Recent short-term drought conditions have emphasized the need to better understand the delicate balance between abundance, sustainability, and scarcity of groundwater in the Ozark Plateaus aquifer system. In 2014, the U.S. Geological Survey began construction of a groundwater-flow model as a tool for the assessment of groundwater availability in the Ozark Plateaus aquifer system. The model was developed to benefit concurrent and future investigations involving groundwater-pumping scenarios, optimization, particle transport, and groundwater-monitoring network analysis.
The groundwater model simulates 116 years (1900–2015) of hydrologic conditions and the response of the groundwater system to changes in stress including changes in recharge and groundwater pumping for water supply. Semiseasonal stress periods were simulated from the later part of 1991 to 2015 and represent higher demand and lower recharge in the spring and summer months and lower demand and higher recharge in the fall and winter months. Groundwater pumping increases throughout the simulation period with a maximum rate of about 600 million gallons per day (Mgal/d).
The process of matching historical hydrologic data for the Ozark Plateaus aquifer system model was accomplished by a combination of manual changes to parameter values and automated calibration methods. Observation data used in the development and evaluation of the model included 19,045 hydraulic-head observations from 6,683 wells within the model area. Observation data also included stream leakage estimates summed to calculate a net gain or net loss value for approximately 81 named streams.
The majority (mean of over 95 percent) of the recharge component is discharged through streams simulated in the model. The total simulated discharge to streams fluctuates seasonally between 7,500 and 17,500 Mgal/d with a mean outflow of 11,500 Mgal/d. Much of the remaining balance between modeled recharge inflows and stream outflows is made up by water moving into or out of storage in the aquifer system resulting in changes in modeled groundwater levels.
The goal of the model was to develop a model capable of suitable accuracy at regional scales. The intent was not to reproduce individual local-scale details, which are typically not possible given the uniform cell size of 1 square mile. Although the model may not represent each local-scale detail, the model can be applied for a better understanding of the regional flow system and to evaluate responses to changes in climate and groundwater pumping.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185035","collaboration":"Water Availability and Use Science Program","usgsCitation":"Clark, B.R., Richards, J.M., and Knierim, K.J., 2018, The Ozark Plateaus Regional Aquifer Study—Documentation of a groundwater-flow model constructed to assess water availability in the Ozark Plateaus: U.S. Geological Survey Report 2018–5035, 33 p., https://doi.org/10.3133/sir20185035.","productDescription":"Report: v, 33 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079993","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":352956,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5035/coverthb2.jpg"},{"id":352962,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://water.usgs.gov/wausp/","text":"Water Availability and Use Science Program"},{"id":352957,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5035/sir20185035.pdf","text":"Report","size":"15.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5035"},{"id":352958,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F718350W","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model of groundwater flow in the Ozark Plateaus aquifer system"}],"country":"United States","otherGeospatial":" Ozark Plateaus aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.3,\n 35.0333\n ],\n [\n -89.25,\n 35.0333\n ],\n [\n -89.25,\n 39.0667\n ],\n [\n -95.3,\n 39.0667\n ],\n [\n -95.3,\n 35.0333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
700 W. Research Blvd.
Fayetteville, AR 72701
Wave attenuation is a central process in the mechanics of a healthy salt marsh. Understanding how wave attenuation varies with vegetation and hydrodynamic conditions informs models of other marsh processes that are a function of wave energy (e.g. sediment transport) and allows for the incorporation of marshes into coastal protection plans. Here, we examine the evolution of wave height across a tidal salt marsh in San Francisco Bay. Instruments were deployed along a cross-shore transect, starting on the mudflat and crossing through zones dominated by Spartina foliosa and Salicornia pacifica. This dataset is the first to quantify wave attenuation for these vegetation species, which are abundant in the intertidal zone of California estuaries. Measurements were collected in the summer and winter to assess seasonal variation in wave attenuation. Calculated drag coefficients of S. foliosa and S. pacifica were similar, indicating equal amounts of vegetation would lead to similar energy dissipation; however, S. pacifica has much greater biomass close to the bed (<20 cm) and retains biomass throughout the year, and therefore, it causes more total attenuation. S. foliosa dies back in the winter, and waves often grow across this section of the marsh. For both vegetation types, attenuation was greatest for low water depths, when the vegetation was emergent. For both seasons, attenuation rates across S. pacifica were the highest and were greater than published attenuation rates across similar (Spartina alterniflora) salt marshes for the comparable depths. These results can inform designs for marsh restorations and management plans in San Francisco Bay and other estuaries containing these species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2018.02.001","usgsCitation":"Foster-Martinez, M.R., Lacy, J.R., Ferner, M.C., and Variano, E., 2018, Wave attenuation across a tidal marsh in San Francisco Bay: Coastal Engineering, v. 136, p. 26-40, https://doi.org/10.1016/j.coastaleng.2018.02.001.","productDescription":"15 p.","startPage":"26","endPage":"40","ipdsId":"IP-090999","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468882,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2018.02.001","text":"Publisher Index Page"},{"id":353003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.50717163085938,\n 38.00049145082287\n ],\n [\n -122.44949340820312,\n 38.00049145082287\n ],\n [\n -122.44949340820312,\n 38.03132654864846\n ],\n [\n -122.50717163085938,\n 38.03132654864846\n ],\n [\n -122.50717163085938,\n 38.00049145082287\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"136","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6f5e4b0da30c1bfbfb3","contributors":{"authors":[{"text":"Foster-Martinez, Madeline R.","contributorId":201705,"corporation":false,"usgs":false,"family":"Foster-Martinez","given":"Madeline","email":"","middleInitial":"R.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":731210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lacy, Jessica R. 0000-0002-2797-6172","orcid":"https://orcid.org/0000-0002-2797-6172","contributorId":201703,"corporation":false,"usgs":true,"family":"Lacy","given":"Jessica","email":"","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":731209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferner, Matthew C.","contributorId":176972,"corporation":false,"usgs":false,"family":"Ferner","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":731211,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Variano, Evan A.","contributorId":67793,"corporation":false,"usgs":true,"family":"Variano","given":"Evan A.","affiliations":[],"preferred":false,"id":731212,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194969,"text":"ofr20171136 - 2018 - Water-quality, bed-sediment, and biological data (October 2015 through September 2016) and statistical summaries of data for streams in the Clark Fork Basin, Montana","interactions":[],"lastModifiedDate":"2018-09-25T06:35:26","indexId":"ofr20171136","displayToPublicDate":"2018-03-30T00:00:00","publicationYear":"2018","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":"2017-1136","title":"Water-quality, bed-sediment, and biological data (October 2015 through September 2016) and statistical summaries of data for streams in the Clark Fork Basin, Montana","docAbstract":"Water, bed sediment, and biota were sampled in selected streams from Butte to near Missoula, Montana, as part of a monitoring program in the upper Clark Fork Basin of western Montana. The sampling program was led by the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, to characterize aquatic resources in the Clark Fork Basin, with emphasis on trace elements associated with historic mining and smelting activities. Sampling sites were on the Clark Fork and selected tributaries. Water samples were collected periodically at 20 sites from October 2015 through September 2016. Bed-sediment and biota samples were collected once at 13 sites during August 2016.
This report presents the analytical results and quality-assurance data for water-quality, bed-sediment, and biota samples collected at sites from October 2015 through September 2016. Water-quality data include concentrations of selected major ions, trace elements, and suspended sediment. Samples for analysis of turbidity were collected at 13 sites, whereas samples for analysis of dissolved organic carbon were collected at 10 sites. In addition, samples for analysis of nitrogen (nitrate plus nitrite) were collected at two sites. Daily values of mean suspended-sediment concentration and suspended-sediment discharge were determined for three sites. Seasonal daily values of turbidity were determined for five sites. Bed-sediment data include trace-element concentrations in the fine-grained (less than 0.063 millimeter) fraction. Biological data include trace-element concentrations in whole-body tissue of aquatic benthic insects. Statistical summaries of water-quality, bed-sediment, and biological data for sites in the upper Clark Fork Basin are provided for the period of record.
