UNCERTAINTY ANALYSIS OF BMP EFFECTWENESS FOR CONTROLLING NITROGEN FROM URBAN NONPOINT SOURCES1

ABSTRACT: The effectiveness of urban Best Management Practices (BMPs) in achieving the No-Net-Increase Policy (NNTP), a policy designed to limit nonpoint nitrogen loading to Long Island Sound (US), is analyzed. A unit loading model is used to simulate annual nitrogen exported from the Norwalk River watershed (Connecticut) under current and future conditions. A probabilistic uncertainty analysis is used to incorporate uncertainty in nitrogen export coefficients and BMP nitrogen removal effectiveness. The inclusion of uncertainty in BMP effectiveness and nitrogen export coefficients implies that additional BMPs, or BMPs with a greater effectiveness in nitrogen removal, will be required to achieve the NNIP. Even though including uncertainty leads to an increase in BMP implementation rates or BMP effectiveness, this type of analysis provides the decision maker with a more realistic assessment of the likelihood that implementing BMPs as a management strategy will be successful. Monte Carlo simulation results indicate that applying BMPs to new urban developments alone will not be sufficient to achieve the NNIP since BMPs are not 100 percent effective in removing the increase in nitrogen caused by urbanization. BMPs must also be applied to selected existing urban areas. BMPs with a nitrogen removal effectiveness of 40–60 percent, probably the highest level of removal that can be expected over an entire watershed, must be applied to at least 75 percent of the existing urban area to achieve the NNIP This high rate of application is not likely to be achieved in urbanized watersheds in the LIS watershed; therefore, additional point source control will be necessary to achieve the NNIP     

DETERMINATION OF BEST TIMING FOR POULTRY WASTE DISPOSAL: A MODELING APPROACH1

ABSTRACT: Confined production of poultry results in significant volumes of waste material which are typically disposed of by land application. Concerns over the potential environmental impacts of poultry waste disposal have resulted in ongoing efforts to develop management practices which maintain high quality of water downstream of disposal areas. The timing of application to minimize waste constituent losses is a management practice with the potential to ensure high quality of streams, rivers, and lakes downstream of receiving areas. This paper describes the development and application of a method to identify which time of year is best, from the standpoint of surface water quality, for land application of poultry waste. The procedure consists of using a mathematical simulation model to estimate average nitrogen and phosphorus losses resulting from different application timings, and then identifying the timings which minimize losses of these nutrients. The procedure was applied to three locations in Arkansas, and three different criteria for optimality of application timing were investigated. One criterion was oriented strictly to water quality, one was oriented only to crop production, and the last was a combination. The criteria resulted in different windows of time being identified as optimal. Optimal windows also varied with location of the receiving area. The results indicate that it is possible to land-apply poultry waste at times which both minimize nutrient losses and maximize crop yield.     

INTERPLAY BETWEEN PARAMETER UNCERTAINTY AND MODEL AGGREGATION ERROR1

ABSTRACTS: Modeling error can be divided into two basic components: use of an incorrect model and input parameter uncertainty. Incorrect model usage can be further subdivided into inappropriate model selection and inherent modeling error due to process aggregation. Total modeling error is a culmination of these various modeling error components, with overall optimization requiring reductions in all. A technique, utilizing Monte Carlo analysis, is employed to investigate the relative importance of input parameter uncertainty versus process aggregation error. An expanded form of the Streeter-Phelps dissolved oxygen equation is used to demonstrate the application of this technique. A variety of scenarios are analyzed to illustrate the relative obfuscation of each modeling error component. Under certain circumstances an aggregated model performs better than a more complex model, which perfectly simulates the real system. Alternately, process aggregation error dominates total modeling error for other situations. The ability to differentiate modeling error impact is a function of the desired or imposed model performance level (accuracy tolerance).     

A FRAMEWORK FOR PHOSPHORUS TRANSPORT MODELING IN THE LAKE OKEECHOBEE WATERSHED1

ABSTRACT: A modeling framework was developed to determine phosphorus loadings to Lake Okeechobee from watersheds located north of the lake. This framework consists of the land-based model CREAMS-WT, the in-stream transport model QUAL2E, and an interface procedure to format the land-based model output for use by the in-stream model. QUAL2E hydraulics and water quality routines were modified to account for flow routing and phosphorus retention in both wetlands and stream channels. Phosphorus loadings obtained from previous applications of CREAMS-WT were used by QUAL2E, and calibration and verification showed that QUAL2E accurately simulated seasonal and annual phosphorus loadings from a watershed. Sensitivity and uncertainty analyses indicated that the accuracy of monthly loadings can be improved by using better estimates of in-stream phosphorus decay rates, ground water phosphorus concentrations, and runoff phosphorus concentrations as input to QUAL2E.     

