Variation in stream network relationships and geospatial predictions of watershed conductivity.

Michael G McManus, Ellen D'Amico, Elizabeth M Smith, Robyn Polinsky, Jerry Ackerman, Kip Tyler
Author Information
  1. Michael G McManus: Center for Environmental Measurement and Modeling, Office of Research and Development, United States Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268 USA.
  2. Ellen D'Amico: Pegasus Technical Services c/o United States Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268 USA.
  3. Elizabeth M Smith: Water Division, United States Environmental Protection Agency, Region IV, 61 Forsyth Street Southwest, Atlanta, Georgia 30303 USA.
  4. Robyn Polinsky: Water Division, United States Environmental Protection Agency, Region IV, 61 Forsyth Street Southwest, Atlanta, Georgia 30303 USA.
  5. Jerry Ackerman: Laboratory Services and Applied Science Division, United States Environmental Protection Agency, Region IV, 980 College Station Road, Athens, Georgia 30605 USA.
  6. Kip Tyler: Water Division, United States Environmental Protection Agency, Region IV, 61 Forsyth Street Southwest, Atlanta, Georgia 30303 USA.

Abstract

Secondary salinization, the increase of anthropogenically-derived salts in freshwaters, threatens freshwater biota and ecosystems, drinking water supplies, and infrastructure. The various anthropogenic sources of salts and their locations in a watershed may result in secondary salinization of river and stream networks through multiple inputs. We developed a watershed predictive assessment to investigate the degree to which topology, land-cover, and land-use covariates affect stream specific conductivity (SC), a measure of salinity. We used spatial stream network models to predict SC throughout an Appalachian stream network in a watershed affected by surface coal mining. During high-discharge conditions, 8 to 44% of stream km in the watershed exceeded the SC benchmark of 300 μS/cm, which is meant to be protective of aquatic life in the Central Appalachian ecoregion. During low-discharge conditions, 96 to 100% of stream km exceeded the benchmark. The 2 different discharge conditions altered the spatial dependency of SC among the stream monitoring sites. During most low discharges, SC was a function of upstream-to-downstream network distances, or flow-connected distances, among the sites. Flow-connected distances are indicative of upstream dependencies affecting stream SC. During high discharge, SC was related to both flow-connected distances and flow-unconnected distances (i.e., distances between sites on different branches of the network). Flow-unconnected distances are indicative of processes on adjacent branches and their catchments affecting stream SC. With sites distributed from headwaters to the watershed outlet, the extent of impacts from secondary salinization could be better spatially predicted and assessed with spatial stream network models than with models assuming spatial independence. Importantly, the assessment also recognized the multi-scale spatial relationships that can occur between the landscape and stream network.

Keywords

References

  1. Proc Natl Acad Sci U S A. 2014 May 13;111(19):7030-5 [PMID: 24753575]
  2. Ecol Lett. 2013 May;16(5):707-19 [PMID: 23458322]
  3. Environ Monit Assess. 2006 Oct;121(1-3):571-96 [PMID: 16897525]
  4. Water Res. 2018 Apr 15;133:8-18 [PMID: 29353698]
  5. Environ Toxicol Chem. 2013 Feb;32(2):296-303 [PMID: 23161531]
  6. PLoS One. 2018 Jul 25;13(7):e0197758 [PMID: 30044790]
  7. J Am Water Resour Assoc. 2017 Aug;53(4):944-960 [PMID: 30034212]
  8. Ecology. 2010 Mar;91(3):644-51 [PMID: 20426324]
  9. Sci Total Environ. 2012 Feb 15;417-418:1-12 [PMID: 22264919]
  10. Environmetrics. 2015 Aug;26(5):327-338 [PMID: 27563267]
  11. Environ Toxicol Chem. 2015 Nov;34(11):2603-10 [PMID: 26053694]
  12. Environ Manage. 2014 Oct;54(4):919-33 [PMID: 24990807]
  13. J Am Water Resour Assoc. 2018 Apr;54(2):323-345 [PMID: 30245566]
  14. Sci Total Environ. 2016 Aug 1;560-561:170-8 [PMID: 27101452]
  15. Environ Sci Technol. 2012 Aug 7;46(15):8115-22 [PMID: 22788537]
  16. Proc Natl Acad Sci U S A. 2018 Jan 23;115(4):E574-E583 [PMID: 29311318]
  17. Environ Pollut. 2013 Feb;173:157-67 [PMID: 23202646]
  18. Environ Monit Assess. 2007 Jun;129(1-3):359-78 [PMID: 17057969]
  19. Proc Natl Acad Sci U S A. 2011 Dec 27;108(52):20929-34 [PMID: 22160676]
  20. Environ Toxicol Chem. 2017 Mar;36(3):576-600 [PMID: 27808448]
  21. Environ Sci Technol. 2017 Aug 1;51(15):8324-8334 [PMID: 28704046]

Grants

  1. EPA999999/Intramural EPA

Word Cloud

Created with Highcharts 10.0.0streamSCnetworkdistanceswatershedspatialsalinizationsitessecondaryconductivitymodelsconditionsdischargesaltsassessmentspecificAppalachiansurfaceminingkmexceededbenchmarkdifferentamongmonitoringflow-connectedindicativeaffectingbranchesrelationshipsSecondaryincreaseanthropogenically-derivedfreshwatersthreatensfreshwaterbiotaecosystemsdrinkingwatersuppliesinfrastructurevariousanthropogenicsourceslocationsmayresultrivernetworksmultipleinputsdevelopedpredictiveinvestigatedegreetopologyland-coverland-usecovariatesaffectmeasuresalinityusedpredictthroughoutaffectedcoalhigh-discharge844%300μS/cmmeantprotectiveaquaticlifeCentralecoregionlow-discharge96100%2altereddependencylowdischargesfunctionupstream-to-downstreamFlow-connectedupstreamdependencieshighrelatedflow-unconnectedieFlow-unconnectedprocessesadjacentcatchmentsdistributedheadwatersoutletextentimpactsbetterspatiallypredictedassessedassumingindependenceImportantlyalsorecognizedmulti-scalecanoccurlandscapeVariationgeospatialpredictionsblockkrigingautocorrelationstreams

Similar Articles

Cited By (2)