","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171136","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Dodge, K.A., Hornberger, M.I., and Turner, M.A., 2018, Water-quality, bed-sediment, and biological data (October 2015 through September 2016) and statistical summaries of data for streams in the Clark Fork Basin, Montana: U.S. Geological Survey Open-File Report 2017–1136, 118 p., https://doi.org/10.3133/ofr20171136.","productDescription":"Report: vi, 118 p.; Data Release","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-089429","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":352885,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79C6WDM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-Quality, Bed-Sediment, and Biological Data (October 2015 through September 2016) and Statistical Summaries of Data for Streams in the Clark Fork Basin, Montana"},{"id":352884,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pubs/of/2017/1136/ofr20171136.pdf","text":"Report","size":"3.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2017–1136"},{"id":352883,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pubs/of/2017/1136/coverthb2.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114,\n 45.85\n ],\n [\n -112.41485595703125,\n 45.85\n ],\n [\n -112.41485595703125,\n 47\n ],\n [\n -114,\n 47\n ],\n [\n -114,\n 45.85\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Evidence of disease and mortalities of young of the year (age‐0) Smallmouth Bass Micropterus dolomieu has occurred during the late spring and summer in many parts of the Susquehanna River watershed since 2005. To better understand contributing factors, fish collected from multiple areas throughout the watershed as well as out‐of‐basin reference populations (Allegheny and Delaware River basins; experimental ponds, Kearneysville, West Virginia) were examined grossly and histologically for abnormalities. Tissue contaminant concentrations were determined from whole‐body homogenates, and water contaminant concentrations were estimated using time‐integrated passive samplers at selected sites. Observed or isolated pathogens included bacteria, predominantly motile Aeromonas spp. and Flavobacterium columnare ; largemouth bass virus, and parasites, including trematode metacercariae, cestodes, and the myxozoan Myxobolus inornatus . Although these pathogens were found in age‐0 Smallmouth Bass from multiple sites, no one pathogen was consistently associated with mortality. Chemicals detected in tissue included polychlorinated biphenyl (PCB) congeners, organochlorine, and current‐use pesticides. Pyraclostrobin, PCB congeners 170 and 187, cis‐chlordane and trans‐nonachlor were detected in all Susquehanna watershed samples but rarely in samples from the reference site. The findings support the idea that there is no single cause for disease of age‐0 Smallmouth Bass; rather the cumulative effects of co‐infections and potential immunomodulation by environmental stressors during a sensitive developmental life stage may lead to mortality. Identifying the most important risk factors will be necessary for more in‐depth analyses of individual stressors and better management of the habitat and fish populations.
","language":"English","publisher":"Wiley","doi":"10.1002/aah.10009","usgsCitation":"Walsh, H.L., Blazer, V., Smith, G., Lookenbill, M., Alvarez, D.A., and Smalling, K., 2018, Risk factors associated with mortality of age-0 Smallmouth Bass in the Susquehanna River basin, Pennsylvania: Journal of Aquatic Animal Health, v. 30, no. 1, p. 65-80, https://doi.org/10.1002/aah.10009.","productDescription":"16 p.","startPage":"65","endPage":"80","ipdsId":"IP-088555","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":468883,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/aah.10009","text":"Publisher Index Page"},{"id":376952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Susquehanna River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.475341796875,\n 39.74943369178247\n ],\n [\n -75.443115234375,\n 39.74943369178247\n ],\n [\n -75.443115234375,\n 41.95131994679697\n ],\n [\n -78.475341796875,\n 41.95131994679697\n ],\n [\n -78.475341796875,\n 39.74943369178247\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Walsh, Heather L. 0000-0001-6392-4604","orcid":"https://orcid.org/0000-0001-6392-4604","contributorId":213348,"corporation":false,"usgs":false,"family":"Walsh","given":"Heather","email":"","middleInitial":"L.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":794683,"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":794684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Geoffrey","contributorId":199064,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey","affiliations":[],"preferred":false,"id":794685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lookenbill, Michael 0000-0001-5857-8276","orcid":"https://orcid.org/0000-0001-5857-8276","contributorId":236910,"corporation":false,"usgs":false,"family":"Lookenbill","given":"Michael","email":"","affiliations":[{"id":17703,"text":"Pennsylvania Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":794686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alvarez, David A. 0000-0002-6918-2709","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":220763,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":794687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216337,"text":"70216337 - 2018 - Hierarchical modeling assessment of the influence of watershed stressors on fish and invertebrate species in Gulf of Mexico estuaries","interactions":[],"lastModifiedDate":"2020-11-12T15:39:40.108738","indexId":"70216337","displayToPublicDate":"2018-03-28T09:35:38","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Hierarchical modeling assessment of the influence of watershed stressors on fish and invertebrate species in Gulf of Mexico estuaries","docAbstract":"The northern Gulf of Mexico (GoM) spans five U.S. states and encompasses estuaries that vary greatly in size, shape, upstream river input, eutrophication status, and biotic communities. Given the variability among these estuaries, assessing their biological condition relative to anthropogenic stressors is challenging, but important to regional fisheries management and habitat conservation initiatives. Here, a hierarchical generalized linear modeling approach was developed to predict species presence in bottom trawl samples, using data from 33 estuaries over a nineteen-year study period. This is the first GoM estuary assessment to leverage Gulf-wide trawl data to develop species-level indicators and a quantitative index of estuary disturbance. After controlling for sources of variability at the sampling event, estuary, state, and sampling program levels, our approach screened for statistically significant relationships between watershed-level anthropogenic stressors and fish and invertebrate species presence. Modeling results indicate species level indicators with sensitivities to landscape stressor gradients. The most influential stressors include total anthropogenic land use, crop land use, and the number of toxic release sites in upstream watersheds, as well as agriculture in the shoreline buffer, each of which was significantly related to between 21% and 39% of the 57 species studied. Averaging the effects of these influential stressors across species, we develop a quantitative estuary stress index that can be compared against benchmark conditions. In general, disturbance levels were greatest in estuaries west of the Mississippi delta and in highly developed estuaries in southwest Florida. Estuaries from the Florida panhandle to the eastern Mississippi delta had less anthropogenic stress.
We conducted a large-scale assessment of unconventional oil and gas (UOG) development effects on brook trout (Salvelinus fontinalis) distribution. We compiled 2231 brook trout collection records from the Upper Susquehanna River Watershed, USA. We used boosted regression tree (BRT) analysis to predict occurrence probability at the 1:24,000 stream-segment scale as a function of natural and anthropogenic landscape and climatic attributes. We then evaluated the importance of landscape context (i.e., pre-existing natural habitat quality and anthropogenic degradation) in modulating the effects of UOG on brook trout distribution under UOG development scenarios. BRT made use of 5 anthropogenic (28% relative influence) and 7 natural (72% relative influence) variables to model occurrence with a high degree of accuracy [Area Under the Receiver Operating Curve (AUC) = 0.85 and cross-validated AUC = 0.81]. UOG development impacted 11% (n = 2784) of streams and resulted in a loss of predicted occurrence in 126 (4%). Most streams impacted by UOG had unsuitable underlying natural habitat quality (n = 1220; 44%). Brook trout were predicted to be absent from an additional 26% (n = 733) of streams due to pre-existing non-UOG land uses (i.e., agriculture, residential and commercial development, or historic mining). Streams with a predicted and observed (via existing pre- and post-disturbance fish sampling records) loss of occurrence due to UOG tended to have intermediate natural habitat quality and/or intermediate levels of non-UOG stress. Simulated development of permitted but undeveloped UOG wells (n = 943) resulted in a loss of predicted occurrence in 27 additional streams. Loss of occurrence was strongly dependent upon landscape context, suggesting effects of current and future UOG development are likely most relevant in streams near the probability threshold due to pre-existing habitat degradation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.02.062","usgsCitation":"Merriam, E.R., Petty, J.T., Maloney, K.O., Young, J.A., Faulkner, S., Slonecker, E.T., Milheim, L., Hailegiorgis, A., and Niles, J.M., 2018, Brook trout distributional response to unconventional oil and gas development: Landscape context matters: Science of the Total Environment, v. 628-629, p. 338-349, https://doi.org/10.1016/j.scitotenv.2018.02.062.","productDescription":"12 p.","startPage":"338","endPage":"349","ipdsId":"IP-093043","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":468888,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2018.02.062","text":"Publisher Index Page"},{"id":352823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Pennsylvania","otherGeospatial":"Upper Susquehanna River Watershed","volume":"628-629","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6f6e4b0da30c1bfbfbd","contributors":{"authors":[{"text":"Merriam, Eric R.","contributorId":203597,"corporation":false,"usgs":false,"family":"Merriam","given":"Eric","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":731842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petty, J. Todd","contributorId":166749,"corporation":false,"usgs":false,"family":"Petty","given":"J.","email":"","middleInitial":"Todd","affiliations":[{"id":24497,"text":"West Virginia