Selection Of Best Management Practices Research Articles

Stormwater runoff to an impaired lake: impacts and solutions.

Stormwater runoff can represent a significant source of pollutants to downstream water bodies. An integrated assessment was conducted for the Spring Lake watershed in western Michigan because of concerns that increased impervious land cover in the watershed, especially in sub-basins adjacent to Spring Lake, would result in greater stormwater runoff and pollutant loads. Spring Lake has a history of high total phosphorus (TP) concentrations and cyanobacterial blooms. An alum treatment, paid for by an assessment, was applied to Spring Lake in 2005 to control internal phosphorus loading; hence, there was an economic incentive for stakeholders to limit new phosphorus from entering the lake, which otherwise would reduce the long-term efficacy of the alum treatment. This study provides a novel six-step process that identifies priority areas and optimally reduces nonpoint sources of pollution. We identified a suite of best management practices to be placed in the watershed, assessed their optimal locations based on pollutant sources, and modeled the degree to which their implementation would reduce TP and total suspended solids. Application of the modeled best management practices (BMPs) resulted in a 15 % reduction in TP load and a 17 % reduction in total suspended solid load. Reductions were not uniform throughout the watershed, with the greatest reductions closest to Spring Lake. We also developed a flow chart for BMP selection, which may be transferable to other watersheds with similar issues.

Spatial optimization of watershed management practices for nitrogen load reduction using a modeling-optimization framework

Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions.

Developing and testing a best management practices tool for estimating effectiveness of nonpoint source pollution control

Best management practices (BMPs) are being implemented to reduce nonpoint sources pollution in China and worldwide. The effectiveness of individual BMPs is usually assessed through controlled experiments. Field evaluation of the effectiveness of a great majority of BMPs is limited by cost of implementation, site-specific response, and length of time needed to document reliable response. Nonpoint source pollution (NPS) models are increasingly relied upon to estimate water quality response to BMP implementation. This study has developed and tested a tool for estimating the effectiveness of BMPs in controlling NPS pollution by collecting and analyzing existing data from previous studies. It includes 60 BMPs grouped into six classes. Data from combined soil and slope analyses were used to design a BMP effectiveness estimator driven by hydrologic soil groups and slope classes using Visual Basic for Applications and Structured Query Language. This tool includes five primary functions, namely: (1) effectiveness assessment for single or combined BMP, (2) statistical analyses for BMP effectiveness; (3) informational inquiry about individual BMPs; (4) updating and editing the BMPs database; and (5) external expansion as new information becomes available. The tool was tested and validated for BMPs selection in a 27.28 km2 watershed, located within the upper watershed of the Miyun Reservoir in Beijing. Results demonstrate that the tool can be served as an efficient and easy tool for identifying potentially useful BMPs in agricultural areas in China. Overall, this will also allow water environmental professionals to make more confident and accurate BMP recommendations and manage watersheds more effectively.

Agricultural costs of the Chesapeake Bay total maximum daily load.

This study estimates costs to agricultural producers of the Watershed Implementation Plans (WIPs) developed by states in the Chesapeake Bay Watershed to comply with the Chesapeake Bay total maximum daily load (TMDL) and potential cost savings that could be realized by a more efficient selection of agricultural Best Management Practices (BMPs) and spatial targeting of BMP implementation. The cost of implementing the WIPs between 2011 and 2025 is estimated to be about $3.6 billion (in 2010 dollars). The annual cost associated with full implementation of all WIP BMPs from 2025 onward is about $900 million. Significant cost savings can be realized through careful and efficient BMP selection and spatial targeting. If retiring up to 25% of current agricultural land is included as an option, Bay-wide cost savings of about 60% could be realized compared to the WIPs.

