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