Literature DB >> 33747635

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

Michael G McManus1,2, Ellen D'Amico3,4, Elizabeth M Smith5,6, Robyn Polinsky5,7, Jerry Ackerman8,9, Kip Tyler5,10.   

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.

Entities:  

Keywords:  block kriging; discharge; monitoring; secondary salinization; spatial autocorrelation; specific conductivity; streams; surface mining

Year:  2020        PMID: 33747635      PMCID: PMC7970528          DOI: 10.1086/710340

Source DB:  PubMed          Journal:  Freshw Sci        ISSN: 2161-9549            Impact factor:   2.034


  21 in total

Review 1.  The effects of mountaintop mines and valley fills on the physicochemical quality of stream ecosystems in the central Appalachians: a review.

Authors:  Michael B Griffith; Susan B Norton; Laurie C Alexander; Amina I Pollard; Stephen D LeDuc
Journal:  Sci Total Environ       Date:  2012-01-20       Impact factor: 7.963

2.  A mixed-model moving-average approach to geostatistical modeling in stream networks.

Authors:  Erin E Peterson; Jay M Ver Hoef
Journal:  Ecology       Date:  2010-03       Impact factor: 5.499

3.  Patterns of spatial autocorrelation in stream water chemistry.

Authors:  Erin E Peterson; Andrew A Merton; David M Theobald; N Scott Urquhart
Journal:  Environ Monit Assess       Date:  2006-08-01       Impact factor: 2.513

4.  Network analysis reveals multiscale controls on streamwater chemistry.

Authors:  Kevin J McGuire; Christian E Torgersen; Gene E Likens; Donald C Buso; Winsor H Lowe; Scott W Bailey
Journal:  Proc Natl Acad Sci U S A       Date:  2014-04-21       Impact factor: 11.205

5.  Seasonal pattern of anthropogenic salinization in temperate forested headwater streams.

Authors:  Anthony J Timpano; Carl E Zipper; David J Soucek; Stephen H Schoenholtz
Journal:  Water Res       Date:  2018-01-08       Impact factor: 11.236

6.  Relationship of land use and elevated ionic strength in Appalachian watersheds.

Authors:  Susan M Cormier; Samuel P Wilkes; Lei Zheng
Journal:  Environ Toxicol Chem       Date:  2012-12-28       Impact factor: 3.742

7.  IMPROVING PREDICTIVE MODELS OF IN-STREAM PHOSPHORUS CONCENTRATION BASED ON NATIONALLY-AVAILABLE SPATIAL DATA COVERAGES.

Authors:  Murray W Scown; Michael G McManus; John H Carson; Christopher T Nietch
Journal:  J Am Water Resour Assoc       Date:  2017-08

Review 8.  Modelling dendritic ecological networks in space: an integrated network perspective.

Authors:  Erin E Peterson; Jay M Ver Hoef; Dan J Isaak; Jeffrey A Falke; Marie-Josée Fortin; Chris E Jordan; Kristina McNyset; Pascal Monestiez; Aaron S Ruesch; Aritra Sengupta; Nicholas Som; E Ashley Steel; David M Theobald; Christian E Torgersen; Seth J Wenger
Journal:  Ecol Lett       Date:  2013-03-04       Impact factor: 9.492

9.  Validation and comparison of geostatistical and spline models for spatial stream networks.

Authors:  A M Rushworth; E E Peterson; J M Ver Hoef; A W Bowman
Journal:  Environmetrics       Date:  2015-04-07       Impact factor: 1.900

10.  Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine.

Authors:  Andrew A Pericak; Christian J Thomas; David A Kroodsma; Matthew F Wasson; Matthew R V Ross; Nicholas E Clinton; David J Campagna; Yolandita Franklin; Emily S Bernhardt; John F Amos
Journal:  PLoS One       Date:  2018-07-25       Impact factor: 3.240

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