This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.
","language":"English","publisher":"Department of Energy","doi":"10.2172/1769795","usgsCitation":"Varadharajan, C., Kumar, V., Willard, J., Zwart, J.A., Sadler, J.M., Weierbach, H., Perciano, T., Mueller, J., Hendrix, V., and Christianson, D., 2021, Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality: Technical Report, 5 p., https://doi.org/10.2172/1769795.","productDescription":"5 p.","ipdsId":"IP-126904","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":453494,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1769795","text":"External Repository"},{"id":421340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Varadharajan, Charuleka","contributorId":242712,"corporation":false,"usgs":false,"family":"Varadharajan","given":"Charuleka","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":883290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883292,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weierbach, Helen","contributorId":290549,"corporation":false,"usgs":false,"family":"Weierbach","given":"Helen","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883293,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perciano, Talita 0000-0002-2388-1803","orcid":"https://orcid.org/0000-0002-2388-1803","contributorId":290546,"corporation":false,"usgs":false,"family":"Perciano","given":"Talita","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883294,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Juliane 0000-0001-8627-1992","orcid":"https://orcid.org/0000-0001-8627-1992","contributorId":290539,"corporation":false,"usgs":false,"family":"Mueller","given":"Juliane","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883295,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hendrix, Valerie 0000-0001-9061-8952","orcid":"https://orcid.org/0000-0001-9061-8952","contributorId":290533,"corporation":false,"usgs":false,"family":"Hendrix","given":"Valerie","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":883296,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Christianson, Danielle","contributorId":265829,"corporation":false,"usgs":false,"family":"Christianson","given":"Danielle","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":883297,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70223670,"text":"70223670 - 2021 - Biological and anthropogenic influences on macrophage aggregates in white perch Morone americana from Chesapeake Bay, USA","interactions":[],"lastModifiedDate":"2021-09-01T13:34:03.647085","indexId":"70223670","displayToPublicDate":"2021-02-11T08:18:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Biological and anthropogenic influences on macrophage aggregates in white perch Morone americana from Chesapeake Bay, USA","title":"Biological and anthropogenic influences on macrophage aggregates in white perch Morone americana from Chesapeake Bay, USA","docAbstract":"The response of macrophage aggregates in fish to a variety of environmental stressors has been useful as a biomarker of exposure to habitat degradation. Total volume of macrophage aggregates (MAV) was estimated in the liver and spleen of white perch Morone americana from Chesapeake Bay using stereological approaches. Hepatic and splenic MAV were compared between fish populations from the rural Choptank River (n = 122) and the highly urbanized Severn River (n = 131). Hepatic and splenic MAV increased with fish age, were greater in females from the Severn River only, and were significantly greater in fish from the more polluted Severn River (higher concentrations of polycyclic aromatic hydrocarbons, organochlorine pesticides, and brominated diphenyl ethers). Water temperature and dissolved oxygen had a significant effect on organ volumes, but not on MAV. Age and river were most influential on hepatic and splenic MAV, suggesting that increased MAV in Severn River fish resulted from chronic exposures to higher concentrations of environmental contaminants and other stressors. Hemosiderin was abundant in 97% of spleens and was inversely related to fish condition and positively related to fish age and trematode infections. Minor amounts of hemosiderin were detected in 30% of livers and positively related to concentrations of benzo[a] pyrene metabolite equivalents in the bile. This study demonstrated that hepatic and splenic MAV were useful indicators in fish from the 2 tributaries with different land use characteristics and concentrations of environmental contaminants. More data are needed from additional tributaries with a wider gradient of environmental impacts to validate our results in this species.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03555","usgsCitation":"Matsche, M.A., Blazer, V., Pulster, E., and Mazik, P.M., 2021, Biological and anthropogenic influences on macrophage aggregates in white perch Morone americana from Chesapeake Bay, USA: Diseases of Aquatic Organisms, v. 143, p. 79-100, https://doi.org/10.3354/dao03555.","productDescription":"22 p.","startPage":"79","endPage":"100","ipdsId":"IP-122515","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":388726,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.83837890625,\n 36.78289206199065\n ],\n [\n -75.65185546874999,\n 36.78289206199065\n ],\n [\n -75.65185546874999,\n 39.67337039176558\n ],\n [\n -76.83837890625,\n 39.67337039176558\n ],\n [\n -76.83837890625,\n 36.78289206199065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Matsche, Mark A","contributorId":194275,"corporation":false,"usgs":false,"family":"Matsche","given":"Mark","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":822263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":822264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pulster, Erin","contributorId":236999,"corporation":false,"usgs":false,"family":"Pulster","given":"Erin","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":822265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822266,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217895,"text":"sim3468 - 2021 - Machine-learning predictions of redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, south-central United States","interactions":[],"lastModifiedDate":"2021-02-11T18:29:36.148544","indexId":"sim3468","displayToPublicDate":"2021-02-10T14:57:26","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3468","displayTitle":"Machine-Learning Predictions of Redox Conditions in Groundwater in the Mississippi River Valley Alluvial and Claiborne Aquifers, South-Central United States","title":"Machine-learning predictions of redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, south-central United States","docAbstract":"Machine-learning models developed by the U.S. Geological Survey were used to predict iron concentrations and the probability of dissolved oxygen (DO) concentrations exceeding a threshold of 1 milligram per liter (mg/L) in groundwater in aquifers of the Mississippi embayment physiographic region. DO and iron concentrations are driven by and reflect the oxidation-reduction (redox) conditions in groundwater. Predictions from boosted regression trees, a type of machine-learning model, of iron concentration and DO threshold probability were used to categorize redox zones in the Mississippi River Valley alluvial aquifer (MRVA), middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ). Model predictions indicated that DO concentrations greater than 1 mg/L are uncommon across the MRVA. DO events (where the predicted probability was greater than 0.5) tended to occur on the margins of the MRVA and in upland areas where MCAQ and LCAQ units crop out at the surface or are at shallow depth. Predicted iron concentrations were higher in the MRVA than in the MCAQ and LCAQ. Uncertainty in predicted iron concentrations tended to be high in areas where measured concentrations were also high, resulting in small areas (encompassing less than 1.5 percent of the areal extent of the MRVA) of predicted iron concentrations that exceeded 100,000 micrograms per liter. Despite the large magnitude of overpredicted iron concentrations, the general proportion and spatial distribution of predicted iron concentrations reflected observed concentrations in groundwater wells. Where the probability of exceeding a DO concentration of 1 mg/L was 0.8 or more and the iron concentration was less than 1,000 micrograms per liter, aquifers were categorized as oxic. Oxic conditions were mostly in the uplands where MCAQ and LCAQ units crop out at the margins of the modeled area. The MRVA was mostly anoxic, which was controlled by DO threshold probabilities less than 0.1. The predictions and redox zones support conceptual models of redox conditions in the Mississippi embayment. The MRVA is predominantly anoxic with high iron concentrations. In the Claiborne aquifers (including the MCAQ and LCAQ), groundwater flows along regional flow paths toward the axis of the Mississippi embayment (the approximate location of the Mississippi River), the residence time in the aquifer increases, DO is consumed, and iron concentrations generally increase. Elevated concentrations of trace elements, such as manganese and arsenic, are often associated with reducing conditions in anoxic and mixed anoxic zones, but other factors such as sediment mineralogy affect the occurrence and distribution of these constituents.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3468","collaboration":"National Water Quality Program","usgsCitation":"Knierim, K.J., Kingsbury, J.A., and Haugh, C.J., 2021, Machine-learning predictions of redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, south-central United States: U.S. Geological Survey Scientific Investigations Map 3468, 16 p., 3 sheets, https://doi.org/10.3133/sim3468.","productDescription":"Pamphlet: v, 16 p.; 3 Sheets: 34.3 inches x 24.7 inches or smaller; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-117970","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":383180,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N108JM","text":"USGS data release","linkHelpText":"Machine-learning model predictions and rasters of dissolved oxygen probability, iron concentration, and redox conditions in groundwater in the Mississippi River Valley alluvial and Claiborne aquifers"},{"id":383179,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3468/sim3468_sheet03.pdf","text":"Sheet 3","size":"3.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3468 Sheet 3"},{"id":383178,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3468/sim3468_sheet02.pdf","text":"Sheet 2","size":"6.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3468 Sheet 2"},{"id":383177,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3468/sim3468_sheet01.pdf","text":"Sheet 1","size":"7.