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Assessing Potential Effects of Highway Runoff on Receiving-Water Quality in Oregon using Surrogate Water-Quality Data Sets John Risley, Gregory E. Granato, and William Fletcher Eugene, Oregon April 22, 2015
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USGS – ODOT Study In 2012 the U.S. Geological Survey Oregon Water Science Center and the Oregon Department of Transportation began a jointly funded study assessing the potential effects of highway runoff on receiving-water quality at selected sites in Oregon using the Stochastic Empirical Loading and Dilution Model (SELDM)
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USGS - ODOT Study Report Risley, J.C., and Granato, G.E., 2014, Assessing potential effects of highway runoff on receiving-water quality at selected sites in Oregon with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2014–5099, 74 p.
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USGS – ODOT Study Objectives: 1. Refine precipitation/runoff data inputs for Oregon SELDM applications. 2. Evaluate impacts of storm-water runoff for six highway sites. 3. Compute upstream basin and highway water-quality statistics and transport curves for Oregon SELDM applications. 4. Provide a report describing Oregon SELDM applications.
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SELDM Overview SELDM estimates combinations of contaminant loads/concentrations from a highway and an upstream basin using Monte Carlo methods. Created by Greg Granato--USGS Massachusetts Water Science Center Funded by Federal Highway Administration
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SELDM Overview http://water.usgs.gov/osw/streamstats/
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SELDM Components 1. Storm-event precipitation 2. Upstream basin streamflow 3. Highway storm runoff 4. Upstream basin and highway runoff water quality 5. Optional – Lake basin analysis 6. Optional – Evaluation of BMPs 7. SELDM model output files
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Best Management Practices 1.Flow volume reduction option --Infiltration, evaporation, and absorption 2.Hydrograph extension option --Allows better downstream dilution 3.Water-quality modification option --Settling and filtration
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Water-Quality Treatment: TSS Reduction
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Six Study Sites
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Highway catchment drainage areas: 3.85 to 11.83 acres Upstream basin drainage areas: 0.16 to 6.56 square miles Two sites: Urbanized Four remaining sites: Forest or agricultural with < 5% imperviousness
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Modeling Water-Quality Constituents Chemical concentrations from the highway catchment and upstream basin can be defined in SELDM as: 1. Random Mean, SD, and skew 2. A transport curve A function of streamflow 3. Dependent on another constituent Example: Sediment
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Constituents of Interest CadmiumLead ChlorideNickel ChromiumPhosphorus CopperZinc Iron
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Oregon Department of Environmental Quality Criteria
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Constituents of Interest Concentrations and loads were simulated for the six study site highway catchments and upstream basins using surrogate water-quality data.
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Water-Quality Surrogate Data Highway Catchments: Measured QW data from similar U.S. highway sites with similar climate, road width, catchment size, and average daily traffic (ADT) load. Highway-Runoff Database (HRDB). Data from California, Massachusetts, Pennsylvania, Florida, and Wisconsin sites.
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Water-Quality Surrogate Data Upstream Basins: Measured water ‑ quality data from nearby urban and rural USGS streamflow sites within the same ecoregion. Random statistics used for non-urban Willamette ecoregion sites. Transport curves created for urban sites. Random statistics and transport curves used for southern Wall Creek site.
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Tyron Creek at Interstate 5 Upstream basin Basin area (square miles)0.63 Percent forest11.2 Percent urban99.8 Impervious fraction0.48 Highway catchment Area (acres)11.8 Impervious fraction0.99 Total lanes10 Average daily traffic21,400 -
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Tyron Creek at Interstate 5
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Flows at Tyron Creek at Interstate 5
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Copper Concentrations at Tyron Creek at Interstate 5
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Copper Loads at Tyron Creek at Interstate 5
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Take Home Message Analyses show that potential effects of highway runoff on receiving-water quality downstream of the highway depends on: (1) The ratio of the upstream and highway catchment drainage areas (dilution). (2) The quality of the water upstream of the highway.
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Take Home Message These analyses also show that the probability of exceeding a water-quality criterion may depend on the input statistics used, thus careful selection of representative values is important. If time and funding are available-- measuring streamflow and water-quality data at a site of interest is preferable to surrogate data.
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Questions?
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