Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Upon reproductive failure, many bird species make a secondary attempt at nesting (hereafter, “renesting”). Renesting may be an effective strategy to maximize current and lifetime reproductive success, but individuals face uncertainty in the probability of success because reproductive attempts initiated later in the breeding season often have reduced nest, pre-fledging, and post-fledging brood survival. We evaluated renesting propensity, renesting intervals, and renest reproductive success of Piping Plovers (Charadrius melodus) by following 1,922 nests and 1,785 unique breeding adults from 2014 to 2016 in the Northern Great Plains of the United States. The apparent renesting rate for individuals was 25% for reproductive attempts that failed in the nest stage (egg laying and incubation) and only 1.2% for reproductive attempts when broods were lost. Renesting propensity declined if reproductive attempts failed during the brood-rearing stage, nests were depredated, reproductive failure occurred later in the breeding season, or individuals had previously renested that year. Additionally, plovers that nested on reservoirs were less likely to renest compared to other habitats. Renesting intervals declined when individuals had not already renested, were after-second-year adults without known prior breeding experience, and moved short distances between nest attempts. Renesting intervals also decreased if the attempt failed later in the season. Overall, reproductive success and daily nest survival were lower for renests than first nests throughout the breeding season. Furthermore, renests on reservoirs had reduced apparent reproductive success and daily nest survival unless the predicted amount of habitat on reservoirs increased within the breeding season. Our results provide important demographic measures for this threatened species and suggest that predation- and water-management strategies that maximize success of early nests would be more likely to increase productivity. Altogether, renesting appears to be an unproductive reproductive strategy to replace lost reproductive attempts for Piping Plovers breeding in the Northern Great Plains.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duz066","usgsCitation":"Swift, R.J., Anteau, M.J., Ring, M., Toy, D.L., and Sherfy, M.H., 2020, Low renesting propensity and reproductive success make renesting unproductive for the threatened Piping Plover (Charadrius melodus): The Condor, v. 2, no. 122, duz066, 18 p., https://doi.org/10.1093/condor/duz066.","productDescription":"duz066, 18 p.","ipdsId":"IP-108250","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457680,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz066","text":"Publisher Index Page"},{"id":437105,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VAS8P7","text":"USGS data release","linkHelpText":"Renesting propensity, intervals, and reproductive success data for the Northern Great Plains Piping Plover, a threatened shorebird species 2014-2016"},{"id":375685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.39208984375,\n 48.21003212234042\n ],\n [\n -103.71093749999999,\n 48.10743118848039\n ],\n [\n -102.89794921875,\n 48.3416461723746\n ],\n [\n -101.97509765625,\n 47.90161354142077\n ],\n [\n -101.4697265625,\n 47.82790816919329\n ],\n [\n -100.78857421875,\n 47.76886840424207\n ],\n [\n -100.52490234375,\n 47.010225655683485\n ],\n [\n -100.43701171875,\n 46.558860303117164\n ],\n [\n -100.39306640625,\n 46.10370875598026\n ],\n [\n -100.21728515624999,\n 45.81348649679973\n ],\n [\n -100.39306640625,\n 44.731125592643274\n ],\n [\n -100.37109375,\n 44.449467536006935\n ],\n [\n -99.6240234375,\n 44.449467536006935\n ],\n [\n -99.03076171875,\n 45.120052841530544\n ],\n [\n -98.96484375,\n 46.08847179577592\n ],\n [\n -98.94287109375,\n 46.99524110694593\n ],\n [\n -99.29443359375,\n 47.54687159892238\n ],\n [\n -99.95361328125,\n 48.22467264956519\n ],\n [\n -101.1181640625,\n 48.647427805533546\n ],\n [\n -102.41455078125,\n 48.647427805533546\n ],\n [\n -103.18359375,\n 48.90805939965008\n ],\n [\n -104.4580078125,\n 48.951366470947725\n ],\n [\n -104.65576171875,\n 48.472921272487824\n ],\n [\n -104.39208984375,\n 48.21003212234042\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","issue":"122","noUsgsAuthors":false,"publicationDate":"2020-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Swift, Rose J. 0000-0001-7044-6196","orcid":"https://orcid.org/0000-0001-7044-6196","contributorId":212082,"corporation":false,"usgs":true,"family":"Swift","given":"Rose","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ring, Megan M. 0000-0001-8331-8492","orcid":"https://orcid.org/0000-0001-8331-8492","contributorId":225026,"corporation":false,"usgs":true,"family":"Ring","given":"Megan M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toy, Dustin L. 0000-0001-5390-5784 dtoy@usgs.gov","orcid":"https://orcid.org/0000-0001-5390-5784","contributorId":5150,"corporation":false,"usgs":true,"family":"Toy","given":"Dustin","email":"dtoy@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherfy, Mark H. 0000-0003-3016-4105 msherfy@usgs.gov","orcid":"https://orcid.org/0000-0003-3016-4105","contributorId":125,"corporation":false,"usgs":true,"family":"Sherfy","given":"Mark","email":"msherfy@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791041,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211506,"text":"70211506 - 2020 - American eels produce and release bile acids that vary across life stage","interactions":[],"lastModifiedDate":"2020-07-29T14:41:29.589294","indexId":"70211506","displayToPublicDate":"2020-02-18T09:37:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"title":"American eels produce and release bile acids that vary across life stage","docAbstract":"The American eel (Anguilla rostrata ) is an imperilled fish hypothesized to use conspecific cues, in part, to coordinate long‐distance migration during their multistage life history. Here, holding water and tissue from multiple American eel life stages was collected and analysed for the presence, profile and concentration of bile acids. Distinct bile acid profiles were identified in glass, elver, yellow eel and silver eel holding waters using ultraperformance liquid chromatography high‐resolution mass spectrometry and principal component analysis. Taurochenodeoxycholic acid, taurodeoxycholic acid, cholic acid, deoxycholic acid, taurolithocholic acid and taurocholic acid were detected in whole tissue of American glass eels and elvers, and in liver, intestine and gallbladder samples of late‐stage yellow eels. Bile acids were not a major component of silver eel washings or tissue. This study is novel because little was previously known about bile acids produced and emitted into the environment by American eels. Future behavioural studies could evaluate whether any bile acids produced by American eels influence conspecific migratory behaviour.
