Journal of Environmental Engineering

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.

The total maximum daily load (TMDL) concept provides the basis for regulating pollution load from riverine sources to impaired water bodies. However, load is comprised of two components: flow and concentration. These two components may have confounding, or even conflicting, effects on waterbody attributes of concern. This is particularly the case for dynamic, advective systems, such as estuaries. Resolving these components is critical for properly predicting the response of impaired systems to watershed management actions. The Neuse River Estuary in North Carolina is an example of such an impaired system. Nitrogen has been identified as the pollutant of concern, and the process of developing a TMDL for nitrogen is underway. We, therefore, analyze the extensive data that have been collected for the Neuse River and estuary to investigate spatiotemporal relationships between river flow, riverine total nitrogen (TN) inputs, water temperature, dissolved inorganic nitrogen concentration, algal density, and primary productivity. Results support the belief that phytoplankton in the estuary are under substantial riverine control. However, the riverine TN concentration alone has only a minor role in determining estuarine chlorophyll $a\_$ concentration. River flow has a stronger influence, likely through its effects on down-estuary nitrogen delivery, residence time, salinity, and turbidity. These results imply that using riverine nitrogen load as the metric to evaluate watershed nutrient management may not be appropriate. While nitrogen controls should reduce loads in the long term, in the short term, river flow is the dominant component of load and has the opposite effect of nitrogen on algae at the up-estuary locations.

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.

Bias in the design of stream-sampling networks can be a major cause of inaccurate characterization of ambient water quality. At the state level, sampling bias can impact a state’s ability to produce an accurate assessment of the water quality of all state waters. At the federal level, this bias has hindered attempts by the U.S. Environmental Protection Agency (U.S. EPA) to produce nationwide assessments of water quality. Three types of bias commonly occur in water-quality assessments: design, analytical, and statistical. This paper focuses on “design bias,” especially spatial-design bias, in stream monitoring networks. A geographic information system method is described to develop an environmental index to help recognize spatial design bias, and to prioritize areas (subwatersheds) for sampling. The environmental index, which is developed using data that are generally available from federal and state agencies, provides a means of differentiating the component parts of a watershed, its subwatersheds, in terms of two sets of features: natural landscape features and anthropogenic features or “stressors.” Together, these features largely determine the variability of the quantity and quality of water discharged from watersheds. Subwatersheds with higher environmental-index values are expected to have more variable water quality over the course of a year than subwatersheds with lower index values. They are also expected to exert a greater influence on basinwide water quality. These properties of the index make it useful for (1) identifying the possible presence of spatial bias in existing watershed-sampling networks, (2) making sampling design decisions, and (3) aggregating data from subwatersheds into basinwide measures of water quality. Development and use of the index is demonstrated in a watershed in southern New Hampshire.

Diminished clarity has been listed as a water quality impairment in Chesapeake Bay. The CE-QUAL-ICM eutrophication model has been revised and recalibrated to provide management guidance in alleviating impaired clarity. The algorithms used to model light attenuation and suspended solids are presented herein. Computed and observed total and volatile solids and light attenuation are examined in several formats. A solids budget constructed for the bay identifies major solids sources as internal production, bank erosion, and watershed loading. Sensitivity to loading sources and a key management scenario are examined. Major but feasible reductions in solids and nutrient loads, coupled with reductions in bank erosion, are calculated to meet clarity goals at the 1-m depth in the main bay and major eastern embayments. Careful examination of model results at small scales is required to verify large-scale findings, however. We recommend major improvements in monitoring, computation of light attenuation, and in sediment transport modeling to improve the state of the art in modeling and management of water clarity.

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.

A methodology for analyzing uncertain parameter ranges prior to model calibration or uncertainty analysis is presented. The method considers parameters that exist in complex models and are typically difficult to set using site-specific data (i.e., parameters that have suggested ranges, national average ranges, or ranges set with land characteristic data). The method applies Monte Carlo runs and an interval-spaced sensitivity analysis to determine the parts of parameter ranges that will most likely cause unrealistic model results. An application of the method is presented using the Soil and Water Assessment Tool model as applied to the Cannonsville Reservoir system watershed for hydrology and sediment simulations. Results indicate that after parameter range reduction, the model output range was reduced by an order of magnitude, thereby reducing the uncertainty of the model and aiding the calibration effort. Sediment transport is difficult to monitor and model in its many stages of transport so significant uncertainty in the sediment erosion and transport parameters for this model still exist. This uncertainty will impact the application of the model for Total Maximum Daily Load development and management decisions.

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.

