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1 Recent Findings from the National Stormwater Best Management Practices Database Project: Applied Research in Practice Marcus Quigley 1 and Eric Strecker 2 1 GeoSyntec Consultants, 629 Massachusetts Avenue, Boxborough, Massachusetts, 01719; PH (978) ; FAX (978) ; mquigley@geosyntec.com 2 GeoSyntec Consultants, 838 SW First Avenue, Suite 430, Portland, Oregon, 97201; PH (503) ; FAX (503) ; estrecker@geosyntec.com Over the last year the National Stormwater Best Management Practices (BMP) Database Project team has completed a revised analysis of water quality and quantity data collected. The current database contains nearly 200 BMP studies. The findings of the reanalysis have significant implications for the implementation, modeling, and management of runoff. Based on the results of the analysis we now have (for some BMP types that are well represented in the Database) sufficient information to begin to not only statistically quantify water quality performance of BMPs, but to better understand some relationships between design and performance. This understanding has been extended, with the addition of the newer data, to include hydrologic characteristics of various BMPs types showing their ability to provide volume reduction. Project findings have become the basis for a number of ongoing BMP research projects which are discussed in this paper. Specifically the results of the statistical analysis conducted as part of the project are useful as the basis for probabilistic modeling of BMP effluent concentrations and loads. This paper provides examples from the results of the analysis of the database and presents a discussion of the application of those results in practice for watershed modeling, BMP selection, and stormwater design. Introduction The USEPA (Environmental Protection Agency)/ASCE (American Society of Civil Engineers) National Stormwater BMP (Best Management Practice) Database has been under development since 1994, under a USEPA grant project with the Urban Water Resources Research Council (UWRRC) of ASCE (Urbonas, 1994). The project has included the development of recommended protocols for BMP performance (Urbonas, 1994 and Strecker 1994), a compilation of existing BMP information and loading of suitable data into a specially designed database ( and an initial assessment of the results of the analyses of the database (Strecker et. al., 2001). In addition a detailed guidance document on BMP monitoring has been developed, entitled Urban Stormwater BMP Performance Monitoring: A Guidance Manual for Meeting the National Stormwater BMP Database Requirements (available for download at: Many studies have assessed the ability of stormwater treatment BMPs (e.g., wet ponds, grass swales, stormwater wetlands, sand filters, dry detention, etc.) to reduce pollutant concentrations and loadings in stormwater. Although some of these monitoring projects conducted to date have done an excellent job of describing the effectiveness of specific BMPs and BMP systems, there has been a distinct lack of standards and protocols for conducting BMP assessment and monitoring work. These problems become readily apparent for persons seeking to summarize the information gathered from a number of 1

2 individual BMP evaluations. Inconsistent study methods, lack of associated design information, and varying reporting protocols make wide-scale assessments difficult, if not impossible. (Strecker et al. 2001; Urbonas 1994) For example, individual studies often include the analysis of different constituents and utilize different methods for data collection and analysis, as well as report varying degrees of information on BMP design and flow characteristics. The differences in monitoring strategies and data evaluation alone contribute significantly to the wide ranges of BMP efficiency (typically percentage removal) that has been reported in literature to date. Persons seeking to better understand BMP performance have few (if any) scientifically sound resources identifying data on the effectiveness of BMPs for improving stormwater runoff water quality. The National Stormwater Best Management Practices Database represents the only currently available national effort to compile BMP performance monitoring data. The information the database contains and the results stemming from analysis of the data offer the researcher, state or federal regulator, or other stakeholder an opportunity to examine and apply the findings from studies from around the nation (and internationally) to better manage and improve water quality in receiving waters. To that end, the database team has spent considerable time addressing the issue of how performance should be evaluated so that our findings are transferable across studies and provide a rigorous basis for evaluation of performance. One of the major findings of the EPA/ASCE BMP Database efforts to date has been that BMP pollutant removal performance for most pollutants is believed best assessed by the following: (Strecker et. al., 2001): How much stormwater runoff is prevented? (Hydrological Source Control) How much of the runoff that occurs is treated by the BMP or not? Of the runoff treated, what is the effluent quality? For some pollutants the amount of material captured is also important, as well as how the BMP mitigates temperature and/or flow changes. Percent removal of pollutants is a highly problematic method for assessing performance and has resulted in some significant errors in BMP performance reporting (Strecker, et. al., 2001). The Database is being used in a number of ways to directly improve the BMP selection process and better understand how to model and predict BMP performance. The availability of sufficient data to support the application of statistical models of BMP effluent quality is driving long-term pollutant loading estimation methodologies under post-construction scenarios. Although direct benefits to the technical community in the short-term are being realized through the availability of the database and research-friendly flat-files of analysis results, the true success of the project will most clearly be demonstrated though continued population of the database with increasingly useful data sets, practice-wide increased emphasis on collection of statistically valid data, and use of standard monitoring, analysis, and reporting protocols disseminated by the project. 2