University, Morgantown, WV","active":true,"usgs":false}],"preferred":false,"id":731843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":731841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":731844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Faulkner, Stephen 0000-0001-5295-1383 faulkners@usgs.gov","orcid":"https://orcid.org/0000-0001-5295-1383","contributorId":146152,"corporation":false,"usgs":true,"family":"Faulkner","given":"Stephen","email":"faulkners@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":731845,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":731846,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Milheim, Lesley E. lmilheim@usgs.gov","contributorId":2560,"corporation":false,"usgs":true,"family":"Milheim","given":"Lesley E.","email":"lmilheim@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":731847,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hailegiorgis, Atesmachew","contributorId":196129,"corporation":false,"usgs":false,"family":"Hailegiorgis","given":"Atesmachew","email":"","affiliations":[],"preferred":false,"id":731848,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Niles, Jonathan M.","contributorId":146975,"corporation":false,"usgs":false,"family":"Niles","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":35657,"text":"Susquehanna University, Selinsgrove, PA","active":true,"usgs":false}],"preferred":false,"id":731849,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70196242,"text":"70196242 - 2018 - Influence of governance structure on green stormwater infrastructure investment","interactions":[],"lastModifiedDate":"2018-03-28T11:52:18","indexId":"70196242","displayToPublicDate":"2018-03-28T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1563,"text":"Environmental Science and Policy","active":true,"publicationSubtype":{"id":10}},"title":"Influence of governance structure on green stormwater infrastructure investment","docAbstract":"Communities are faced with the challenge of meeting regulatory requirements mandating reductions in water pollution from stormwater and combined sewer overflows (CSO). Green stormwater infrastructure and gray stormwater infrastructure are two types of water management strategies communities can use to address water pollution. In this study, we used long-term control plans from 25 U.S. cities to synthesize: the types of gray and green infrastructure being used by communities to address combined sewer overflows; the types of goals set; biophysical characteristics of each city; and factors associated with the governance of stormwater management. These city characteristics were then used to identify common characteristics of “green leader” cities—those that dedicated >20% of the control plan budget in green infrastructure. Five “green leader” cities were identified: Milwaukee, WI, Philadelphia, PA, Syracuse, NY, New York City, NY, and Buffalo, NY. These five cities had explicit green infrastructure goals targeting the volume of stormwater or percentage of impervious cover managed by green infrastructure. Results suggested that the management scale and complexity of the management system are less important factors than the ability to harness a “policy window” to integrate green infrastructure into control plans. Two case studies—Philadelphia, PA, and Milwaukee, WI—indicated that green leader cities have a long history of building momentum for green infrastructure through a series of phases from experimentation, demonstration, and finally—in the case of Philadelphia—a full transition in the approach used to manage CSOs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsci.2018.03.008","usgsCitation":"Hopkins, K.G., Grimm, N.B., and York, A.M., 2018, Influence of governance structure on green stormwater infrastructure investment: Environmental Science and Policy, v. 84, p. 124-133, https://doi.org/10.1016/j.envsci.2018.03.008.","productDescription":"10 p.","startPage":"124","endPage":"133","ipdsId":"IP-093747","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":468886,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsci.2018.03.008","text":"Publisher Index Page"},{"id":352822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"84","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6f6e4b0da30c1bfbfbf","contributors":{"authors":[{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":731838,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grimm, Nancy B.","contributorId":44058,"corporation":false,"usgs":false,"family":"Grimm","given":"Nancy","email":"","middleInitial":"B.","affiliations":[{"id":24511,"text":"Arizona State University, Tempe AZ USA 85287","active":true,"usgs":false}],"preferred":false,"id":731839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"York, Abigail M.","contributorId":203596,"corporation":false,"usgs":false,"family":"York","given":"Abigail","email":"","middleInitial":"M.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":731840,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194996,"text":"sir20185002 - 2018 - Flood-inundation and flood-mitigation modeling of the West Branch Wapsinonoc Creek Watershed in West Branch, Iowa","interactions":[],"lastModifiedDate":"2018-03-26T16:43:14","indexId":"sir20185002","displayToPublicDate":"2018-03-26T15:00:00","publicationYear":"2018","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":"2018-5002","title":"Flood-inundation and flood-mitigation modeling of the West Branch Wapsinonoc Creek Watershed in West Branch, Iowa","docAbstract":"The U.S. Geological Survey (USGS) in cooperation with the city of West Branch and the Herbert Hoover National Historic Site of the National Park Service assessed flood-mitigation scenarios within the West Branch Wapsinonoc Creek watershed. The scenarios are intended to demonstrate several means of decreasing peak streamflows and improving the conveyance of overbank flows from the West Branch Wapsinonoc Creek and its tributary Hoover Creek where they flow through the city and the Herbert Hoover National Historic Site located within the city.
Hydrologic and hydraulic models of the watershed were constructed to assess the flood-mitigation scenarios. To accomplish this, the models used the U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS) version 4.2 to simulate the amount of runoff and streamflow produced from single rain events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS) version 5.0 was then used to construct an unsteady-state model that may be used for routing streamflows, mapping areas that may be inundated during floods, and simulating the effects of different measures taken to decrease the effects of floods on people and infrastructure.
Both models were calibrated to three historic rainfall events that produced peak streamflows ranging between the 2-year and 10-year flood-frequency recurrence intervals at the USGS streamgage (05464942) on Hoover Creek. The historic rainfall events were calibrated by using data from two USGS streamgages along with surveyed high-water marks from one of the events. The calibrated HEC–HMS model was then used to simulate streamflows from design rainfall events of 24-hour duration ranging from a 20-percent to a 1-percent annual exceedance probability. These simulated streamflows were incorporated into the HEC–RAS model.
The unsteady-state HEC–RAS model was calibrated to represent existing conditions within the watershed. HEC–RAS model simulations with the existing conditions and streamflows from the design rainfall events were then done to serve as a baseline for evaluating flood-mitigation scenarios. After these simulations were completed, three different flood-mitigation scenarios were developed with HEC–RAS: a detention-storage scenario, a conveyance improvement scenario, and a combination of both. In the detention-storage scenario, four in-channel detention structures were placed upstream from the city of West Branch to attenuate peak streamflows. To investigate possible improvements to conveying floodwaters through the city of West Branch, a section of abandoned railroad embankment and an old truss bridge were removed in the model, because these structures were producing backwater areas during flooding events. The third scenario combines the detention and conveyance scenarios so their joint efficiency could be evaluated. The scenarios with the design rainfall events were run in the HEC–RAS model so their flood-mitigation effects could be analyzed across a wide range of flood magnitudes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185002","collaboration":"Prepared in cooperation with the city of West Branch and the National Park Service","usgsCitation":"Cigrand, C.V., 2018, Flood-inundation and flood-mitigation modeling of the West Branch Wapsinonoc Creek Watershed in West Branch, Iowa: U.S. Geological Survey Scientific Investigations Report 2018–5002, 36 p., https://doi.org/10.3133/sir20185002.","productDescription":"viii, 36 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-090129","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":352733,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5002/sir20185002.pdf","text":"Report","size":"3.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5002"},{"id":352732,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5002/coverthb.jpg"}],"country":"United States","state":"Iowa","city":"West Branch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.40693664550781,\n 41.64264409952472\n ],\n [\n -91.32488250732422,\n 41.64264409952472\n ],\n [\n -91.32488250732422,\n 41.72289932945416\n ],\n [\n -91.40693664550781,\n 41.72289932945416\n ],\n [\n -91.40693664550781,\n 41.64264409952472\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 S. Clinton Street
Iowa City, IA 52240
From August through December 2015, beachcast bird survey programs reported increased deposition of common murres (Uria aalge) on central and northern California beaches, but not on southern California beaches. Coastal wildlife rehabilitation centers received more than 1,000 live, stranded, and debilitated murres from Sonoma County to San Luis Obispo County during August–October. Approximately two-thirds of admitted birds were after-hatch-year birds in emaciated body condition and in various stages of molt, with extremely worn plumage. Necropsies were done on a sample (n=35) of birds to determine the probable cause of death of beachcast carcasses. Most birds examined during necropsy were emaciated, with starvation the most likely cause of death. Birds were also tested for underlying infectious diseases at the US Geological Survey National Wildlife Health Center (NWHC) and harmful algal bloom toxins at the University of California, Santa Cruz and the National Oceanographic and Atmospheric Administration's Northwest Fisheries Science Center. Twenty-four out of 29 tested birds had detectable levels of domoic acid, and no indication of infectious disease was found. Emaciation is thought to be the cause of death for these birds, with a large warm water anomaly and harmful algal bloom playing a secondary detrimental role.