Pollution characteristics of lead, zinc, arsenic, and cadmium in short-term storm water roof runoff in a suburban area

Pollution of hazardous elements in roof runoff has been a potential danger to aquatic ecosystem as documented in previous studies. In this study, concentrations of lead (Pb), zinc (Zn), arsenic (As), and cadmium (Cd) in roof runoff were measured for short-term rainfall events. The concentrations of the selected elements varied to a high degree during the rainfall period, and the well-known first-flush phenomenon was not observed due to the short-term rainfall patterns. Based on event mean concentrations (EMCs) and fluxes, the elemental pollution may be ranked as Zn >> Pb > As > Cd. The average levels of Pb and Zn exceeded the fourth class value and fifth class value of the environmental quality standards for surface water in China. The relative high pollution could be caused by atmospheric deposition and the erosion of crust steel appurtenance installed above the rooftop. Furthermore, elements in particulate form dominated the roof runoff pollution, and the ratios of elements in particulate form to total amounts were 0.6 (As), 0.9 (Pb), 0.7 (Cd), and 0.5 (Zn). This study cleared the pollution characteristics of hazardous elements in short-term storm water roof runoff; it was useful for the selection of best management practices.

Spatial Optimization of Best Management Practices to Attain Water Quality Targets

Diffuse nutrient loads are a common problem in developed and agricultural watersheds. While there has been substantial investment in best management practices (BMPs) to reduce diffuse pollution, there remains a need to better prioritize controls at the watershed scale as reflected in recent US-EPA guidance for watershed planning and Total Maximum Daily Load development. We implemented spatial optimization techniques among four diffuse source pathways in a mixed-use watershed in Northern Vermont to maximize total reduction of phosphorus loading to streams while minimizing associated costs. We found that within a capital cost range of 138 to 321 USD ha-1 a phosphorus reduction of 0.29 to 0.38 kg ha−1 year−1, is attainable. Optimization results are substantially more cost-effective than most scenarios identified by stakeholders. The maximum diffuse phosphorus load reduction equates to 1.25 t year−1using the most cost-effective technologies for each diffuse source at a cost of $3,464,260. However, 1.13 t year−1 could be reduced at a much lower cost of $976,417. This is the practical upper limit of achievable diffuse phosphorus reduction, above which additional spending would not result in substantially more phosphorus reduction. Watershed managers could use solutions along the resulting Pareto optimal curve to select optimal combinations of BMPs based on a water quality target or available funds. The results demonstrate the power of using spatial optimization methods to arrive at a cost-effective selection of BMPs and their distribution across a landscape.

A Web-Based Model to Estimate the Impact of Best Management Practices

The Spreadsheet Tool for the Estimation of Pollutant Load (STEPL) can be used for Total Maximum Daily Load (TMDL) processes, since the model is capable of simulating the impacts of various best management practices (BMPs) and low impact development (LID) practices. The model computes average annual direct runoff using the Soil Conservation Service Curve Number (SCS-CN) method with average rainfall per event, which is not a typical use of the SCS-CN method. Five SCS-CN-based approaches to compute average annual direct runoff were investigated to explore estimated differences in average annual direct runoff computations using daily precipitation data collected from the National Climate Data Center and generated by the CLIGEN model for twelve stations in Indiana. Compared to the average annual direct runoff computed for the typical use of the SCS-CN method, the approaches to estimate average annual direct runoff within EPA STEPL showed large differences. A web-based model (STEPL WEB) was developed with a corrected approach to estimate average annual direct runoff. Moreover, the model was integrated with the Web-based Load Duration Curve Tool, which identifies the least cost BMPs for each land use and optimizes BMP selection to identify the most cost-effective BMP implementations. The integrated tools provide an easy to use approach for performing TMDL analysis and identifying cost-effective approaches for controlling nonpoint source pollution.

Comparing the Selection and Placement of Best Management Practices in Improving Water Quality Using a Multiobjective Optimization and Targeting Method

Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA) and a watershed model (Soil and Water Assessment Tool—SWAT). For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS), and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.