Recent experimental studies have detected the presence of anoxic microzones in hyporheic sediments. These microzones are small‐scale anoxic pores, embedded within oxygen‐rich porous media and can act as anaerobic reaction sites producing reduction compounds such as nitrous oxide, a potent greenhouse gas. Microbes are a key control on nutrient transformation in hyporheic sediment, but their associated biomass growth is also capable of altering hydraulic flux, leading to potential bioclogging. Here, we developed one of the first computational modeling approaches that combined hydraulics and microbial conditions to explore the continuous evolution of microzones in stream sediments. The model assessed stream and sediment conditions with different hydraulic flux (0.1–1.0 m/day Darcy flux), nutrient concentrations (O2 = 8 mg/L, OrgC = 20 mg/L, NO−3 = 1.5–3 mg/L, and NH3 = 0.5–1 mg/L), and biomass scenarios (with and without). The model domain is a pore network model with random sized pore‐throat radii creating heterogeneous and anisotropic flow that is representative of a natural streambed composed of medium sand with a hydraulic conductivity of 0.8 m/day. Results from 30 day simulations indicate that hyporheic microzone formation will occur and microzone distributions are not simply controlled by residence time alone, rather by the complex interactions of hydraulic flux, nutrient concentrations, and biomass, with bioclogging having strong feedbacks on both hydraulics and nutrients. Under all conditions with biomass growth, anoxic microzones were unstable, perishing a few days after formation, because bioclogging primarily occurs near the influent (downwelling) area of the hyporheic zone. In turn, this bioclogging shifts transport conditions from advection‐dominated to diffusion‐dominated transport, removing all oxic regions in the hyporheic zone. Overall, results from the modeling show that anoxic microzones are likely to form under many hyporheic zone conditions, and be dynamic through space and time as they are dependent on both hydraulic flux and nutrient transport.
","language":"English","publisher":"Wiley","doi":"10.1029/2019WR025971","usgsCitation":"Chowdhury, S.R., Zarnetske, J., Phanikumar, M., Briggs, M.A., Day-Lewis, F.D., and Singha, K., 2020, Formation criteria for hyporheic anoxic microzones: Assessing interactions of hydraulics, nutrients and biofilms: Water Resources Research, v. 56, no. 3, e2019WR025971, 15 p., https://doi.org/10.1029/2019WR025971.","productDescription":"e2019WR025971, 15 p.","ipdsId":"IP-113836","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":372602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Chowdhury, S. R.","contributorId":222748,"corporation":false,"usgs":false,"family":"Chowdhury","given":"S.","email":"","middleInitial":"R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarnetske, J.","contributorId":222749,"corporation":false,"usgs":false,"family":"Zarnetske","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phanikumar, M.S.","contributorId":222750,"corporation":false,"usgs":false,"family":"Phanikumar","given":"M.S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":783077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":783074,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":783078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Singha, K.","contributorId":201025,"corporation":false,"usgs":false,"family":"Singha","given":"K.","email":"","affiliations":[],"preferred":false,"id":783079,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208575,"text":"70208575 - 2020 - Does Lake Erie still have sufficient oxythermal habitat for cisco Coregonus artedi?","interactions":[],"lastModifiedDate":"2020-04-06T21:58:32.077746","indexId":"70208575","displayToPublicDate":"2020-02-15T06:15:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Does Lake Erie Still Have Sufficient Oxythermal Habitat for Cisco Coregonus artedi?","title":"Does Lake Erie still have sufficient oxythermal habitat for cisco Coregonus artedi?","docAbstract":"In Lake Erie, cisco Coregonus artedi once supported one of the most valuable freshwater fisheries on earth, yet overfishing caused their eventual extirpation from the lake. With warming lake temperatures, some have questioned whether Lake Erie still contains suitable oxythermal conditions for cisco. Using published oxythermal thresholds for cisco and oxythermal profiles from Lake Erie, we sought to answer two questions critical to cisco restoration science. First, is cisco habitat still available during the most restrictive periods? Second, what is the distribution of cisco habitat during these times? Beta regression was used to determine that cisco habitat was most limited during the month of August, and that August of 2010 was the most restrictive period in the time series. We then used Empirical Bayesian Kriging (EBK) to map the spatial extent of cisco habitat during these times. EBK maps revealed large areas of summer refugia for cisco in Lake Erie, even during the least favorable periods. Most of the Central and East Basins contain suitable habitat during the average August, yet during August of 2010, suitable conditions were limited to the eastern edge of the Central Basin and the deep waters of the East Basin. These findings align well with historical accounts of cisco landings. While suitable oxythermal habitat still exists for cisco in Lake Erie, future restoration efforts, if attempted, will partially depend on: 1) better management of nutrient inputs, 2) the realization of future climate scenarios, and 3) the ability of cisco to adapt to a changing lake.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.01.019","usgsCitation":"Schmitt, J., Vandergoot, C.S., O’Malley, B.P., and Kraus, R., 2020, Does Lake Erie still have sufficient oxythermal habitat for cisco Coregonus artedi?: Journal of Great Lakes Research, v. 46, no. 2, p. 330-338, https://doi.org/10.1016/j.jglr.2020.01.019.","productDescription":"9 p.","startPage":"330","endPage":"338","ipdsId":"IP-112702","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":372406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Erie ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.21044921875,\n 42.13082130188811\n ],\n [\n -83.507080078125,\n 41.68932225997044\n ],\n [\n -82.4853515625,\n 41.36031866306708\n ],\n [\n -81.968994140625,\n 41.48389104267175\n ],\n [\n -81.650390625,\n 41.48389104267175\n ],\n [\n -81.419677734375,\n 41.68111756290652\n ],\n [\n -80.540771484375,\n 41.94314874732696\n ],\n [\n -79.27734374999999,\n 42.374778361114195\n ],\n [\n -78.826904296875,\n 42.827638636242284\n ],\n [\n -78.837890625,\n 42.90011265525328\n ],\n [\n -79.1015625,\n 42.91620643817353\n ],\n [\n -79.541015625,\n 42.924251753870685\n ],\n [\n -80.013427734375,\n 42.827638636242284\n ],\n [\n -80.299072265625,\n 42.80346172417078\n ],\n [\n -80.562744140625,\n 42.62587560259137\n ],\n [\n -80.91430664062499,\n 42.67435857693381\n ],\n [\n -81.2109375,\n 42.69858589169842\n ],\n [\n -81.45263671875,\n 42.69051116998238\n ],\n [\n -81.82617187499999,\n 42.431565872579185\n ],\n [\n -82.0458984375,\n 42.342305278572816\n ],\n [\n -82.518310546875,\n 42.09007006868398\n ],\n [\n -82.891845703125,\n 42.01665183556825\n ],\n [\n -83.21044921875,\n 42.13082130188811\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schmitt, Joseph","contributorId":222565,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":782571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergoot, Christoper S.","contributorId":222566,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christoper","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":782572,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Malley, Brian P. bomalley@usgs.gov","contributorId":5615,"corporation":false,"usgs":true,"family":"O’Malley","given":"Brian","email":"bomalley@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":782573,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":782574,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208051,"text":"sir20205006 - 2020 - Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18","interactions":[],"lastModifiedDate":"2022-04-25T20:56:36.421159","indexId":"sir20205006","displayToPublicDate":"2020-02-14T15:35:24","publicationYear":"2020","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":"2020-5006","displayTitle":"Potential Groundwater Recharge Rates for Two Subsurface-Drained Agricultural Fields, Southeastern Minnesota, 2016–18","title":"Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18","docAbstract":"Subsurface drainage is used to efficiently drain saturated soils to support productive agriculture in poorly drained terrains. Although subsurface drainage alters the water balance for agricultural fields, its effect on groundwater resources and groundwater recharge is poorly understood. In Minnesota, subsurface drainage has begun to increase in southeastern Minnesota, even though this part of the State is underlain by permeable karstic bedrock aquifers with only a thin layer of glacial sediments separating these aquifers from land surface.