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3468 Sheet 1"},{"id":383176,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3468/sim3468_pamphlet.pdf","text":"Pamphlet","size":"1.55MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3468 Pamphlet"},{"id":383175,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3468/coverthb2.jpg"}],"country":"United States","state":"Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Claiborne Aquifer, Mississippi Rier Valley Alluvial Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.04296874999999,\n 33.568861182555565\n ],\n [\n -94.04296874999999,\n 32.40779154205701\n ],\n [\n -93.33984375,\n 31.71882222408327\n ],\n [\n -91.95556640625,\n 31.147006308556566\n ],\n [\n -91.351318359375,\n 31.11879439598953\n ],\n [\n -90.274658203125,\n 31.522361470421437\n ],\n [\n -87.978515625,\n 31.5504526754715\n ],\n [\n -87.593994140625,\n 31.728167146023935\n ],\n [\n -87.78076171875,\n 32.45415593941475\n ],\n [\n -89.12109375,\n 32.93492866908233\n ],\n [\n -88.934326171875,\n 35.567980458012094\n ],\n [\n -88.8134765625,\n 37.23032838760387\n ],\n [\n -89.329833984375,\n 37.03763967977139\n ],\n [\n -90.362548828125,\n 36.48314061639213\n ],\n [\n -91.86767578124999,\n 35.25459097465022\n ],\n [\n -94.04296874999999,\n 33.568861182555565\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The Soldier Meadows spring complex provides habitat for the desert dace, an endemic and threatened fish. The spring complex has been altered with the construction of irrigation ditches that remove water from natural stream channels. Irrigation ditches generally provide lower quality habitat for the desert dace. Land and wildlife management agencies are interested in increasing habitat extent and quality by filling in irrigation ditches and restoring streamflow to natural channels. The U.S. Geological Survey measured streamflow, surveyed topography, and combined light detection and ranging data to create a two-dimensional hydraulic model of the study area to understand how restoration would change streamflow extents and hydraulic characteristics. Streamflow measurements indicate that, except for a section of one irrigation ditch at the upstream end of the study area, the total volume of streamflow diverted into the irrigation ditches in the study area was minimal. Hydraulic modeling indicates filling in the irrigation ditch at the upper end of the study area would return streamflow to the natural channel, resulting in an increase in natural channel surface water extent, and a reduction of irrigation ditch surface water flow. The result would be a more heterogenous natural stream channel, ranging from shallow and slow to narrow and fast.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205143","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Morris, C.M., 2021, Evaluation of streamflow extent and hydraulic characteristics of a restored channel at Soldier Meadows, Black Rock Desert–High Rock Canyon Emigrant Trails National Conservation Area, Nevada: U.S. Geological Survey Scientific Investigations Report 2020–5143, 22 p., https://doi.org/10.3133/sir20205143.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-110000","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":383124,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O0GII7","linkHelpText":"Geospatial data and surface-water model archive for evaluation of streamflow extent and hydraulic characteristics of a restored channel at Soldier Meadows, Black Rock Desert–High Rock Canyon Emigrant Trails National Conservation Area, Nevada"},{"id":383123,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5143/images"},{"id":383122,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5143/sir20205143.xml"},{"id":383121,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5143/sir20205143.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383120,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5143/covrthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Black Rock Desert, High Rock Canyon Emigrant Trails National Conservation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.40765380859375,\n 40.734770989672406\n ],\n [\n -118.35845947265625,\n 40.734770989672406\n ],\n [\n -118.35845947265625,\n 41.45919537950706\n ],\n [\n -119.40765380859375,\n 41.45919537950706\n ],\n [\n -119.40765380859375,\n 40.734770989672406\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Green Peter and Foster Dams have altered natural seasonal temperature patterns in the South and Middle Santiam Rivers of the Willamette River Basin in northwestern Oregon. Cold-water releases from Green Peter Dam, upstream of Foster Lake, contribute to the cool-water conditions at Foster Dam. In summer, unseasonably cold water typically is discharged from Foster Dam into the Foster Dam fish ladder, which may be one factor contributing to the low numbers of upstream migrating Chinook salmon (Oncorhynchus tshawytscha) that enter the fish ladder. The U.S. Army Corps of Engineers is leading efforts to improve conditions for Chinook salmon upstream and downstream of these dams by considering structural alterations to Foster Dam and by exploring changes to the way the dams are operated.
The U.S. Geological Survey assisted the U.S. Army Corps of Engineers by using previously calibrated numerical models of flow and water quality for Green Peter and Foster Lakes and for the South Santiam River downstream of Foster Dam. These two-dimensional hydrodynamic and water-quality (CE-QUAL-W2) models were used to test scenarios of altered dam operations and alternate water-management strategies. Results of these scenarios provide information and insights into how the mixing and thermal characteristics of the lakes are affected by dam operations, how the mixing and timing of upstream source waters reaching Foster Dam are affected by dam operations, how river and fish-ladder temperature targets might be achieved, and how quickly (or slowly) such changes in the lakes and downstream river reaches occur, relative to typical unmodified operations at Green Peter and Foster Dams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205145","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Sullivan, A.B., and Rounds, S.A., 2021, Modeling water temperature response to dam operations and water management in Green Peter and Foster Lakes and the South Santiam River, Oregon: U.S. Geological Survey Scientific Investigations Report 2020–5145, 27 p., https://doi.org/10.3133/sir20205145.","productDescription":"Report: vi, 27 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-117626","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":383125,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5145/coverthb.jpg"},{"id":383126,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5145/sir20205145.pdf","text":"Report","size":"12.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5145"},{"id":383127,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C1YRV3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"CE–QUAL–W2 water-quality model for Green Peter and Foster Lakes and the South Santiam River, Oregon: 2002-2011"}],"country":"United States","state":"Oregon","otherGeospatial":"Foster Lake, Green Peter Lake, South Santiam River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.08807373046875,\n 44.29436701558004\n ],\n [\n -122.09518432617186,\n 44.29436701558004\n ],\n [\n -122.09518432617186,\n 44.775986224030376\n ],\n [\n -123.08807373046875,\n 44.775986224030376\n ],\n [\n -123.08807373046875,\n 44.29436701558004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Seasonal migrations of fish populations can have large effects on lake nutrient budgets and food web dynamics, but the addition of a migrating non‐native species may alter these dynamics. The Round Goby (Neogobius melanostomus) arrived in Lake Ontario (USA/Canada) about 20 years ago with a documented history of annual offshore–inshore migrations in its native range. Here we combined nearshore, fixed‐plot video with offshore trawl data to document the annual migration of this population over multiple years. This behaviour was correlated with seasonal, nearshore temperature changes. The population size structure and mean fish length of returning fish were smaller than those of out‐migrating fish. The out‐migrating population contained an estimated 37.7 metric tonnes of phosphorous; and we estimated roughly 20 metric tonnes were translocated to and remained in offshore waters over the winter months, representing an important nutrient subsidy to a variety of offshore piscivorous fish. Lake Sturgeon (Acipenser fulvescens) have incorporated Round Goby extensively into their diet and consume a size range of fish matching the size range of missing Round Goby that fail to return to the nearshore. We conclude Round Goby are an important prey within the food web of Lake Ontario and translocate roughly 6.5% of the monthly total phosphorous load entering from surface waters. Further investigations of the nutrient content, population size structure and fate of migrating Round Goby in Lake Ontario are warranted to clarify the extent of this prey and nutrient subsidy in ongoing assessments of lake condition.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12568","usgsCitation":"Pennuto, C., Mehler, K., Weidel, B., Lantry, B.F., and Bruestle, E., 2021, Dynamics of the seasonal migration of Round Goby (Neogobius melanostomus, Pallas 1814) and implications for the Lake Ontario food web: Ecology of Freshwater Fish, v. 30, no. 2, p. 151-161, https://doi.org/10.1111/eff.12568.","productDescription":"11 p.","startPage":"151","endPage":"161","ipdsId":"IP-118165","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.925537109375,\n 43.265206318396025\n ],\n [\n 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bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lantry, Brian F. 0000-0001-8797-3910 bflantry@usgs.gov","orcid":"https://orcid.org/0000-0001-8797-3910","contributorId":3435,"corporation":false,"usgs":true,"family":"Lantry","given":"Brian","email":"bflantry@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bruestle, Eric","contributorId":251746,"corporation":false,"usgs":false,"family":"Bruestle","given":"Eric","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":810467,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218253,"text":"70218253 - 2021 - Sequestration of microfibers and other microplastics by green algae, Cladophora, in the US Great Lakes","interactions":[],"lastModifiedDate":"2021-02-22T14:17:36.844083","indexId":"70218253","displayToPublicDate":"2021-02-10T08:13:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Sequestration of microfibers and other microplastics by green algae, Cladophora, in the US Great Lakes","docAbstract":"Daunting amounts of microplastics are present in surface waters worldwide. A main category of microplastics is synthetic microfibers, which originate from textiles. These microplastics are generated and released in laundering and are discharged by wastewater treatment plants or enter surface waters from other sources. The polymers that constitute many common synthetic microfibers are mostly denser than water, and eventually settle out in aquatic environments. The interaction of these microfibers with submerged aquatic vegetation has not been thoroughly investigated but is potentially an important aquatic sink in surface waters. In the Laurentian Great Lakes, prolific growth of macrophytic Cladophora creates submerged biomass with a large amount of surface area and the potential to collect and concentrate microplastics. To determine the number of synthetic microfibers in Great Lakes Cladophora, samples were collected from Lakes Erie and Michigan at multiple depths in the spring and summer of 2018. After rinsing and processing the algae, associated synthetic microfibers were quantified. The average loads of synthetic microfibers determined from the Lake Erie and Lake Michigan samples were 32,000 per kg (dry weight (dw)) and 34,000 per kg (dw), respectively, 2–4 orders of magnitude greater than loads previously reported in water and sediment. To further explore this sequestration of microplastics, fresh and aged Cladophora were mixed with aqueous mixtures of microfibers or microplastic in the laboratory to simulate pollution events. Microscopic analyses indicated that fresh Cladophora algae readily interacted with microplastics via adsorptive forces and physical entanglement. These interactions mostly cease upon algal senescence, with an expected release of microplastics in benthic sediments. Collectively, these findings suggest that synthetic microfibers are widespread in Cladophora algae and the affinity between microplastics and Cladophora may offer insights for removing microplastic pollution.