3 Recent Findings from the Database The project team has completed a revised assessment of the expanded database. Table 1 presents an overview of the BMPs in analysis data set (as of November 2002), including the number of data records for each BMP type. New BMP information is being provided to the database team at about a rate of 15 to 20 studies per year. These are studies that meet the protocols established for BMP monitoring and reporting. The nearly 200 studies now in the database (August 2003) compares with the total of just over 60 BMP studies in the database during the initial evaluation. Each study has again been analyzed in a consistent manner as described in Strecker, et. al. 2001) and on the project web site. The results now available include basic summary statistics and statistics on log-transformed data, comparisons of influent and effluent water quality through parametric and non-parametric hypothesis tests, tests of normality and log-normality, and tests of equal variance. In this evaluation, the project team has been specifically focused on the effects of BMPs on hydrology and effluent quality. The project team has also for the first time been able to demonstrate the first statistically valid assessments of the relationship between BMP design criteria and performance. Evaluation of Hydrology One of the goals of the database project was to provide better information on the effects of BMPs on hydrology and whether some BMPs may have some benefits over others in terms of reducing volume of runoff (Hydrological Source Control-HSC). For example, one would expect that a wet pond might not significantly decrease the volume of runoff, but a biofilter might, given the contact with more frequently drier soils and resulting evapotranspiration and/or infiltration. Accurately measuring flow during storm conditions is very difficult (EPA, 2002). In a field test of over 20 different flow measurement technologies and approaches, FHWA (2001) found that flow measurements can be upwards of 50% or more off of the expected true flow. Therefore appreciable variability in the hydrologic data in the database is to be expected as a result of measurement error. Figure 1 presents plots of inflow versus outflow for Biofilters (Swales and filter strips), Detention Basins (dry ponds), Retention Ponds (wet ponds), and Wetland Basins. Biofilters showed an average of 20% less volume of runoff on a storm-by-storm basis and were consistently lower for almost all storm events. The other BMPs showed a large scatter, but generally showed an increase in runoff volumes. While showing an increase on a storm-by-storm basis, dry ponds tended to have many more storms that were lower in outflow. Figure 2, shows the distribution of the ratio of total outflow volumes to inflow volumes for a number of BMP types based on data available in the Database. From these analyses, it is apparent that detention basins (dry ponds) and biofilters (vegetated swales, overland flow, etc.) appear to contribute significantly to volume reductions, even though they were likely not specifically designed to do so. Based upon the recommended criteria above for assessing BMP performance, it appears that there is a basis for factoring in volume and resulting pollutant load reductions into BMP 3

