A WATER QUALITY‐BASED APPROACH FOR WATERSHED WIDE BMP STRATEGIES1

ABSTRACT: Watershed management strategies generally involve controlling nonpoint source pollution by implementing various best management practices (BMPs). Currently, stormwater management programs in most states use a performance‐based approach to implement onsite BMPs. This approach fails to link the onsite BMP performance directly to receiving water quality benefits, and it does not take into account the combined treatment effects of all the stormwater management practices within a watershed. To address these issues, this paper proposes a water quality‐based BMP planning approach for effective nonpoint source pollution control at a watershed scale. A coupled modeling system consisting of a watershed model (HSPF) and a receiving water quality model (CE‐QUAL‐W2) was developed to establish the linkage between BMP performance and receiving water quality targets. A Monte Carlo simulation approach was utilized to develop alternative BMP strategies at a watershed level. The developed methodology was applied to the Swift Creek Reservoir watershed in Virginia, and the results show that the proposed approach allows for the development of BMP strategies that lead to full compliance with water quality requirements.     

HYDROLOGIC MODELING OF A BIOINFILTRATION BEST MANAGEMENT PRACTICE1

ABSTRACT: The goal of this research was to develop a methodology for modeling a bioinfiltration best management practice (BMP) built in a dormitory area on the campus of Villanova University in Pennsylvania. The objectives were to quantify the behavior of the BMP through the different seasons and rainfall events; better understand the physical processes governing the system's behavior; and develop design criteria. The BMP was constructed in 2001 by excavating within an existing traffic island, backfilling with a sand/soil mixture, and planting with salt tolerant grasses and shrubs native to the Atlantic shore. It receives runoff from the asphalt (0.26 hectare) and turf (0.27 hectare) surfaces of the watershed. Monitoring supported by the hydrologic model shows that the facility infiltrates a significant fraction of the annual precipitation, substantially reducing the delivery of nonpoint source pollution and erosive surges downstream. A hydrologic model was developed using HEC‐HMS to represent the site and the BMP using Green‐Ampt and kinematic wave methods. Instruments allow comparison of the modeled and measured water budget parameters. The model, incorporating seasonally variable parameters, predicts the volumes infiltrated and bypassed by the BMP, confirming the applicability of the selected methods for the analysis of bioinfiltration BMPs.     

Trip-Based Explanatory Variables For Estimating Vehicle Fuel Consumption and Emission Rates

The current state-of-practice in the US for estimatingvehicle emissions is based on a single traffic-relatedexplanatory variable, namely average speed. Research,however, has demonstrated that the use of average speed asa single traffic-related variable is insufficient for theestimation of vehicle emissions. For example, although theEnvironmental Protection Agency (EPA) MOBILE5 model wouldindicate that a slowing of traffic typically increasesemissions, empirical research indicates the opposite inmany cases.The objective of this paper is to identify criticalaggregate trip variables as potential explanatory variablesfor the estimation of a vehicle's fuel consumption andemissions. Subsequently, statistical models for estimatingfuel consumption and emissions of hydrocarbon (HC), carbonmonoxide (CO), and oxides of nitrogen (NO_x) aredeveloped using these critical variables that include theaverage speed, speed variability, the level ofdeceleration, and the level of acceleration. The proposedmodels are demonstrated to be consistent with microscopicenergy and emission model estimates that are based on thevehicle's instantaneous speed and acceleration levels(coefficient of determination ranges from 0.88 to 0.96).     

基于神经网络的温度预测

室内温度与诸多影响因素之间的非线性、复杂性等关系 ,给建模、预测带来了难度 ,引入了人工神经网络 ;利用人工神经网络的非线性、并行计算和自学习特性进行建模 ,实现了对温度模拟     

WATER QUALITY MODELING OF ALTERNATIVE AGRICULTURAL SCENARIOS IN THE U.S. CORN BELT1

ABSTRACT: Simulated water quality resulting from three alternative future land‐use scenarios for two agricultural watersheds in central Iowa was compared to water quality under current and historic land use/land cover to explore both the potential water quality impact of perpetuating current trends and potential benefits of major changes in agricultural practices in the U.S. Corn Belt. The Soil Water Assessment Tool (SWAT) was applied to evaluate the effect of management practices on surface water discharge and annual loads of sediment and nitrate in these watersheds. The agricultural practices comprising Scenario 1, which assumes perpetuation of current trends (conversion to conservation tillage, increase in farm size and land in production, use of currently‐employed Best Management Practices (BMPs)) result in simulated increased export of nitrate and decreased export of sediment relative to the present. However, simulations indicate that the substantial changes in agricultural practices envisioned in Scenarios 2 and 3 (conversion to conservation tillage, strip intercropping, rotational grazing, conservation set‐asides and greatly extended use of best management practices (BMPs) such as riparian buffers, engineered wetlands, grassed waterways, filter strips and field borders) could potentially reduce current loadings of sediment by 37 to 67 percent and nutrients by 54 to 75 percent. Results from the study indicate that major improvements in water quality in these agricultural watersheds could be achieved if such environmentally‐targeted agricultural practices were employed. Traditional approaches to water quality improvement through application of traditional BMPs will result in little or no change in nutrient export and minor decreases in sediment export from Corn Belt watersheds.     