Multi‐objective optimization for diffuse pollution control at zero cost

AbstractAgricultural best management practices (BMPs) are gaining ground as a means of mitigating diffuse nutrient pollution of surface waters in agricultural catchments; however, their cost‐effectiveness depends on the location‐specific characteristics of the land on which they are applied. To identify acceptable catchment management solutions with respect to environmental and economic objectives, a decision support tool (DST) is used in this study. The DST integrates the river basin soil and water assessment tool (SWAT) model that serves as the nonpoint source pollution estimator into an optimization framework consisting of a multi‐objective genetic algorithm that searches for optimal selection and location of BMPs in the landscape. A three‐objective optimization problem has been previously solved for the Arachtos catchment in western Greece including the implementation costs of several types of BMPs such as nutrient application, crop, soil and livestock management and total annual diffuse losses of total phosphorus (TP) and nitrate‐nitrogen (NO3‐N) from land to surface waters. In the present study, a solution of negligible total cost for the whole catchment was selected from optimal two‐dimensional trade‐off curves of cost‐TP and cost‐NO3‐N, aiming to complement previously analysed management options and further enhance decision‐making in this catchment. The zero cost solution led to 30 and 20% reductions in TP and NO3‐N river concentrations, respectively, corresponding to contour cultivation without tillage in corn, fertilizer management in alfalfa as well as livestock and manure management along with the establishment of filter strips at the edge of some corn and pastureland fields. The proposed methodology enabled the identification of a low cost, and possibly more favourable, compared to previous findings, combination of BMPs that ensures good quality of river water. It helps to provide the basis for sustainable land‐use planning and management in large agricultural landscapes, thus aiding decision‐making and cost‐effective implementation of Environmental Directives.

Implementation of BMP Strategies for Adaptation to Climate Change and Land Use Change in a Pasture-Dominated Watershed

Implementing a suite of best management practices (BMPs) can reduce non-point source (NPS) pollutants from various land use activities. Watershed models are generally used to evaluate the effectiveness of BMP performance in improving water quality as the basis for watershed management recommendations. This study evaluates 171 management practice combinations that incorporate nutrient management, vegetated filter strips (VFS) and grazing management for their performances in improving water quality in a pasture-dominated watershed with dynamic land use changes during 1992–2007 by using the Soil and Water Assessment Tool (SWAT). These selected BMPs were further examined with future climate conditions (2010–2069) downscaled from three general circulation models (GCMs) for understanding how climate change may impact BMP performance. Simulation results indicate that total nitrogen (TN) and total phosphorus (TP) losses increase with increasing litter application rates. Alum-treated litter applications resulted in greater TN losses, and fewer TP losses than the losses from untreated poultry litter applications. For the same litter application rates, sediment and TP losses are greater for summer applications than fall and spring applications, while TN losses are greater for fall applications. Overgrazing management resulted in the greatest sediment and phosphorus losses, and VFS is the most influential management practice in reducing pollutant losses. Simulations also indicate that climate change impacts TSS losses the most, resulting in a larger magnitude of TSS losses. However, the performance of selected BMPs in reducing TN and TP losses was more stable in future climate change conditions than in the BMP performance in the historical climate condition. We recommend that selection of BMPs to reduce TSS losses should be a priority concern when multiple uses of BMPs that benefit nutrient reductions are considered in a watershed. Therefore, the BMP combination of spring litter application, optimum grazing management and filter strip with a VFS ratio of 42 could be a promising alternative for use in mitigating future climate change.

Optimization Model for BMP Placement in a Reservoir Watershed

The purpose of this study was to develop an optimization model for the optimal placement of structural best management practices (BMPs) at the watershed scale for a reservoir. In this research, the authors considered three types of structural BMPs (i.e., a wet detention pond, a grass swale, and an infiltration trench). The complete model consists of three interacting submodels: a watershed water quality simulation model [the Hydrologic Simulation Program-FORTRAN (HSPF) model], a reservoir water quality model (the CE-QUAL-W2 model); and an optimization model that uses a well-developed genetic algorithm (GA) software. This research used the Feitsui Reservoir and its watershed in northern Taiwan as the area of application. For the optimization model, the objective function was to minimize the total annual cost of BMPs, within the constraints of achieving minimum water quality standards for the concentrations of phosphorus, ammonium, nitrate-nitrite and for the total suspended solids in the reservoir. The study first simulated the discharge and pollutant loads for the watershed in 2002–2003 by using HSPF. The reservoir model CE-QUAL-W2 was then used to simulate variations in pollutant concentrations in the reservoir on the basis of the input discharge and pollutant loads from HSPF. The optimization model was finally used to search for the near-optimal selection and placement of BMPs in the watershed. The results obtained from the completed model effectively demonstrate its viability in improving water quality in the reservoir by adopting optimal BMPs placement strategies.