AN ANALYSIS OF SEASONAL FECAL COLIFORM LEVELS IN THE TCHEFUNCTE RWER1

ABSTRACT: A model for estimating seasonal fecal coliform concentrations in the Tchefuncte River as a function of river discharge was developed. Data on fecal coliform concentration were obtained from the Louisiana Department of Health and Hospitals and were available for a period of 15 years (1975 through 1992) from three locations. Stream flow data were obtained from a gaging station of the U. S. Geological Survey at Folsom, Louisiana. These data were available for 49 years (1943 through 1991). The climate of the area is characterized by different precipitation/runoff mechanisms for the summer and winter seasons. The division for seasons used in this analysis was May through October (summer season), and November through April (winter season). Because of the combined effects of climatic mechanisms causing precipitation and the seasonal variation of evapotranspiration, runoff is greater in the winter season resulting in higher fecal coliform counts in the Tchefuncte River. Statistical analysis revealed a statistically significant relationship between fecal coliform concentration and discharge for each season, at each of three sites on the Tchefuncte River.     

DEEP RESERVOIR THERMAL STRATIFICATION MODEL1

ABSTRACT. A method of predicting reservoir temperature profiles is presented. The model requires monthly averaged meteorological and hydrological data along with reservoir physical characteristics. The one-dimensional profiles are predicted by solving the time-dependent energy equation with an assumed epilimnetic depth and a diffusion coefficient applicable below the epilimnion. The model is verified using an existing reservoir.     

IMPLEMENTATION OF AN OPTIMIZATION MODEL FOR OPERATION OF A METROPOLITAN RESERVOIR SYSTEM1

ABSTRACT: A deterministic dynamic programming optimization model with a refining sectioning search procedure is developed and implemented to find least cost withdrawal and release patterns for water supple from a multiple reservoir system serving a metropolitan area. Applications are made to teh four reservoir system operated by the city of Dallas, Texas. A realistic cost structure, including nonlinear power consumption, block rate unit power costs, and flow dependent power consumption for intracity water distribution, is utilized. Applications are made to find least cost operating patterns and, as well, by inclusion of a water loss penalty function, supply patterns which will reduce evaporation water losses for the Dallas system.     

NONPOINT SOURCE POLLUTION POTENTIAL IN AN AGRICULTURAL WATERSHED IN NORTHWESTERN PENNSYLVANIA1

ABSTRACT: A 155,947 ha portion of the Shenango River watershed in western Pennsylvania was evaluated as to the potential impact of agriculture drainage on water quality. Approximately a third of the area is being used as either cropland or pasture with approximately an equal percentage in forest lands. Eleven subwatersheds were evaluated as to their potential for nonpoint source pollution according to the criteria established by the Pennsylvania Department of Environmental Resources for the Chesapeake Bay Pollution Abatement Program. The individual components and overall rating for each subwatershed were then evaluated as to their correlation with four water quality variables based on 104 samples collected at 26 sampling stations throughout the watershed. There was a significant correlation between the overall rating factor for each subwatershed and each of the four water quality variables. In general, the watershed delivery factor, animal nutrient factor, and management factors were correlated with fecal coliform and phosphorus in the receiving streams, whereas the ground water delivery factor appeared to be more important in determining nitrate concentrations in these streams. These results indicate that manure and nutrient management, along with the exclusion of livestock from streams and the enhancement and/or replacement of riparian wetlands, are important approaches in reducing agricultural impacts in fresh water ecosystems.     

ANALYSIS OF UNCERTAINTY IN FLOOD PLAIN MAPPING1

ABSTRACT: Components contributing to uncertainty in the location of the flood plain fringe of a mapped flood plain are identified and examined to determine their relative importance. First-order uncertainty analysis is used to provide a procedure for quantifying the magnitude of uncertainty in the location of the flood plain fringe. Application of the procedure indicated that one standard deviation of uncertainty in flood plain inundation width was about one third of the mean computed inundation width for several flood population-flood geometry combinations. Suggested mapping criteria, which directly incorporate uncertainty estimates, are given. While these criteria are more suitable for use in developing areas than in flood plains that have had extensive development, the analysis procedure can be used to accommodate property owners who challenge the validity of estimated flood fringe boundaries. Use of uncertainty analysis in flood plain mapping should enhance the credibility of the final plan.     