INTEGRATED MODELING FOR WATERSHED MANAGEMENT: MULTIPLE OBJECTIVES AND SPATIAL EFFECTS1

ABSTRACT: This paper presents an optimization framework for prioritizing sites for wetlands restoration on a watershed or landscape scale. The framework is designed for analyzing the potential environmental impacts of alternative management strategies while accounting for economic constraints, thereby aiding decision makers in explicitly considering multiple management objectives. The modeling strategy consists of two phases. First, relationships between the configuration of land use types in a watershed and valued ecosystem services are specified mathematically. Second, those functions are incorporated into a spatial optimization model that allows comparisons of the expected environmental impacts and economic costs of management strategies that change the configuration of land use in the watershed. By way of a stylized example, this paper develops the general structure of the framework, presents simulation results based on two production functions for ecosystem services, and discusses the potential utility of the methodology for watershed management.     

APPLICATION OF AN ENVIRONMENTAL AND ECONOMIC MODELING SYSTEM FOR WATERSHED ASSESSMENTS1

ABSTRACT: A National Pilot Project (NPP) on Livestock and the Environment was initiated in 1992 to help provide solutions to environmental problems associated with livestock production. A major development of the NPP was the Comprehensive Economic and Environmental Optimization Tool‐Livestock and Poultry (CEEOT‐LP), an integrated modeling system designed to produce economic and environmental indicators for alternative policy scenarios applied to intensive livestock production watersheds. The system consists of a farm‐level economic model (FEM) and two environmental models: the field‐scale APEX model and the watershed‐level SWAT model. To date, CEEOT‐LP has been applied to two watersheds in Texas and one in Iowa. Predicted reductions in P losses for two P‐based manure application rate scenarios, relative to baseline conditions, ranged from ?4 to ?54 percent across the three watersheds; however, N loss impacts ranged from a decrease of 34 percent to an increase of 79 percent. For five other alternative scenarios that were simulated for only one watershed, N and P loss impacts ranged between a reduction of 78 percent to an increase of 20 percent. Aggregate watershed‐level economic impacts of the seven scenarios spanned a spectrum of a 27 percent decrease to a 25 percent increase in profit, relative to the baseline.     

CONSTRUCTING A PROBLEM SOLVING ENVIRONMENT TOOL FOR HYDROLOGIC ASSESSMENT OF LAND USE CHANGE1

ABSTRACT: This paper describes the integration of a comprehensive hydrological model known as the Hydrological Simulation Program Fortran (HSPF) into a problem solving environment (PSE) for watershed management. The original PSE concept was a structure providing web‐based access to a suite of models, including HSPF and other models of in‐stream hydrodynamics, biological impacts and economic effects, for the watershed‐wide assessment of alternative land use scenarios. The present paper describes only the HSPF integration into the PSE program. Example applications to the 148 square kilometer (57 square mile) Back Creek subwatershed in the upper Roanoke River system (1,479 square kilometers or 571 square miles) in southwest Virginia are used to illustrate important concepts and linkages between land development and hydrological change using hypothetical' what if'scenarios. The features of HSPF and its limitations in this context are discussed. The paper as such is a proof‐of‐concept paper and not a completion report. It is intended to describe the PSE tool building process rather than analysis of the many possible simulation outcomes. However, the dominance of raw imperviousness as a contributor to hydrograph response is apparent in all the PSE simulations described in this paper.     

SUBWATERSHED SPATIAL ANALYSIS TOOL: DISCRETIZATION OF A DISTRIBUTED HYDROLOGIC MODEL BY STATISTICAL CRITERIA1

ABSTRACT: Complex hydrologic models, designed for simulating larger watersheds, require a huge amount of input data. Most of these models use spatially distributed data as inputs. Spatial data can be aggregated or disaggregated for use as input to a model, which can impact model outputs. Although, it is efficient to minimize data redundancy by aggregating the spatial data, upscaling reduces the detail/resolution of input information and increases model uncertainty. On the other hand, a large number of model inputs with high degrees of disaggregation take more computer time and space to process. Hence, a balance between striving for a maximum level of aggregation and a minimum level of information loss has to be achieved. This study presents a definition of an appropriate level of discretization, derived by establishing a relationship between a model's efficiency and the number of subwater‐sheds modeled. An entropy based statistical approach/tool called Subwatershed Spatial Analysis Tool (SUSAT) was developed to find an objective choice of an appropriate level of discretization. The new approach/tool was applied to three watersheds, each representing different hydrologic conditions, using a hydrologic model. Coefficients of efficiency and entropy estimated at different levels of discretization were used to validate the success of the new approach.     

INTEGRATING SCIENCE AND TECHNOLOGY TO SUPPORT STREAM NATURALIZATION NEAR CHICAGO,ILLINOIS1

ABSTRACT: Many urban and suburban communities in the Midwest are seeking to establish sustainable, morphologically and hydraulically varied, yet dynamically stable fluvial systems that are capable of supporting healthy, biologically diverse aquatic ecosystems — a process known as stream naturalization. This paper describes an integrated research program that seeks to develop a scientific and technological framework to support two stream naturalization projects near Chicago, Illinois. The research program integrates theory and methods in fluvial geomorphology, aquatic ecology, hydraulic engineering and social theory. Both the conceptual and the practical challenges of that integration are discussed. Scientific and technical support emphasize the development of predictive tools to evaluate the performance of possible naturalization designs at scales most appropriate to community based projects. Social analysis focuses on place based evaluations of how communities formulate an environmental vision and then, through decision making, translate this vision into specific stream naturalization strategies. Integration of scientific and technical with social components occurs in the context of community based decision making as the predictive tools are employed by project scientists to help local communities translate their environmental visions into concrete environmental designs. Social analysis of this decision making process reveals how the interplay between the community's vision of what they want the watershed to become, and the scientific perspective on what the watershed can become to achieve the community's environmental goals, leads to the implementation of specific stream naturalization practices.