Decision support for diffuse pollution management

The effort to manage diffuse pollution at the catchment scale is an ongoing challenge that needs to take into account trade-offs between environmental and economic objectives. Best Management Practices (BMPs) are gaining ground as a means to address the problem, but their application (and impact) is highly dependant on the characteristics of the crops and of the land in which they are to be applied. In this paper, we demonstrate a new methodology and associated decision support tool that suggests the optimal location for placing BMPs to minimise diffuse surface water pollution at the catchment scale, by determining the trade-off among economic and multiple environmental objectives. The decision support tool consists of a non-point source (NPS) pollution estimator, the SWAT (Soil and Water Assessment Tool) model, a genetic algorithm (GA), which serves as the optimisation engine for the selection and placement of BMPs across the agricultural land of the catchment, and of an empirical economic function for the estimation of the mean annual cost of BMP implementation. In the proposed decision support tool, SWAT was run a number of times equal to the number of tested BMPs, to predict nitrates nitrogen (N-NO3) and total phosphorus (TP) losses from all the agricultural Hydrologic Response Units (HRUs) and possible BMPs implemented on them. The results were then saved in a database which was subsequently used for the optimisation process. Fifty different BMPs, including sole or combined changes in livestock, crop, soil and nutrient application management in alfalfa, corn and pastureland fields, were evaluated in the reported application of the tool in a catchment in Greece, by solving a three-objective optimisation process (cost, TP and N-NO3). The relevant two-dimensional trade-off curves of cost-TP, cost-N-NO3 and N-NO3–TP are presented and discussed. The strictest environmental target, expressed as a 45% reduction of TP at the catchment outlet, which also resulted in a 25% reduction of the annual N-NO3 yield was met at an affordable annual cost of 25 €/person by establishing an optimal combination of BMPs. The methodology could be used to assist in a more cost-effective implementation of environmental legislation.

Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-II based search method. The model was tested for the selection and placement of BMPs in Wildcat Creek Watershed, a corn dominated watershed located in northcentral Indiana, to reduce nitrogen, phosphorus, sediment, and pesticide losses from the watershed. The Pareto optimal fronts (plotted as spider plots) generated between the optimized objective functions can be used to make management decisions to achieve desired water quality goals with minimum BMP implementation and maintenance cost for the watershed. Also these solutions were geographically mapped to show the locations where various BMPs should be implemented. The solutions with larger pollution reduction consisted of buffer filter strips that lead to larger pollution reduction with greater costs compared to other alternatives.

Selection and placement of best management practices used to reduce water quality degradation in Lincoln Lake watershed

An increased loss of agricultural nutrients is a growing concern for water quality in Arkansas. Several studies have shown that best management practices (BMPs) are effective in controlling water pollution. However, those affected with water quality issues need water management plans that take into consideration BMPs selection, placement, and affordability. This study used a nondominated sorting genetic algorithm (NSGA‐II). This multiobjective algorithm selects and locates BMPs that minimize nutrients pollution cost‐effectively by providing trade‐off curves (optimal fronts) between pollutant reduction and total net cost increase. The usefulness of this optimization framework was evaluated in the Lincoln Lake watershed. The final NSGA‐II optimization model generated a number of near‐optimal solutions by selecting from 35 BMPs (combinations of pasture management, buffer zones, and poultry litter application practices). Selection and placement of BMPs were analyzed under various cost solutions. The NSGA‐II provides multiple solutions that could fit the water management plan for the watershed. For instance, by implementing all the BMP combinations recommended in the lowest‐cost solution, total phosphorous (TP) could be reduced by at least 76% while increasing cost by less than 2% in the entire watershed. This value represents an increase in cost of $5.49 ha−1 when compared to the baseline. Implementing all the BMP combinations proposed with the medium‐ and the highest‐cost solutions could decrease TP drastically but will increase cost by $24,282 (7%) and $82,306 (25%), respectively.