Macroalgae in the Laurentian Great Lakes contain high loads of synthetic microfibers, both entangled and adsorbed, which likely account for an important fraction of microplastics in these surface waters.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2021.116695","usgsCitation":"Peller, J.R., Nevers, M., Byappanahalli, M., Nelson, C., Babu, B.G., Evans, M.A., Kostelnik, E., Keller, M., Johnston, J., and Shidler, S., 2021, Sequestration of microfibers and other microplastics by green algae, Cladophora, in the US Great Lakes: Environmental Pollution, v. 276, 116695, 11 p., https://doi.org/10.1016/j.envpol.2021.116695.","productDescription":"116695, 11 p.","ipdsId":"IP-124829","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":383415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Lake Michigan, Leeland Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.02844238281249,\n 44.90646871709883\n ],\n [\n -85.67138671875,\n 44.90646871709883\n ],\n [\n -85.67138671875,\n 45.18978009667531\n ],\n [\n -86.02844238281249,\n 45.18978009667531\n ],\n [\n -86.02844238281249,\n 44.90646871709883\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peller, Julie R.","contributorId":48889,"corporation":false,"usgs":false,"family":"Peller","given":"Julie","email":"","middleInitial":"R.","affiliations":[{"id":12645,"text":"Indiana University - Northwest","active":true,"usgs":false}],"preferred":false,"id":810719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X byappan@usgs.gov","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":147923,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","email":"byappan@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Cassie","contributorId":251861,"corporation":false,"usgs":false,"family":"Nelson","given":"Cassie","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":810722,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Babu, Bharath Ganesh","contributorId":251862,"corporation":false,"usgs":false,"family":"Babu","given":"Bharath","email":"","middleInitial":"Ganesh","affiliations":[{"id":48129,"text":"Valparaiso University","active":true,"usgs":false}],"preferred":false,"id":810723,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":810724,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kostelnik, Eddie","contributorId":251863,"corporation":false,"usgs":false,"family":"Kostelnik","given":"Eddie","email":"","affiliations":[{"id":48129,"text":"Valparaiso University","active":true,"usgs":false}],"preferred":false,"id":810725,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Keller, Morgan","contributorId":251864,"corporation":false,"usgs":false,"family":"Keller","given":"Morgan","email":"","affiliations":[{"id":48129,"text":"Valparaiso University","active":true,"usgs":false}],"preferred":false,"id":810726,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Johnston, Jenna","contributorId":251865,"corporation":false,"usgs":false,"family":"Johnston","given":"Jenna","email":"","affiliations":[{"id":48129,"text":"Valparaiso University","active":true,"usgs":false}],"preferred":false,"id":810727,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shidler, Sarah","contributorId":251866,"corporation":false,"usgs":false,"family":"Shidler","given":"Sarah","email":"","affiliations":[{"id":50405,"text":"Renishaw, Inc.","active":true,"usgs":false}],"preferred":false,"id":810728,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70218488,"text":"70218488 - 2021 - Microbial pathogens and contaminants of emerging concern in groundwater at an urban subsurface stormwater infiltration site","interactions":[],"lastModifiedDate":"2021-03-02T13:20:22.839507","indexId":"70218488","displayToPublicDate":"2021-02-10T07:14:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Microbial pathogens and contaminants of emerging concern in groundwater at an urban subsurface stormwater infiltration site","docAbstract":"Urban stormwater may contain a variety of pollutants, including viruses and other pathogens, and contaminants of emerging concern (pharmaceuticals, artificial sweeteners, and personal care products). In vulnerable geologic settings, the potential exists for these contaminants to reach underlying aquifers and contaminate drinking water wells. Viruses and other pathogens, as well as other contaminants of emerging concern, were measured in stormwater and groundwater at an urban site containing a stormwater cistern and related subsurface infiltration gallery, three shallow lysimeter wells, and a monitoring well. Five of 12 microbial targets were detected more than once across the eight rounds of sampling and at multiple sampling points, with human-specific Bacteroides detected most frequently. The microbial and chemical contaminants present in urban stormwater were much lower in the water table monitoring well than the vadose zone lysimeters. There may be numerous causes for these reductions, but they are most likely related to transit across fine-grained sediments that separate the water table from the vadose zone at this location. Precipitation amount prior to sample collection was significantly associated with microbial load. A significant relation between microbial load and chloride-bromide ratio was also observed. The reduction in number and concentrations of contaminants found in the monitoring well indicates that although geologically sensitive aquifers receiving urban stormwater effluent in the subsurface may be prone to contamination, those with a protective cap of fine-grained sediments are less vulnerable. These results can inform stormwater infiltration guidance relative to drinking water wells, with an emphasis on restricting infiltration near water supply wells finished in geologically sensitive aquifers to reduce public health risks.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.145738","usgsCitation":"de Lambert, J.R., Walsh, J.F., Scher, D.P., Firnstahl, A.D., and Borchardt, M.A., 2021, Microbial pathogens and contaminants of emerging concern in groundwater at an urban subsurface stormwater infiltration site: Science of the Total Environment, v. 775, 145738, 9 p., https://doi.org/10.1016/j.scitotenv.2021.145738.","productDescription":"145738, 9 p.","ipdsId":"IP-123128","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":383710,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","city":"Roseville","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.19908142089844,\n 44.99151235226668\n ],\n [\n -93.11771392822266,\n 44.99151235226668\n ],\n [\n -93.11771392822266,\n 45.03714091439948\n ],\n [\n -93.19908142089844,\n 45.03714091439948\n ],\n [\n -93.19908142089844,\n 44.99151235226668\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"775","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"de Lambert, Jane R.","contributorId":214334,"corporation":false,"usgs":false,"family":"de Lambert","given":"Jane","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":811198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walsh, James F.","contributorId":214333,"corporation":false,"usgs":false,"family":"Walsh","given":"James","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":811199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scher, Deanna P.","contributorId":252948,"corporation":false,"usgs":false,"family":"Scher","given":"Deanna","email":"","middleInitial":"P.","affiliations":[{"id":36357,"text":"Minnesota Department of Health","active":true,"usgs":false}],"preferred":false,"id":811200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":210973,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":811202,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218701,"text":"70218701 - 2021 - Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers","interactions":[],"lastModifiedDate":"2021-04-16T13:59:46.362577","indexId":"70218701","displayToPublicDate":"2021-02-10T07:11:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7747,"text":"Acta Polytechnica","active":true,"publicationSubtype":{"id":10}},"title":"Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers","docAbstract":"Exsolution and re-dissolution of CO2 gas within heterogeneous porous media are investigated using experimental data and mathematical modeling. In a set of bench-scale experiments, water saturated with CO2 under a given pressure is injected into a 2-D water-saturated porous media system, causing CO2 gas to exsolve and migrate upwards. A layer of fine sand mimicking a heterogeneity within a shallow aquifer is present in the tank to study accumulation and trapping of exsolved CO2. Then, clean water is injected into the system and the accumulated CO2 dissolves back into the flowing water. Simulated exsolution and dissolution mass transfer processes are studied using both nearequilibrium and kinetic approaches and compared to experimental data under conditions that do and do not include lateral background water flow. The mathematical model is based on the mixed hybrid finite element method that allows for accurate simulation of both advection- and diffusion- dominated processes.
