Role of Effluent Permit Trading in Total Maximum Daily Load Programs: Overview and Uncertainty and Reliability Implications

Many jurisdictions in the United States are currently preparing total maximum daily load (TMDL) programs for stream segments that come under Section 303(d) of the Clean Water Act. Among the options being considered by many state pollution control agencies is that of permit trading, otherwise known as permit transfers, transferable permits, emissions trading, bubbles, pollution rights, marketable effluent permits, and transferable discharge permits. Under such programs, a permit to discharge into a watercourse, issued as part of a wasteload allocation program, is treated as a marketable commodity. This paper presents a qualitative discussion of the strengths and weaknesses of permit trading in the context of a TMDL program, and discusses the circumstances that favor it. The paper also presents hypothetical quantitative findings to illustrate the circumstances under which a regional administrator might wish to adopt a program of permit trading, and if so, what type of permits would suit it best.     

Integrated Hydrodynamic and Water Quality Modeling System to Support Nutrient Total Maximum Daily Load Development for Wissahickon Creek, Pennsylvania

This paper presents a hydrodynamic and water quality modeling system for Wissahickon Creek, Pa. Past data show that high nutrient levels in Wissahickon Creek were linked to large diurnal fluctuations in oxygen concentration, which combining with the deoxygenation effect of carbonaceous biological oxygen demand (CBOD) causes violations of dissolved oxygen (DO) standards. To obtain quantitative knowledge about the cause of the DO impairment, an integrated modeling system was developed based on a linked environmental fluid dynamics code (EFDC) and water quality simulation program for eutrophication (WASP/EUTRO5) modeling framework. The EFDC was used to simulate hydrodynamic and temperature in the stream, and the resulting flow information were incorporated into the WASP/EUTRO5 to simulate the fate and transport of nutrients, CBOD, algae, and DO. The standard WASP/EUTRO5 model was enhanced to include a periphyton dynamics module and a diurnal DO simulation module to better represent the prototype. The integrated modeling framework was applied to simulate the creek for a low flow period when monitoring data are available, and the results indicate that the model is a reasonable numerical representation of the prototype.     

Estimating Stream Temperature from Air Temperature: Implications for Future Water Quality

This study examines the air temperature/stream temperature relationship at a geographically diverse set of streams. We evaluate the general temperature relationships (both linear and nonlinear) that apply to these streams, and then examine how changes in stream temperature associated with climate variability or climate warming might affect dissolved oxygen levels. The majority of streams showed an increase in water temperature of about 0.6–0.8°C for every 1°C increase in air temperature, with very few streams displaying a linear 1:1 air/water temperature trend. For most of the streams, a nonlinear model produced a better fit than did a simple linear model. Understanding the relationship between air temperature and water temperature is important if people want to estimate how stream temperatures are likely to respond to anticipated future increases in surface air temperature. Surface water temperature in many streams will likely increase 2 to 3°C as air temperature increases 3 to 5°C. At sites with currently low dissolved oxygen content, an increase in summer stream temperatures could cause the dissolved oxygen levels to fall into a critically low range, threatening the health of many aquatic species.     

Models Quantify the Total Maximum Daily Load Process

Mathematical models have been used for many years to assist in the management of water quality. The total maximum daily load (TMDL) process is no exception; models represent the means by which the assimilative capacity of a water body can be quantified and a waste load allocation can be determined such that the assimilative capacity is not exceeded. Unfortunately, in many TMDLs, the use of models has not always adhered to the best modeling practices that have been developed over the past half-century. This paper presents what are felt to be the most important principles of good modeling practice relative to all of the steps in developing and applying a model for computing a TMDL. These steps include: Problem definition and setting management objectives; data synthesis for use in modeling; model selection; model calibration and, if possible confirmation; model application; iterative modeling; and model postaudit. Since mathematical modeling of aquatic systems is not an exact science, it is essential that these steps be fully transparent to all TMDL stakeholders through comprehensive documentation of the entire process, including specification of all inputs and assumptions. The overriding consideration is that data richness and quality govern the level of model complexity that can be applied to a given system. The model should never be more complex than the data allow. Also, in applying a model, one should always attempt to quantify the uncertainty in predictions. In general, quantifying uncertainty is easier with simple models, which is another reason to begin with a simple framework.     