Improvement of Urban Drainage System Performance under Climate Change Impact: Case Study

Urban floods have adverse impacts on the performance of urban infrastructures and the life of residents. The floods cause heavy damages and perturbation in the serviceability of urban infrastructures as well as transportation. Therefore, different factors affecting the urban water flood characteristics should be considered in urban development planning, especially in metropolitan areas. In recent years, climate change and its consequences have affected the total components of the water cycle as well as floods. These effects are intensified in urban areas because of the anthropogenic effects they have on the water cycle, such as reducing the infiltration capacity of basins, construction regardless of the channel’s right of way, and disposal of sediment and solid wastes into channels that will decrease the channels’ safe carrying capacity. In this way, incorporating climate change impact on urban water studies could help to achieve more reliable results to be applied in real-time planning of urban areas through selection of best management practices (BMPs). In this study, an algorithm for selecting the BMPs to improve the system performance and reliability in dealing with urban flash floods is proposed that considers the anthropogenic and climate change effects. The suggested algorithm is applied to the Tehran metropolitan area drainage system as a case study. First, the future rainfall pattern of the study area under climate change impact is simulated. Then, the effectiveness of present and future development projects for improvement of drainage system performance is evaluated under different scenarios. Also, the effect of solid wastes and sediments carried with surface runoff in system performance is considered. Finally, feasibility of suggested BMPs and their effectiveness in urban flood management as well as their related costs and benefits are considered. The results of the study show the significance of using analytical and management tools in assessing and improving the urban drainage system.

Application of the Analytical Hierarchical Process for Improved Selection of Storm-Water BMPs

Engineers and planners responsible for the selection and design of storm-water best management practices (BMPs) face a formidable array of practices and techniques for managing the quality of urban runoff. Primary factors affecting the choice of BMP invariably include the performance capability of a given class of BMPs and the new impervious cover fraction arising from development projects. We introduce the analytical hierarchal process (AHP) as an analytical framework for BMP selection, through priority ranking of storm water control objectives and BMP performance metrics. Using the AHP, a BMP priority ranking is accomplished through the construction of pairwise relative rankings of the selection criteria and then of the BMPs themselves against the respective selection criteria. The use of normalized ranking vectors, obtained through eigenvector approximation, is employed to provide a final ranking of BMP choices that then can be carefully reviewed by the design engineer. Two BMP selection applications a...

Development of a multiobjective optimization tool for the selection and placement of best management practices for nonpoint source pollution control

Best management practices (BMPs) are effective in reducing the transport of agricultural nonpoint source pollutants to receiving water bodies. However, selection of BMPs for placement in a watershed requires optimization of the available resources to obtain maximum possible pollution reduction. In this study, an optimization methodology is developed to select and place BMPs in a watershed to provide solutions that are both economically and ecologically effective. This novel approach develops and utilizes a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The BMP tool replaces the dynamic linkage of the distributed parameter watershed model during optimization and therefore reduces the computation time considerably. Total pollutant load from the watershed, and net cost increase from the baseline, were the two objective functions minimized during the optimization process. The optimization model, consisting of a multiobjective genetic algorithm (NSGA‐II) in combination with a watershed simulation tool (Soil Water and Assessment Tool (SWAT)), was developed and tested for nonpoint source pollution control in the L'Anguille River watershed located in eastern Arkansas. The optimized solutions provided a trade‐off between the two objective functions for sediment, phosphorus, and nitrogen reduction. The results indicated that buffer strips were very effective in controlling the nonpoint source pollutants from leaving the croplands. The optimized BMP plans resulted in potential reductions of 33%, 32%, and 13% in sediment, phosphorus, and nitrogen loads, respectively, from the watershed.

