07:36.608501","indexId":"sir20225003","displayToPublicDate":"2022-03-09T13:55:00","publicationYear":"2022","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":"2022-5003","displayTitle":"Response of Green Lake, Wisconsin, to Changes in Phosphorus Loading, With Special Emphasis on Near-Surface Total Phosphorus Concentrations and Metalimnetic Dissolved Oxygen Minima","title":"Response of Green Lake, Wisconsin, to changes in phosphorus loading, with special emphasis on near-surface total phosphorus concentrations and metalimnetic dissolved oxygen minima","docAbstract":"Green Lake is the deepest natural inland lake in Wisconsin, with a maximum depth of about 72 meters. In the early 1900s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity) with low dissolved oxygen (DO) concentrations occurring in only the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake have increased; these changes have led to increased algal production and low DO concentrations not only in the deepest areas but also in the middle of the water column (metalimnion). The U.S. Geological Survey has routinely monitored the lake since 2004 and its tributaries since 1988. Results from this monitoring led the Wisconsin Department of Natural Resources (WDNR) to list the lake as impaired because of low DO concentrations in the metalimnion, and they identified elevated TP concentrations as the cause of impairment.
As part of this study by the U.S. Geological Survey, in cooperation with the Green Lake Sanitary District, the lake and its tributaries were comprehensively sampled in 2017–18 to augment ongoing monitoring that would further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water-quality models were then used to determine the causes of the low DO concentrations and the magnitudes of P-load reductions needed to improve the water quality of the lake enough to meet multiple water-quality goals, including the WDNR’s criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900s but, since the late 1970s, have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–18 (the baseline period for this study), the near-surface geometric mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were above the 0.015-mg/L WDNR criterion for the lake), and the metalimnetic DO minimum concentrations (MOMs) measured in August ranged from 1.0 to 4.7 mg/L. The degradation in water quality was assumed to have been caused by excessive P inputs to the lake; therefore, the TP inputs to the lake were estimated. The mean annual external P load during 2014–18 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. During fall turnover, internal sediment recycling contributed an additional 7,040 kilograms that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there was a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, quantified by Secchi disk depths, had a greater response to decreases in P loading than to increases in P loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration in the east side of the lake, with higher TP concentrations than in the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr is equivalent to a 46-percent reduction in the potentially controllable external P sources (all external sources except for precipitation, atmospheric deposition, and waterfowl) from those measured during water years 2014–18. The total external P loading would need to decrease to 7,680 kg/yr (a 17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to decrease to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June–September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM–AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that MOMs are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM–AED results indicated that settling of organic material during summer might be slowed by the colder, denser, and more viscous water in the metalimnion and thus increase DO consumption. Based on empirical evidence from a comparison of MOMs with various meteorological, hydrologic, water quality, and in-lake physical factors, MOMs were lower during summers, when metalimnetic water temperatures were warmer, near-surface Chl-a and TP concentrations were higher, and Secchi depths were lower. GLM–AED results indicated that the external P load would need to be reduced to about 4,060 kg/yr, a 57-percent reduction from that measured in 2014–18, to eliminate the occurrence of MOMs less than 5 mg/L during more than 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake; however, it may take several years for the full effects of the external-load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
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U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726