ANALYSIS OF LAKE-BOTTOM SEDIMENT TO ESTIMATE HISTORICAL NONPOINT-SOURCE PHOSPHORUS LOADS1

ABSTRACT Bottom sediment in Hillsdale Lake, Kansas, was analyzed to estimate the annual load of total phosphorus deposited in the lake from nonpoint sources. Topographic, bathymetric, and sediment-core data were used to estimate the total mass of phosphorus in the lake-bottom sediment. Available streamflow and water-quality data were used to compute the mean annual mass of phosphorus (dissolved plus suspended) exiting the lake. The mean annual load of phosphorus added to the lake from point sources was estimated from previous studies. A simple mass balance then was used to compute the mean annual load of phosphorus from non-point sources. The total mass of phosphorus in the lake-bottom sediment was estimated to be 924,000 kg, with a mean annual load of 62,000 kg. The mean annual mass of phosphorus exiting in the lake outflow was estimated to be about 8,000 kg. The mean annual loads of phosphorus added to the lake from point and nonpoint sources were estimated to be 5,000 and 65,000 kg, respectively. Thus, the contribution to the total mean annual phosphorus load in Hillsdale Lake is about 7 percent from point sources and about 93 percent from nonpoint sources.     

WATER QUALITY PROJECTIONS: PREIMPOUNDMENT CASE STUDY1

ABSTRACT: Recent regulations require impact statements for major water development projects, including reservoirs that will be used for water supply, recreation, and pollution control. A water quantity/quality model was developed and used for making water quality projections of a proposed reservoir in Montgomery County, Maryland. The study area is uncommon in that there is an extensive water quality data base. The results indicate that land use changes will have a significant effect on water quality and that the proposed reservoir will improve the quality of the surface waters downstream from the reservoir. A major effect of land use changes is the increase in the variability of water quality.     

MODELING CONCENTRATION VARIATIONS IN HIGH-CAPACITY WELLS: IMPLICATIONS FOR GROUNDWATER SAMPLING1

ABSTRACT: High-capacity wells are used as a convenient and economical means of sampling groundwater quality. Although the inherent limitations of using these wells are generally recognized, little has been done to investigate how these wells actually sample groundwater. A semi-analytical particle tracking model is used to illustrate the influence of variable vertical contaminant distributions and aquifer heterogeneity on the composition of water samples from these wells during short pumping periods. The hypothetical pumping well used in the simulations is located in an unconfined, alluvial aquifer with a shallow water table and concentration gradients of nitrate-nitrogen contamination. This is a typical setting for many irrigated areas in the United States. The main conclusions are: (1) high-capacity wells underestimate the average amount of contamination within an aquifer; (2) shapes of concentration-time curves for high-capacity wells appear to be governed by the distribution of the contaminant and travel times to the well; (3) variables such as well construction, pumping rate, and hydrogeologic properties contribute to the magnitude of the concentration-time curves at individual high-capacity wells; and (4) a sampling strategy using concentration-time curves based on the behavioral characteristics of the well rather than individual samples will provide a much better framework for interpreting spatial contaminant distributions.     

Model Based Nitrate TMDLs for Two Agricultural Watersheds of Southeastern Minnesota1

Abstract: In this study, a set of nitrogen reduction strategies were modeled to evaluate the feasibility of improving water quality to meet total maximum daily loads (TMDLs) in two agricultural watersheds. For this purpose, a spatial‐process model was calibrated and used to predict monthly nitrate losses (1994‐96) from Sand and Bevens Creek watersheds located in south‐central Minnesota. Statistical comparison of predicted and observed flow and nitrate losses gave r² coefficients of 0.75 and 0.70 for Sand Creek watershed and 0.72 and 0.67 for Bevens Creek watershed, respectively. Modeled alternative agricultural management scenarios included: six different N application rates over three application timings and three different percentages of crop land with subsurface drainage. Predicted annual nitrate losses were then compared with nitrate TMDLs assuming a 30% reduction in observed nitrate losses is required. Reductions of about 33 (8.6 to 5.8 kg/ha) and 35% (23 to 15 kg/ha) in existing annual nitrate losses are possible for Sand and Bevens Creek watersheds, respectively, by switching the timing of fertilizer application from fall to spring. Trends towards increases in tile‐drained crop land imply that attaining nitrate TMDLs in future may require other alternative management practices in addition to fertilizer management such as partial conversion of crop land to pasture.     

FOREST PRACTICES AS NONPOINT SOURCES OF POLLUTION IN NORTH AMERICA1

ABSTRACT: Forest management activities may substantially alter the quality of water draining forests, and are regulated as nonpoint sources of pollution. Important impacts have been documented, in some cases, for undesirable changes in stream temperature and concentrations of dissolved oxygen, nitrate-N, and suspended sediments. We present a comprehensive summary of North American studies that have examined the impacts of forest practices on each of these parameters of water quality. In most cases, retention of forested buffer strips along streams prevents unacceptable increases in stream temperatures. Current practices do not typically involve addition of large quantities of fine organic material to streams, and depletion of streamwater oxygen is not a problem; however, sedimentation of gravel streambeds may reduce oxygen diffusion into spawning beds in some cases. Concentrations of nitrate-N typically increase substantially after forest harvesting and fertilization, but only a few cases have resulted in concentrations approaching the drinking-water standard of 10 mg of nitrate-NIL. Road construction and harvesting increase suspended sediment concentrations in streamwater, with highly variable results among regions in North America. The use of best management practices usually prevents unacceptable increases in sediment concentrations, but exceptionally large responses (especially in relation to intense storms) are not unusual.     

