Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs)

Abstract

For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment, visit http://www.usgs.gov or call 1-888-ASK-USGS.For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprodTo order this and other USGS information products, visit http://store.usgs.gov Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.Suggested citation: Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014-5037, 37 p., http://dx.doi.org/10.3133/sir20145037. ISSN 2328-0328 (online) iii AcknowledgmentsThe author thanks the many people who reviewed this manual and the associated digital media. Susan Jones of the Federal Highway Administration, Peter Weiskel, Toby Feaster, Teresa Rasmussen, Elizabeth Enright, and Kevin Breen of the U.S. Geological Survey for providing thoughtful and thorough technical and editorial reviews of this report, the software, and the associated CD-ROM. Jane Clary, the project manager of the International BMP database project worked closely with the author to answer questions and resolve a number of issues with data in the January 2012 version of that database as they were uncovered in this analysis. By Gregory E. Granato AbstractThe U.S. Geological Survey (USGS) developed the Stochastic Empirical Loading and Dilution Model (SELDM) in cooperation with the Federal Highway Administration (FHWA) to indicate the risk for stormwater concentrations, flows, and loads to be above user-selected water-quality goals and the potential effectiveness of mitigation measures to reduce such risks. SELDM models the potential effect of mitigation measures by using Monte Carlo methods with statistics that approximate the net effects of structural and nonstructural best management practices (BMPs). In this report, structural BMPs are defined as the components of the drainage pathway between the source of runoff and a stormwater discharge location that affect the volume, timing, or quality of runoff. SELDM uses a simple stochastic statistical model of BMP performance to develop planning-level estimates of runoff-event characteristics. This statistical approach can be used to represent a single BMP or an assemblage of BMPs. The SELDM BMP-treatment module has provisions for stochastic modeling of three stormwater treatments: volume reduction, hydrograph extension, and water-quality treatment. In SELDM, these three treatment variables are modeled by using the trapezoidal distribution and the rank co...

Estimating Risks for Water-Quality Exceedances of Total-Copper from Highway and Urban Runoff under Predevelopment and Current Conditions with the Stochastic Empirical Loading and Dilution Model (SELDM)

“…This graph shows that the highway runoff is a small proportion of concurrent downstream storm flows (less than 10 percent) for most storms in these simulations. Dilution factors for highway-swale discharge are lower than for highway-runoff discharges because the swale infiltrates runoff and extends the pavement-runoff hydrograph, which dilutes the discharge in a larger proportion of the upstream stormflow (Granato, 2014). The urban runoff from the large impervious MS4 areas, however, is a large part of the stormflows upstream of the highway in scenario 2.…”

“…SELDM uses Monte Carlo methods to generate a population of random events that are grouped into annual-load accounting years, but does not represent any particular time period or a particular time series (Granato 2013a). Precipitation, upstream pre-storm flows, runoff from the highway and the upstream basin, and flow modifications by the grassy swale were simulated by using standard methods described by Granato (2010;2013a,b;2014). Statistics for precipitation, pre-storm flow, and runoff coefficients from the FHWA 2010 dataset were used (Granato, 2010;2013a,b).…”

“…Decisionmakers have become increasingly aware of the importance of considering random variation in the quantity and quality of highway runoff, urban runoff, and upstream stormflows for estimating the potential adverse effects of runoff on receiving waters (U.S. Environmental Protection Agency, 1985, 1998, 2014Novotny, 2004;Langseth and Brown, 2011). The Stochastic Empirical Loading and Dilution Model (SELDM) uses Monte Carlo methods to generate stormflows, concentrations, and loads from a site of interest and an upstream basin (Granato, 2013a).…”

Quantitative Analysis of Urban Pluvial Flood Alleviation by Open Surface Water Systems in New Towns: Comparing Almere and Tianjin Eco-City

“…There are a variety of BMPs available, and EPA has published a list of storm water BMPs for local governments, builders and property owners ("National Menu of Storm water Best Management Practices"). However, there are uncertainties in effectiveness because BMP monitoring requires a high degree of expertise [19]. Besides, the BMPs approach is also highly case sensitive.…”

Data driven comprehensive assessment of the performance of stormwater best management practices.

“…Typically, stormwater BMPs can abate the overflow area during storm events through reducing the volume of the runoff by redirecting stormwater flow into the groundwater system. Simultaneously, it improves receiving water quality by attenuating peak flows of the runoff (Granato 2014).…”