High-Frequency Diel Dissolved Oxygen Stream Data Modeled for Variable Temperature and Scale

Diel dissolved oxygen (DO) concentrations and temperature were sensed at high-frequency and modeled in an eastern Iowan stream, Clear Creek, in an agricultural setting. The magnitude of the diel changes in DO and temperature were largest at the upstream (headwater) station. Inclusion of temperature change factors increased the accuracy of modeling results and yielded estimates of the reaeration rate constant, primary production rate, and respiration rate. The DO modeling of the high-frequency measurements (15-min intervals) revealed a temperature-driven nonlinear reaeration process that led to increases in nighttime DO concentrations. The DO modeling results from three sensing stations in the watershed revealed decreasing trends in primary productivity, respiration, and the reaeration rate constant with increasing drainage area. Light extinction from suspended solids was the main factor limiting net primary production. As a result, the P/R ratio also decreased with increasing drainage area. High-frequency sensor data and DO modeling revealed the effects of temperature and watershed scale on the primary factors that dictate diel DO dynamics in a stream setting.     

Biological Assessment and Criteria Improve Total Maximum Daily Load Decision Making

Mandated total maximum daily load (TMDL) analyses present an excellent opportunity to restore the nation’s degraded waters. The current norm for TMDL practice is, however, unlikely to achieve this goal without improved water quality standards plus systematic monitoring and assessment using biological criteria. Better than chemical and physical criteria alone, biological criteria link human actions, their impacts on water bodies, and societal goals, which are expressed as designated uses. To be adequate, monitoring should improve understanding of the connections among stressor, exposure, and response gradients. Water quality standards, monitoring, and assessment can improve water resources because they track water body condition, not the number of TMDLs completed. Federal and state leadership must set policy goals, as required by the Clean Water Act, and provide adequate fiscal and professional resources. States with high-quality programs should serve as models. Administrators should use the advances made in 2 decades of water resource science to improve their water management programs. Without such improvements, those involved in the TMDL process will continue to be frustrated, and the nation’s waters will continue to decline.     

Estimation of Average Stream Velocity

Stream tracer studies provide information supporting diverse applications in environmental research and management. Average stream velocity through a reach is often estimated from tracer temporal profiles. This Technical Note addresses the calculation of average reach velocity. It is shown here that, under steady flow, average reach velocity over a fixed distance equals the spatial harmonic mean velocity. Similarly, the average reach velocity from the point of tracer injection to a fixed downstream measurement site is equal to downstream distance divided by the harmonic mean tracer time-of-travel, rather than the commonly used temporal centroid.     

Simplified Databased Total Maximum Daily Loads, or the World is Log-Normal

The total maximum daily load (TMDL) approaches that have relied mostly on deterministic modeling have inherent problems with considerations of a margin of safety and estimating probabilities of excursions of water quality standards expressed in terms of magnitude, duration, and frequency. A tiered probabilistic TMDL approach is proposed in this paper. A simple databased Tier I TMDL that uses statistical principles has been proposed for watersheds that have adequate water quality databases enabling statistical evaluations. Studies have shown that for many pollutants, event mean concentrations in runoff, wastewater loads, and concentrations in the receiving waters follow the log-normal probability distribution. Other probability distributions are also applicable. Tier II Monte Carlo simulation, using a simpler deterministic or black box water quality model as a transfer function, can then be used to generate time series of data, which fills the data gaps and allows estimation of probabilities of excursions of chronic standards that are averaged over periods of 4 or 30 days. Statistical approaches, including Monte Carlo, allow replacement of an arbitrary margin of safety by a quantitative estimation of uncertainty and enable linking the model results to the standards defined in terms of magnitude, frequency, and duration.     