To gain a better understanding of groundwater recharge effects from subsurface drainage, the U.S. Geological Survey (USGS), in cooperation with the Legislative-Citizen Commission on Minnesota Resources, led a 2-year hydrologic study to investigate this connection for two agricultural fields in southeastern Minnesota with subsurface drainage. A total of three monitoring plots were used between the two agricultural fields: two monitoring plots that included an actively drained area with peripheral, undrained areas, and a third monitoring plot without any subsurface drainage. Multiple piezometer transects were set up across the three monitoring plots to characterize the unsaturated zone and shallow water-table flow using pressure transducers and soil moisture probes. From these piezometers, groundwater recharge rates were derived using two different methods: the RISE Water-Table Fluctuation (WTF) method and the DRAINMOD model. In addition to these two methods, the USGS Soil-Water-Balance (SWB) model was used to estimate potential recharge rates for three different monitoring plots.
In addition to deriving groundwater recharge rates, the hydrologic budget was analyzed to interpret the water-table surface elevation and soil volumetric water content time series. At one of the two drained plots, the transects exhibited varying water-table surface elevation patterns. Frequent backflow from the adjacent ditch caused subsurface drainage flow to slow down or stop drainage through the main collector drain and cause pipe pressurization, so the closest transect appeared to be mostly controlled by the drain pressurization, whereas the farthest transect was more efficiently drained. Both of the drained monitoring plots had an elevation gradient parallel to the pattern tiles, sloping downward towards the collector drain that aggregated the parallel lines into a single drain. Because the transects were set at different gradients in the field, some of the water-table surface elevation differences were also attributed to lateral flow towards the lowest parts of the field.
Three methods were used to derive potential groundwater recharge rates: the RISE WTF method, the USGS SWB model, and DRAINMOD-derived deep seepage rates. Potential groundwater recharge rates, using the RISE WTF method, across all piezometers were 1.55 and 1.94 inches per year, respectively, for water years 2017 and 2018. More differentiation of potential recharge rates between different piezometer types occurred for water year 2018. Although the difference was slightly more than 1 inch between the drained and nondrained piezometers for water year 2018, this difference was statistically significant based on a t-test with a p-value of 0.036 (α=0.05). When looking at recharge based on distance from the drain, the subsurface drain did not affect potential recharge, although other factors such as variability in screen depths, well construction, and specific yield variability cannot be eliminated. The SWB model was also used to estimate potential recharge rates for water years 2017–18, with rates between 2.44 and 5.92 inches per year for the two drained sites, generally higher than the RISE WTF estimates. DRAINMOD-derived potential recharge rates were generally the highest of the three methods, with potential recharge rates varying from 2.07 to 9.49 inches per year.
Overall, there was a lack of agreement between the three methods. These results were not remarkable, considering the fundamental differences in the methodology for each method. However, all methods did show a fundamental difference between piezometers within the drained area and piezometers outside the drained area, including the third undrained monitoring plot. The drained areas show a lower overall potential groundwater recharge compared to the nondrained areas for all three estimates. The difference for the 2018 recharge estimates was slightly higher than 1 inch for the RISE WTF method, the difference was almost double for the nine sites for the DRAINMOD model, and the difference between the drain and undrained plots was even more significant for the SWB model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205006","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources","usgsCitation":"Smith, E.A., and Berg, A.M., 2020, Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5006, 57 p., https://doi.org/10.3133/sir20205006.","productDescription":"Report: ix, 54 p.; 5 Appendixes; 3 Data Releases; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112919","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":372354,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendixes.xlsx","text":"Appendix 1 and 2","size":"3.55 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5006 Appendixes"},{"id":372353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006.pdf","text":"Report","size":"4.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5006"},{"id":372352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5006/coverthb.jpg"},{"id":372355,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table1.1.csv","text":"Appendix 1.1","size":"1.55 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 1.1"},{"id":372356,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table1.2.csv","text":"Appendix 1.2","size":"1.66 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 1.2"},{"id":372357,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table2.1.csv","text":"Appendix 2.1","size":"13.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 2.1"},{"id":372358,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table2.2.csv","text":"Appendix 2.2","size":"13.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 2.2"},{"id":372359,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P987N30U","text":"USGS data release","linkHelpText":"DRAINMOD simulations for two agricultural drainage sites in western Fillmore County, southeastern Minnesota"},{"id":372360,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90N4AWG","text":"USGS data release","linkHelpText":"Soil-Water Balance model datasets used to estimate recharge for southeastern Minnesota, 2014–2018"},{"id":372361,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94LMOPP","text":"USGS data release","linkHelpText":"Potential groundwater recharge estimates based on a groundwater rise analysis technique for two agricultural sites in southeastern Minnesota, 2016–2018"},{"id":372362,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS dataset","linkHelpText":"– USGS groundwater data for Minnesota in USGS water data for the Nation"},{"id":399628,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109687.htm"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.4167,\n 43.595\n ],\n [\n -92.45,\n 43.595\n ],\n [\n -92.45,\n 43.5444\n ],\n [\n -92.4167,\n 43.5444\n ],\n [\n -92.4167,\n 43.595\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911