","language":"English","publisher":"Czech Technical University","doi":"10.14311/AP.2021.61.0077","usgsCitation":"Fucik, R., Solovsky, J., Plampin, M.R., Wu, H., Mikyska, J., and Illangasekare, T.H., 2021, Computational methodology to analyze the effect of mass transfer rate on attenuation of leaked carbon dioxide in shallow aquifers: Acta Polytechnica, v. 61, no. SI, 12 p., https://doi.org/10.14311/AP.2021.61.0077.","productDescription":"12 p.","ipdsId":"IP-114423","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":453509,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14311/ap.2021.61.0077","text":"Publisher Index Page"},{"id":384057,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"SI","noUsgsAuthors":false,"publicationDate":"2021-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Fucik, Radek 0000-0001-7040-9184","orcid":"https://orcid.org/0000-0001-7040-9184","contributorId":254378,"corporation":false,"usgs":false,"family":"Fucik","given":"Radek","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Solovsky, Jakub","contributorId":254380,"corporation":false,"usgs":false,"family":"Solovsky","given":"Jakub","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plampin, Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":811429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Hao","contributorId":254382,"corporation":false,"usgs":false,"family":"Wu","given":"Hao","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":811430,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mikyska, Jiri","contributorId":254383,"corporation":false,"usgs":false,"family":"Mikyska","given":"Jiri","email":"","affiliations":[{"id":39686,"text":"Czech Technical University in Prague","active":true,"usgs":false}],"preferred":false,"id":811431,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Illangasekare, Tissa H.","contributorId":194933,"corporation":false,"usgs":false,"family":"Illangasekare","given":"Tissa","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":811432,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217868,"text":"tm16B1 - 2021 - Multi-taxa database data dictionary","interactions":[],"lastModifiedDate":"2021-02-10T12:59:29.690403","indexId":"tm16B1","displayToPublicDate":"2021-02-09T15:16:40","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"16-B1","displayTitle":"Multi-Taxa Database Data Dictionary","title":"Multi-taxa database data dictionary","docAbstract":"The conservation of biological resources relies on the successful management of ecological and physiological research data. The Western Ecological Research Center of the U.S. Geological Survey is working with researchers, land managers, and decision makers from non-government organizations and city, county, state, and federal resource agencies to develop data management methods. Access to the most current and applicable research data available in making sound decisions to conserve species diversity is foundational. We sought to accomplish several goals in developing the data management strategy used in the Multi-Taxa database. Data persistence and availability are primary goals of well-developed databases. By documenting and sharing the structure and definitions of Multi-Taxa database, we hope to further the successful management of these crucial data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm16B1","collaboration":"Prepared in cooperation with San Diego Association of Governments (SanDAG)","usgsCitation":"Watson, E., Rochester, C.J., Brown, C.W., Holmes, D.A., Hathaway, S.A., and Fisher, R.N., 2021, Multi-taxa database data dictionary: U.S. Geological Survey Techniques and Methods 16–B1, 149 p., https://doi.org/10.3133/tm16B1.","productDescription":"Report: xvi, 149 p., 5 Appendixes; 3 Datasets","numberOfPages":"149","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119276","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":383110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/16/b1/covrthb.jpg"},{"id":383111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383112,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx1.pdf","text":"Appendix 1","size":"700 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of All Database Tables"},{"id":383113,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx2.pdf","text":"Appendix 2","size":"200 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of Tables Associated with Survey Events"},{"id":383114,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx3.pdf","text":"Appendix 3","size":"240 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of Tables Associated with Sites"},{"id":383115,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx4.pdf","text":"Appendix 4","size":"250 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of Tables Associated with Taxa Observations"},{"id":383116,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_appx5.pdf","text":"Appendix 5","size":"240 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Entity Relationship Diagram of Tables Associated with Habitat Observations"},{"id":383117,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_field_def.csv","text":"Field Definitions","size":"175 KB","linkFileType":{"id":7,"text":"csv"}},{"id":383118,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_lookup_table_def.csv","text":"Lookup Table Definitions","size":"25 KB","linkFileType":{"id":7,"text":"csv"}},{"id":383119,"rank":10,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/tm/16/b1/tm16b1_table_def.csv","text":"Table Definitions","size":"10 KB","linkFileType":{"id":7,"text":"csv"}}],"contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
If not managed properly, modern agricultural practices can alter surface and groundwater quality and drinking water resources resulting in potential negative effects on aquatic and terrestrial ecosystems. Exposure to agriculturally derived contaminant mixtures has the potential to alter habitat quality and negatively affect fish and other aquatic organisms. Implementation of conservation practices focused on improving water quality continues to increase particularly in agricultural landscapes throughout the United States. The goal of this study was to determine the consequences of land management actions on the primary drivers of contaminant mixtures in five agricultural watersheds in the Chesapeake Bay, the largest watershed of the Atlantic Seaboard in North America where fish health issues have been documented for two decades. Surface water was collected and analyzed for 301 organic contaminants to determine the benefits of implemented best management practices (BMPs) designed to reduce nutrients and sediment to streams in also reducing contaminants in surface waters. Of the contaminants measured, herbicides (atrazine, metolachlor), phytoestrogens (formononetin, genistein, equol), cholesterol and total estrogenicity (indicator of estrogenic response) were detected frequently enough to statistically compare to seasonal flow effects, landscape variables and BMP intensity. Contaminant concentrations were often positively correlated with seasonal stream flow, although the magnitude of this effect varied by contaminant across seasons and sites. Land-use and other less utilized landscape variables including biosolids, manure and pesticide application and percent phytoestrogen producing crops were inversely related with site-average contaminant concentrations. Increased BMP intensity was negatively related to contaminant concentrations indicating potential co-benefits of BMPs for contaminant reduction in the studied watersheds. The information gained from this study will help prioritize ecologically relevant contaminant mixtures for monitoring and contributes to understanding the benefits of BMPs on improving surface water quality to better manage living resources in agricultural landscapes inside and outside the Chesapeake Bay watershed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.145687","usgsCitation":"Smalling, K., Devereux, O., Gordon, S.E., Phillips, P.J., Blazer, V., Hladik, M.L., Kolpin, D., Meyer, M., Sperry, A., and Wagner, T., 2021, Environmental and anthropogenic drivers of contaminants in agricultural watersheds with implications for land management: Science of the Total Environment, v. 774, 145687, 14 p., https://doi.org/10.1016/j.scitotenv.2021.145687.","productDescription":"145687, 14 p.","ipdsId":"IP-118914","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science 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California","interactions":[],"lastModifiedDate":"2021-02-10T18:00:22.216537","indexId":"ofr20201102","displayToPublicDate":"2021-02-09T10:33:12","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1102","displayTitle":"Using High Resolution Satellite and Telemetry Data to Track Flooded Habitats, Their Use by Waterfowl, and Evaluate Effects of Drought on Waterfowl and Shorebird Bioenergetics in California","title":"Using high resolution satellite and telemetry data to track flooded habitats, their use by waterfowl, and evaluate effects of drought on waterfowl and shorebird bioenergetics in California","docAbstract":"Wetland managers in the Central Valley of California, a dynamic hydrological landscape, require information regarding the amount and location of existing wetland habitat to make decisions on how to best use water resources to support multiple wildlife objectives, particularly during drought. Scientists from the U.S. Geological Survey Western Ecological Research Center (WERC), Point Blue Conservation Science (Point Blue), and the U.S. Fish and Wildlife Service (USFWS) partnered to learn how wetland and flooded agricultural habitats used by waterfowl and shorebirds change during the non-breeding season (July–April) particularly during drought. During extreme drought conditions, the ability to provide sufficient water for wildlife often depends on the timing of water deliveries to managed wetlands and winter-flooded crop fields and decisions on whether to fallow croplands. Waterfowl and shorebirds could be particularly affected by these decisions because they typically rest and feed in flooded habitats. Poor habitat conditions resulting from spatially or temporally suboptimal water deliveries (that is, mismatch between waterfowl habitat needs and timing and location of flooded habitats) could reduce waterfowl hunting opportunities and body condition. Point Blue scientists developed a system for near real-time tracking of habitats used by waterfowl, shorebirds, and some other wetland-dependent “waterbirds” (www.pointblue.org/watertracker) and to assess the impacts of drought on habitat availability and on waterfowl and shorebird bioenergetics. The WERC researchers linked these data with near real-time tracking (telemetry) data of duck locations throughout the Valley. The team used these two datasets to relate duck locations to open-water characteristics and to learn how waterfowl use habitats under spatially and temporally changing conditions during drought and non-drought periods. We found that recent extreme drought (2013–15) significantly changed the timing and magnitude of flooding and consequently reduced the availability of habitats used by waterfowl and shorebirds more than other recent historic droughts 2000–11. Drought reduced irrigations of moist soil seed plants and thus there was lower food energy available for waterfowl. Analyses using bioenergetics models indicated that, overall, extreme drought increased food energy deficits (total number of deficit days) for shorebirds and waterfowl. Our analysis indicated a strong direct relationship between duck locations and classified habitat derived from open-water data during the wintering period (October–March). This result helps confirm the application of dynamic water data to identify flooded areas that provide waterfowl habitat. Presence of open water at a 1-hectare resolution can be used effectively to identify flooded landscape areas available as habitat for ducks. Our discoveries from evaluating use of space by ducks also indicated that nighttime feeding locations of ducks were concentrated nearby primary roosts and that foraging distances could depend on hydrologic dynamics of location (Suisun Marsh versus California excluding Suisun Marsh) and time of season (early, middle, late). Other results indicated that some areas on the California landscape with extremely reliable water supplies could receive consistent use by ducks year after year (in essence, almost drought proof). The Water Tracker is set up to automatically track wetland habitat and food availability each year and is making these data available to water and wetland managers. Results from this research are a significant step toward understanding how waterfowl and shorebird habitats can be optimally managed on the landscape to support desired populations of these migratory birds during extreme drought.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201102","collaboration":"Prepared in cooperation with the Southwest Climate Adaptation Science Center of the U.S. Geological Survey and the Regional Inventory and Monitoring Program of the U.S. Fish and Wildlife Service","usgsCitation":"Matchett, E.L., Reiter, M., Overton, C.T., Jongsomjit, D., and Casazza, M.L., 2021, Using high resolution satellite and telemetry data to track flooded habitats, their use by waterfowl, and evaluate effects of drought on waterfowl and shorebird bioenergetics in California: U.S. Geological Survey Open-File Report 2020–1102, 59 p., https://doi.org/10.3133/ofr20201102.","productDescription":"Report: xi, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","ipdsId":"IP-102884","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":383074,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2020/1102/images"},{"id":383073,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P922KDU6","linkHelpText":"Classification of waterfowl habitat and quantification of interannual space use and movement distance from primary roosts to night feeding locations by waterfowl in California for October–March of 2015 through 2018"},{"id":383071,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1102/ofr20201102.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1102/covrthb.jpg"},{"id":383072,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2020/1102/ofr20201102.xml"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 36.06686213257888\n ],\n [\n -119.44335937499999,\n 35.137879119634185\n ],\n [\n -118.828125,\n 34.813803317113155\n ],\n [\n -118.30078125,\n 35.137879119634185\n ],\n [\n -118.49853515625,\n 35.71083783530009\n ],\n [\n -119.39941406249999,\n 37.33522435930639\n ],\n [\n -120.47607421874999,\n 38.16911413556086\n ],\n [\n -120.89355468749999,\n 38.58252615935333\n ],\n [\n -121.22314453124999,\n 39.11301365149975\n ],\n [\n -121.640625,\n 39.977120098439634\n ],\n [\n -121.97021484374999,\n 40.74725696280421\n ],\n [\n -122.3876953125,\n 41.0130657870063\n ],\n [\n -122.84912109375,\n 40.613952441166596\n ],\n [\n -122.87109375,\n 40.07807142745009\n ],\n [\n -122.6953125,\n 38.993572058209466\n ],\n [\n -122.08007812499999,\n 37.68382032669382\n ],\n [\n -121.37695312499999,\n 36.96744946416934\n ],\n [\n -120.234375,\n 35.99578538642032\n ],\n [\n -120.234375,\n 36.06686213257888\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
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3020 State University Drive East
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Dissolved organic carbon (DOC) impacts water quality, the carbon cycle, and the ecology of aquatic systems. Understanding what controls DOC is therefore critical for improving large-scale models and best management practices for aquatic ecosystems. The two main processes of DOC transformation and removal, photochemical and microbial DOC degradation, work in tandem to modify and remineralize DOC within natural waters. Here, we examined both the photo- and microbial remineralization of DOC (photolability and biolability), and the indirect phototransformation of DOC into biolabile DOC (photoprimed biolability) for samples that capture the spatiotemporal and hydrological gradients of the Connecticut River watershed. The majority of DOC exported from this temperate watershed was photolabile and the concentration of photolabile DOC correlated with UV absorbance at 254 nm (r2 = 0.86). Phototransformation of DOC also increased biolability, and the total photolabile DOC (sum of photolabile and photoprimed biolabile DOC) showed a stronger correlation with UV absorbance at 254 nm (r2 = 0.92). We estimate that as much as 49% (SD = 3.3%) and 10% (SD = 1.1%) of annual DOC export from the Connecticut River is directly photolabile and photoprimable, respectively. Thus, 2.82 Gg C year−1 (SD = 0.67 Gg C year−1) or 1.13 Mg C km−2 year−1 (SD = 0.27 km−2 year−1) of total photolabile DOC escapes photochemical degradation within the river network to be exported from the Connecticut River each year.