","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/2017-07-179","usgsCitation":"Gibble, C., Duerr, R., Bodenstein, B., Lindquist, K., Lindsey, J., Beck, J., Henkel, L.A., Roletto, J., Harvey, J., and Kudela, R., 2018, Investigation of a largescale common murre (Uria aalge) mortality event in California in 2015: Journal of Wildlife Diseases, v. 54, no. 3, p. 569-574, https://doi.org/10.7589/2017-07-179.","productDescription":"6 p.; Data Release","startPage":"569","endPage":"574","ipdsId":"IP-089006","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":418297,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79W0DQR"},{"id":352756,"rank":1,"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 -124.69482421875,\n 33.5963189611327\n ],\n [\n -118.10302734374999,\n 33.5963189611327\n ],\n [\n -118.10302734374999,\n 40.93011520598305\n ],\n [\n -124.69482421875,\n 40.93011520598305\n ],\n [\n -124.69482421875,\n 33.5963189611327\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6f7e4b0da30c1bfbfda","contributors":{"authors":[{"text":"Gibble, Corinne","contributorId":203498,"corporation":false,"usgs":false,"family":"Gibble","given":"Corinne","affiliations":[{"id":36634,"text":"California Department of Fish and Wildlife, Marine Wildlife Veterinary Care & Research Center, 151 McAllister Way, Santa Cruz, CA 95060 USA","active":true,"usgs":false}],"preferred":false,"id":731656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duerr, Rebecca","contributorId":203499,"corporation":false,"usgs":false,"family":"Duerr","given":"Rebecca","email":"","affiliations":[{"id":36635,"text":"International Bird Rescue, 4369 Cordelia Rd, Fairfield, CA 94534 USA","active":true,"usgs":false}],"preferred":false,"id":731657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bodenstein, Barbara L. 0000-0001-7946-0103 bbodenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7946-0103","contributorId":189820,"corporation":false,"usgs":true,"family":"Bodenstein","given":"Barbara","email":"bbodenstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":731655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindquist, Kirsten","contributorId":203500,"corporation":false,"usgs":false,"family":"Lindquist","given":"Kirsten","email":"","affiliations":[{"id":36636,"text":", Farallones Marine Sanctuary Association, 991 Marine Drive, San Francisco, CA 94129 USA","active":true,"usgs":false}],"preferred":false,"id":731658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Jackie","contributorId":203501,"corporation":false,"usgs":false,"family":"Lindsey","given":"Jackie","email":"","affiliations":[{"id":36637,"text":"Moss Landing Marine Laboratories, 8272 Moss Landing Road, Moss Landing, CA 95039 USA","active":true,"usgs":false}],"preferred":false,"id":731659,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beck, Jessie","contributorId":169807,"corporation":false,"usgs":false,"family":"Beck","given":"Jessie","email":"","affiliations":[{"id":25597,"text":"Oikonos Ecosystem Knowledge","active":true,"usgs":false}],"preferred":false,"id":731660,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Henkel, Laird A.","contributorId":84288,"corporation":false,"usgs":true,"family":"Henkel","given":"Laird","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":731662,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roletto, Jan","contributorId":152297,"corporation":false,"usgs":false,"family":"Roletto","given":"Jan","email":"","affiliations":[{"id":18902,"text":"Gulf of the Farallones National Marine Sanctuary","active":true,"usgs":false}],"preferred":false,"id":731663,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Harvey, Jim","contributorId":203502,"corporation":false,"usgs":false,"family":"Harvey","given":"Jim","email":"","affiliations":[],"preferred":false,"id":731664,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kudela, Raphael","contributorId":196461,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","affiliations":[],"preferred":false,"id":731661,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70196198,"text":"70196198 - 2018 - Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands","interactions":[],"lastModifiedDate":"2018-03-26T10:12:45","indexId":"70196198","displayToPublicDate":"2018-03-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands","docAbstract":"Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2017.12.008","usgsCitation":"Poitras, T.B., Villarreal, M.L., Waller, E.K., Nauman, T.W., Miller, M.E., and Duniway, M.C., 2018, Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands: Journal of Arid Environments, v. 153, p. 76-87, https://doi.org/10.1016/j.jaridenv.2017.12.008.","productDescription":"12 p.","startPage":"76","endPage":"87","ipdsId":"IP-084812","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468897,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2017.12.008","text":"Publisher Index Page"},{"id":437977,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SWDWLS","text":"USGS data release","linkHelpText":"Grassland State and Transition Map of Canyonlands National Park Needles District and Indian Creek Grazing Allotment"},{"id":352759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.20797729492188,\n 37.81737834565083\n ],\n [\n -109.58862304687499,\n 37.81737834565083\n ],\n [\n -109.58862304687499,\n 38.494443887725055\n ],\n [\n -110.20797729492188,\n 38.494443887725055\n ],\n [\n -110.20797729492188,\n 37.81737834565083\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"153","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6f7e4b0da30c1bfbfe0","contributors":{"authors":[{"text":"Poitras, Travis B. 0000-0001-8677-1743 tpoitras@usgs.gov","orcid":"https://orcid.org/0000-0001-8677-1743","contributorId":195168,"corporation":false,"usgs":true,"family":"Poitras","given":"Travis","email":"tpoitras@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":731644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":731643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waller, Eric K. 0000-0002-9169-9210","orcid":"https://orcid.org/0000-0002-9169-9210","contributorId":203496,"corporation":false,"usgs":true,"family":"Waller","given":"Eric","email":"","middleInitial":"K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true}],"preferred":true,"id":731645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Mark E.","contributorId":91580,"corporation":false,"usgs":false,"family":"Miller","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":6959,"text":"National Park Service Southeast Utah Group","active":true,"usgs":false}],"preferred":false,"id":731648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731647,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196192,"text":"70196192 - 2018 - Drivers of solar radiation variability in the McMurdo Dry Valleys, Antarctica","interactions":[],"lastModifiedDate":"2018-04-02T13:38:04","indexId":"70196192","displayToPublicDate":"2018-03-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of solar radiation variability in the McMurdo Dry Valleys, Antarctica","docAbstract":"Annually averaged solar radiation in the McMurdo Dry Valleys, Antarctica has varied by over 20 W m−2 during the past three decades; however, the drivers of this variability are unknown. Because small differences in radiation are important to water availability and ecosystem functioning in polar deserts, determining the causes are important to predictions of future desert processes. We examine the potential drivers of solar variability and systematically eliminate all but stratospheric sulfur dioxide. We argue that increases in stratospheric sulfur dioxide increase stratospheric aerosol optical depth and decrease solar intensity. Because of the polar location of the McMurdo Dry Valleys (77–78°S) and relatively long solar ray path through the stratosphere, terrestrial solar intensity is sensitive to small differences in stratospheric transmissivity. Important sources of sulfur dioxide include natural (wildfires and volcanic eruptions) and anthropogenic emission.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41598-018-23390-7","usgsCitation":"Obryk, M., Fountain, A.G., Doran, P., Lyons, B., and Eastman, R., 2018, Drivers of solar radiation variability in the McMurdo Dry Valleys, Antarctica: Scientific Reports, v. 8, no. 1, Article number: 5002; 7 p., https://doi.org/10.1038/s41598-018-23390-7.","productDescription":"Article number: 5002; 7 p.","ipdsId":"IP-086694","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468895,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-23390-7","text":"Publisher Index Page"},{"id":352761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":352746,"type":{"id":15,"text":"Index Page"},"url":"https://www.nature.com/articles/s41598-018-23390-7"}],"otherGeospatial":"McMurdo Dry Valleys, Antarctica","volume":"8","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-22","publicationStatus":"PW","scienceBaseUri":"5afee6f8e4b0da30c1bfbfe4","contributors":{"authors":[{"text":"Obryk, Maciej K. 0000-0002-8182-8656","orcid":"https://orcid.org/0000-0002-8182-8656","contributorId":203477,"corporation":false,"usgs":true,"family":"Obryk","given":"Maciej","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":731594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fountain, Andrew G.","contributorId":10410,"corporation":false,"usgs":false,"family":"Fountain","given":"Andrew","email":"","middleInitial":"G.