A rapid batch extraction method was evaluated to estimate potential for total dissolved solids (TDS) release by 65 samples of rock from coal and gas-bearing strata of the Appalachian Basin in eastern USA. Three different extractant solutions were considered: deionized water (DI), DI equilibrated with 10% CO2 atmosphere (DI + CO2), or 30% H2O2 under 10% CO2 (H2O2+CO2). In all extractions, 10 g of pulverized rock (<0.5-mm) were mixed with 20 mL of extractant solution and shaken for 4 h at 50 rpm and 20–22 °C. The 65 rock samples were classified as coal (n=3), overburden (n = 17), coal refuse that had weathered in the field (n = 14), unleached coal refuse that had oxidized during indoor storage (n = 20), gas-bearing shale (n = 10), and pyrite (n = 1). Extracts were analyzed for specific conductance (SC), TDS, pH, and major and trace elements, and subsequently speciated to determine ionic contributions to SC. The pH of extractant blanks decreased in the order DI (6.0), DI + CO2 (5.1), and H2O2+CO2 (2.6). The DI extractant was effective for mobilizing soluble SO4 and Cl salts. The DI + CO2 extractant increased weathering of carbonates and resulted in equivalent or greater TDS than the DI leach of the same material. The H2O2+CO2 extractant increased weathering of sulfides (and carbonates) and resulted in the greatest TDS production and lowest pH values. Of the 65 samples, 19 had leachate chemistry data from previous column experiments and 35 were paired to 10 field sites with leachate chemistry data. When accounting for the water-to-rock ratio, TDS from DI and DI + CO2 extractions were correlated to TDS from column experiments while TDS from H2O2+CO2 extractions was not. In contrast to column experiments, field SC was better correlated to SC measured from H2O2+CO2 extractions than DI extractions. The field SC and SC from H2O2+CO2 extractions were statistically indistinguishable for 7 of 9 paired data sets while SC from DI extractions underestimated field SC in 5 of 9 cases. Upscaling comparisons suggest that (1) weathering reactions in the field are more aggressive than DI water or synthetic rainwater extractants used in batch or column tests, and (2) a batch extraction method utilizing 30% H2O2 (which is mildly acidic without CO2 enrichment) could be effective for identifying rocks that will release high amounts of TDS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2020.104540","usgsCitation":"Castillo-Meza, L.E., Cravotta, C., Tasker, T.L., Warner, N.R., Daniels, W.L., Orndorff, Z.W., Bergstresser, T., Douglass, A., Kimble, G., Streczywilk, J., Barton, C., Thompson, A., and Burgos, W.D., 2020, Batch extraction method to estimate total dissolved solids (TDS) release from coal refuse and overburden: Applied Geochemistry, v. 115, 104540, 16 p., https://doi.org/10.1016/j.apgeochem.2020.104540.","productDescription":"104540, 16 p.","ipdsId":"IP-106585","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":467297,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/102448","text":"External Repository"},{"id":377359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"115","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Castillo-Meza, L. E.","contributorId":237999,"corporation":false,"usgs":false,"family":"Castillo-Meza","given":"L.","email":"","middleInitial":"E.","affiliations":[{"id":47676,"text":"Department of Civil and Environmental Engineering, The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":795778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tasker, T. L.","contributorId":238000,"corporation":false,"usgs":false,"family":"Tasker","given":"T.","email":"","middleInitial":"L.","affiliations":[{"id":47676,"text":"Department of Civil and Environmental Engineering, The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":795780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, N. R.","contributorId":238001,"corporation":false,"usgs":false,"family":"Warner","given":"N.","email":"","middleInitial":"R.","affiliations":[{"id":47676,"text":"Department of Civil and Environmental Engineering, The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":795781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Daniels, W. L.","contributorId":238002,"corporation":false,"usgs":false,"family":"Daniels","given":"W.","email":"","middleInitial":"L.","affiliations":[{"id":47677,"text":"Department of Crop and Soil Science, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":795782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Orndorff, Z. W.","contributorId":238003,"corporation":false,"usgs":false,"family":"Orndorff","given":"Z.","email":"","middleInitial":"W.","affiliations":[{"id":47677,"text":"Department of Crop and Soil Science, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":795783,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bergstresser, T.","contributorId":238004,"corporation":false,"usgs":false,"family":"Bergstresser","given":"T.","email":"","affiliations":[{"id":47678,"text":"Geochemical Testing Laboratory","active":true,"usgs":false}],"preferred":false,"id":795784,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Douglass, A.","contributorId":238005,"corporation":false,"usgs":false,"family":"Douglass","given":"A.","email":"","affiliations":[{"id":47678,"text":"Geochemical Testing Laboratory","active":true,"usgs":false}],"preferred":false,"id":795785,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kimble, G.","contributorId":238006,"corporation":false,"usgs":false,"family":"Kimble","given":"G.","email":"","affiliations":[{"id":47678,"text":"Geochemical Testing Laboratory","active":true,"usgs":false}],"preferred":false,"id":795786,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Streczywilk, J.","contributorId":238007,"corporation":false,"usgs":false,"family":"Streczywilk","given":"J.","email":"","affiliations":[{"id":47678,"text":"Geochemical Testing Laboratory","active":true,"usgs":false}],"preferred":false,"id":795787,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Barton, C.","contributorId":238008,"corporation":false,"usgs":false,"family":"Barton","given":"C.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":795788,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Thompson, A","contributorId":238009,"corporation":false,"usgs":false,"family":"Thompson","given":"A","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":795789,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Burgos, W. D.","contributorId":238010,"corporation":false,"usgs":false,"family":"Burgos","given":"W.","email":"","middleInitial":"D.","affiliations":[{"id":47676,"text":"Department of Civil and Environmental Engineering, The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":795790,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70218261,"text":"70218261 - 2020 - Groundwater model simulations of stakeholder-identified scenarios in a high-conflict irrigated area","interactions":[],"lastModifiedDate":"2021-02-23T13:10:39.001834","indexId":"70218261","displayToPublicDate":"2020-02-14T07:04:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater model simulations of stakeholder-identified scenarios in a high-conflict irrigated area","docAbstract":"This study investigated collaborative groundwater‐flow modeling and scenario analysis in the Little Plover River basin, Wisconsin, USA where an unconfined aquifer supplies groundwater for agricultural irrigation, industrial processing, municipal water supply, and stream baseflow. We recruited stakeholders with diverse interests to identify, prioritize, and evaluate scenarios defined as management changes to the landscape. Using a groundwater flow model, we simulated the top 10 stakeholder‐ranked scenarios under historically informed dry, average, and wet weather conditions and evaluated the ability of scenarios to meet government‐defined stream flow performance measures. Results show that multiple changes to the landscape are necessary to maintain optimum stream flow, particularly during dry years. Yet, when landscape changes from three scenarios—transferring water from the local waste water treatment plant to basin headwaters, moving municipal wells further from the river and downstream, and converting 240 acre (97 ha) of irrigated land to unirrigated land—were simulated in combination, the probability of meeting or exceeding optimum flows rose to 75, 65, and 34% at upper, mid, and lower stream gages, respectively, in dry climate conditions. Discussions with stakeholders reveal that the collaborative model and scenario analysis process resulted in social learning that built upon the existing complex and dynamic institutional landscape. The approach provided a forum for solution‐based discussions, and the model served as an important mediation tool for the development and evaluation of community‐defined scenarios in a high conflict environment. Today, stakeholders continue to work collaboratively to overcome challenges and implement voluntary solutions in the basin.