","language":"English","publisher":"Springer","doi":"10.1007/s00027-021-00778-8","usgsCitation":"Yoon, B., Hosen, J.D., Kyzivat, E., Fair, J., Weber, L.C., Aho, K.S., Lowenthal, R., Matt, S., Sobczak, W.V., Shanley, J.B., Morrison, J., Saiers, J.E., Stubbins, A., and Raymond, P.A., 2021, Export of photolabile and photoprimable dissolved organic carbon from the Connecticut River: Aquatic Sciences, v. 83, 23, 17 p., https://doi.org/10.1007/s00027-021-00778-8.","productDescription":"23, 17 p.","ipdsId":"IP-094783","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":389152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Connecticut River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.26806640624999,\n 41.36031866306708\n ],\n [\n -72.13623046875,\n 41.95131994679697\n ],\n [\n -72.18017578125,\n 42.293564192170095\n ],\n [\n -72.24609375,\n 42.8115217450979\n ],\n [\n -72.18017578125,\n 43.197167282501276\n ],\n [\n -71.91650390625,\n 43.89789239125797\n ],\n [\n -71.60888671875,\n 44.134913443750726\n ],\n [\n -71.279296875,\n 44.465151013519616\n ],\n [\n -71.21337890625,\n 44.98034238084973\n ],\n [\n -71.08154296875,\n 45.3521452458518\n ],\n [\n -71.4990234375,\n 45.27488643704891\n ],\n [\n -71.9384765625,\n 45.07352060670971\n ],\n [\n -72.333984375,\n 44.08758502824516\n ],\n [\n -72.70751953125,\n 43.32517767999296\n ],\n [\n -72.88330078125,\n 42.84375132629021\n ],\n [\n -72.99316406249999,\n 42.309815415686664\n ],\n [\n -73.037109375,\n 41.49212083968776\n ],\n [\n -72.70751953125,\n 41.21172151054787\n ],\n [\n -72.35595703125,\n 41.19518982948959\n ],\n [\n -72.26806640624999,\n 41.36031866306708\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Yoon, B. 0000-0002-8959-3855","orcid":"https://orcid.org/0000-0002-8959-3855","contributorId":245946,"corporation":false,"usgs":false,"family":"Yoon","given":"B.","email":"","affiliations":[{"id":49377,"text":"Department of Marine and Environmental Sciences, Northeastern University, Boston, MA USA","active":true,"usgs":false}],"preferred":false,"id":823178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hosen, Jacob D.","contributorId":149188,"corporation":false,"usgs":false,"family":"Hosen","given":"Jacob","email":"","middleInitial":"D.","affiliations":[{"id":17663,"text":"Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, Maryland, United States","active":true,"usgs":false}],"preferred":false,"id":823179,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kyzivat, Ethan","contributorId":241078,"corporation":false,"usgs":false,"family":"Kyzivat","given":"Ethan","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":823180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fair, Jennifer H","contributorId":241077,"corporation":false,"usgs":false,"family":"Fair","given":"Jennifer H","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":823181,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weber, Lisa C.","contributorId":124586,"corporation":false,"usgs":true,"family":"Weber","given":"Lisa","email":"","middleInitial":"C.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":823182,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aho, Kelly S.","contributorId":241075,"corporation":false,"usgs":false,"family":"Aho","given":"Kelly","email":"","middleInitial":"S.","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":823183,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lowenthal, Rachel","contributorId":241079,"corporation":false,"usgs":false,"family":"Lowenthal","given":"Rachel","email":"","affiliations":[{"id":48197,"text":"Yale","active":true,"usgs":false}],"preferred":false,"id":823184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Matt, Serena","contributorId":194108,"corporation":false,"usgs":false,"family":"Matt","given":"Serena","affiliations":[],"preferred":false,"id":823185,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sobczak, W. V.","contributorId":41983,"corporation":false,"usgs":true,"family":"Sobczak","given":"W.","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":823186,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823187,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Morrison, Jonathan 0000-0002-1756-4609 jmorriso@usgs.gov","orcid":"https://orcid.org/0000-0002-1756-4609","contributorId":2274,"corporation":false,"usgs":true,"family":"Morrison","given":"Jonathan","email":"jmorriso@usgs.gov","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823188,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Saiers, James E.","contributorId":191842,"corporation":false,"usgs":false,"family":"Saiers","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":823189,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Stubbins, Aron","contributorId":80949,"corporation":false,"usgs":true,"family":"Stubbins","given":"Aron","affiliations":[],"preferred":false,"id":823190,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Raymond, Peter A.","contributorId":172876,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":17883,"text":"Yale School of Forestry and Environmental Studies, New Haven, CT","active":true,"usgs":false}],"preferred":false,"id":823191,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70254486,"text":"70254486 - 2021 - Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX)","interactions":[],"lastModifiedDate":"2024-05-28T14:47:15.444274","indexId":"70254486","displayToPublicDate":"2021-02-09T09:42:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7749,"text":"Frontiers in Climate","active":true,"publicationSubtype":{"id":10}},"title":"Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX)","docAbstract":"The mitigation of losses due to extreme climate events and long-term climate adaptation requires climate informed decision-making. In the past few decades, several remote sensing and modeled-based Earth observations (EOs) have been developed to provide an unprecedented global overview and routine monitoring of climate and its impacts on vegetation and hydrologic conditions, with the goal of supporting informed decision-making. However, their usage in decision-making is particularly limited in climate-risk vulnerable and in situ data-scarce regions such as sub-Saharan Africa, due to lack of access to EOs. Here, we describe the Early Warning eXplorer (EWX), which was developed to address this crucial limitation and facilitate the application of EOs in decision-making, particularly in the food and water-insecure regions of the world. First, the EWX's core framework, which includes (i) the Viewer, (ii) GeoEngine, and (iii) Support Applications, is described. Then, a comprehensive overview of the Viewer, which is a web-based interface used to access EOs, is provided. This includes a description of (i) the maps and associated features to access gridded EO data and anomalies for different temporal averaging periods, (ii) time series graphs and associated features to access EOs aggregated over polygons such as administrative boundaries, and (iii) commonly used EOs served by the EWX that provide assessments of climate and vegetation conditions. Next, examples are provided to demonstrate how EWX can be used to monitor development, progression, spatial extent, and severity of climate-driven extreme events to support timely decisions related to mitigation of food insecurity and flooding impacts. Finally, the value of a regional implementation of EWX at the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya, is highlighted. Regional implementation of the EWX facilitates access to regionally focused EOs and their availability at polygon boundaries most relevant to the local decision-makers. Similar instances of EWX implemented in other regions, especially those susceptible to food and water security, will likely further enhance the application of EOs for informed decision-making.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2020.583509","usgsCitation":"Shukla, S., Landsfeld, M., Anthony, M., Budde, M., Husak, G., Rowland, J., and Funk, C., 2021, Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX): Frontiers in Climate, v. 2, 583509, 16 p., https://doi.org/10.3389/fclim.2020.583509.","productDescription":"583509, 16 p.","ipdsId":"IP-120483","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453527,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2020.583509","text":"Publisher Index Page"},{"id":429328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":901558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":901559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anthony, Michelle 0000-0001-6646-2134","orcid":"https://orcid.org/0000-0001-6646-2134","contributorId":336955,"corporation":false,"usgs":false,"family":"Anthony","given":"Michelle","affiliations":[{"id":80923,"text":"KBR Technical Support Services Contract (TSSC)","active":true,"usgs":false}],"preferred":false,"id":901560,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Husak, Greg 0000-0003-2647-7870","orcid":"https://orcid.org/0000-0003-2647-7870","contributorId":331302,"corporation":false,"usgs":false,"family":"Husak","given":"Greg","email":"","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":901562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901563,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901564,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219165,"text":"70219165 - 2021 - Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales","interactions":[],"lastModifiedDate":"2021-03-29T13:00:49.94313","indexId":"70219165","displayToPublicDate":"2021-02-09T07:56:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales","docAbstract":"Understanding