Importance of Field Data in Stream Water Quality Modeling Using QUAL2E-UNCAS

The Stream Water Quality Model QUAL2E-UNCAS is widely used to simulate the dissolved oxygen of streams under steady flow conditions. It is the latest version of a series of water quality models that have a long history in systems analysis in water quality management, and has been applied to a number of streams and rivers around the world. This paper summarizes the conceptual representation of the computer model, briefly reviews a number of applications of the model that have been published in open literature, describes the included uncertainty analysis capability, and discusses the importance of field data in model predictions. Experience with the QUAL2E model has proven the importance of site-specific data to model predictions. An accurate representation of the properties of the system significantly contributes to simulation success.     

Rapid Calculation of Oxygen in Streams: Approximate Delta Method

The “approximate delta method” is a simple procedure for simultaneous calculation of the stream reaeration coefficient, primary production rate, and respiration rate from a single-station stream diurnal profile of dissolved oxygen (DO). It approximates the exact graphs of results for the “delta method” reported in 1991 by Chapra and Di Toro by means of simple logistic curve-fitting approximations. The necessity of reading graphs or of obtaining numerical solutions is thereby avoided, so making it suitable for inclusion in a decision support system, particularly for streams reaeration coefficients less than 10?day?1 and for moderate photoperiods (10–14?h). Worked examples are given for streams in the USA and in New Zealand. Results are used to show that the constellation of parameters for the three fundamental processes is much more important than their individual values in calculating diurnal DO profiles. Independent measurement of the reaeration coefficient enhances the utility of the method, by enabling separate calculation of production and respiration rates.     

Temporal and Spatial Variations in Bulk Chlorine Decay within a Water Supply System

Modern water treatment must maintain an acceptable balance between the microbial safety of potable water supply, the costs of treatment, and the formation of potentially harmful disinfection by-products (DBPs). In order to achieve the optimum balance, it is essential to understand and predict both the formation of DBP and the decay of chlorine, in relation to source water, treatment processes, storage, and supply. Reported herein are new data which demonstrate the lack of durability, precision, and accuracy associated with earlier empirical chlorine decay rate equations. This work develops an improved methodology for the prediction of variation in chlorine decay rates in distribution systems enabling practical, cost-effective prediction of the effects of both seasonal variations and management interventions on chlorine levels at treatment works and in distribution systems.     

Improving Total Maximum Daily Loads with Lessons Learned from Long-Term Detailed Monitoring

Many thousands of impaired water segments in the United States will be the subject of total maximum daily load (TMDL) determinations in the next decade. Many of these load allocations will be established with access to only minimal local data. Long-term and detailed datasets from other locations can facilitate this process by offering general insights into the processes that interact to produce the chemistry observed in a particular waterbody over time. These insights can lead to more enlightened interpretation of sparse but locally relevant water quality data. They can also inform the design of implementation monitoring to evaluate success of TMDLs. Finally, study of such datasets reveals biases that may result from inappropriate sampling design or data interpretation algorithms, and may lead to erroneous conclusions about the success or failure of a TMDL program in a specific watershed.     

Regional Water Management Modeling for Decision Support in Irrigated Agriculture

Efficient water management is one of the key elements in successful operation of irrigation schemes in arid and semiarid regions. An integrated water management model was developed by combining an unsaturated flow model and a groundwater simulation model. These combined models serve as a tool for decision making in irrigation water management to maintain the water tables at a safe depth. The integrated model was applied on a regional scale in Sirsa Irrigation Circle, covering about a 0.42 million ha area in the northwestern part of Haryana, India, which is faced with serious waterlogging and salinity problems in areas underlain with saline ground irrigated by the canal network. The model was calibrated using the agrohydrologic data for the period 1977–1981 and validated for the period 1982–1990 by keeping the calibrating parameters unchanged. The model was used to study the long-term impact of two water management interventions related to the canal irrigation system—change in pricing system of irrigation water, and water supply according to demand—on the extent of waterlogging risk. Both of these strategies, if implemented, would considerably reduce aquifer recharge and consequently waterlogging risk, compared to the existing practice. The water supply according to demand strategy was slightly more effective in reducing aquifer recharge than the water pricing intervention. The implementation of the proposed water pricing policy would pose no problem in fitting into the existing irrigation system, and thus it would be easier to implement, compared to the water supply according to demand strategy, when taking technical, financial, and social considerations into account.     

