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Statements

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dbr:Storm_Water_Management_Model
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dbr:Stochastic_empirical_loading_and_dilution_model
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Stochastic empirical loading and dilution model
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The stochastic empirical loading and dilution model (SELDM) is a stormwater quality model. SELDM is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to pr
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The stochastic empirical loading and dilution model (SELDM) is a stormwater quality model. SELDM is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. Although SELDM is, nominally, a highway runoff model is can be used to estimate flows concentrations and loads of runoff-quality constituents from other land use areas as well. SELDM was developed by the U.S. Geological Survey so the model, source code, and all related documentation are provided free of any copyright restrictions according to U.S. copyright laws and the USGS Software User Rights Notice. SELDM is widely used to assess the potential effect of runoff from highways, bridges, and developed areas on receiving-water quality with and without the use of mitigation measures. Stormwater practitioners evaluating highway runoff commonly use data from the Highway Runoff Database (HRDB) with SELDM to assess the risks for adverse effects of runoff on receiving waters. SELDM is a stochastic mass-balance model A mass-balance approach (figure 1) is commonly applied to estimate the concentrations and loads of water-quality constituents in receiving waters downstream of an urban or highway-runoff outfall. In a mass-balance model, the loads from the upstream basin and runoff source area are added to calculate the discharge, concentration, and load in the receiving water downstream of the discharge point. SELDM can do a stream-basin analysis and a lake-basin analysis. The stream-basin analysis uses a stochastic mass-balance analysis based on multi-year simulations including hundreds to thousands of runoff events. SELDM generates storm-event values for the site of interest (the highway site) and the upstream receiving stream to calculate flows, concentrations, and loads in the receiving stream downstream of the stormwater outfall. The lake-basin analysis also is a stochastic multi-year mass-balance analysis. The lake-basin analysis uses the highway loads that occur during runoff periods, the total annual loads from the lake basin to calculate annual loads to and from the lake. The lake basin analysis uses the volume of the lake and pollutant-specific attenuation factors to calculate a population of average-annual lake concentrations. The annual flows and loads SELDM calculates for the stream and lake analyses also can be used to estimate total maximum daily loads (TMDLs) for the site of interest and the upstream lake basin. The TMDL can be based on the average of annual loads because product of the average load times the number of years of record will be the sum-total load for that (simulated) period of record. The variability in annual values can be used to estimate the risk of exceedance and the margin of safety for the TMDL analysis
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