There are significant uncertainties associated with certain aspects of the total maximum daily load (TMDL) estimation. Selection of the “margin of safety (MOS)” term is typically made by subjectively assigning to it a small percentage (5–10%) of the TMDL load. To introduce some objectivity into the MOS estimation, the first-order error analysis (FOEA) was utilized to quantify the MOS term in the TMDL formulation. A case study, which was based on a previous study entitled “Nitrate TMDL Development for Muddy Creek/Dry River, Virginia,” is presented in this paper. Besides computational efficiency, one of the major advantages of FOEA is its capability of determining the relative importance of the various parameters that contribute to the overall variance of the model output. Precipitation was found by far to be the most dominant source of uncertainty. Furthermore, a relationship was established to link the pollutant loads with the FOEA output concentrations. The results from testing different TMDL allocation scenarios demonstrate that with the increase of relative percentage of nonpoint sources load reduction in the total load reduction, the portion needed to be reserved for MOS increases as well. The MOS term can be related to the variability in rainfall and therefore would be different for different locations in the country. In summary, as a practical, less subjective and reliable approach to TMDL uncertainty analysis, the use of the FOEA is considered as a viable alternative to the current simple explicit and implicit methods in estimating the MOS term for TMDL calculations.

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.

Best management practices (BMPs) are practical control measures (including technological, economic, and institutional considerations) that have been demonstrated to effectively minimize water quality impacts. The use of BMPs is widely accepted as the most appropriate method of controlling nonpoint sources of pollution because BMPs prevent or minimize pollution rather than retrospectively respond to it. Still, there is a stigma that BMPs do not afford quite the same degree of protection or assurance of pollution control that effluent treatment and process controls do for point sources. Here we provide a brief history of BMPs and their emergence as a practical water pollution control tool for nonpoint source activities, with a focus on the history of forestry BMPs. This history demonstrates the variety of BMPs used to avoid or minimize the generation of nonpoint source pollutants or reduce delivery of these materials to streams. It also demonstrates the extensive testing of BMP effectiveness that has been conducted throughout the United States. Those who must select or design BMPs face difficult issues about balancing desirable and undesirable inputs of watershed materials and energy to streams. We show that BMPs and nonpoint source control programs are not a “weak sister” of effluent treatment and point source control efforts, and are effectively addressing extremely complex and variable watershed conditions. Best management practices continue to evolve as research identifies new environmental concerns and control options and, as the primary tool for controlling nonpoint source pollution, play a key role in addressing water quality limited waterbodies.

The margin of safety (MOS), designed to account for uncertainty in calculations, is a required element of a total maximum daily load (TMDL). It is widely recognized that the margin of safety component is not currently being rigorously addressed throughout the TMDL program. This work investigates the existing barriers to rigorous consideration of the margin of safety, and presents a method designed with attributes to address the existing problems. Rigorous application of the margin of safety is presently inhibited by: (1) limited practical experience in defining TMDL uncertainty; (2) absence of policy regarding the desired degree of protection to be provided by the MOS; and (3) data-poor/high-uncertainty situations with MOS values so large as to make implementation impractical. The method provided here consists of first defining the desired level of protection to be provided by the MOS, which requires policy input. The second step consists of selecting between a range of implicit and explicit approaches for determining the MOS. Guidance is provided on explicitly defining the MOS associated with implicit approaches. The final step considers the implementation feasibility of the resulting MOS, with a potential outcome of the process being an adaptive management approach to the TMDL.

This paper describes adaptive watershed management, which combines concepts for adaptive management and watershed management to address the various uncertain elements in a total maximum daily load (TMDL). The paper discusses how adaptive watershed management allows initial progress to be made while additional information is collected and incorporated in the TMDL. Adaptive watershed management differs from the conventional TMDL approach as a result of feedback loops, which allow managers to proceed with implementation of controls in a progressive manner, avoiding unproductive and irresolvable debate over uncertainty in the numeric value of the TMDL or the efficacy of the controls. Over time, improvements in monitoring, modeling, TMDL analysis, water quality targets, and control actions contribute to the improved effectiveness of the TMDL. The adaptive watershed management approach can be applied in situations dominated by nonpoint sources or having significant uncertainty in any number of issues. The paper includes examples of previous uses of adaptive approaches, a discussion of additional elements that need to be considered, and identification of regulatory and other obstacles.

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.

Stakeholder involvement is essential to the development of a total maximum daily load (TMDL) and its implementation plan. A tool, beyond a simulation model, is needed to support the decision making process that requires negotiation and compromise among stakeholders. The decision support system (DSS) described herein has a TMDL module to calculate various combinations of point and nonpoint loads that can meet the water quality criteria. Its Consensus module allows stakeholders to formulate, evaluate, modify, and vote for alternatives. The DSS displays bar charts for pollution loads from various subwatersheds and attributes the nonpoint loads to land uses. The water quality consequence of the pollution loads is output in maps, which shows sections meeting criteria in green and those not in red. The DSS requires a front end effort of site specific adaptation and model calibration. An Internet-based stakeholder process was developed to allow more concerned citizens to participate in management decisions.