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)

Dates

Creation: 2020-12-14
Publication Date: 2021-06-11

Citation

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.

Summary

Municipal Separate Storm Sewer System (MS4) permitees including the California Department of Transportation need information about potential loads and yields (loads per unit area) of constituents of concern in stormwater runoff. These entities also need information about the potential effectiveness of stormwater best management practices (BMPs) used to mitigate the effects of runoff. This information is needed to address total maximum daily load (TMDL) regulations. This model archive describes approaches used by the U.S. Geological Survey in cooperation with CalTrans for assessing long-term annual yields of highway and urban runoff in selected areas of California with version 1.1.0 of the Stochastic Empirical Loading and Dilution Model [...]

Summary

Municipal Separate Storm Sewer System (MS4) permitees including the California Department of Transportation need information about potential loads and yields (loads per unit area) of constituents of concern in stormwater runoff. These entities also need information about the potential effectiveness of stormwater best management practices (BMPs) used to mitigate the effects of runoff. This information is needed to address total maximum daily load (TMDL) regulations. This model archive describes approaches used by the U.S. Geological Survey in cooperation with CalTrans for assessing long-term annual yields of highway and urban runoff in selected areas of California with version 1.1.0 of the Stochastic Empirical Loading and Dilution Model (SELDM). In this study SELDM was used to do 368 analyses to examine highway- and urban-runoff yields for 53 runoff-quality constituents. The analyses include 222 random-seed analyses, 60 regional highway-runoff analyses, 24 regional urban-runoff analyses, and 62 focused TMDL-area analyses. Results for all these analyses are provided in this model archive. Although application of results from this study may have considerable uncertainty for predicting loads from any particular stormwater outfall, the results do provide robust estimates to support basin-scale planning-level analyses in California. These analyses also provide regional estimates inside and outside California for the 12 U.S. Environmental Protection Agency level III ecoregions that lie in-whole or in-part within the state of California.

Contacts

Author :: Gregory E Granato, Paul J Friesz
Point of Contact :: Gregory E Granato
Metadata Contact :: Gregory E Granato
Publisher :: U.S. Geological Survey
USGS Mission Area :: Water Resources
SDC Data Owner :: New England Water Science Center
Distributor :: U.S. Geological Survey - ScienceBase

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HighwayEcoregionTIA060.zip “Regional highway simulations TIA 60%”		21.89 MB	application/zip
HighwayEcoregionTIA100.zip “Regional highway simulations TIA 100%”		21.82 MB	application/zip
HighwayEcoregionTIA100MediaFilter.zip “Regional highway simulations TIA 100% with media filter”		21.82 MB	application/zip
HighwayEcoregionTIA100Strip.zip “Regional highway simulations TIA 100% with grass strip”		21.95 MB	application/zip
HighwayEcoregionTIA100Swale.zip “Regional highway simulations TIA 100% with grass swale”		21.9 MB	application/zip
HighwayLevel2PrecipTIA060.zip “Local highway simulations TIA 60%”		54 MB	application/zip
HighwayLevel2PrecipTIA100.zip “Local highway simulations TIA 100%”		53.84 MB	application/zip
HighwayTIA100SeedAnalysis.zip “Random seed analysis”		494.13 MB	application/zip
HRDBv1.1.0a.zip “Highway runoff database v 1.1.0a”		9.81 MB	application/zip
ReadMe.txt “ReadMe file in tab-delimited text format”		148.28 KB	text/plain
ReadMe.xml “ReadMe file in xml format” Original FGDC Metadata	View	15.28 KB	application/fgdc+xml
SELDM-CalTrans.zip “The SELDM database application files”		8.17 MB	application/zip
SELDM-MonteCarloCode.zip “The SELDM Monte Carlo code using visual basic for applications in text files”		105.29 KB	application/zip
SummaryResults.zip “Annual flow and yield statistics in text-file format”		718 KB	application/zip
UrbanEcoregionTIA100.zip “Regional urban simulations TIA 100%”		43.62 MB	application/zip

Related External Resources

Type: Citation

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.	https://doi.org/10.3133/tm4C3
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
Lantin, A., Larsen, L., Vyas, A., Barrett, M., Leisenring, M., Koryto, K., and Pechacek, L., 2019, Approaches for determining and complying with TMDL requirements related to roadway stormwater runoff: National Cooperative Highway Research Program Research Report 918, 133 p.	https://doi.org/10.17226/25473
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, v. 2672, no. 39, 9 p.	https://doi.org/10.1177/0361198118758679
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, v. 2638, no. 1, p. 104–112.	https://doi.org/10.3141/2638-12
Granato, G.E., Medalie, Laura, and Spaetzel, A.B., 2021, Statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey data release	https://doi.org/10.5066/P9X3ECTD
Granato, G.E., 2021, Best Management Practices Statistical Estimator (BMPSE), version 1.2.0b: U.S. Geological Survey software release	https://doi.org/10.5066/P9XBPIOB

Type: Related Primary Publication

Granato, G.E., and Friesz, P.J., 2021, Approaches for assessing 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 Scientific Investigations Report 2021–5043, 37 p., https://doi.org/10.3133/sir20215043.	https://doi.org/10.3133/sir20215043

Type: Publication that references this resource

Granato, G.E., Spaetzel, A.B., and Jeznach, L.C., 2023, Approaches for assessing flows, concentrations, and loads of highway and urban runoff and receiving-stream stormwater in southern New England with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2023–5087, 152 p., https://doi.org/10.3133/sir20235087.	https://doi.org/10.3133/sir20235087

Purpose

This data release is a model archive for simulations designed to examine highway- and urban-runoff yields (loads per unit area) for 53 runoff-quality constituents. It contains simulation results for 12 U.S. Environmental Protection Agency level III ecoregions that lie in-whole or in-part within the state of California. It also contains simulation results for 31 areas of the state with one or more stream reaches that are subject to total maximum daily load (TMDL) regulations.

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)

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