A MODELING APPROACH FOR STORM WATER QUANTITY AND QUALITY CONTROL VIA DETENTION BASINS1

ABSTRACT: Storm water detention basins have historically been employed for quantity (i.e., flooding) control only. However, recently it has been suggested that these basins may also provide a practical means of storm water quality control. This paper presents the formulation of a mathematical modeling approach which may be used by professionals to simultaneously design detention basins for the dual purpose of storm water quantity and quality control. Model simulations demonstrate that for a given basin, pollutant removal increases as storm frequency increases. The importance of particle size distribution and settling velocity for net pollutant removal is illustrated, The design procedure is demonstrated, and pollutant loading diagrams for estimating pollutant removal as a function of storm size are developed.     

THE USE OF A SIMPLE MODEL AND UNCERTAINTY ANALYSIS IN LAKE MANAGEMENT1

ABSTRACT: A simple, black-box lake model was developed for phosphorus, using nonlinear regression analysis on a data base of north temperate lakes. The uncertainty associated with the model was then combined with the parameter uncertainty and the independent variable uncertainty to provide an estimate of the confidence limits associated with a predicted value. The prediction uncertainty is often neglected, yet it is an important measure of the usefulness of a model. Prediction uncertainty reflects the modeler's confidence in the model, and it should be used by a decision maker as a weight indicating the value of the model prediction. A procedure is outlined that combined lake modeling and uncertainty analysis for use in lake quality assessment and lake management. An example is provided illustrating the use of this procedure in nutrient budget sampling design, data analysis, and the evaluation of lake management strategies for a 208 program in New Hampshire.     

INTEGRATION OF GEOGRAPHIC INFORMATION SYSTEMS AND A COMPUTER MODEL TO EVALUATE IMPACTS OF AGRICULTURAL RUNOFF ON WATER QUALITY1

ABSTRACT: This study integrates an Agricultural Non-Point Source Pollution Model (AGNPS), the Geographic Resource Analysis Support System (GRASS) (U.S. Army Corps of Engineers, 1987), and GRASS WATERWORKS (a hydrologic modeling tool box being developed at the Michigan State University Center for Remote Sensing) to evaluate the impact of agricultural runoff on water quality in the Cass River, a subwatershed of Saginaw Bay. AGNPS is used to estimate the amounts, origin, and distribution of sediment, nitrogen (N), and phosphorus (P) in the watershed. GRASS and GRASS WATERWORKS are used to generate parameters needed for AGNPS from digital maps, which include soil association, land use, watershed boundaries, water features, and digital elevation. Outputs of the model include spatially distributed estimates of volume and peak runoff, overland and channel erosion, sediment yields, and concentrations of nitrogen and phosphorus. Management scenarios are explored in the AGNPS model to minimize sedimentation and nutrient loading. Scenarios evaluated include variations in crop cover, tillage methods, and other agricultural management practices. In addition, areas vulnerable to erosion are identified for best management practices.     

GROUPWISE MODELING STUDY OF BACTERIALLY IMPAIRED WATERSHEDS IN TEXAS: CLUSTERING ANALYSIS1

ABSTRACT: Under the Clean Water Act (CWA) program, the Texas Commission on Environmental Quality (TCEQ) listed 110 stream segments in the year 2000 with pathogenic bacteria impairment. A study was conducted to evaluate the probable sources of pollution and characterize the watersheds associated with these impaired water bodies. The primary aim of the study was to group the water bodies into clusters having similar watershed characteristics and to examine the possibility of studying them as a group by choosing models for total maximum daily load (TMDL) development based on their characteristics. This approach will help to identify possible sources and determine appropriate models and hence reduce the number of required TMDL studies. This in turn will help in reducing the effort required to restore the health of the impaired water bodies in Texas. The main characteristics considered for the classification of water bodies were land use distribution within the watershed, density of stream network, average distance of land of a particular use to the closest stream, household population, density of on‐site sewage facilities (OSSFs), bacterial loading from different types of farm animals and wildlife, and average climatic conditions. The climatic data and observed instream fecal coliform bacteria concentrations were analyzed to evaluate seasonal variability of instream water quality. The grouping of water bodies was carried out using the multivariate statistical techniques of factor analysis/principal component analysis, cluster analysis, and discriminant analysis. The multivariate statistical analysis resulted in six clusters of water bodies. The main factors that differentiated the clusters were found to be bacterial contribution from farm animals and wildlife, density of OSSFs, density of households connected to public sewers, and land use distribution.