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":731595,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doran, Peter","contributorId":203478,"corporation":false,"usgs":false,"family":"Doran","given":"Peter","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":731596,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, Berry","contributorId":203479,"corporation":false,"usgs":false,"family":"Lyons","given":"Berry","email":"","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":731597,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eastman, Ryan","contributorId":203480,"corporation":false,"usgs":false,"family":"Eastman","given":"Ryan","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":731598,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195558,"text":"fs20183009 - 2018 - Hot water in the Long Valley Caldera—The benefits and hazards of this large natural resource","interactions":[],"lastModifiedDate":"2018-04-19T12:58:50","indexId":"fs20183009","displayToPublicDate":"2018-03-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-3009","title":"Hot water in the Long Valley Caldera—The benefits and hazards of this large natural resource","docAbstract":"The volcanic processes that have shaped the Long Valley Caldera in eastern California have also created an abundant supply of natural hot water. This natural resource provides benefits to many users, including power generation at the Casa Diablo Geothermal Plant, warm water for a state fish hatchery, and beautiful scenic areas such as Hot Creek gorge for visitors. However, some features can be dangerous because of sudden and unpredictable changes in the location and flow rate of boiling water. The U.S. Geological Survey monitors several aspects of the hydrothermal system in the Long Valley Caldera including temperature, flow rate, and water chemistry.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183009","collaboration":"Prepared in cooperation with the U.S. Forest Service","usgsCitation":"Evans, W.C., Hurwitz, S., Bergfeld, D., and Howle, J.F., 2018, Hot water in the Long Valley Caldera—The benefits and hazards of this large natural resource: U.S. Geological Survey Fact Sheet 2018–3009, 4 p., https://doi.org/10.3133/fs20183009.","productDescription":"4 p.","ipdsId":"IP-092280","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":352768,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3009/fs2018_3009.pdf","text":"Report","size":"4.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3009"},{"id":352767,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3009/cover_thb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Long Valley Caldera","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.0694808959961,\n 37.591383348725785\n ],\n [\n -118.68667602539062,\n 37.591383348725785\n ],\n [\n -118.68667602539062,\n 37.765286825037926\n ],\n [\n -119.0694808959961,\n 37.765286825037926\n ],\n [\n -119.0694808959961,\n 37.591383348725785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
Hydrothermal alteration can create low‐permeability zones, potentially resulting in elevated pore‐fluid pressures, within a volcanic edifice. Strength reduction by rock alteration and high pore‐fluid pressures have been suggested as a mechanism for edifice flank instability. Here we combine numerical models of multiphase heat transport and groundwater flow with a slope‐stability code that incorporates three‐dimensional distributions of strength and pore‐water pressure to address the following questions: (1) What permeability distributions and contrasts produce elevated pore‐fluid pressures in a stratovolcano? (2) What are the effects of these elevated pressures on flank stability? (3) Finally, what are the effects of magma intrusion on potential flank failure in an edifice? Simulation results show that under a range of plausible parameters, water tables in a stratovolcano can be elevated or perched. These elevated water tables result in universally lower stability (lower factor of safety) compared with equivalent dry edifices, indicating a higher likelihood of flank collapse. Low‐permeability (<1 × 10−17 m2) layers such as altered pyroclastic deposits or breccias can result in locally saturated regions (perched water) and lower factors of safety near the ground surface but may actually reduce liquid water saturation and pore pressures in the core of the edifice and thus may favor small, shallow collapses over larger, deeper collapses. Magma intrusion into the base of the edifice increases pore‐fluid pressures and decreases the factor of safety. However, the shear strength of edifice rocks also exerts a significant control on stability, so both mechanical properties and pore‐fluid pressures are important for stability assessments.
Water relations in plant communities are influenced both by contrasting functional groups (grasses, shrubs) and by climate change via complex effects on interception, uptake and transpiration. We modelled the effects of functional group replacement and biomass increase, both of which can be outcomes of invasion and vegetation management, and climate change on ecological drought (soil water potential below which photosynthesis stops) in 340 semiarid grassland sites over 30‐year periods. Relative to control vegetation (climate and site‐determined mixes of functional groups), the frequency and duration of drought were increased by shrubs and decreased by annual grasses. The rankings of shrubs, control vegetation, and annual grasses in terms of drought effects were generally consistent in current and future climates, suggesting that current differences among functional groups on drought effects predict future differences. Climate change accompanied by experimentally‐increased biomass (i.e. the effects of invasions that increase community biomass, or management that increases productivity through fertilization or respite from grazing) increased drought frequency and duration, and advanced drought onset. Our results suggest that the replacement of perennial temperate semiarid grasslands by shrubs, or increased biomass, can increase ecological drought both in current and future climates.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JG004173","usgsCitation":"Wilson, S.D., Schlaepfer, D., Bradford, J.B., Lauenroth, W.K., Duniway, M.C., Hall, S.A., Jamiyansharav, K., Jia, G., Lkhagva, A., Munson, S.M., Pyke, D.A., and Tietjen, B., 2018, Functional group, biomass, and climate change effects on ecological drought in semiarid grasslands: Journal of Geophysical Research G: Biogeosciences, v. 123, no. 3, p. 1072-1085, https://doi.org/10.1002/2017JG004173.","productDescription":"14 p.","startPage":"1072","endPage":"1085","ipdsId":"IP-093452","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":468900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017jg004173","text":"Publisher Index Page"},{"id":352743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-25","publicationStatus":"PW","scienceBaseUri":"5afee6f8e4b0da30c1bfbff0","contributors":{"authors":[{"text":"Wilson, Scott D.","contributorId":181519,"corporation":false,"usgs":false,"family":"Wilson","given":"Scott","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":731572,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schlaepfer, Daniel R.","contributorId":105189,"corporation":false,"usgs":false,"family":"Schlaepfer","given":"Daniel R.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":731573,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731574,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":731575,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":731576,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hall, Sonia A.","contributorId":181518,"corporation":false,"usgs":false,"family":"Hall","given":"Sonia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":731577,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jamiyansharav, Khishigbayar","contributorId":181522,"corporation":false,"usgs":false,"family":"Jamiyansharav","given":"Khishigbayar","email":"","affiliations":[],"preferred":false,"id":731578,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jia, Gensuo","contributorId":181520,"corporation":false,"usgs":false,"family":"Jia","given":"Gensuo","email":"","affiliations":[],"preferred":false,"id":731579,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lkhagva, Ariuntsetseg","contributorId":181521,"corporation":false,"usgs":false,"family":"Lkhagva","given":"Ariuntsetseg","email":"","affiliations":[],"preferred":false,"id":731580,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":731581,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":731571,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tietjen, Britta","contributorId":181517,"corporation":false,"usgs":false,"family":"Tietjen","given":"Britta","email":"","affiliations":[],"preferred":false,"id":731582,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70196186,"text":"70196186 - 2018 - The role of frozen soil in groundwater discharge predictions for warming alpine watersheds","interactions":[],"lastModifiedDate":"2018-04-27T16:38:29","indexId":"70196186","displayToPublicDate":"2018-03-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"The role of frozen soil in groundwater discharge predictions for warming alpine watersheds","docAbstract":"Climate warming may alter the quantity and timing of groundwater discharge to streams in high alpine watersheds due to changes in the timing of the duration of seasonal freezing in the subsurface and snowmelt recharge. It is imperative to understand the effects of seasonal freezing and recharge on groundwater discharge to streams in warming alpine watersheds as streamflow originating from these watersheds is a critical water resource for downstream users. This study evaluates how climate warming may alter groundwater discharge due to changes in seasonally frozen ground and snowmelt using a 2‐D coupled flow and heat transport model with freeze and thaw capabilities for variably saturated media. The model is applied to a representative snowmelt‐dominated watershed in the Rocky Mountains of central Colorado, USA, with snowmelt time series reconstructed from a 12 year data set of hydrometeorological records and satellite‐derived snow covered area. Model analyses indicate that the duration of seasonal freezing in the subsurface controls groundwater discharge to streams, while snowmelt timing controls groundwater discharge to hillslope faces. Climate warming causes changes to subsurface ice content and duration, rerouting groundwater flow paths but not altering the total magnitude of future groundwater discharge outside of the bounds of hydrologic parameter uncertainties. These findings suggest that frozen soil routines play an important role for predicting the future location of groundwater discharge in watersheds underlain by seasonally frozen ground.