A Multilevel Model of the Impact of Farm‐Level Best Management Practices on Phosphorus Runoff1

Abstract: Multilevel or hierarchical models have been applied for a number of years in the social sciences but only relatively recently in the environmental sciences. These models can be developed in either a frequentist or Bayesian context and have similarities to other methods such as empirical Bayes analysis and random coefficients regression. In essence, multilevel models take advantage of the hierarchical structure that exists in many multivariate datasets; for example, water quality measurements may be taken from individual lakes, lakes are located in various climatic zones, lakes may be natural or man‐made, and so on. The groups, or levels, may effectively yield different responses or behaviors (e.g., nutrient load response in lakes) that often make retaining group membership more effective when developing a predictive model than when working with either all of the data together or working separately with the individuals. Here, we develop a multilevel model of the impact of farm level best management practices (BMPs) on phosphorus runoff. The result of this research is a model with parameters which vary with key practice categories and thus may be used to evaluate the effectiveness of these practices on phosphorus runoff. For example, it was found that the effect of fertilizer application rate on farm‐scale phosphorus loss is a function of the application method, the hydrologic soil group, and the land use (crop type). Further, results indicate that the most effective method for controlling fertilizer loss is through soil injection. In summary, the resultant multilevel model can be used to estimate phosphorus loss from farms and hence serve as a useful tool for BMP selection.

A Tool for Estimating Best Management Practice Effectiveness in Arkansas

Best management practices (BMPs) are being implemented across the United States to combat water pollution by nutrient and sediment often with little knowledge about the actual effectiveness of the BMPs. Previously, most BMP studies have focused on determining the effectiveness of a single BMP; often the knowledge acquired from these individual studies has been applicable only to the study site. However, these studies can be compiled over a wide range of site conditions and used collectively to obtain reliable estimates of BMP effectiveness. In this regard, a BMP tool was developed for use in Arkansas. The underlying database contains over 120 references and includes 163 agricultural BMPs grouped into 14 classes and 147 urban BMPs grouped into 8 classes. This tool will facilitate effectiveness-based BMP selection for agricultural and urban applications by providing BMP effectiveness estimates based on site characteristics. Because of the State's rapid urban development and heavy focus on agriculture, this tool will find use among watershed planners and local and state agencies alike. The tool can be used as a standalone application or can be linked with other applications. This tool can also be easily expanded to include data from other areas beyond Arkansas and the surrounding region or be used as a template for other BMP tools. This tool, along with a users' manual, will be available for no charge to any interested user.

BMP Analysis System for Watershed-Based Stormwater Management

Best Management Practices (BMPs) are measures for mitigating nonpoint source (NPS) pollution caused mainly by stormwater runoff. Established urban and newly developing areas must develop cost effective means for restoring or minimizing impacts, and planning future growth. Prince George's County in Maryland, USA, a fast-growing region in the Washington, DC metropolitan area, has developed a number of tools to support analysis and decision making for stormwater management planning and design at the watershed level. These tools support watershed analysis, innovative BMPs, and optimization. Application of these tools can help achieve environmental goals and lead to significant cost savings. This project includes software development that utilizes GIS information and technology, integrates BMP processes simulation models, and applies system optimization techniques for BMP planning and selection. The system employs the ESRI ArcGIS© as the platform, and provides GIS-based visualization and support for developing networks including sequences of land uses, BMPs, and stream reaches. The system also provides interfaces for BMP placement, BMP attribute data input, and decision optimization management. The system includes a stand-alone BMP simulation and evaluation module, which complements both research and regulatory nonpoint source control assessment efforts, and allows flexibility in the examining various BMP design alternatives. Process based simulation of BMPs provides a technique that is sensitive to local climate and rainfall patterns. The system incorporates a meta-heuristic optimization technique to find the most cost-effective BMP placement and implementation plan given a control target, or a fixed cost. A case study is presented to demonstrate the application of the Prince George's County system. The case study involves a highly urbanized area in the Anacostia River (a tributary to Potomac River) watershed southeast of Washington, DC. An innovative system of management practices is proposed to minimize runoff, improve water quality, and provide water reuse opportunities. Proposed management techniques include bioretention, green roof, and rooftop runoff collection (rain barrel) systems. The modeling system was used to identify the most cost-effective combinations of management practices to help minimize frequency and size of runoff events and resulting combined sewer overflows to the Anacostia River.