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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Gregory E. Granato</dc:contributor>
  <dc:contributor>Kira M. Glover-Cutter</dc:contributor>
  <dc:creator>Adam J. Stonewall</dc:creator>
  <dc:date>2019</dc:date>
  <dc:description>&lt;p class="p1"&gt;The Stochastic Empirical Loading and Dilution&amp;nbsp;Model (SELDM) was developed by the U.S. Geological&amp;nbsp;Survey (USGS) in cooperation with the Federal Highway&amp;nbsp;Administration to simulate stormwater quality. To assess the&amp;nbsp;effects of runoff, SELDM uses a stochastic mass-balance&amp;nbsp;approach to estimate combinations of pre-storm streamflow,&amp;nbsp;stormflow, highway runoff, event mean concentrations&amp;nbsp;(EMCs) and stormwater constituent loads from a site of&amp;nbsp;interest. In addition, SELDM can be used to assess the effects&amp;nbsp;of stormwater Best Management Practices (BMPs), which&amp;nbsp;are designed to mitigate the adverse effects of runoff into a&amp;nbsp;waterbody.&amp;nbsp;&lt;br&gt;&lt;/p&gt;&lt;p class="p1"&gt;Adverse effects of stormwater on receiving waters&amp;nbsp;are one of the greatest unsolved water-quality problems&amp;nbsp;Nationwide. State DOTs, municipalities, Federal facilities,&amp;nbsp;and private property owners who manage impervious surfaces&amp;nbsp;need information about the potential magnitude of their&amp;nbsp;contributions and the potential effectiveness of methods to&amp;nbsp;mitigate the adverse effects of runoff. Because the efficacy of&amp;nbsp;at-site controls are limited, information about the potential&amp;nbsp;effectiveness of alternative strategies is needed.&amp;nbsp;&lt;/p&gt;&lt;p class="p1"&gt;The USGS, in cooperation with the Oregon Department&amp;nbsp;of Transportation (ODOT), conducted a study to research&amp;nbsp;methods in which SELDM can be used to enhance the&amp;nbsp;efficiency of ODOT’s stormwater program, support the&amp;nbsp;development of a stormwater banking program, and meet&amp;nbsp;environmental goals. Results can be used to develop a&amp;nbsp;strategic, systems-level approach to stormwater management&amp;nbsp;by considering entire watersheds instead of individual road&amp;nbsp;crossings. Two watersheds, Bear Creek and Mill Creek,&amp;nbsp;in western Oregon were selected for analysis. Within&amp;nbsp;each watershed, seven road crossings were selected for&amp;nbsp;demonstrating the utility of SELDM in nested basins.&lt;/p&gt;&lt;p class="p1"&gt;Precipitation statistics, pre-storm streamflow, runoff&amp;nbsp;coefficients, and hydrograph recession factors were calculated&amp;nbsp;for each location and used in SELDM to simulate flow,&amp;nbsp;water-quality concentrations, and constituent loads in the&amp;nbsp;upstream basin, from the highway (or developed area), and&amp;nbsp;downstream from the road crossing. Three water-quality&amp;nbsp;constituents were selected for modeling: suspended-sediment&amp;nbsp;concentration (SSC), total phosphorus (TP), and total copper&amp;nbsp;(TCu). Using water-quality transport curves, the relations&amp;nbsp;between streamflow and SSC and between streamflow and&amp;nbsp;TP were simulated. Concentrations of TCu were simulated by&amp;nbsp;configuring a linear relation between SSC and TCu. A generic&amp;nbsp;BMP was simulated using the median treatment statistics&amp;nbsp;for flow reductions, hydrograph extensions, concentration&amp;nbsp;reductions, and minimum irreducible concentrations from nine&amp;nbsp;BMP categories with data from the 2012 International BMP&amp;nbsp;database.&amp;nbsp;&lt;/p&gt;&lt;p class="p1"&gt;Five simulation scenarios were modeled for&amp;nbsp;demonstrative purposes. These simulations were used to&amp;nbsp;evaluate potential effects of different watershed properties,&amp;nbsp;water-quality inputs, and stormwater mitigation measures.