","language":"English","publisher":"AGU","doi":"10.1002/2017WR022098","usgsCitation":"Evans, S.G., Ge, S., Voss, C.I., and Molotch, N.P., 2018, The role of frozen soil in groundwater discharge predictions for warming alpine watersheds: Water Resources Research, v. 54, no. 3, p. 1599-1615, https://doi.org/10.1002/2017WR022098.","productDescription":"17 p.","startPage":"1599","endPage":"1615","ipdsId":"IP-093839","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468898,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017wr022098","text":"Publisher Index Page"},{"id":352744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.64702033996582,\n 40.03182061333687\n ],\n [\n -105.57663917541504,\n 40.03182061333687\n ],\n [\n -105.57663917541504,\n 40.05902304741144\n ],\n [\n -105.64702033996582,\n 40.05902304741144\n ],\n [\n -105.64702033996582,\n 40.03182061333687\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-07","publicationStatus":"PW","scienceBaseUri":"5afee6f8e4b0da30c1bfbff2","contributors":{"authors":[{"text":"Evans, Sarah G.","contributorId":203464,"corporation":false,"usgs":false,"family":"Evans","given":"Sarah","email":"","middleInitial":"G.","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":731568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ge, Shemin","contributorId":203465,"corporation":false,"usgs":false,"family":"Ge","given":"Shemin","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":731569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":731567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Molotch, Noah P. 0000-0003-4733-8060","orcid":"https://orcid.org/0000-0003-4733-8060","contributorId":203466,"corporation":false,"usgs":false,"family":"Molotch","given":"Noah","email":"","middleInitial":"P.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":731570,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196180,"text":"ofr20181047 - 2018 - Groundwater quality in the Mokelumne, Cosumnes, and American River Watersheds, Sierra Nevada, California","interactions":[],"lastModifiedDate":"2018-08-15T15:22:57","indexId":"ofr20181047","displayToPublicDate":"2018-03-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1047","title":"Groundwater quality in the Mokelumne, Cosumnes, and American River Watersheds, Sierra Nevada, California","docAbstract":"Groundwater provides more than 40 percent of California’s drinking water. To protect this vital resource, the State of California created the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Program’s Priority Basin Project assesses the quality of groundwater resources used for drinking water supply and increases public access to groundwater-quality information. In the Mokelumne, Cosumnes, and American River Watersheds of the Sierra Nevada, many rural households rely on private wells for their drinking-water supplies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181047","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Fram, M.S., and Shelton, J.L., 2018, Groundwater quality in the Mokelumne, Cosumnes, and American River Watersheds, Sierra Nevada, California: U.S. Geological Survey Open-File Report 2018-1047, 4 p., https://doi.org/10.3133/ofr20181047.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-094012","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437981,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JXP","text":"USGS data release","linkHelpText":"GROUNDWATER-QUALITY DATA IN THE MOKELUMNE, COSUMNES, AND AMERICAN RIVER WATERSHEDS SHALLOW AQUIFER STUDY UNIT, 2016-2017: RESULTS FROM THE CALIFORNIA GAMA PRIORITY BASIN PROJECT"},{"id":352749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1047/coverthb.jpg"},{"id":352750,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1047/ofr20181047_.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1047"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.25,\n 38.18314846626173\n ],\n [\n -120,\n 38.18314846626173\n ],\n [\n -120,\n 39.35341418045878\n ],\n [\n -121.25,\n 39.35341418045878\n ],\n [\n -121.25,\n 38.18314846626173\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
This report describes (1) work done between January 2015 and May 2017 as part of the U.S. Geological Survey (USGS) hexavalent chromium, Cr(VI), background study and (2) the summative-scale approach to be used to estimate the extent of anthropogenic (man-made) Cr(VI) and background Cr(VI) concentrations near the Pacific Gas and Electric Company (PG&E) natural gas compressor station in Hinkley, California. Most of the field work for the study was completed by May 2017. The summative-scale approach and calculation of Cr(VI) background were not well-defined at the time the USGS proposal for the background Cr(VI) study was prepared but have since been refined as a result of data collected as part of this study. The proposed summative scale consists of multiple items, formulated as questions to be answered at each sampled well. Questions that compose the summative scale were developed to address geologic, hydrologic, and geochemical constraints on Cr(VI) within the study area. Each question requires a binary (yes or no) answer. A score of 1 will be assigned for an answer that represents data consistent with anthropogenic Cr(VI); a score of –1 will be assigned for an answer that represents data inconsistent with anthropogenic Cr(VI). The areal extent of anthropogenic Cr(VI) estimated from the summative-scale analyses will be compared with the areal extent of anthropogenic Cr(VI) estimated on the basis of numerical groundwater flow model results, along with particle-tracking analyses. On the basis of these combined results, background Cr(VI) values will be estimated for “Mojave-type” deposits, and other deposits, in different parts of the study area outside the summative-scale mapped extent of anthropogenic Cr(VI).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181045","collaboration":"Prepared in cooperation with the Lahontan Regional Water Quality Control Board and the State Water Resources Control Board","usgsCitation":"Izbicki, J.A., and Groover, K., 2018, Natural and man-made hexavalent chromium, Cr(VI), in groundwater near a mapped plume, Hinkley, California—study progress as of May 2017, and a summative-scale approach to estimate background Cr(VI) concentrations: U.S. Geological Survey Open-File Report 2018–1045, 28 p., https://doi.org/10.3133/ofr20181045.","productDescription":"vi, 28 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-095489","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":352720,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1045/ofr20181045.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1045"},{"id":352719,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1045/coverthb.jpg"}],"country":"United States","state":"California","city":"Hinkley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.2333,\n 34.8667\n ],\n [\n -117.0667,\n 34.8667\n ],\n [\n -117.0667,\n 35.0333\n ],\n [\n -117.2333,\n 35.0333\n ],\n [\n -117.2333,\n 34.8667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
This report describes a new methodology for using sparse (weekly or less frequent observations) and potentially highly censored pesticide monitoring data to simulate daily pesticide concentrations and associated quantities used for acute and chronic exposure assessments, such as the annual maximum daily concentration. The new methodology is based on a statistical model that expresses log-transformed daily pesticide concentration in terms of a seasonal wave, flow-related variability, long-term trend, and serially correlated errors. Methods are described for estimating the model parameters, generating conditional simulations of daily pesticide concentration given sparse (weekly or less frequent) and potentially highly censored observations, and estimating concentration extremes based on the conditional simulations. The model can be applied to datasets with as few as 3 years of record, as few as 30 total observations, and as few as 10 uncensored observations. The model was applied to atrazine, carbaryl, chlorpyrifos, and fipronil data for U.S. Geological Survey pesticide sampling sites with sufficient data for applying the model. A total of 112 sites were analyzed for atrazine, 38 for carbaryl, 34 for chlorpyrifos, and 33 for fipronil. The results are summarized in this report; and, R functions, described in this report and provided in an accompanying model archive, can be used to fit the model parameters and generate conditional simulations of daily concentrations for use in investigations involving pesticide exposure risk and uncertainty.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175159","collaboration":"National Water Quality Program","usgsCitation":"Vecchia, A.V., 2018, Model methodology for estimating pesticide concentration extremes based on sparse monitoring data: U.S. Geological Survey Scientific Investigations Report 2017–5159, 47 p., https://doi.org/10.3133/sir20175159.","productDescription":"Report: viii, 47 p.; Appendix; Data release","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090885","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":352536,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NV9H50","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Files to Support SEAWAVE-QEX Model for Simulating Concentrations of Selected Pesticides in the Continental United States, 1992–2012"},{"id":352529,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/science/mission-areas/water/national-water-quality-program?qt-programs_l2_landing_page=0#qt-programs_l2_landing_page","text":"National Water Quality Program"},{"id":352528,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5159/downloads/","text":"Model Archive","description":"SIR 2017–5159 Model Archive"},{"id":352526,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5159/coverthb.jpg"},{"id":352527,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5159/sir20175159.pdf","text":"Report","size":"2.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5159"}],"contact":"Director, Dakota Water Science Center, North Dakota Office
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
The U.S. Geological Survey, in cooperation with the City of Sioux Falls, South Dakota, began developing a groundwater-flow model of the Big Sioux aquifer in 2014 that will enable the City to make more informed water management decisions, such as delineation of areas of the greatest specific yield, which is crucial for locating municipal wells. Innovative tools are being evaluated as part of this study that can improve the delineation of the hydrogeologic framework of the aquifer for use in development of a groundwater-flow model, and the approach could have transfer value for similar hydrogeologic settings. The first step in developing a groundwater-flow model is determining the hydrogeologic framework (vertical and horizontal extents of the aquifer), which typically is determined by interpreting geologic information from drillers’ logs and surficial geology maps. However, well and borehole data only provide hydrogeologic information for a single location; conversely, nearly continuous geophysical data are collected along flight lines using airborne electromagnetic (AEM) surveys. These electromagnetic data are collected every 3 meters along a flight line (on average) and subsequently can be related to hydrogeologic properties. AEM data, coupled with and constrained by well and borehole data, can substantially improve the accuracy of aquifer hydrogeologic framework delineations and result in better groundwater-flow models.