4 performance. Since the majority of BMPs in the database were not designed to explicitly provide volume reduction, it is expected that as BMPs that are specifically designed to meet this goal (e.g., lower impact development, etc.) are tested and information added into the database, that a similar analysis would yield results which echo this shift in the design paradigm. Table 1. Number of BMPs and Data Records (events or event mean concentrations) in the National BMP Database as of Spring # of BMPs in Category with Design Information Precipitation Records for BMP type Flow Records for Water Quality Records for BMP Type Detention Basins Grass Filter Strips ,251 Media Filter ,144 Porous Pavement Retention Pond ,293 Percolation Trench and Dry Well Wetland Channel and Swale ,241 Wetland Basin ,320 Hydrodynamic Devices ,186 Total ,869 45,720 Water Quality Performance The analysis of water quality performance data of the BMPs that have been conducted by the authors is comprised of three levels: 1) a comprehensive evaluation of effluent versus influent water quality; 2) comparisons of effluent quality amongst BMP types; and 3) comparisons of performance versus design attributes for BMP types and individual BMPs. Even with the increase in data in the database since the last evaluation, the total number of BMPs in any one category is still small as compared to the number of design parameters that can be potentially investigated. The approach that the team has taken is to develop groupings of BMPs by design factors. That is, our approach has been to develop categories of design parameters that are expected to affect performance, group BMPs into those that meet all or most of the factors (e.g., length to width ratios; volume of facility as compared to average storm inflow, etc.) and then explore if a difference in performance can be established and potentially explained by these assessments of these grouped design factors. 4

5 Figure 1. Comparison of Individual Storm Inflow and Outflow Volumes for Indicated BMPs (N= number of BMPs included; n= number of storm events) Outflow (watershed inches) Outflow (watershed inches) Biofilters (N=16) (Swale and Filter Strips) Inflow (watershed inches) Retention Ponds (N=20) (Wet Ponds) Average Ratio (Out/In) = Inflow (watershed inches) 5 Outflow (watershed inches) Outflow (watershed inches) Detention Basins (N=11) (Dry Ponds) n=144 n=75 Average Ratio (Out/In) = Inflow (watershed inches) n=276 n= Average Ratio (Out/In) = 1.12 Wetland Basins (N=10) Average Ratio (Out/In) = Inflow (watershed inches) Figure 3 presents plots shows a box plot of the fractions of Total Suspended Solids (TSS) removed and the box plots of effluent quality of BMP types. As has been found previously (Strecker et. al., 2001), the effluent quality is much less variable than fraction removed. It appears that percent removal is more or less just a function of inflow concentration. Recent analysis of the expanded database shows that effluent quality can be assumed to be different among different BMP types. It appears that retention ponds (wet ponds) and Wetlands can achieve lower concentrations of TSS than other BMPs, while hydrodynamic devices were the lowest performers (higher effluent concentrations) on average. Similar results have been found for other constituents with some variations. Figure 4 shows the result for comparing Total Phosphorus and Total Copper concentrations for the same BMP categories. Wetlands and wet ponds are more consistent performers, while the other BMPs vary with regards to effluent quality results. The lowest effluent quality achieved for Phosphorus is on the order of 50 to 60 ug/l. This contrasts with some TMDLs where the ultimate phosphorus goal has been 10 to 20 ug/l and showing achievement of such goals by misapplication of percent removal approaches. As mentioned above, the project team has begun to explore individual BMP designs (sizing, etc.) relative to performance. Some initial results of the expanded database have been encouraging. For example, the previous effort during the initial work was not able to statistically find a potential relationship between performance of retention ponds and wetlands and their treatment volume relative to measured storm events.

6 Something that the authors expected to see for suspended solids. Figure 5 shows a scatter plot of Retention Ponds (with a permanent pool) effluent quality versus the ratio of the treatment volume to mean monitored storm event volume, and a box plot of Retention Pond mean effluent quality for sites with a ratio less than one and greater than one. The plots clearly demonstrate that at those sites where the treatment volume was greater than the average size storm event monitored, the effluent quality was significantly lower. It was also demonstrated that the central tendency influent concentrations were not statistically different between the large and small ponds (Figure 6). In addition, the variability of effluent quality for the larger retention ponds was lower. These results are expected, but it is one of the first times that they have been demonstrated statistically. Figure 2. Box and Whisker Plot of the Ratio of Total Monitored Storm Outflow to Inflow Volumes by. Ratio of Total Monitored Outflow to Total Monitored Inflow Biofilters Detention Ponds Hydrodynamic Devices Media Filters Retention Ponds Wetland Basins Wetland Channels Upper Inner Fence 3rd Quartile Upper 95% CL Median Lower 95% CL 1st Quartile Lower Inner Fence Outside Value Figure 3 - Box Plots of the Fractions of Total Suspended Solids (TSS) Removed and of Effluent Quality of Selected s. Fraction of TSS Removed TSS (mg/l) Upper Inner Fence 3 rd Quartile Upper 95% CL Median Lower 95% CL 1 st Quartile Lower Inner Fence Outside Value Bioswales Detention Basins Media Filters Hydro - Dynamic Devices Retention Basins Wetlands Bioswales Detention Basins Hydro Dynamic Devices Retention Basins Wetlands 6