&amp;nbsp;Instream EMCs were compared to hypothetical water-quality&amp;nbsp;criteria for suspended sediment, total phosphorus, and total&amp;nbsp;copper to demonstrate the concept of water-quality risk&amp;nbsp;analysis. For all five scenarios, it was assumed that highway runoff concentrations were independent of location or average&amp;nbsp;annual daily traffic. These five scenarios are as follows:&lt;br&gt;• Simulation Scenario 1—Natural Conditions (hereafter&amp;nbsp;Simulation Scenario 1) represents conditions in an&amp;nbsp;undeveloped watershed. This scenario demonstrates&amp;nbsp;that the strategic placement of a hypothetical road&amp;nbsp;crossing within a watershed could be used to avoid&amp;nbsp;exceeding water-quality standards of TP and SSC,&amp;nbsp;but that no location choice results in meeting TCu&amp;nbsp;standards. Implementation of BMP had the most&amp;nbsp;pronounced effects on downstream water-quality&amp;nbsp;constituent EMCs at road crossings with the highest&amp;nbsp;ratio of highway catchment area to upstream drainage&amp;nbsp;area, but the largest effect of BMP treatment on mean&amp;nbsp;annual load is based on highway catchment area alone.&lt;/p&gt;&lt;p class="p1"&gt;• Simulation Scenario 2—Current Conditions (hereafter&amp;nbsp;Simulation Scenario 2) represents current watershed&amp;nbsp;conditions, where all developed area upstream from the&amp;nbsp;road crossing was modeled as a highway and combined&amp;nbsp;with the undeveloped part of the upstream drainage&amp;nbsp;area (scenario 2A) and where the output from scenario&amp;nbsp;2A is used for the upstream area (developed area and&amp;nbsp;the undeveloped area), and where the road crossing&amp;nbsp;&amp;nbsp;is added as usual (scenario 2B). Scenario 2 results&amp;nbsp;indicate that attaining water-quality standards is more&amp;nbsp;difficult with upstream developed areas. Specific road-crossing sites can be selected to achieve the fewest&amp;nbsp;water-quality exceedances per year, but water-quality&amp;nbsp;targets are not met without BMP implementation, and&amp;nbsp;in some instances are not achievable even with BMP&amp;nbsp;implementation. Results from this scenario also serve&amp;nbsp;to quantify the upper limit of constituent reduction if&amp;nbsp;funding were available to implement BMPs to large&amp;nbsp;areas of development, and to quantify how much area&amp;nbsp;would need BMP implementation to achieve water-quality targets.&amp;nbsp;&lt;/p&gt;&lt;p class="p1"&gt;• Simulation Scenario 3—Alternative Road Layouts&amp;nbsp;(hereafter Simulation Scenario 3) was designed&amp;nbsp;to assess the sensitivity of SELDM to various&amp;nbsp;road layouts. In this scenario, different highway&amp;nbsp;configurations were superimposed at one road&amp;nbsp;crossing. Results indicate that downstream waterquality constituent EMCs did not exhibit much&amp;nbsp;variation, but annual water-quality constituent loads&amp;nbsp;varied considerably.&lt;br&gt;• Simulation Scenario 4—Varying Road Width (hereafter Simulation Scenario 4) was designed to assess the&amp;nbsp;sensitivity of SELDM to road width. Similar to&amp;nbsp;scenario 3, the results indicate little variation in&amp;nbsp;downstream water-quality constituent EMCs, but&amp;nbsp;annual water-quality constituent loads increased in&amp;nbsp;proportion to road width.&lt;br&gt;• Simulation scenario 5—Changes to Impervious Area&amp;nbsp;(hereafter Simulation Scenario 5) was designed&amp;nbsp;to investigate the effects of changing amounts of&amp;nbsp;imperviousness upstream from the road crossing.&amp;nbsp;&amp;nbsp;Results indicate that the downstream water-quality&amp;nbsp;constituent EMCs are highly correlated with the&amp;nbsp;percentage of impervious area upstream.&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.3133/sir20195053</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>U.S. Geological Survey</dc:publisher>
  <dc:title>Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the stochastic empirical loading and dilution model</dc:title>
  <dc:type>reports</dc:type>
</oai_dc:dc>