Model for the Simulation of Water Flows in Irrigation Districts. I: Description

Significant improvements in the profitability and sustainability of irrigated areas can be obtained by the application of new technologies. In this work, a model for the simulation of water flows in irrigation districts is presented. The model is based on the combination of a number of modules specialized on surface irrigation, open channel distribution networks, crop growth modeling, irrigation decision making, and hydrosaline balances. These modules are executed in parallel, and are connected by a series of variables. The surface irrigation module is based on a numerical hydrodynamic routine solving the Saint Venant equations, including the heterogeneity of soil physical properties. The simulation of water conveyance is performed on the basis of the capacity of the elements of the conveyance network. Crop growth is simulated using a scheme derived from the well-known model CropWat. The irrigation decision making module satisfies water orders considering water stress, yield sensitivity to stress, multiple water sources, and the network capacity. Finally, the hydrosaline module is based on a steady state approach, and provides estimations of the volume and salinity of the irrigation return flows for the whole irrigation season. The application of the model to district irrigation management and modernization studies may be limited by the volume of data required. In a companion paper, the model is calibrated, validated, and applied to a real irrigation district.     

Logistic Regression Model for Evaluating Soil Liquefaction Probability Using CPT Data

A simple model for evaluating liquefaction probability using cone penetration test (CPT) data is developed based on logistic regression analyses of 396 case histories. The proposed model uses the normalized cone penetration resistance and soil behavior type index as input parameters; therefore, only CPT testing is necessary for evaluating the liquefaction probability of a site. The selection of the model parameters and the expression of equations are based on results of probability examinations and rigorous statistical analyses. Moreover, the derivation of the logistic regression model is presented in a system of equations. The incorporation of these procedures in developing the model not only fully satisfies the statistic requirements but also highlights the physical meanings of the model parameters. Comparisons of the proposed probability model with previously proposed deterministic and probabilistic approaches are performed to demonstrate the improvements. For practical purposes, the developed model is implemented to establish the relationship between the factor of safety against liquefaction and the probability of liquefaction.     

Simulation of Metals Total Maximum Daily Loads and Remediation in a Mining-Impacted Stream

Simulation of metals transport was performed to help develop metals total maximum daily loads (TMDLs) and evaluate remediation alternatives in a mountain stream in Montana impacted by hundreds of abandoned hardrock metal mines. These types of watersheds are widespread in Montana and many other areas of the western United States. Impacts from abandoned hardrock or metal mines include loadings of sediment, metals, and other pollutants causing impairment of multiple beneficial uses and exceedances of water quality standards. The United States Environmental Protection Agency (EPA) Water Quality Analysis Simulation Program (WASP) was used to model and evaluate TMDLs for several heavy metals in Tenmile Creek, a mountain stream supplying drinking water to the City of Helena, Mont. The model was calibrated for baseflow conditions and validated using data collected by the EPA and the United States Geological Survey, and used to assess existing metals loadings and losses, including interactions between metals in water and bed sediment, uncertainty, water quality standard exceedances, TMDLs, potential source areas, and required reductions in loadings. During baseflow conditions, adits and point sources contribute significant metals loadings to Tenmile Creek. Exceedances of standards are widespread throughout the stream under both baseflow and higher flow conditions. Adsorption and precipitation onto bed sediments play a primary role in losses from the water column in some areas. Modeling results indicate that some uncertainty exists in the metal partition coefficients associated with sediment, significance of precipitation reactions, and in locations of unidentified sources and losses of metals. TMDLs and loading reductions were calculated based on variations in flow, concentrations, loadings, and standards (which vary with hardness) along the mainstem. In most cases, considerable reductions in loadings are required to achieve TMDLs and water quality standards. Reductions in loadings from point sources, mine waste near watercourses, and streambed sediment can help improve water quality, but alteration of the water supply scheme and increasing baseflow will also be needed.     