patterns of biodiversity is a key goal of ecology and is especially pressing in the current human‐caused biodiversity crisis. In wetland ecosystems, human impacts are centered around hydrologic manipulation including the common practice of wetland diking and impoundment. Constraining how wetland management influences plant biodiversity patterns across spatial scales will provide information on how best to modify actions to preserve biodiversity and ecosystem function in managed wetlands. Here, we compare patterns of plant diversity and species presence, abundance, and community composition at several spatial scales among tidal wetlands along an estuarine salinity gradient and managed wetlands that were formerly tidal. Managed impounded wetlands had decreased alpha and gamma diversity of rare species, with less than 60% of the species richness found in tidal brackish wetlands at several spatial scales. There was little change in the overall pattern of alpha, beta, and gamma diversity for common species in impounded wetlands; however, dominant tidal brackish species, primarily perennial rhizomatous graminoids, were replaced with management target plants and non‐native annual grasses in impounded wetlands. This species replacement led to over 60% of impounded sites being classified as containing novel plant assemblages. An additional 25% of impounded sites were classified as containing tidal saline plant assemblages, suggesting potential soil salinization. Along the estuarine gradient, patchiness and codominance of common plant species drove high diversity and turnover in tidal brackish wetlands, while it remains unclear whether tidal fresh or brackish wetlands maximize rare plant diversity. With reduced species richness, altered functional dominants, and novel or saline assemblages, impounded brackish wetlands may require careful water management to balance native plant biodiversity, associated ecosystem processes, and waterfowl requirements.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3366","usgsCitation":"Jones, S., Janousek, C.N., Casazza, M.L., Takekawa, J., and Thorne, K., 2021, Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales: Ecosphere, v. 12, no. 2, e03366, 19 p., https://doi.org/10.1002/ecs2.3366.","productDescription":"e03366, 19 p.","ipdsId":"IP-121980","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453532,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3366","text":"Publisher Index Page"},{"id":436516,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZG1Y72","text":"USGS data release","linkHelpText":"Impounded and tidal wetland plant diversity and composition across spatial scales, San Francisco Bay-Delta, California, USA (2016-2018)"},{"id":384713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.93701171874999,\n 36.89719446989036\n ],\n [\n -121.57470703125,\n 36.89719446989036\n ],\n [\n -121.57470703125,\n 38.976492485539396\n ],\n [\n -122.93701171874999,\n 38.976492485539396\n ],\n [\n -122.93701171874999,\n 36.89719446989036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Scott 0000-0002-1056-3785","orcid":"https://orcid.org/0000-0002-1056-3785","contributorId":215602,"corporation":false,"usgs":true,"family":"Jones","given":"Scott","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":813087,"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":813088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takekawa, John Y. 0000-0003-0217-5907","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":203805,"corporation":false,"usgs":false,"family":"Takekawa","given":"John Y.","affiliations":[{"id":36724,"text":"Audubon California, Richardson Bay Audubon Center and Sanctuary, Tiburon, CA","active":true,"usgs":false}],"preferred":false,"id":813089,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813090,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217900,"text":"70217900 - 2021 - Microbial and viral indicators of pathogens and human health risks from recreational exposure to waters impaired by fecal contamination","interactions":[],"lastModifiedDate":"2021-02-10T13:57:39.234893","indexId":"70217900","displayToPublicDate":"2021-02-09T07:55:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5896,"text":"Journal of Sustainable Water in the Built Environment","active":true,"publicationSubtype":{"id":10}},"title":"Microbial and viral indicators of pathogens and human health risks from recreational exposure to waters impaired by fecal contamination","docAbstract":"Fecal indicator bacteria (FIB) (e.g., fecal coliforms, Escherichia coli, and enterococci) have been used for decades to monitor for and protect the public from waterborne pathogens from fecal contamination. However, FIB may not perform well at predicting the presence of waterborne pathogens or human health outcomes from recreational exposure to fecal-contaminated surface waters. Numerous factors can influence the relationship between FIB and pathogens or human health outcomes, including the source(s) of contamination, the type of pathogen(s) present, differences in the survival and behavior of FIB and pathogens in the wastewater conveyance and treatment process, and varying environmental conditions. As a result, different indicators, such as source-specific microbial source tracking (MST) markers and viral fecal indicators, have been used as possible surrogates to better approximate pathogen abundance and human health risks in recreational waters. The performance of these alternative indicators has been mixed, with some promise of viral indicators better approximating viral pathogens than bacterial fecal indicators, and FIB generally more closely associated with bacterial and protozoal pathogen presence than human MST markers. Many of the assays to detect and quantify fecal indicators and pathogens are polymerase chain reaction-based assays, which detect and quantify nucleic acid [deoxyribonucleic acid (DNA) and ribonucleic acid (RNA)] sequences specific to a target of interest. Recent advances in DNA and RNA sequencing technologies may push the field toward metabarcoding approaches, where multiple targets can be detected and quantified simultaneously. Metabarcoding is currently more applicable to bacterial and protozoal assessments than viral assessments based on a lack of universal metabarcoding markers for viruses. Innovative technologies, such as biosensors and nanotechnologies, may provide more sensitive and accurate tools to detect and quantify pathogens. When a specific pathogen is of concern for a recreational water body, a practical approach in estimating the likelihood of human health outcomes is the application of quantitative microbial risk assessments (QMRAs). Quantitative microbial risk assessments can be used to model the likelihood of pathogen-specific human health outcomes from recreational exposure as a function of a surrogate indicator. Inputs for QMRAs include the ratio between the indicator to be monitored and the pathogen of interest, the concentration of the indicator, the amount of water ingested, and the likelihood of the health outcome based on the estimated amount of pathogen consumed. There are numerous unknowns about the behavior and survival of fecal indicators and pathogens in environmental waters. Developing accurate models to predict pathogen concentrations from fecal indicators in recreational waters will require a better understanding of these unknowns. Current methods and technologies for detecting and quantifying fecal indicators and pathogens are limited due to the rare and patchy nature of pathogens. Technological advances may help improve sensitivity for detecting and quantifying pathogens.
Basic data on river bathymetry is critical for numerous applications in river research and management and is increasingly obtained via remote sensing, but the noisy, pixelated appearance of image‐derived depth maps can compromise subsequent analyses. We hypothesized that this noise originates from reflectance from an irregular water surface and introduced a framework for mitigating these effects by Inferring Bathymetry from Averaged River Images (IBARI). This workflow produces time‐averaged images from video frames stabilized to account for platform motion and/or computes a spatial average from an ensemble simulated by randomly shifting images relative to themselves. We used field observations of water depth and helicopter‐based videos from a clear‐flowing river to assess the potential of this approach to improve depth retrieval. Our results indicated that depths inferred from averaged images were more accurate and precise than those inferred from single frames; observed versus predicted regression R2 increased from 0.80 to 0.88. In addition, IBARI significantly enhanced the texture of image‐derived depth maps, leading to smoother, more coherent representations of channel morphology. Depth retrieval improved with image sequence duration, but the number of images was more important than the length of time encompassed; shorter acquisitions at higher frame rates would economize data collection. We also demonstrated the potential to scale up the IBARI workflow by producing a mosaic of bathymetric maps derived from averaged images acquired at several hovering waypoints distributed along a 2.36 km reach. This approach is well‐suited to data collected from helicopters and small unmanned aircraft systems.