The North Branch Au Sable River, located in the northern lower peninsula of Michigan near Lovells, Michigan, has historically been known for its brook trout (Salvelinus fontinalis) and its status as a blue ribbon trout stream; however, within the past few decades, there has been a decline in fish population. The objectives of this study were to assess if concentrations of organic chemicals were present in quantities in the North Branch Au Sable River that may potentially harm aquatic species and to establish current baseline concentrations of organic chemicals against which future data can be compared. Passive sampling technology was used to collect information on the concentration, occurrence, transport, and fate of organic chemicals; these samplers absorb dissolved organic chemicals in the river over several weeks, as the timing and intensity of pesticide applications and the frequency of storm events and irrigation can cause fluctuations in organic chemical loading to surface waters. The chemical classes investigated as part of this study included pesticides (both legacy [organochlorine] and current use), polychlorinated biphenyls, polybrominated diphenyl ethers (PBDEs), and polycyclic aromatic hydrocarbons (PAHs).
Passive samplers, including semipermeable membrane devices and polar organic chemical integrative samplers, were deployed at four locations along the North Branch Au Sable River, near Lovells, Mich., in June 2018 for a total of 28 days. Several organic chemicals were detected in the North Branch Au Sable River at low concentrations. Organic chemicals were detected at every sampling location on the North Branch Au Sable River; however, not all chemicals were detected at every location. The highest number of organic chemicals were detected at the most downstream sampling site (North Branch Au Sable River at Kellogg's Bridge), and the lowest number of organic chemicals were detected at the next site upstream (North Branch Au Sable River at Twin Bridge Road). The organic contaminants most frequently detected at all sampling locations include the legacy pesticides pentachloroanisole, trans-chlordane, p,p'-dichlorodiphenyldichloroethylene, and p,p'-dichlorodiphenyltrichloroethane; the PBDE PBDE-28; and the PAHs 2-methylphenanthrene and perylene.
Organic chemical concentrations detected on the North Branch Au Sable River were below almost all water-quality benchmarks included in this report. However, low concentrations of organic chemicals may still pose a risk to aquatic organisms and throughout the trophic hierarchy because of low-dose additive and synergistic mixture effects, transgenerational effects, and a lack of established water-quality benchmarks for many organic chemicals. This report provides data on the current (2018) state of the North Branch Au Sable River and provided a baseline of organic contaminant data against which future data on the North Branch Au Sable River can be evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205002","collaboration":"Prepared for the Mason-Griffith Founders Chapter of Trout Unlimited in cooperation with Lovells Township, Michigan","usgsCitation":"Brennan, A.K., and Alvarez, D.A., 2020, Evaluation of legacy and emerging organic chemicals using passive sampling devices on the North Branch Au Sable River near Lovells, Michigan, June 2018: U.S. Geological Survey Scientific Investigations Report 2020–5002, 21 p., https://doi.org/10.3133/sir20205002.","productDescription":"vi, 21 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-112993","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399625,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109682.htm"},{"id":372284,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5002/sir20205002.pdf","text":"Report","size":"1.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5002"},{"id":372283,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5002/coverthb.jpg"}],"country":"United States","state":"Michigan","county":"Crawford County","city":"Lovells","otherGeospatial":"North Branch Au Sable River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.6833,\n 45\n ],\n [\n -84.25,\n 45\n ],\n [\n -84.25,\n 44.65\n ],\n [\n -84.6833,\n 44.65\n ],\n [\n -84.6833,\n 45\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911–4107
Cattail (Typha) is an iconic emergent wetland plant found worldwide. By producing an abundance of wind-dispersed seeds, cattail can colonize wetlands across great distances, and its rapid growth rate, large size, and aggressive expansion result in dense stands in a variety of aquatic ecosystems such as marshes, ponds, lakes, and riparian areas. Cattail can also quickly dominate disturbed areas with waterlogged soils such as roadside ditches, retention areas, and fringes of stormwater ponds. These dense stands impact local plant and animal life, biogeochemical cycling, and wetland hydrology, which in turn alter wetland functions. Over recent decades, the distribution and abundance of cattail in North America has increased as a result of human disturbances to natural water cycles and increased nutrient loads. In addition, highly competitive nonnative and hybrid taxa have worsened the rapid spread of cattail. Because cattail invasion and expansion often change wetlands in undesirable ways, wetland managers often respond with widespread management efforts, though these efforts may have short-lived or weak effects. Notwithstanding the negative impacts, cattail provides beneficial ecosystem services including the reduction of pollution through bioremediation and the production of biofuel material.
Despite the widespread distribution and invasive characteristics of cattail, a comprehensive review and synthesis of past and current research on cattail was lacking. To address this gap, a diverse team of researchers produced a paper that details the spread and management of cattail throughout North America, summarizing 4 decades of research from more than 650 references (Bansal and others, 2019). This fact sheet highlights the primary topics covered in the paper.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193076","usgsCitation":"Bansal, S., Tangen, B., Lishawa, S., Newman, S., and Wilcox, D., 2020, A review of Cattail (Typha) invasion in North American wetlands: U.S. Geological Survey Fact Sheet 2019-3076, 6 p., https://doi.org/10.3133/fs20193076.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-112132","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":371754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3076/coverthb.jpg"},{"id":372286,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3076/fs20193076.pdf","text":"Report","size":"15.3 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3076"}],"contact":"Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401-7317
The U.S. Geological Survey, in cooperation with the Ute Mountain Ute Tribe (UMUT), initiated a study in 2016 to increase understanding of the hydrogeology and chemistry of groundwater within select areas of the Ute Mountain Ute Reservation (UMUR) in Colorado and Utah, identify vulnerabilities to the system and other natural resources, and outline information needs to aid in the understanding and protection of groundwater resources. The results presented for this study can be used to support the UMUT’s goal of protecting their vital groundwater resources on the UMUR.