Concentration–Duration–Frequency Curves for pH in a Stream in the Great Smoky Mountains

Multiparameter monitors (sondes) were installed at four sites in a stream in the Great Smoky Mountains National Park. These sondes recorded pH, turbidity, and other parameters every 15?min. The data were analyzed to determine the time-connected duration of excursions below various pH levels (pH 4.5, 5.0, 5.5, 6.0, and 6.5). The durations for excursions below each pH level were assigned probability quantiles and plotted. This created a concentration–duration–frequency curve similar to precipitation intensity–duration–frequency curves. The pH excursion events appear to follow a Poisson arrival process and were fitted to a corresponding exponential distribution. The mean event duration for each pH level is the fitting parameter, μ, for the exponential distribution, and μ followed an exponential trend with pH. The characteristics of the concentration–duration–frequency family of curves capture the information contained in the original data and potentially allow comparisons with toxicity data as well as a temporal and spatial comparison.     

Determining Attribute Weights in a CBR Model for Early Cost Prediction of Structural Systems

This paper compares the performance of three optimization techniques, namely feature counting, gradient descent, and genetic algorithms (GA) in generating attribute weights that were used in a spreadsheet-based case based reasoning (CBR) prediction model. The generation of the attribute weights by using the three optimization techniques and the development of the procedure used in the CBR model are described in this paper in detail. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of 29 residential building projects. The results indicated that GA-augmented CBR performed better than CBR used in association with the other two optimization techniques. The study is of benefit primarily to researchers as it compares the impact attribute weights generated by three different optimization techniques on the performance of a CBR prediction tool.     

Multiple Linear Regression Model for Total Bed Material Load Prediction

A new total bed material load equation that is applicable for rivers in Malaysia was developed using multiple linear regression analyses. A total of 346 hydraulic and sediment data were collected from nine natural and channelized rivers having diverse catchment characteristics in Malaysia. The governing parameters were carefully selected based on literature survey and field experiments, examined and grouped into five categories namely mobility, transport, sediment, shape, and flow resistance parameters. The most influential parameters from each group were selected by using all possible regression model method. The suitable model selection criteria namely the R-square, adjusted R-square, mean square error, and Mallow’s Cp statistics were employed. The accuracy of the derived model is determined using the discrepancy ratio, which is a ratio of the calculated values to the measured values. The best performing models that give the highest percentage of prediction from the validation data were chosen. In general, the newly derived model is best suited for rivers with uniform sediment size distribution with a d50 value within the range of 0.37–4.0 mm and performs better than the commonly used Graf, Yang, and Ackers–White total bed material load equations.     

Decision Support Systems for Efficient Irrigation in the Middle Rio Grande Valley

Water is the lifeblood of the American West and the foundation of its economy, but it remains its scarcest resource. The explosive population growth in western United States, the emerging additional need for water for environmental uses, and the national importance of the domestic food production are driving major conflicts between these competing water uses. The case of the Middle Rio Grande illustrates the problem very well. The river is the ecological backbone of the Chihuahuan Desert region in the western United States, and supports its dynamic and diverse ecology, including the fish and wildlife habitat. The Rio Grande Silvery Minnow is federally listed as an endangered species, and the irrigated agriculture in the Middle Rio Grande has come under increasing pressure to reduce its water consumption and maintain the desired level of service to its water users. This paper will present the writers ongoing research on options to make irrigation system operations more efficient in the Middle Rio Grande Conservancy District (MRGCD). Specifically, it will describe formulation and implementation of a decision support system (DSS) that can assist the MRGCD managers to more efficiently plan and implement their water delivery operations, thereby reducing river diversions. The MRGCD DSS uses linear programming to find an optimum water delivery schedule for canal service areas in the MRGCD irrigation system. The computer model is presently formulated along with the related data sets for two of the four divisions in the MRGCD. For the past 3?years, the model has been validated in the field and the evaluation indicates that the model recommendations are realistic and represent current management practices. The future plans are to complete the data files for the irrigation networks in the remaining two divisions and concurrently help the MRGCD implement the DSS to guide water delivery operation.