7 Figure 4 - Box Plots of Effluent Quality of Selected s for Total Phosphorus and Total Copper. Total Phosphorus (mg/l) Total Copper (mg/l) Upper Inner Fence 3 rd Quartile Upper 95% CL Median Lower 95% CL 1 st Quartile Lower Inner Fence Outside Value 01 Bioswales Detention Media Hydro - Retention Wetlands Basins Filters Dynamic Basins Devices 0.1 Bioswales Detention Media Hydro - Basins Filters Dynamic Devices Retention Basins Wetlands Recent Applications - Probabilistic Modeling of Effluent Concentrations As described previously in this paper, the authors strongly encourage describing BMP performance in terms of measures that are readily transferable between sites, specifically a statistical characterization of achievable effluent quality. GeoSyntec and a number of researchers around the country are currently using the Database as a primary data source for a number of local, state, and national projects. Stochastic Loading Analysis Under Post BMP Conditions Deterministic approaches for estimating loading to receiving waters from non-point sources fall short in providing any means for assessing uncertainty in the predicted results. The authors have applied Monte Carlo analysis tools for predicating the combined effect of complex environmental phenomena on long-term loading to water bodies. This approach (and a number of similar statistical modeling approaches) can be used to provide a means for assessing loading where the stochastic behavior of the following are well defined: Runoff event volumes from long term continuous simulation hydrologic models (e.g., SWMM). Flows in receiving waters. Runoff event mean concentrations (available from historical or nearby land use or similar monitoring) for pre-development conditions. BMP effluent concentrations (for specific BMPs this data is available from the Database). An example is provided in Figure 7 below. One of the major concerns for researches engaged in watershed level modeling efforts is accounting of uncertainty. The use of the Database in combination with modeling approaches that explicitly account for uncertainty provides significant additional understanding of our ability to accurately predict the results of BMP implementation projects. 7

8 Current National Level Research Projects Using the Database Two major research projects are currently underway that are focusing BMP performance at the unit process level and both projects are using the Database project findings and accumulated monitoring study data sets as major data sources. These projects are examples of how the Database project team anticipates the data will be used by the technical community on other similar projects, both now and into the future as the database continues to grow. In both studies the Database will be used a source of BMP level event mean concentration, environmental, and design data. The technical community is often divided on the use of analytical models versus the use of black box empirical analyses of monitoring data. These two projects demonstrate that these approaches are not mutually exclusive and that a combination of analytical models and empirical data analysis is most likely our best chance of better understanding structural control performance. The first of these two projects is the Water Environment Research Foundation Project (WERF) Critical Assessment of Stormwater Control Selection Issues. The overall goal of the WERF project is to provide guidance for selection and sizing of most stormwater runoff quality controls (Best Management Practices BMPs) based on literature information, available data, and fundamental principles of unit operations to the extent possible. The purpose of the WERF project is to apply fundamental environmental engineering principles of unit operations to evaluation and selection of BMPs for urban and urbanizing areas. The emphasis of the project is on structural BMPs for urban stormwater runoff quality control. The work includes an evaluation of the ability of some BMPs to reduce the volumes of runoff and therefore reduce pollutant loads as well as reduce physical stream impacts. The second project using the database as a primary data source is the National Cooperative Highway Research Program (NCHRP) project Evaluation of Best Management Practices for Highway Runoff Control. The purpose of the NCHRP project is to evaluate the basic scientific and technical criteria that can be used for the quantitative assessment of wet-weather flow (WWF) control alternatives for highways and other highway-related facilities, and to apply the results of the evaluation for use in facilitating effective implementation of these controls. The Database is a primary source of data on structural controls in the near-highway environment and is being used in a manner similar to the project approach for the above described WERF project. Where analytical models can be developed, the Database is being used as a source of model calibration data marrying the empirical black box approach of EMC statistical evaluations with intra-event analytical and numerical modeling. 8

