This presentation describes the use of the Stochastic Empirical Loading and Dilution Model (SELDM) and the Highway Runoff Database (HRDB) for addressing total maximum daily loads (TMDLs) and other issues of concern for runoff from highways and other developed areas. SELDM is a simple-to-use runoff quality model that can be used to simulate runoff from highways and other land uses to obtain flows, concentrations, and loads of stormwater. The Highway Runoff Database holds highway runoff data and provides data and statistics that can be used with SELDM or independently for other analyses. More information is available on-line at https://www.usgs.gov/SELDM
This presentation was delivered at the FHWA National Stormwater Practitioners Virtual Forum October 18th, 2021
Related reports:
Barbaro, Henry, 2015, Using the Stochastic Empirical Loading and Dilution Model (SELDM) to assess nutrient-impaired waters: in Use and Implementation of the Federal Highway Administration Stochastic Empirical Loading and Dilution Model (SELDM): Oregon and Massachusetts: http://www.trb.org/HydraulicsHydrology/Blurbs/172268.aspx
Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S.Geological Survey Techniques and Methods, book 4, chap. C3, 112 p. , CD–ROM. https://doi.org/10.3133/tm4C3
Granato, G.E., 2021, Stochastic Empirical Loading and Dilution Model (SELDM) software archive: U.S. Geological Survey software release, https://doi.org/10.5066/P9PYG7T5
Granato, G.E., and Friesz, P.J., 2021, Model archive for analysis of long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey data release, https://doi.org/10.5066/P9B02EUZ.
Granato, G.E., and Jones, S.C., 2017, Estimating Total Maximum Daily Loads with the Stochastic Empirical Loading and Dilution Model: Transportation Research Record, Journal of the Transportation Research Board, No. 2638, p. 104-112. https://doi.org/10.3141/2638-12
Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136
Jeznach, L.C., and Granato, G.E., 2020, Comparison of SELDM simulated total-phosphorus concentrations with ecological impervious-area criteria: Journal of Environmental Engineering: v. 146, No. 8, 10 p. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001763
National Academies of Sciences, Engineering, and Medicine 2019. Approaches for Determining and Complying with TMDL Requirements Related to Roadway Stormwater Runoff. Washington, DC: The National Academies Press. https://doi.org/10.17226/25473.
Spaetzel, A.B., Steeves, P.A., and Granato, G.E., 2020, Basin characteristics and point locations of road crossings in Connecticut, Massachusetts, and Rhode Island for highway-runoff mitigation analyses using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey data release, https://doi.org/10.5066/P9VK1MCG.
Stonewall, A.J., Granato, G.E., and Haluska, T.L., 2018, Assessing roadway contributions to stormwater flows, concentrations and loads by using the StreamStats application: Transportation Research Record, Journal of the Transportation Research Board, Volume: 2672 issue: 39, p. 79-87. https://doi.org/10.1177/0361198118758679
Stonewall, A.J., Granato, G.E., and Glover-Cutter, K.M., 2019, 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: U.S. Geological Survey Scientific Investigations Report 2019–5053, 116 p., https://doi.org/10.3133/sir20195053.
U.S. Environmental Protection Agency, 2009, Guidance on the Development, Evaluation, and Application of Environmental Models: U.S. Environmental Protection Agency EPA/100/K-09/003,98 p. https://www.epa.gov/sites/default/files/2015-04/documents/cred_guidance_0309.pdf
Weaver, J.C., Stillwell, C.C., Granato, G.E., McDaniel, A.H., Lipscomb, B.S., and Mullins, R.M, 2021, Application of the North Carolina Stochastic Empirical Loading and Dilution Model (SELDM) to Assess Potential Impacts of Highway Runoff: U.S. Geological Survey data release, https://doi.org/10.5066/P9LCXHLN.
This presentation was delivered at the FHWA National Stormwater Practitioners Virtual Forum October 18th, 2021
Related reports:
Barbaro, Henry, 2015, Using the Stochastic Empirical Loading and Dilution Model (SELDM) to assess nutrient-impaired waters: in Use and Implementation of the Federal Highway Administration Stochastic Empirical Loading and Dilution Model (SELDM): Oregon and Massachusetts: http://www.trb.org/HydraulicsHydrology/Blurbs/172268.aspx
Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S.Geological Survey Techniques and Methods, book 4, chap. C3, 112 p. , CD–ROM. https://doi.org/10.3133/tm4C3
Granato, G.E., 2021, Stochastic Empirical Loading and Dilution Model (SELDM) software archive: U.S. Geological Survey software release, https://doi.org/10.5066/P9PYG7T5
Granato, G.E., and Friesz, P.J., 2021, Model archive for analysis of long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey data release, https://doi.org/10.5066/P9B02EUZ.
Granato, G.E., and Jones, S.C., 2017, Estimating Total Maximum Daily Loads with the Stochastic Empirical Loading and Dilution Model: Transportation Research Record, Journal of the Transportation Research Board, No. 2638, p. 104-112. https://doi.org/10.3141/2638-12
Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136
Jeznach, L.C., and Granato, G.E., 2020, Comparison of SELDM simulated total-phosphorus concentrations with ecological impervious-area criteria: Journal of Environmental Engineering: v. 146, No. 8, 10 p. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001763
National Academies of Sciences, Engineering, and Medicine 2019. Approaches for Determining and Complying with TMDL Requirements Related to Roadway Stormwater Runoff. Washington, DC: The National Academies Press. https://doi.org/10.17226/25473.
Spaetzel, A.B., Steeves, P.A., and Granato, G.E., 2020, Basin characteristics and point locations of road crossings in Connecticut, Massachusetts, and Rhode Island for highway-runoff mitigation analyses using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey data release, https://doi.org/10.5066/P9VK1MCG.
Stonewall, A.J., Granato, G.E., and Haluska, T.L., 2018, Assessing roadway contributions to stormwater flows, concentrations and loads by using the StreamStats application: Transportation Research Record, Journal of the Transportation Research Board, Volume: 2672 issue: 39, p. 79-87. https://doi.org/10.1177/0361198118758679
Stonewall, A.J., Granato, G.E., and Glover-Cutter, K.M., 2019, 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: U.S. Geological Survey Scientific Investigations Report 2019–5053, 116 p., https://doi.org/10.3133/sir20195053.
U.S. Environmental Protection Agency, 2009, Guidance on the Development, Evaluation, and Application of Environmental Models: U.S. Environmental Protection Agency EPA/100/K-09/003,98 p. https://www.epa.gov/sites/default/files/2015-04/documents/cred_guidance_0309.pdf
Weaver, J.C., Stillwell, C.C., Granato, G.E., McDaniel, A.H., Lipscomb, B.S., and Mullins, R.M, 2021, Application of the North Carolina Stochastic Empirical Loading and Dilution Model (SELDM) to Assess Potential Impacts of Highway Runoff: U.S. Geological Survey data release, https://doi.org/10.5066/P9LCXHLN.
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