Day-roost selection by Lasiurine tree bats during winter and their response to dormant season fires is unknown in the southeastern United States where dormant season burning is widely applied. Although fires historically were predominantly growing season, they now occur in the dormant season in this part of the Coastal Plain to support a myriad of stewardship activities, including habitat management for game species. To examine the response of bats to landscape condition and the application of prescribed fire, in the winter of 2019, we mist-netted and affixed radio-transmitters to 16 Lasiurine bats, primarily Seminole bats (Lasiurus seminolus) at Camp Blanding Joint Training Center in northern Florida. We then located day-roost sites to describe roost attributes. For five Seminole bats, one eastern red bat (Lasiurus borealis), and one hoary bat (Lasiurus cinereus), we applied prescribed burns in the roost area to observe bat response in real-time. Generally, Seminole bats selected day-roosts in mesic forest stands with high mean fire return intervals. At the roost tree scale, Seminole day-roosts tended to be larger, taller and in higher canopy dominance classes than surrounding trees. Seminole bats roosted in longleaf (Pinus palustris), slash (Pinus elliotii) and loblolly pine (Pinus taeda) more than expected based on availability, whereas sweetbay (Magnolia virginiana), water oak (Quercus nigra) and turkey oak (Quercus laevis), were roosted in less than expected based on availability. Of the seven roosts subjected to prescribed burns, only one male Seminole bat and one male eastern red bat evacuated during or immediately following burning. In both cases, these bats had day-roosted at heights lower than the majority of other day-roosts observed during our study. Our results suggest Seminole bats choose winter day-roosts that both maximize solar exposure and minimize risks associated with fire. Nonetheless, because selected day-roosts largely were fire-dependent or tolerant tree species, application of fire does need to periodically occur to promote recruitment and retention of suitable roost sites.
In the spring of 2019, ice sheets transported down-stream during a large streamflow rise event in the lower Minnesota River destroyed an index-velocity streamgage at the Minnesota River at Fort Snelling State Park, Minnesota (U.S. Geological Survey station 05330920; hereafter referred to as “Ft. Snelling”). The streamgage previously used an acoustic Doppler velocity meter to provide instantaneous streamflow and suspended-sedimentation concentration (SSC) data in backwater conditions caused by the confluence with the Mississippi River. In response, the U.S. Geological Survey cooperated with the U.S. Army Corps of Engineers and Lower Minnesota River Watershed District to develop linear regression models that estimate SSCs and suspended-sand concentrations (sand) at the destroyed streamgage using streamflow data from an upstream site Minnesota River near Jordan, Minn. (U.S. Geological Survey station 05330000, hereafter referred to as “Jordan”).
Simple linear regression models were developed for selected positions on the streamflow hydrograph to estimate SSC and sand at Ft. Snelling from the streamflow at Jordan. Statistically significant models could not be developed for estimating SSC at low streamflows and sand at high streamflows. Models developed to estimate sand were more uncertain than models used to estimate SSC, and models using streamflow to predict SSC and sand were more uncertain than models using acoustic backscatter to predict SSC. Annual loads of SSC and sand estimated from these models show the dynamic nature of sediment transport and storage in this section of the lower Minnesota River. These models and the associated ancillary data can help with management decisions that are crucial in managing aquatic habitat, supporting power production, and commercial navigation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211005","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers and Lower Minnesota River Watershed District","usgsCitation":"Groten, J.T., Hendrickson, J.S., and Loomis, L.R., 2021, Estimation of suspended sediment at a discontinued streamgage on the lower Minnesota River at Fort Snelling State Park, Minnesota: U.S. Geological Survey Open-File Report 2021–1005, 12 p., https://doi.org/10.3133/ofr20211005.","productDescription":"Report: vi, 12 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-121668","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":383100,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1005/ofr20211005.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1005"},{"id":383101,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AIULOQ","text":"USGS data release","linkHelpText":"Suspended-sediment and sand concentrations, streamflow, acoustic data, linear regression models, and loads for the Lower Minnesota River, 2012 -2019"},{"id":383108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1005/coverthb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Fort Snelling State Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.22311401367188,\n 44.819107339295684\n ],\n [\n -93.1801986694336,\n 44.85124448203336\n ],\n [\n -93.16577911376953,\n 44.879471418146686\n ],\n [\n -93.14414978027344,\n 44.89187715629887\n ],\n [\n -93.15067291259766,\n 44.89503897537852\n ],\n [\n -93.17436218261719,\n 44.89601180781499\n ],\n [\n -93.19427490234375,\n 44.89114748105545\n ],\n [\n -93.19599151611328,\n 44.87557887053108\n ],\n [\n -93.21247100830078,\n 44.859275967357476\n ],\n [\n -93.22208404541016,\n 44.85270483540896\n ],\n [\n -93.23856353759766,\n 44.84102097157541\n ],\n [\n -93.23856353759766,\n 44.82763029742812\n ],\n [\n -93.22895050048828,\n 44.81862027505869\n ],\n [\n -93.22311401367188,\n 44.819107339295684\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Multiple nations and private entities are pushing to make landing humans on Mars a reality. The majority of proposed mission architectures envision ‘living off the land’ by leveraging Martian water-ice deposits for fuel production and other purposes. Fortunately for mission designers, water ice exists on Mars in plentiful volumes. The challenge is isolating accessible ice deposits within regions that optimize other preferred landing-site conditions. Here we present the first results of the Mars Subsurface Water Ice Mapping (SWIM) project, which has the aim of searching for buried ice resources across the mid-latitudes. Through the integration of orbital datasets in concert with new data-processing techniques, the SWIM project assesses the likelihood of ice by quantifying the consistency of multiple, independent data sources with the presence of ice. Concentrating our efforts across the majority of the northern hemisphere, our composite ice-consistency maps indicate that the broad plains of Arcadia and the extensive glacial networks across Deuteronilus Mensae match the greatest number of remote-sensing criteria for accessible ice-rich, subsurface material situated equatorwards of the contemporary ice-stability zone.
Southern California has a long history of damaging debris flows after wildfire. Despite recurrent loss, forecasts of the frequency and magnitude of postfire debris flows are not available for the region like they are for earthquakes. Instead, debris flow hazards are typically assessed in a reactive manner after wildfires. Such assessments are crucial for evaluating debris flow risk by postfire emergency response teams; however, time between the fire and first rainstorm is often insufficient to fully develop and implement effective emergency response plans like those in place for earthquakes. Here, we use both historical distributions of fire and precipitation frequency and empirical models of postfire debris flow likelihood and volume to map the expected frequency and magnitude of postfire debris flows across southern California. We find that at least small debris flows can be expected almost every year, while major debris flows capable of damaging 40 or more structures have a recurrence interval between 10 and 13 years, a return interval that is comparable to a magnitude 6.7 earthquake. A sensitivity analysis to possible future changes in current fire and precipitation regimes indicates that debris flow activity in southern California is more sensitive to increases in precipitation intensity than increases in fire frequency and severity. Projected increases in rainfall intensity of 18% result in an overall 110% increase in the probability of major debris flows. Our results, in combination with an assessment of exposure, can be used to prioritize watersheds for further analysis and possible prefire mitigation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020EF001735","usgsCitation":"Kean, J.W., and Staley, D.M., 2021, Forecasting the frequency and magnitude of postfire debris flows across southern California: Earth's Future, v. 9, no. 3, e2020EF001735, 19 p., https://doi.org/10.1029/2020EF001735.","productDescription":"e2020EF001735, 19 p.","ipdsId":"IP-124894","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453549,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020ef001735","text":"Publisher Index Page"},{"id":436518,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91GIT04","text":"USGS data release","linkHelpText":"Gridded estimates of postfire debris flow frequency and magnitude for southern California"},{"id":384885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.76171875,\n 32.565333160841035\n ],\n [\n -114.2578125,\n 32.565333160841035\n ],\n [\n -114.2578125,\n 35.209721645221386\n ],\n [\n -120.76171875,\n 35.209721645221386\n ],\n [\n -120.76171875,\n 32.565333160841035\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":813546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":813547,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70207509,"text":"sir20195146 - 2021 - Water-level conditions in the confined aquifers of the New Jersey Coastal Plain, 2013","interactions":[],"lastModifiedDate":"2021-02-12T20:58:10.337303","indexId":"sir20195146","displayToPublicDate":"2021-02-05T13:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5146","displayTitle":"Water-Level Conditions in the Confined Aquifers of the New Jersey Coastal Plain, 2013","title":"Water-level conditions in the confined aquifers of the New Jersey Coastal Plain, 2013","docAbstract":"The Coastal Plain aquifers of New Jersey provide an important source of water for more than 3.5 million people. In 2013, groundwater withdrawals from 10 confined aquifers of the New Jersey Coastal Plain totaled about 190 million gallons per day. Steadily increasing withdrawals from the late 1800s to the early 1990s resulted in declining water levels and the formation of regional cones of depression in many confined Coastal Plain aquifers. Starting in 1978, the U.S. Geological Survey (USGS) began mapping the potentiometric surfaces of the major confined Coastal Plain aquifers every 5 years to provide a regional assessment of groundwater conditions.
In a study conducted by the USGS, in cooperation with the New Jersey Department of Environmental Protection, water levels in 10 confined aquifers of the New Jersey Coastal Plain were measured and evaluated to provide a regional overview of groundwater conditions during fall 2013. Water levels were measured in 987 wells in New Jersey, and parts of Pennsylvania and Delaware. Potentiometric-surface maps were prepared for, in ascending order of age, the confined Cohansey aquifer of Cape May County, Rio Grande water-bearing zone, Atlantic City 800-foot sand, Piney Point aquifer, Vincentown aquifer, Wenonah-Mount Laurel aquifer, Englishtown aquifer system, and the Upper, Middle, and Lower aquifers of the Potomac-Raritan-Magothy (PRM) aquifer system.