Hydrogeologic conditions were characterized for the surficial aquifer contained in Quaternary-age unconsolidated surficial deposits and the Dakota aquifer contained in the Cretaceous-age Dakota Sandstone. In the surficial aquifer, median depth to water ranges from about 5.4 to 17.2 feet below land surface in the Farm and Ranch Enterprise area and 11 to 34 feet below land surface in the Towaoc area, and the water table slopes generally southwest or south. A map of depth to the top of the Dakota Sandstone was constructed from existing well data. Depths range from zero in outcrop areas to more than 3,000 feet below land surface on mesas in the southeastern part of the UMUR.
Groundwater-chemistry data were collected by the UMUT from 13 springs and 31 wells from 1996 through 2017. Specific conductance was much lower for samples from springs than from wells; median values were 512 and 6,024 microsiemens per centimeter at 25 degrees Celsius, respectively. Spring samples were well oxygenated. A few well samples were anoxic (dissolved oxygen concentrations less than 0.5 milligrams per liter [mg/L]), indicating reducing conditions in the aquifer. About 75 percent of spring samples had fresh water (total dissolved solids concentrations less than 1,000 mg/L), and about 85 percent of well samples had brackish or highly saline water (total dissolved solids concentrations greater than 1,000 mg/L). Water type for springs on the Ute Mountains was calcium bicarbonate. Lower-altitude springs had a calcium-sulfate water type. Most well samples had sodium as the dominant cation, and sulfate, bicarbonate, and chloride as the dominant anions. Fluoride concentrations in about 45 percent of well samples were greater than an agricultural-use standard of 2 mg/L.
Nitrate plus nitrite concentrations in most spring and well samples were less than about 1.6 mg/L per liter. Concentrations in samples from wells in the irrigated agricultural area were elevated; the maximum concentration was 78.5 mg/L. About one-half of the trace-element samples had concentrations that were less than laboratory reporting limits. Only aluminum, arsenic, and selenium in spring samples, and boron and selenium in well samples, were detected at concentrations greater than surface-water standards or water-quality standards for agricultural use of groundwater.
Only three organic compounds, the pesticides alachlor and atrazine and the volatile organic compound di(2-ethylhexyl) phthalate, were detected in well samples. The Escherichia coli bacteria was detected in 47 and 23 percent of samples from wells and springs, respectively. The E. coli detections included samples from three culturally significant springs, which did not meet the UMUT cultural-use standard of total absence of E. coli.
Tritium and carbon-14 were the primary environmental tracers used for interpreting groundwater ages for Lopez 2 Spring and five wells (AP–1, 5000 Block, Cottonwood Spring, Goodknight, and SE Toe). Water from the AP–1 well contained a mixture of pre- and post-1950s recharge. Tritium and carbon-14 recharge ages for Lopez 2 Spring (post-1950s in age), Goodknight and SE Toe wells (pre-1950s in age), and Cottonwood Spring well (primarily pre-1950s in age) are supported by helium-4 data. The helium-4 data for the 5000 Block well are inconsistent with the tritium and carbon-14 age of pre-1950s recharge because of interference caused by high methane concentrations in the water.
Springs and surficial deposits are more vulnerable to contamination from anthropogenic chemicals than deeper bedrock wells. Bedrock aquifers are vulnerable in areas where the geologic formations containing the aquifers are exposed at the land surface. Groundwater in deep bedrock aquifers is likely thousands of years old and is not currently affected by present-day land uses. Both shallow and deep groundwater are vulnerable to naturally occurring salts and minerals, such as of total dissolved solids, major ions, nitrate, and trace elements.
Effects of a changing climate on water resources and other ecological characteristics of the UMUR could include changes in evapotranspiration, a decrease in snowpack, decreased aquifer recharge and flow of springs, a decrease in soil moisture, and increased occurrence of wildfires and forest mortality. Of particular interest for the UMUT are possible effects of a changing climate on medicinal and culturally important plants and springs
Several information needs were identified during this study that would aid in the understanding and protection of groundwater resources on the UMUR. These include well-completion information for bedrock wells, the collection of environmental tracer data at additional wells, the addition of methane and hydrocarbon analysis to well sampling plans, and the resampling of springs and wells that were last sampled in 2002 or earlier.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195122","collaboration":"Prepared in cooperation with the Ute Mountain Ute Tribe","usgsCitation":"Bauch, N.J., and Arnold, L.R., 2020, Hydrogeologic characterization, groundwater chemistry, and vulnerability assessment, Ute Mountain Ute Reservation, Colorado and Utah: U.S. Geological Survey Scientific Investigations Report 2019–5122, 76 p., https://doi.org/10.3133/sir20195122.","productDescription":"Report: ix, 76 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-095027","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":399604,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109676.htm"},{"id":372110,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4MOB6","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for estimating depth to the top of the Dakota Sandstone, Ute Mountain Ute Reservation, Colorado, 2017"},{"id":372108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5122/coverthb.jpg"},{"id":372109,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5122/sir20195122.pdf","text":"Report","size":"8.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5122"}],"country":"United States","state":"Colorado","otherGeospatial":"Ute Mountain Ute Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0333,\n 37\n ],\n [\n -108.2667,\n 37\n ],\n [\n -108.2667,\n 37.3564\n ],\n [\n -109.0333,\n 37.3564\n ],\n [\n -109.0333,\n 37\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Pinnipeds are commonly monitored using aerial photographic surveys at land- or ice-based sites, where animals come ashore for resting, pupping, molting, and to avoid predators. Although these counts form the basis for monitoring population change over time, they do not provide information regarding where animals occur in the water, which is often of management and conservation interest. In this study, we developed a hierarchical model that links counts of pinnipeds at terrestrial sites to sightings-at-sea and estimates abundance, spatial distribution, and the proportion of time spent on land (attendance probability). The structure of the model also allows for the inclusion of predictors that may explain variation in ecological and observation processes. We applied the model to Steller sea lions (Eumetopias jubatus) in Glacier Bay, Alaska using counts of sea lions from aerial photographic surveys and opportunistic in-water sightings from vessel surveys. Glacier Bay provided an ideal test and application of the model because data are available on attendance probability based on long-term monitoring. We found that occurrence in the water was positively related to proximity to terrestrial sites, as would be expected for a species that engages in central-place foraging. The proportion of sea lions in attendance at terrestrial sites and overall abundance estimates were consistent with reports from the literature and monitoring programs. The model we describe has benefit and utility for park managers who wish to better understand the overlap between pinnipeds and visitors, and the framework that we present has potential for application across a variety of study systems and taxa.