AEM data were acquired using the Resolve frequency-domain AEM system to map the Big Sioux aquifer in the region of the city of Sioux Falls. The survey acquired more than 870 line-kilometers of AEM data over a total area of about 145 square kilometers, primarily over the flood plain of the Big Sioux River between the cities of Dell Rapids and Sioux Falls. The U.S. Geological Survey inverted the survey data to generate resistivity-depth sections that were used in two-dimensional maps and in three-dimensional volumetric visualizations of the Earth resistivity distribution. Contact lines were drawn using a geographic information system to delineate interpreted geologic stratigraphy. The contact lines were converted to points and then interpolated into a raster surface. The methods used to develop elevation and depth maps of the hydrogeologic framework of the Big Sioux aquifer are described herein.
The final AEM interpreted aquifer thickness ranged from 0 to 31 meters with an average thickness of 12.8 meters. The estimated total volume of the aquifer was 1,060,000,000 cubic meters based on the assumption that the top of the aquifer is the land-surface elevation. A simple calculation of the volume (length times width times height) of a previous delineation of the aquifer estimated the aquifer volume at 378,000,000 cubic meters; thus, the estimation based on AEM data is more than twice the previous estimate. The depth to top of Sioux Quartzite, which ranged in depth from 0 to 90 meters, also was delineated from the AEM data.
Director, Dakota Water Science Center, South Dakota Office
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
Tributyltin (TBT) has been recognized as an endocrine disrupting chemical (EDC) for several decades. However, only in the last decade, was its primary endocrine mechanism of action (MeOA) elucidated—interactions with the nuclear retinoid-X receptor (RXR), peroxisome proliferator-activated receptor γ (PPARγ), and their heterodimers. This molecular initiating event (MIE) alters a range of reproductive, developmental, and metabolic pathways at the organism level. It is noteworthy that a variety of MeOAs have been proposed over the years for the observed endocrine-type effects of TBT; however, convincing data for the MIE was provided only recently and now several researchers have confirmed and refined the information on this MeOA. One of the most important lessons learned from years of research on TBT concerns apparent species sensitivity. Several aspects such as the rates of uptake and elimination, chemical potency, and metabolic capacity are all important for identifying the most sensitive species for a given chemical, including EDCs. For TBT, much of this was discovered by trial and error, hence important relationships and important sensitive taxa were not identified until several decades after its introduction to the environment. As recognized for many years, TBT-induced responses are known to occur at very low concentrations for molluscs, a fact that has more recently also been observed in fish species. This review explores the MeOA and effects of TBT in different species (aquatic molluscs and other invertebrates, fish, amphibians, birds, and mammals) according to the OECD Conceptual Framework for Endocrine Disruptor Testing and Assessment (CFEDTA). The information gathered on biological effects that are relevant for populations of aquatic animals was used to construct Species Sensitivity Distributions (SSDs) based on No Observed Effect Concentrations (NOECs) and Lowest Observed Effect Concentrations (LOECs). Fish appear at the lower end of these distributions, showing that they are as sensitive as molluscs, and for some species, even more sensitive. Concentrations in the range of 1 ng/L for water exposure (10 ng/g for whole-body burden) have been shown to elicit endocrine-type responses, whereas mortality occurs at water concentrations ten times higher. Current screening and assessment methodologies as compiled in the OECD CFEDTA are able to identify TBT as a potent endocrine disruptor with a high environmental risk for the original use pattern. If those approaches had been available when TBT was introduced to the market, it is likely that its use would have been regulated sooner, thus avoiding the detrimental effects on marine gastropod populations and communities as documented over several decades.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reviews of environmental contamination and toxicology Volume 245","language":"English","publisher":"Springer","doi":"10.1007/398_2017_8","usgsCitation":"Lagadic, L., Katsiadaki, I., Biever, R.C., Guiney, P., Karouna-Renier, N., Schwarz, T., and Meador, J., 2018, Tributyltin: Advancing the science on assessing endocrine disruption with an unconventional endocrine-disrupting compound, chap. of Reviews of environmental contamination and toxicology Volume 245, v. 245, p. 65-127, https://doi.org/10.1007/398_2017_8.","productDescription":"63 p.","startPage":"65","endPage":"127","ipdsId":"IP-075961","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":352685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"245","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-09","publicationStatus":"PW","scienceBaseUri":"5afee6fae4b0da30c1bfc006","contributors":{"authors":[{"text":"Lagadic, Laurent","contributorId":200679,"corporation":false,"usgs":false,"family":"Lagadic","given":"Laurent","email":"","affiliations":[],"preferred":false,"id":731400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katsiadaki, Ioanna","contributorId":200653,"corporation":false,"usgs":false,"family":"Katsiadaki","given":"Ioanna","email":"","affiliations":[],"preferred":false,"id":731401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biever, Ronald C.","contributorId":200660,"corporation":false,"usgs":false,"family":"Biever","given":"Ronald","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":731402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guiney, Patrick","contributorId":193148,"corporation":false,"usgs":false,"family":"Guiney","given":"Patrick","affiliations":[],"preferred":false,"id":731403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karouna-Renier, Natalie 0000-0001-7127-033X nkarouna@usgs.gov","orcid":"https://orcid.org/0000-0001-7127-033X","contributorId":200983,"corporation":false,"usgs":true,"family":"Karouna-Renier","given":"Natalie","email":"nkarouna@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":731399,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarz, Tamar","contributorId":200733,"corporation":false,"usgs":false,"family":"Schwarz","given":"Tamar","email":"","affiliations":[],"preferred":false,"id":731404,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meador, James P.","contributorId":174075,"corporation":false,"usgs":false,"family":"Meador","given":"James P.","affiliations":[],"preferred":false,"id":731405,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196117,"text":"70196117 - 2018 - Importance of growth rate on mercury and polychlorinated biphenyl bioaccumulation in fish","interactions":[],"lastModifiedDate":"2018-05-29T13:37:56","indexId":"70196117","displayToPublicDate":"2018-03-21T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Importance of growth rate on mercury and polychlorinated biphenyl bioaccumulation in fish","docAbstract":"To evaluate the effect of fish growth on mercury (Hg) and polychlorinated biphenyl (PCB) bioaccumulation, a non–steady‐state toxicokinetic model, combined with a Wisconsin bioenergetics model, was developed to simulate Hg and PCB bioaccumulation in bluegill (Lepomis macrochirus). The model was validated by comparing observed with predicted Hg and PCB 180 concentrations across 5 age classes from 5 different waterbodies across North America. The non–steady‐state model generated accurate predictions for Hg and PCB bioaccumulation in 3 of 5 waterbodies: Apsey Lake (ON, Canada), Sharbot Lake (ON, Canada), and Stonelick Lake (OH, USA). The poor performance of the model for the Detroit River (MI, USA/ON, Canada) and Lake Hartwell (GA/SC, USA), which are 2 well‐known contaminated sites with possibly high heterogeneity in spatial contamination, was attributed to changes in feeding behavior and/or prey contamination. Model simulations indicate that growth dilution is a major component of contaminant bioaccumulation patterns in fish, especially during early life stages, and was predicted to be more important for hydrophobic PCBs than for Hg. Simulations that considered tissue‐specific growth provided some improvement in model performance particularly for PCBs in fish populations that exhibited changes in their whole‐body lipid content with age. Higher variation in lipid growth compared with that of lean dry protein was also observed between different bluegill populations, which partially explains the greater variation in PCB bioaccumulation slopes compared with Hg across sampling sites.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.4114","usgsCitation":"Li, J., Haffner, G.D., Patterson, G., Walters, D., Burtnyk, M.D., and Drouillard, K.G., 2018, Importance of growth rate on mercury and polychlorinated biphenyl bioaccumulation in fish: Environmental Toxicology and Chemistry, v. 37, no. 6, p. 1655-1667, https://doi.org/10.1002/etc.4114.","productDescription":"13 p.","startPage":"1655","endPage":"1667","ipdsId":"IP-090841","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":352682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-19","publicationStatus":"PW","scienceBaseUri":"5afee6fae4b0da30c1bfc002","contributors":{"authors":[{"text":"Li, Jiajia","contributorId":203411,"corporation":false,"usgs":false,"family":"Li","given":"Jiajia","email":"","affiliations":[{"id":36613,"text":"U. Windsor","active":true,"usgs":false}],"preferred":false,"id":731430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haffner, G. Douglas","contributorId":203414,"corporation":false,"usgs":false,"family":"Haffner","given":"G.","email":"","middleInitial":"Douglas","affiliations":[{"id":36613,"text":"U. Windsor","active":true,"usgs":false}],"preferred":false,"id":731433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patterson, Gordon","contributorId":203412,"corporation":false,"usgs":false,"family":"Patterson","given":"Gordon","email":"","affiliations":[{"id":36614,"text":"Michigan Tech","active":true,"usgs":false}],"preferred":false,"id":731431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, David M. 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":4444,"corporation":false,"usgs":true,"family":"Walters","given":"David M.","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":731429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burtnyk, Michael D.","contributorId":203413,"corporation":false,"usgs":false,"family":"Burtnyk","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":36615,"text":"CH2M Hill","active":true,"usgs":false}],"preferred":false,"id":731432,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drouillard, Ken G.","contributorId":127334,"corporation":false,"usgs":false,"family":"Drouillard","given":"Ken","email":"","middleInitial":"G.","affiliations":[{"id":6778,"text":"University of Windsor, Windsor, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":731434,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70195752,"text":"ofr20181032 - 2018 - Synthesis of tree swallow (Tachycineta bicolor) data for Beneficial Use Impairment (BUI) assessment at Wisconsin Areas of Concern","interactions":[],"lastModifiedDate":"2018-03-22T10:22:06","indexId":"ofr20181032","displayToPublicDate":"2018-03-20T16:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1032","displayTitle":"Synthesis of tree swallow (Tachycineta bicolor) data for Beneficial Use Impairment (BUI) assessment at Wisconsin Areas of Concern","title":"Synthesis of tree swallow (Tachycineta bicolor) data for Beneficial Use Impairment (BUI) assessment at Wisconsin Areas of Concern","docAbstract":"Assessment of the “Bird or Animal Deformities or Reproductive Problems” Beneficial Use Impairment (BUI) can be accomplished by (1) comparing tissue concentrations to established background and Lowest Observable Effect Level (LOEL) for reproductive effects, or (2) directly measuring reproductive success at Areas of Concern (AOCs) and statistically comparing those rates to minimally impacted reference locations (non-AOCs). Results from recent tree swallow (Tachycineta bicolor) publications were used to evaluate this BUI based on both approaches. For both endpoints, a 95-percent confidence interval (CI) was used to test for significant differences. Additional information on BUIs, AOCs, and the program in general can be found in the Great Lakes Water Quality Agreement (2012).