Persistent, regionally extensive cones of depression were present in the potentiometric surfaces of the Englishtown aquifer system and Wenonah-Mount Laurel aquifer in Ocean and Monmouth Counties; Wenonah-Mount Laurel and Upper, Middle, and Lower PRM aquifers in Camden County; and Atlantic City 800-foot sand in Atlantic County. Changes in water levels from 2008 to 2013 were measured in many Coastal Plain aquifers in New Jersey. In some areas, water levels continued to decline as a result of pumping, but in other areas water levels continued to recover as a result of regulated decreases in groundwater withdrawals. Since 2008, in the confined Cohansey aquifer in Cape May County, water levels generally did not change; however, cones of depression in the potentiometric surface of the Piney Point aquifer in some areas of Cumberland County deepened by more than 20 feet (ft). In Critical Area 1, an area of restricted withdrawals, measured water levels in the Wenonah-Mount Laurel aquifer declined in parts of southern Monmouth County by more than 10 ft; however, rises in water levels of more than 10 ft were measured in parts of northern Ocean and Monmouth Counties. Since 2008, in Critical Area 2, also an area of restricted withdrawals, measured water levels in the Wenonah-Mount Laurel aquifer rose more than 20 ft in parts of western Burlington County and more than 20 ft in parts of western Camden County. Since 2008, in Critical Area 1, measured water levels in the Englishtown aquifer system declined in parts of eastern Ocean County by more than 10 ft and in southeastern Monmouth County by more than 20 ft; however, rises in water levels of more than 10 ft were measured in other parts of Ocean and Monmouth Counties.
In general, since 2008 in Critical Area 2, in the Upper PRM aquifer, measured water levels continued to rise by 10 ft or more in central and western Burlington and central Camden Counties. In the Middle PRM aquifer in Critical Area 2, measured water levels rose in parts of central Camden County by 10 ft or more. However, measured water levels in the Lower PRM aquifer in Critical Area 2 were more than 10 ft lower in the center of the cone of depression in central Camden County, but measured water levels continued to rise updip from this area in Critical Area 2.
Seasonal water-level fluctuations are presented in time-series hydrographs for 77 wells during 1978–2013. Analyses of long-term water-level changes for the period 2008–13 indicate downward water-level trends at 14 wells (18 percent), upward trends at 34 wells (44 percent), and no substantial change at 29 wells (38 percent). Downward trends were most often observed for wells screened in the Piney Point aquifer and the Atlantic City 800-foot sand. Upward water-level trends were most often measured for wells screened in the PRM aquifer system. Upward water-level trends also were measured for wells in the Englishtown aquifer system and the Wenonah-Mount Laurel aquifer in Critical Area 1 in some areas; however, downward trends and no substantial changes were measured in other areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195146","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Gordon, A.D., Carleton, G.B., and Rosman, R., 2021, Water-level conditions in the confined aquifers of the New Jersey Coastal Plain, 2013: U.S. Geological Survey Scientific Investigations Report 2019–5146, 104 p., 9 pl., https://doi.org/10.3133/sir20195146.","productDescription":"Report: x, 104 p.; 9 Plates: 34 x 44 inches or smaller; Data Release","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073418","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":383040,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate5.pdf","text":"Plate 5","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the Wenonah-Mount Laurel aquifer, 2013"},{"id":383039,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate4.pdf","text":"Plate 4","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the Vincentown aquifer, 2013"},{"id":383038,"rank":6,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate3.pdf","text":"Plate 3","size":"1.19 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the Piney Point aquifer, 2013"},{"id":383036,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate1.pdf","text":"Plate 1","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the confined Cohansey aquifer and the Rio Grande water-bearing zone, 2013"},{"id":383035,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EKA147","text":"USGS data release","linkHelpText":"Geospatial data representing wells open to, and 2013 potentiometric surface contours of, the confined aquifers of the New Jersey Coastal Plain"},{"id":383034,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146.pdf","text":"Report","size":"25.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5146"},{"id":383042,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate7.pdf","text":"Plate 7","size":"1.22 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the Upper Potomac-Raritan-Magothy aquifer, 2013"},{"id":383041,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate6.pdf","text":"Plate 6","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the Englishtown aquifer system, 2013"},{"id":383043,"rank":11,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate8.pdf","text":"Plate 8","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the Middle and undifferentiated Potomac-Raritan-Magothy aquifer, 2013"},{"id":383044,"rank":12,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate9.pdf","text":"Plate 9","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the Lower Potomac-Raritan-Magothy aquifer, 2013"},{"id":383033,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5146/coverthb.jpg"},{"id":383037,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5146/sir20195146_plate2.pdf","text":"Plate 2","size":"1.27 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Potentiometric surface of the Atlantic City 800-foot 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U.S. Geological Survey
3450 Princeton Pike
Suite 110
Lawrenceville, NJ 08648
We collected ground‐penetrating radar (GPR) and frequency‐domain electromagnetic induction (FDEM) profiles in 2011 and 2012 to identify the extent of permafrost relative to surface biomass and solar insolation around Twelvemile Lake near Fort Yukon, Alaska. We compared a Landsat‐derived biomass estimate and modeled solar insolation from a digital elevation model to the geophysical measurements. We show correspondence between vegetation type and biomass relative to permafrost extent and seasonal freeze–thaw. Thicker permafrost (≥25 m) was covered by greater biomass, and seasonal thaw depths in these regions were minimal (1 m). Shallow (1–3 m depth) and thin (20–50 cm) newly forming permafrost or frozen layers from the previous winter occurred below northward oriented slopes with thin biomass cover. South‐facing slopes exhibited permafrost when there was enough biomass to shield incoming solar energy. We developed an artificial neural network to predict permafrost extent across the broader region by mapping GPR‐observed instances of permafrost to FDEM, biomass, and terrain observations with 90.2% accuracy. We identified a strong linear correlation (r = −0.77) between permafrost probability and seasonal thaw depth, indicating that our models may also be used to explore thaw patterns and variability in active layer thickness. This study highlights the combined influence of biomass and terrain on the presence of permafrost and the value of evaluating such parameters via remote sensing to predict permafrost spatial or temporal variability. Incorporating diverse geophysical datasets with in‐situ validation into machine learning models demonstrates a useful approach to upscale estimated permafrost extent across large Arctic expanses.
Human activities such as mining, agriculture, and urbanization have resulted in severe, large‐scale alteration to landform organization and associated geomorphic processes. The mountaintop mining (MTM) region of West Virginia, USA has experienced dramatic topographic alteration, by removing steep slopes and introducing plateau‐like areas at ridgelines and benches on valley fills. The resulting engineered landforms create synthetic landscapes, disconnected from previous geomorphic processes. Invoking the process domain concept, we compare differences in slope‐area relations, cumulative area distributions (CADs), elevation, slope, upslope accumulated area, and a slope*area product before and after mining to adjacent unmined sub‐catchments in five study basins. Differences in the slope‐area relation include a 42% slope reduction in low drainage areas, corresponding to hillslopes, unchanneled valleys, and debris flow dominated channels, which may fall below thresholds required for debris flow processes. The curved slope‐area relation that represents valley incision by debris flows is replaced by slope‐area relations that resemble basins where gullying and the stream power law dominate. Extremely high chemical weathering of unconsolidated valley fills materials may facilitate process domain shifts from debris flows to gullying and fluvial erosion. The characteristic power law scaling break in CADs that represents the headward limit of the channelized network is subdued in post‐mined sites and may reflect headward channelized network extension in mined basins. Slope‐area relations and CADs present a unique topographic signature of MTM activity, potentially providing an analytical approach to assess impacts on underlying geomorphic processes for other synthetic landscapes such as cities or large‐scale agricultural production.
Microhexura montivaga (Spruce-fir Moss Spider) is a federally endangered arachnid endemic to high-elevation montane conifer forests of the southern Appalachian Mountains. The spider is cryptic and difficult to monitor because this species lives in the interface between the bryophyte mat and the rock surface. Since temporary removal of the bryophyte mat is necessary to monitor the spider, surveyors may negatively impact the spider's habitat during monitoring. To help inform survey protocol for this endangered species, we studied reattachment rates of bryophyte mats to rock surfaces after their removal. In 2017, we surveyed sixty 10 cm × 10 cm plots, assigning a plot to either control or treatment (i.e., application of water post-reattachment). We monitored plots for 1 year post-survey to determine reattachment rates. The majority of plots (70%) reestablished after 1 year, whereas 15% did not reattach or showed substantial prolonged (e.g., ∼1 year) desiccation and 15% completely fell off or had 100% prolonged desiccation and were chlorotic. We found that mat depth and overstory canopy cover had no effect on mat reestablishment, although bryophyte type did. We found no difference between treatment and control plots, suggesting that no treatment is needed for mats to reestablish under the conditions described. Rock slope significantly influenced reestablishment rates, highlighting that surveying bryophyte mats on slopes >80% may diminish or destroy habitat. Further research is needed to determine long-term monitoring effects on the spider and its habitat, especially in relation to disturbance regimes and ecological restoration of Picea rubens (Red Spruce).