Submerged aquatic vegetation (SAV) thrives across the estuarine salinity gradient providing valuable ecosystem services. Within the saline portion of estuaries, seagrass areas are frequently cited as hotspots for their role in capturing and retaining organic carbon (Corg). Non-seagrass SAV, located in the fresh to brackish estuarine areas, may also retain significant soil Corg, yet their role remains unquantified. Given rapidly occurring landscape and salinity changes due to human and natural disturbances, landscape level carbon pool estimates from estuarine SAV habitat blue carbon estimates are needed. We assessed Corg stocks in SAV habitat soils from estuarine freshwater to saline habitats (interior deltaic) to saline barrier islands (Chandeleur Island) within the Mississippi River Delta Plain (MRDP), Louisiana, USA. SAV habitats contain Corg stocks equivalent to those reported for other estuarine vegetation types (seagrass, salt marsh, mangrove). Interior deltaic SAV Corg stocks (231.6 ± 19.5 Mg Corg ha−1) were similar across the salinity gradient, and significantly higher than at barrier island sites (56.6 ± 10.4 Mg Corg ha−1). Within the MRDP, shallow water SAV habitat covers up to an estimated 28,000 ha, indicating that soil Corg storage is potentially 6.4 ± 0.1 Tg representing an unaccounted Corg pool. Extrapolated across Louisiana, and the Gulf of Mexico, this represents a major unaccounted pool of soil Corg. As marshes continue to erode, the ability of coastal SAV habitat to offset some of the lost carbon sequestration may be valuable. Our estimates of Corg sequestration rates indicated that conversion of eroding marsh to potential SAV habitat may help to offset the reduction of Corg sequestration rates. Across Louisiana, we estimated SAV to offset this loss by as much as 79,000 Mg C yr−1 between the 1960s and 2000s.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.137217","usgsCitation":"Hillman, E.R., Rivera-Monroy, V., Nyman, A.J., and La Peyre, M., 2020, Estuarine submerged aquatic vegetation habitat provides organic carbon storage across a shifting landscape: Science of the Total Environment, v. 717, 137217, 12 p., https://doi.org/10.1016/j.scitotenv.2020.137217.","productDescription":"137217, 12 p.","ipdsId":"IP-090252","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457780,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.lsu.edu/agrnr_pubs/603","text":"Publisher Index Page"},{"id":395067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.6431884765625,\n 29.776297851831366\n ],\n [\n -88.81072998046875,\n 30.21398171687066\n ],\n [\n -89.56878662109375,\n 30.161751648356894\n ],\n [\n -89.86541748046875,\n 30.401306519203583\n ],\n [\n -90.318603515625,\n 30.557530797259172\n ],\n [\n -91.01074218749999,\n 30.57408532473883\n ],\n [\n -91.15631103515625,\n 30.28990324883237\n ],\n [\n -91.834716796875,\n 29.935895213372444\n ],\n [\n -91.878662109375,\n 29.76437737516313\n ],\n [\n -91.285400390625,\n 29.008140362978157\n ],\n [\n -90.428466796875,\n 28.738763971370293\n ],\n [\n -89.05517578125,\n 28.9120147012556\n ],\n [\n -88.6431884765625,\n 29.776297851831366\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"717","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hillman, E. R.","contributorId":264718,"corporation":false,"usgs":false,"family":"Hillman","given":"E.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":831996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivera-Monroy, V. H.","contributorId":272502,"corporation":false,"usgs":false,"family":"Rivera-Monroy","given":"V. H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":831997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nyman, A. J.","contributorId":265337,"corporation":false,"usgs":false,"family":"Nyman","given":"A.","email":"","middleInitial":"J.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":831998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":831999,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208404,"text":"70208404 - 2020 - Determining the drivers of suspended sediment dynamics in tidal marsh-influenced estuaries using high-resolution ocean color remote sensing","interactions":[],"lastModifiedDate":"2020-03-11T15:23:08","indexId":"70208404","displayToPublicDate":"2020-02-07T13:35:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Determining the drivers of suspended sediment dynamics in tidal marsh-influenced estuaries using high-resolution ocean color remote sensing","docAbstract":"Sediment budgets are a critical metric to assess coastal marsh vulnerability to sea-level rise and declining riverine sediment inputs. However, calculating accurate sediment budgets is challenging in tidal marsh-influenced estuaries where suspended sediment concentrations (SSC) typically vary on scales of hours and meters, and where SSC dynamics are driven by a complex and often site-specific interplay of hydrodynamic and meteorological conditions. The mapping of SSC using ocean-color remote sensing is well established and can help capture the spatio-temporal variability needed to determine the dominant drivers regulating sediment budgets. However, the coarse spatial resolution of traditional ocean-color sensors (1-km) generally precludes their use in coastal-marsh estuaries. Here, using the Plum Island Estuary (Massachusetts, USA) as an example, we demonstrate that high-spatial-resolution maps of SSC derived from Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/B Multispectral Instruments (MSI) can be used to determine the main drivers of SSC dynamics in tidal marsh-influenced estuaries, despite the long revisit time of these sensors. Local empirical algorithms between SSC and remote sensing reflectance were derived and applied to a total of 46 clear-sky scenes collected by the OLI and the MSI between 2013 and 2018. The analysis revealed that this 5-year record was sufficient to capture a representative range of meteorological and tidal conditions required to determine the main drivers of SSC dynamics in this mid-latitude system. The interplay between river and tidal flows dominated SSC dynamics in this estuary, whereas wind-driven resuspension had more moderate effects. The SSC were higher during spring because of increased river discharge due to snowmelt. Tidal asymmetry also enhanced sediment resuspension during flood tides, possibly favoring deposition on marsh platforms. Together, water level, water-level rate of change, river discharge and wind speed were able to explain > 60% of the variability in the main-channel thalweg-averaged SSC, thereby facilitating future prediction of SSC from these readily available variables. This study demonstrates that the existing multi-year records of high-resolution remote sensing can provide a representative depiction of SSC dynamics in hydrodynamically-complex and small-scale estuaries that moderate-resolution ocean color remote sensing and in situ measurements are unable to capture.