For the first metric, there are good background and reproductive effect threshold LOELs for tree swallow egg concentrations for polychlorinated biphenyls (PCBs), dioxins and furans (PCDD/Fs), and mercury, as well as, for some other organic and inorganic contaminants. For the second assessment, comparisons were made between AOC and non-AOC sites for reproductive success, which was measured as the daily probability of egg failure and the percentage of eggs laid that hatched. Multistate modeling was used to assess whether there was an association between the daily probability of egg failure and a suite of contaminants, including PCBs, but also whether there was an association with ecological variables, such as female age and date within season. Both of these ecological variables are known to affect hatching success in birds. The objective of this report is to synthesize the previously published information to assist in the assessment of the “Bird or Animal Deformities or Reproductive Problems” BUI at 16 sites within the 5 Wisconsin AOCs (table 1). The logic behind this interpretation is applicable to other AOCs as well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181032","usgsCitation":"Custer, C.M., Custer, T.W., and Dummer, P.M., 2018, Synthesis of tree swallow (Tachycineta bicolor) data for Beneficial Use Impairment (BUI) assessment at Wisconsin Areas of Concern: U.S. Geological Survey Open-File Report 2018–1032, 8 p., https://doi.org/10.3133/ofr20181032.","productDescription":"iv, 8 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092682","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":352653,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1032/ofr20181032.pdf","text":"Report","size":"111 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1032"},{"id":352652,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1032/coverthb.jpg"}],"country":"United States","state":"Michigan, Minnesota, Wisconsin","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Cross, WI 54603
The daily and annual loads of nitrate plus nitrite and total nitrogen for the Connecticut River at Middle Haddam, Connecticut, were determined for water years 2009 to 2014. The analysis was done with a combination of methods, which included a predefined rating curve method for nitrate plus nitrite and total nitrogen for water years 2009 to 2011 and a custom rating curve method that included sensor measurements of nitrate plus nitrite nitrogen concentration and turbidity along with mean daily flow to determine total nitrogen loads for water years 2011 to 2014. Instantaneous concentrations of total nitrogen were estimated through the use of a regression model based on sensor measurements at 15-minute intervals of nitrate plus nitrite nitrogen and turbidity for water years 2011 to 2014.
Annual total nitrogen loads at the Connecticut River at Middle Haddam ranged from 12,900 to 19,200 metric tons, of which about 42 to 49 percent was in the form of nitrate plus nitrite. The mean 95-percent prediction intervals on daily total nitrogen load estimates were smaller from the custom model, which used sensor data, than those calculated by the predefined model.
Annual total nitrogen load estimates at the Connecticut River at Middle Haddam were compared with the upstream load estimates at the Connecticut River at Thompsonville, Conn. Annual gains in total nitrogen loads between the two stations ranged from 3,430 to 6,660 metric tons. These increases between the two stations were attributed to the effects of increased urbanization and to combined annual discharges of 1,540 to 2,090 metric tons of nitrogen from 24 wastewater treatment facilities in the drainage area between the two stations. The contribution of total nitrogen from wastewater discharge between the two stations had declined substantially before the beginning of this study and accounted for from 31 to 52 percent of the gain in nitrogen load between the Thompsonville and Middle Haddam sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185006","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Mullaney, J.R., Martin, J.W., and Morrison, J., 2018, Nitrogen concentrations and loads for the Connecticut River at Middle Haddam, Connecticut, computed with the use of autosampling and continuous measurements of water quality for water years 2009 to 2014: U.S. Geological Survey Scientific Investigations Report 2018–5006, 22 p., https://doi.org/10.3133/sir20185006.","productDescription":"Report: vii, 22 p.; Data release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091217","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":352399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5006/sir20185006.pdf","text":"Report","size":"4.79 MB ","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5006"},{"id":352631,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ31WT","text":"USGS data release","description":"USGS data release","linkHelpText":"Nitrogen Concentrations and Loads for the Connecticut River at Middle Haddam, Connecticut, Computed With the Use of Autosampling and Continuous Measurements of Water Quality for Water Years 2009 to 2014"},{"id":352409,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5006/coverthb.jpg"}],"country":"United States","state":"Connecticut","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 -72.94097900390625,\n 41.34691753986531\n ],\n [\n -72.18154907226562,\n 41.34691753986531\n ],\n [\n -72.18154907226562,\n 42.04011410708205\n ],\n [\n -72.94097900390625,\n 42.04011410708205\n ],\n [\n -72.94097900390625,\n 41.34691753986531\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
101 Pitkin Street
East Hartford, CT 06108
Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relations between temperature and precipitation and a set of variables measured on the ground at individual wetlands and another set measured via satellite sensors within surrounding 4 km2 landscape blocks. At the block scale, we used evapotranspiration and vegetation greenness as remotely sensed proxies for water availability and to estimate seasonal photosynthetic activity. We used sensors on the ground to coincidentally measure surface-water availability and amphibian calling activity at individual wetlands within blocks. Responses of landscape blocks generally paralleled changes in conditions measured on the ground, but the latter were more dynamic, and changes in ecological conditions on the ground that were critical for biota were not always apparent in measurements of related parameters in blocks. Here, we evaluate the effectiveness of decisions and assumptions we made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines.
","language":"English","publisher":"MDPI","doi":"10.3390/s18030880","usgsCitation":"Gallant, A.L., Sadinski, W.J., Brown, J.F., Senay, G., and Roth, M.F., 2018, Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes: Sensors, v. 18, no. 3, p. 1-38, https://doi.org/10.3390/s18030880.","productDescription":"Article 880; 38 p.","startPage":"1","endPage":"38","ipdsId":"IP-094477","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468903,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s18030880","text":"Publisher Index Page"},{"id":437982,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QF8S3H","text":"USGS data release","linkHelpText":"Data files supporting the paper titled "Complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate lessons from temperate wetland-upland landscapes""},{"id":352650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-16","publicationStatus":"PW","scienceBaseUri":"5afee6fce4b0da30c1bfc012","contributors":{"authors":[{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":731314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sadinski, Walter J. wsadinski@usgs.gov","contributorId":3287,"corporation":false,"usgs":true,"family":"Sadinski","given":"Walter","email":"wsadinski@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":731315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":3241,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":731316,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":152206,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel B.","email":"senay@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":731317,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, Mark F. 0000-0001-5095-1865 mroth@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-1865","contributorId":3286,"corporation":false,"usgs":true,"family":"Roth","given":"Mark","email":"mroth@usgs.gov","middleInitial":"F.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":731318,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}