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Variation in stream network relationships and geospatial predictions of watershed conductivity
McManus, M., E. DAmico, E. Smith, R. Polinsky, J. Ackerman, AND K. Tyler. Variation in stream network relationships and geospatial predictions of watershed conductivity. Freshwater Science. The Society for Freshwater Science, Springfield, IL, 39(4):1-18, (2020). https://doi.org/10.1086/710340
We predicted specific conductivity in streams throughout the 525 stream kilometers of an Appalachian watershed. We used water quality monitoring data with land cover geographic information systems (GIS) data in a spatial stream network model that included the spatial autocorrelation from 60 monitoring sites in the Right Fork Beaver Creek watershed in Floyd and Knott Counties of Eastern Kentucky. We showed that the correlations in conductivity between monitoring sites differed under high versus low discharge conditions of the watershed. By being able to predict water quality, such as specific conductivity, we can better understand how land cover and use in the watershed impacts the stream biota. The ability to predict water quality in a stream network of a watershed can help identify conservation or remediation efforts needed to protect water quality and aquatic life. This research is part of the task 3.03C Evaluate cumulative impacts of fossil fuel and mineral extraction activities on aquatic life from changes in land use, water quantity and quality, and habitat availability of the Safe and Sustainable Waters National Research program.
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.