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111682","usgsCitation":"Zhang, X., Fichot, C., Baracco, C., Guo, R., Neugebauer, S., Bengtsson, Z., Ganju, N., and Fagherazzi, S., 2020, Determining the drivers of suspended sediment dynamics in tidal marsh-influenced estuaries using high-resolution ocean color remote sensing: Remote Sensing, v. 240, 111682, 14 p., https://doi.org/10.1016/j.rse.2020.111682.","productDescription":"111682, 14 p.","ipdsId":"IP-109014","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457785,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2020.111682","text":"Publisher Index Page"},{"id":372176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Plum Island Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.9771728515625,\n 42.72683914955442\n ],\n [\n -70.68328857421875,\n 42.72683914955442\n ],\n [\n -70.68328857421875,\n 42.871938424448466\n ],\n [\n -70.9771728515625,\n 42.871938424448466\n ],\n [\n -70.9771728515625,\n 42.72683914955442\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"240","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Xiaohe","contributorId":213308,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaohe","email":"","affiliations":[],"preferred":false,"id":781753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fichot, Cedric","contributorId":222269,"corporation":false,"usgs":false,"family":"Fichot","given":"Cedric","affiliations":[{"id":40511,"text":"Department of Earth and Environment, Boston University, Boston, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":781754,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baracco, Carly","contributorId":222270,"corporation":false,"usgs":false,"family":"Baracco","given":"Carly","email":"","affiliations":[{"id":40511,"text":"Department of Earth and Environment, Boston University, Boston, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":781755,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guo, Ruizhe","contributorId":222271,"corporation":false,"usgs":false,"family":"Guo","given":"Ruizhe","email":"","affiliations":[{"id":40512,"text":"NASA DEVELOP National Program, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":781756,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Neugebauer, Sydney","contributorId":222272,"corporation":false,"usgs":false,"family":"Neugebauer","given":"Sydney","email":"","affiliations":[{"id":40512,"text":"NASA DEVELOP National Program, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":781757,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bengtsson, Zachary","contributorId":222273,"corporation":false,"usgs":false,"family":"Bengtsson","given":"Zachary","email":"","affiliations":[{"id":40512,"text":"NASA DEVELOP National Program, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":781758,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781752,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fagherazzi, Sergio","contributorId":207153,"corporation":false,"usgs":false,"family":"Fagherazzi","given":"Sergio","email":"","affiliations":[{"id":37465,"text":"Boston University, Earth and Environment, Boston, 02215, USA.","active":true,"usgs":false}],"preferred":false,"id":781759,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228164,"text":"70228164 - 2020 - Water quality and ecological risk assessment of intermittent streamflow through mining and urban areas of San Marcos River sub-basin, Mexico","interactions":[],"lastModifiedDate":"2022-02-07T19:32:21.43433","indexId":"70228164","displayToPublicDate":"2020-02-07T13:10:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10088,"text":"Environmental Nanotechnology, Monitoring & Management","onlineIssn":"2215-1532","active":true,"publicationSubtype":{"id":10}},"title":"Water quality and ecological risk assessment of intermittent streamflow through mining and urban areas of San Marcos River sub-basin, Mexico","docAbstract":"Intermittent rivers are becoming more ecologically stressed worldwide. Flow cessation occurs naturally and spatiotemporally in these systems and anthropogenic activities such as wastewater discharges can have considerable impacts. Public entities mostly monitor water quality in permanent streams, leading to insufficient monitoring of intermittent streams and consequently to their potentially inadequate management.. This study analyzed spatiotemporal patterns of water quality and associated ecological risk through the quantification of physicochemical and microbiological pollutants in the intermittent river system of El Novillo and San Marcos in Northeast Mexico. Results showed that water quality varied geographically and seasonally. Based on national and international criteria, annual averages of water quality parameters analyzed suggested that streamflow in these river systems is of poor quality and poses high ecological risk to aquatic life. In the urban area, annual mean concentrations of Cd and Pb (0.14 and 0.4 mg/L) were 77- and 10-fold higher than their respective water quality criteria (<0.0018 and 0.04 mg/L). Statistically significant (q < 0.05) correlations were identified in concentrations of cyanide, Cd, Cu and Pb between wastewater seeping into the river and streamflow within the urban area. These observations highlight the unique sensitivity of intermittent urban streams to anthropogenic activities and may provide useful information to enhance current water management plans for the El Novillo-San Marcos River system for the protection of ecosystem integrity and human health.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.enmm.2020.100369","usgsCitation":"Lopez, E., Patino, R., Vazquez-Sauceda, M.L., Perez-Castaneda, R., Arellano-Mendez, L.U., Ventura-Houle, R., and Heyer, L., 2020, Water quality and ecological risk assessment of intermittent streamflow through mining and urban areas of San Marcos River sub-basin, Mexico: Environmental Nanotechnology, Monitoring & Management, v. 14, 100369, 9 p., https://doi.org/10.1016/j.enmm.2020.100369.","productDescription":"100369, 9 p.","ipdsId":"IP-109161","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","otherGeospatial":"El Novillo Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.37109375,\n 23.644524198573688\n ],\n [\n -97.998046875,\n 23.644524198573688\n ],\n [\n -97.998046875,\n 25.16517336866393\n ],\n [\n -100.37109375,\n 25.16517336866393\n ],\n [\n -100.37109375,\n 23.644524198573688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lopez, Elisenda","contributorId":274748,"corporation":false,"usgs":false,"family":"Lopez","given":"Elisenda","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833280,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vazquez-Sauceda, Maria L.","contributorId":274749,"corporation":false,"usgs":false,"family":"Vazquez-Sauceda","given":"Maria","email":"","middleInitial":"L.","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833281,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perez-Castaneda, Roberto","contributorId":274750,"corporation":false,"usgs":false,"family":"Perez-Castaneda","given":"Roberto","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833282,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arellano-Mendez, Leonardo U.","contributorId":274751,"corporation":false,"usgs":false,"family":"Arellano-Mendez","given":"Leonardo","email":"","middleInitial":"U.","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833283,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ventura-Houle, Rene","contributorId":274752,"corporation":false,"usgs":false,"family":"Ventura-Houle","given":"Rene","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833284,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heyer, Lorenzo","contributorId":274753,"corporation":false,"usgs":false,"family":"Heyer","given":"Lorenzo","email":"","affiliations":[{"id":56648,"text":"Universidad Autónoma de Tamaulipas","active":true,"usgs":false}],"preferred":false,"id":833285,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}