9 Figure 5 - Scatter Plot of 1) Retention Pond (with permanent pool) TSS Effluent Quality Versus the Ratio of the Permanent Pool Volume to Mean Monitored Effluent Volume and 2) Box Plots of the TSS Effluent Quality of Sites Grouped by a Ratio of Less Than or Greater Than 1 for the Ratio of the Permanent Pool Volume to Mean Monitored Effluent Volume. Effluent Concentration TSS (mg/l) Permanent Pool Volume (watershed meters) Effluent Concentration TSS (mg/l) <1 >1 Permanent Pool Volume (watershed meters) Mean Monitored Effluent Volume (watershed meters) Mean Monitored Effluent Volume (watershed meters) Figure 6 - Box Plot of TSS Influent Quality of Sites Grouped by a Ratio of Less Than or Greater Than 1 for the Ratio of the Permanent Pool Volume to Mean Monitored Effluent Volume. No Difference in Central Tendency is Seen Between Two Groups. Influent Concentration TSS (mg/l) <1 >1 Permanent Pool Volume (watershed meters) Mean Monitored Effluent Volume (watershed meters) Upper Inner Fence 3 rd Quartile Upper 95% CL Median Lower 95% CL 1 st Quartile Lower Inner Fence Outside Value 9

10 Figure 7 - Cumulative Distribution Function for Effluent Total Lead Concentration for Retention Ponds. Probability EMC is Less Than or Equal To Value Shown 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% th 75 Percentile Criteria, 18.6 ug/l Criteria, 477 ug/l Concentration (ug/l) Acknowledgements The project team would like to thank Jesse Pritts, P.E. and Eric Strassler of the Environmental Protection Agency for their support for and participation in the ASCE/EPA National Stormwater Best Management Practices Database Project. The authors would also like to thank the members of ASCE s Urban Water Resources Research Council for their thorough reviews and major contributions to the Database Project: Robert Pitt, P.E., Ph.D., Eugene Driscoll, P.E., Roger Bannerman, P.E., Shaw Yu, P.E., Ph.D., Betty Rushton, Richard Field P.E., Jonathan Jones, P.E., Jane Clary, and Tom Langan. The initial database evaluation findings discussed in this paper are those found by the authors only and not the listed individuals above. REFERENCES EPA (2002). Urban Stormwater BMP Performance Monitoring: A Guidance Manual for Meeting the National Stormwater BMP Database Requirements. EPA 821-C U.S. Environmental Protection Agency, Office of Water, Washington, D.C. April. FHWA. (2001). Guidance Manual for Monitoring Highway Runoff Water Quality. FHWA-EP Federal Highway Administration, U.S. Department of Transportation. Federal Highway Administration. Washington, D.C. Strecker, E.W., M.M. Quigley, B.R. Urbonas, J.E. Jones, and J.K. Clary. (2001). Determining Urban Storm Water BMP Effectiveness. Journal of Water Resources Planning and Management, 127(3), Strecker, E.W. (1994). Constituents and Methods for Assessing BMPs. In Proceedings of the Engineering Foundation Conference on Storm Water Monitoring. August 7-12, Crested Butte, CO. Urbonas, B.R. (1994). Parameters to Report with BMP Monitoring Data. In Proceedings of the Engineering Foundation Conference on Storm Water Monitoring. August 7-12, Crested Butte, CO. 10

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